ALBERT

Description: Remote Sensing, Vol. 10, Pages 1171: Multi-Criteria Evaluation of Snowpack Simulations in Complex Alpine Terrain Using Satellite and In Situ Observations Remote Sensing doi: 10.3390/rs10081171 Authors: Jesús Revuelto Grégoire Lecourt Matthieu Lafaysse Isabella Zin Luc Charrois Vincent Vionnet Marie Dumont Antoine Rabatel Delphine Six Thomas Condom Samuel Morin Alessandra Viani Pascal Sirguey This work presents an extensive evaluation of the Crocus snowpack model over a rugged and highly glacierized mountain catchment (Arve valley, Western Alps, France) from 1989 to 2015. The simulations were compared and evaluated using in-situ point snow depth measurements, in-situ seasonal and annual glacier surface mass balance, snow covered area evolution based on optical satellite imagery at 250 m resolution (MODIS sensor), and the annual equilibrium-line altitude of glaciers, derived from satellite images (Landsat, SPOT, and ASTER). The snowpack simulations were obtained using the Crocus snowpack model driven by the same, originally semi-distributed, meteorological forcing (SAFRAN) reanalysis using the native semi-distributed configuration, but also a fully distributed configuration. The semi-distributed approach addresses land surface simulations for discrete topographic classes characterized by elevation range, aspect, and slope. The distributed approach operates on a 250-m grid, enabling inclusion of terrain shadowing effects, based on the same original meteorological dataset. Despite the fact that the two simulations use the same snowpack model, being potentially subjected to same potential deviation from the parametrization of certain physical processes, the results showed that both approaches accurately reproduced the snowpack distribution over the study period. Slightly (although statistically significantly) better results were obtained by using the distributed approach. The evaluation of the snow cover area with MODIS sensor has shown, on average, a reduction of the Root Mean Squared Error (RMSE) from 15.2% with the semi-distributed approach to 12.6% with the distributed one. Similarly, surface glacier mass balance RMSE decreased from 1.475 m of water equivalent (W.E.) for the semi-distributed simulation to 1.375 m W.E. for the distribution. The improvement, observed with a much higher computational time, does not justify the recommendation of this approach for all applications; however, for simulations that require a precise representation of snowpack distribution, the distributed approach is suggested.

Description: Remote Sensing, Vol. 10, Pages 1169: Multi-Year Analyses of Columnar Aerosol Optical and Microphysical Properties in Xi’an, a Megacity in Northwestern China Remote Sensing doi: 10.3390/rs10081169 Authors: Xiaoli Su Junji Cao Zhengqiang Li Kaitao Li Hua Xu Suixin Liu Xuehua Fan A thorough understanding of aerosol optical properties and their spatio-temporal variability are required to accurately evaluate aerosol effects in the climate system. In this study, a multi-year study of aerosol optical and microphysical properties was firstly performed in Xi’an based on three years of sun photometer remote sensing measurements from 2012 to 2015. The multi-year average of aerosol optical depth (AOD) at 440 nm was about 0.88 ± 0.24 (mean ± SD), while the averaged Ångström Exponent (AE) between 440 and 870 nm was 1.02 ± 0.15. The mean value of single scattering albedo (SSA) was around 0.89 ± 0.03. Aerosol optical depth and AE showed different seasonal variation patterns. Aerosol optical depth was slightly higher in winter (0.99 ± 0.36) than in other seasons (~0.85 ± 0.20), while AE showed its minimum in spring (0.85 ± 0.05) due to the impact of dust episodes. The seasonal variations of volume particle size distribution, spectral refractive index, SSA, and asymmetry factor were also analyzed to characterize aerosols over this region. Based on the aerosol products derived from sun photometer measurements, the classification of aerosol types was also conducted using two different methods in this region. Results show that the dominant aerosol types are absorbers in all seasons, especially in winter, demonstrating the strong absorptivity of aerosols in Xi’an.

Description: Remote Sensing, Vol. 10, Pages 1166: Inventory of Glaciers in the Shaksgam Valley of the Chinese Karakoram Mountains, 1970–2014 Remote Sensing doi: 10.3390/rs10081166 Authors: Haireti Alifu Yukiko Hirabayashi Brian Alan Johnson Jean-Francois Vuillaume Akihiko Kondoh Minoru Urai The Shaksgam Valley, located on the north side of the Karakoram Mountains of western China, is situated in the transition zone between the Indian monsoon system and dry arid climate zones. Previous studies have reported abnormal behaviors of the glaciers in this region compared to the global trend of glacier retreat, so the region is of special interest for glacier-climatological studies. For this purpose, long-term monitoring of glaciers in this region is necessary to obtain a better understanding of the relationships between glacier changes and local climate variations. However, accurate historical and up-to-date glacier inventory data for the region are currently unavailable. For this reason, this study conducted glacier inventories for the years 1970, 1980, 1990, 2000 and 2014 (i.e., a ~10-year interval) using multi-temporal remote sensing imagery. The remote sensing data used included Corona KH-4A/B (1965–1971), Hexagon KH-9 (1980), Landsat Thematic Mapper (TM) (1990/1993), Landsat Enhanced Thematic Mapper Plus (ETM+) (2000/2001), and Landsat Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) (2014/2015) multispectral satellite images, as well as digital elevation models (DEMs) from the Shuttle Radar Topography Mission (SRTM), DEMs generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images (2005–2014), and Advanced Land Observing Satellite (ALOS) World 3D 30 m mesh (AW3D30). In the year 2014, a total of 173 glaciers (including 121 debris-free glaciers) (>0.5 km2), covering an area of 1478 ± 34 km2 (area of debris-free glaciers: 295 ± 7 km2) were mapped. The multi-temporal glacier inventory results indicated that total glacier area change between 1970–2014 was not significant. However, individual glacier changes showed significant variability. Comparisons of the changes in glacier terminus position indicated that 55 (32 debris-covered) glaciers experienced significant advances (~40–1400 m) between 1970–2014, and 74 (32 debris-covered) glaciers experienced significant advances (~40–1400 m) during the most recent period (2000–2014). Notably, small glaciers showed higher sensitivity to climate changes, and the glaciers located in the western part of the study site were exhibiting glacier area expansion compared to other parts of the Shaksgam Valley. Finally, regression analyses indicated that topographic parameters were not the main driver of glacier changes. On the contrary, local climate variability could explain the complex behavior of glaciers in this region.

Description: Symmetry, Vol. 10, Pages 300: Computing Metric Dimension and Metric Basis of 2D Lattice of Alpha-Boron Nanotubes Symmetry doi: 10.3390/sym10080300 Authors: Zafar Hussain Mobeen Munir Maqbool Chaudhary Shin Min Kang Concepts of resolving set and metric basis has enjoyed a lot of success because of multi-purpose applications both in computer and mathematical sciences. For a connected graph G(V,E) a subset W of V(G) is a resolving set for G if every two vertices of G have distinct representations with respect to W. A resolving set of minimum cardinality is called a metric basis for graph G and this minimum cardinality is known as metric dimension of G. Boron nanotubes with different lattice structures, radii and chirality’s have attracted attention due to their transport properties, electronic structure and structural stability. In the present article, we compute the metric dimension and metric basis of 2D lattices of alpha-boron nanotubes.

Description: Symmetry, Vol. 10, Pages 299: Game-Theoretic Solutions for Data Offloading in Next Generation Networks Symmetry doi: 10.3390/sym10080299 Authors: Muhammad Asif Shafi Ullah Khan Rashid Ahmad Dhananjay Singh In recent years, global mobile data traffic has seen an unprecedented increase. This is due to worldwide usage of smart devices, availability of fast internet connections, and the popularity of social media. The Mobile Network Operators (MNOs) are, therefore, facing problems in handling this huge traffic flow. Each type of traffic, including real-time video, audio, and text has its own Quality of Services (QoS) requirements which, if not met, may cause a sufficient loss of profit. Offloading of these traffics can be made more efficient so that values of QoS parameters are enhanced. In this work, we propose an incentive-based game-theoretic frame work for downloading data. The download of each type of data will get an incentive determined by the two-stage Stackelberg game. We model the communication among single Mobile Base Station (MBS) and multiple Access Points (APs) in a crowded metropolitan environment. The leader offers an economic incentive based on the traffic type and followers respond to the incentive and offload traffic accordingly. The model optimizes strategies of both the MBS and APs in order to make the best use of their utilities. For the analysis, we have used a combination of analytical and experimental methods. The numerical outcome characterized a direct process of the best possible offloading ratio and legalized the efficiency of the proposed game. Optimal incentives and optimal offloading was the achievement of our proposed game-theoretic approach. We have implemented the model in MATLAB, and the experimental results show a maximum payoff was achieved and the proposed scheme achieved Nash Equilibria.

Description: Remote Sensing, Vol. 10, Pages 1181: Improved Albedo Estimates Implemented in the METRIC Model for Modeling Energy Balance Fluxes and Evapotranspiration over Agricultural and Natural Areas in the Brazilian Cerrado Remote Sensing doi: 10.3390/rs10081181 Authors: Bruno Silva Oliveira Elisabete Caria Moraes Marcos Carrasco-Benavides Gabriel Bertani Guilherme Augusto Verola Mataveli In this study we assessed METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model performance to estimate energy balance fluxes and evapotranspiration (ET) in two heterogeneous landscapes in the Brazilian Cerrado, including fluxes and ET in both agricultural and natural vegetation. The estimates were evaluated by comparing them to flux tower data collected over sugarcane (USR site), woody savanna (PDG site) and stricto-sensu savanna (RECOR site) areas. The selection of the study years (2005–2007 for USR/PDG sites and 2011–2015 for RECOR site) was based on the availability of meteorological data (to be used as inputs in METRIC) and of flux tower data for energy balance fluxes and ET comparisons. The broadband albedo submodel was adjusted in order to improve Net Radiation estimates. For this adjustment, we applied at-surface solar radiation simulations obtained from the SMARTS2 model under different conditions of land elevation, precipitable water content and solar angles. We also tested the equivalence between the measured crop coefficient (Kc_ec) and the reference evapotranspiration fraction (ETrF or F), seeking to extrapolate from instantaneous to daily values of actual evapotranspiration (ETa). Surface albedo was underestimated by 10% at the USR site (showing a better performance for full crop coverage), by 15% at the PDG site (following the woody savanna dynamics pattern through dry and wet seasons) and was overestimated by 21% at the RECOR site. METRIC was effective in simulating the spatial and temporal variability of energy balance fluxes and ET over agricultural and natural vegetation in the Brazilian Cerrado, with errors within those reported in the literature. Net radiation (Rn) presented consistent results (coefficient of determination (R2) > 0.94) but it was overestimated by 8% and 9% in sugarcane and woody savanna, respectively. METRIC-derived ET estimates showed an agreement with ground data at USR and PDG sites (R2 > 0.88, root mean square error (RMSE) up to 0.87 mm day−1), but at the RECOR site, ET was overestimated by 14% (R2 = 0.96, mean absolute error (MAE) = 0.62 mm.day−1 and RMSE = 0.75 mm day−1). Surface energy balance fluxes and ET were marked by seasonality, with direct dependence on available energy, rainfall distribution, soil moisture and other parameters like albedo and NDVI.

Description: Remote Sensing, Vol. 10, Pages 1182: Detection of Frozen Soil Using Sentinel-1 SAR Data Remote Sensing doi: 10.3390/rs10081182 Authors: Nicolas Baghdadi Hassan Bazzi Mohammad El Hajj Mehrez Zribi The objective of this paper is to evaluate the potential of Sentinel-1 Synthetic Aperture Radar “SAR” data (C-band) for monitoring agricultural frozen soils. First, investigations were conducted from simulated radar signal data using a SAR backscattering model combined with a dielectric mixing model. Then, Sentinel-1 images acquired at a study site near Paris, France were analyzed using temperature data to investigate the potential of the new Sentinel-1 SAR sensor for frozen soil mapping. The results show that the SAR backscattering coefficient decreases when the soil temperature drops below 0 °C. This decrease in signal is the most important for temperatures that ranges between 0 and −5 °C. A difference of at least 2 dB is observed between unfrozen soils and frozen soils. This difference increases under freezing condition when the temperature at the image acquisition date decreases. In addition, results show that the potential of the C-band radar signal for the discrimination of frozen soils slightly decreases when the soil moisture decreases (simulated data were used with soil moisture contents of 20 and 30 vol%). The difference between the backscattering coefficient of unfrozen soil and the backscattering coefficient of frozen soil decreases by approximately 1 dB when the soil moisture decreases from 30 to 20 vol%). Finally, the results show that both VV and VH allow a good detection of frozen soils but the sensitivity of VH is higher by approximately 1.5 dB. In conclusion, this study shows that the difference between a reference image acquired without freezing and an image acquired under freezing conditions is a good tool for detecting frozen soils.

Description: IJGI, Vol. 7, Pages 300: A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080300 Authors: Serajis Salekin Jack H. Burgess Justin Morgenroth Euan G. Mason Dean F. Meason It is common to generate digital elevation models (DEMs) from aerial laser scanning (ALS) data. However, cost and lack of knowledge may preclude its use. In contrast, global navigation satellite systems (GNSS) are seldom used to collect and generate DEMs. These receivers have the potential to be considered as data sources for DEM interpolation, as they can be inexpensive, easy to use, and mobile. The data interpolation method and spatial resolution from this method needs to be optimised to create accurate DEMs. Moreover, the density of GNSS data is likely to affect DEM accuracy. This study investigates three different deterministic approaches, in combination with spatial resolution and data thinning, to determine their combined effects on DEM accuracy. Digital elevation models were interpolated, with resolutions ranging from 0.5 m to 10 m using natural neighbour (NaN), topo to raster (ANUDEM), and inverse distance weighted (IDW) methods. The GNSS data were thinned by 25% (0.389 points m−2), 50% (0.259 points m−2), and 75% (0.129 points m−2) and resulting DEMs were contrast against a DEM interpolated from unthinned data (0.519 points m−2). Digital elevation model accuracy was measured by root mean square error (RMSE) and mean absolute error (MAE). It was found that the highest resolution, 0.5 m, produced the lowest errors in resulting DEMs (RMSE = 0.428 m, MAE = 0.274 m). The ANUDEM method yielded the greatest DEM accuracy from a quantitative perspective (RMSE = 0.305 m and MAE = 0.197 m); however, NaN produced a more visually appealing surface. In all the assessments, IDW showed the lowest accuracy. Thinning the input data by 25% and even 50% had relatively little impact on DEM quality; however, accuracy decreased markedly at 75% thinning (0.129 points m−2). This study showed that, in a time where ALS is commonly used to generate DEMs, GNSS-surveyed data can be used to create accurate DEMs. This study confirmed the need for optimization to choose the appropriate interpolation method and spatial resolution in order to produce a reliable DEM.

Description: Remote Sensing, Vol. 10, Pages 1186: Application of Multi-Sensor Satellite Data for Exploration of Zn–Pb Sulfide Mineralization in the Franklinian Basin, North Greenland Remote Sensing doi: 10.3390/rs10081186 Authors: Amin Beiranvand Pour Tae-Yoon S. Park Yongcheol Park Jong Kuk Hong Basem Zoheir Biswajeet Pradhan Iman Ayoobi Mazlan Hashim Geological mapping and mineral exploration programs in the High Arctic have been naturally hindered by its remoteness and hostile climate conditions. The Franklinian Basin in North Greenland has a unique potential for exploration of world-class zinc deposits. In this research, multi-sensor remote sensing satellite data (e.g., Landsat-8, Phased Array L-band Synthetic Aperture Radar (PALSAR) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) were used for exploring zinc in the trough sequences and shelf-platform carbonate of the Franklinian Basin. A series of robust image processing algorithms was implemented for detecting spatial distribution of pixels/sub-pixels related to key alteration mineral assemblages and structural features that may represent potential undiscovered Zn–Pb deposits. Fusion of Directed Principal Component Analysis (DPCA) and Independent Component Analysis (ICA) was applied to some selected Landsat-8 mineral indices for mapping gossan, clay-rich zones and dolomitization. Major lineaments, intersections, curvilinear structures and sedimentary formations were traced by the application of Feature-oriented Principal Components Selection (FPCS) to cross-polarized backscatter PALSAR ratio images. Mixture Tuned Matched Filtering (MTMF) algorithm was applied to ASTER VNIR/SWIR bands for sub-pixel detection and classification of hematite, goethite, jarosite, alunite, gypsum, chalcedony, kaolinite, muscovite, chlorite, epidote, calcite and dolomite in the prospective targets. Using the remote sensing data and approaches, several high potential zones characterized by distinct alteration mineral assemblages and structural fabrics were identified that could represent undiscovered Zn–Pb sulfide deposits in the study area. This research establishes a straightforward/cost-effective multi-sensor satellite-based remote sensing approach for reconnaissance stages of mineral exploration in hardly accessible parts of the High Arctic environments.

Description: Remote Sensing, Vol. 10, Pages 1184: An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States Remote Sensing doi: 10.3390/rs10081184 Authors: Ian W. Housman Robert A. Chastain Mark V. Finco The Operational Remote Sensing (ORS) program leverages Landsat and MODIS data to detect forest disturbances across the conterminous United States (CONUS). The ORS program was initiated in 2014 as a collaboration between the US Department of Agriculture Forest Service Geospatial Technology and Applications Center (GTAC) and the Forest Health Assessment and Applied Sciences Team (FHAAST). The goal of the ORS program is to supplement the Insect and Disease Survey (IDS) and MODIS Real-Time Forest Disturbance (RTFD) programs with imagery-derived forest disturbance data that can be used to augment traditional IDS data. We developed three algorithms and produced ORS forest change products using both Landsat and MODIS data. These were assessed over Southern New England and the Rio Grande National Forest. Reference data were acquired using TimeSync to conduct an independent accuracy assessment of IDS, RTFD, and ORS products. Overall accuracy for all products ranged from 71.63% to 92.55% in the Southern New England study area and 63.48% to 79.13% in the Rio Grande National Forest study area. While the accuracies attained from the assessed products are somewhat low, these results are similar to comparable studies. Although many ORS products met or exceeded the overall accuracy of IDS and RTFD products, the differences were largely statistically insignificant at the 95% confidence interval. This demonstrates the current implementation of ORS is sufficient to provide data to augment IDS data.

Description: Remote Sensing, Vol. 10, Pages 1196: Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China Remote Sensing doi: 10.3390/rs10081196 Authors: Davide Fornacca Guopeng Ren Wen Xiao Remote mountainous regions are among the Earth’s last remaining wild spots, hosting rare ecosystems and rich biodiversity. Because of access difficulties and low population density, baseline information about natural and human-induced disturbances in these regions is often limited or nonexistent. Landsat time series offer invaluable opportunities to reconstruct past land cover changes. However, the applicability of this approach strongly depends on the availability of good quality, cloud-free images, acquired at a regular time interval, which in mountainous regions are often difficult to find. The present study analyzed burn scar detection capabilities of 11 widely used spectral indices (SI) at 1 to 5 years after fire events in four dominant vegetation groups in a mountainous region of northwest Yunnan, China. To evaluate their performances, we used M-statistic as a burned-unburned class separability index, and we adapted an existing metric to quantify the SI residual burn signal at post-fire dates compared to the maximum severity recorded soon after the fire. Our results show that Normalized Burn Ratio (NBR) and Normalized Difference Moisture Index (NDMI) are always among the three best performers for the detection of burn scars starting 1 year after fire but not for the immediate post-fire assessment, where the Mid Infrared Burn Index, Burn Area Index, and Tasseled Cap Greenness were superior. Brightness and Wetness peculiar patterns revealed long-term effects of fire in vegetated land, suggesting their potential integration to assist other SI in burned area detection several years after the fire event. However, in general, class separability of most of the SI was poor after one growing season, due to the seasonal rains and the relatively fast regrowth rate of shrubs and grasses, confirming the difficulty of assessment in mountainous ecosystems. Our findings are meaningful for the selection of a suitable SI to integrate in burned area detection workflows, according to vegetation type and time lag between image acquisitions.

Description: Remote Sensing, Vol. 10, Pages 1192: Automating Parameter Learning for Classifying Terrestrial LiDAR Point Cloud Using 2D Land Cover Maps Remote Sensing doi: 10.3390/rs10081192 Authors: Chen-Chieh Feng Zhou Guo The automating classification of point clouds capturing urban scenes is critical for supporting applications that demand three-dimensional (3D) models. Achieving this goal, however, is met with challenges because of the varying densities of the point clouds and the complexity of the 3D data. In order to increase the level of automation in the point cloud classification, this study proposes a segment-based parameter learning method that incorporates a two-dimensional (2D) land cover map, in which a strategy of fusing the 2D land cover map and the 3D points is first adopted to create labelled samples, and a formalized procedure is then implemented to automatically learn the following parameters of point cloud classification: the optimal scale of the neighborhood for segmentation, optimal feature set, and the training classifier. It comprises four main steps, namely: (1) point cloud segmentation; (2) sample selection; (3) optimal feature set selection; and (4) point cloud classification. Three datasets containing the point cloud data were used in this study to validate the efficiency of the proposed method. The first two datasets cover two areas of the National University of Singapore (NUS) campus while the third dataset is a widely used benchmark point cloud dataset of Oakland, Pennsylvania. The classification parameters were learned from the first dataset consisting of a terrestrial laser-scanning data and a 2D land cover map, and were subsequently used to classify both of the NUS datasets. The evaluation of the classification results showed overall accuracies of 94.07% and 91.13%, respectively, indicating that the transition of the knowledge learned from one dataset to another was satisfactory. The classification of the Oakland dataset achieved an overall accuracy of 97.08%, which further verified the transferability of the proposed approach. An experiment of the point-based classification was also conducted on the first dataset and the result was compared to that of the segment-based classification. The evaluation revealed that the overall accuracy of the segment-based classification is indeed higher than that of the point-based classification, demonstrating the advantage of the segment-based approaches.

Description: Remote Sensing, Vol. 10, Pages 1197: Quantifying Drought Propagation from Soil Moisture to Vegetation Dynamics Using a Newly Developed Ecohydrological Land Reanalysis Remote Sensing doi: 10.3390/rs10081197 Authors: Yohei Sawada Despite the importance of the interaction between soil moisture and vegetation dynamics to understand the complex nature of drought, few land reanalyses explicitly simulate vegetation growth and senescence. In this study, I provide a new land reanalysis which explicitly simulates the interaction between sub-surface soil moisture and vegetation dynamics by the sequential assimilation of satellite microwave brightness temperature observations into a land surface model (LSM). Assimilating satellite microwave brightness temperature observations improves the skill of a LSM to simultaneously simulate soil moisture and the seasonal cycle of leaf area index (LAI). By analyzing soil moisture and LAI simulated by this new land reanalysis, I identify the drought events which significantly damage LAI on the climatological day-of-year of the LAI’s seasonal peak and quantify drought propagation from soil moisture to LAI in the global snow-free region. On average, soil moisture in the shallow soil layers (0–0.45 m) quickly recovers from the drought condition before the climatological day-of-year of the LAI’s seasonal peak while soil moisture in the deeper soil layer (1.05–2.05 m) and LAI recover from the drought condition approximately 100 days after the climatological day-of-year of the LAI’s seasonal peak.

Description: Remote Sensing, Vol. 10, Pages 1202: Potential of Photochemical Reflectance Index for Indicating Photochemistry and Light Use Efficiency in Leaves of European Beech and Norway Spruce Trees Remote Sensing doi: 10.3390/rs10081202 Authors: Daniel Kováč Petra Veselovská Karel Klem Kristýna Večeřová Alexander Ač Josep Peñuelas Otmar Urban Hyperspectral reflectance is becoming more frequently used for measuring the functions and productivity of ecosystems. The purpose of this study was to re-evaluate the potential of the photochemical reflectance index (PRI) for evaluating physiological status of plants. This is needed because the reasons for variation in PRI and its relationships to physiological traits remain poorly understood. We examined the relationships between PRI and photosynthetic parameters in evergreen Norway spruce and deciduous European beech grown in controlled conditions during several consecutive periods of 10–12 days between which the irradiance and air temperature were changed stepwise. These regime changes induced significant changes in foliar biochemistry and physiology. The responses of PRI corresponded particularly to alterations in the actual quantum yield of photosystem II photochemistry (ΦPSII). Acclimation responses of both species led to loss of PRI sensitivity to light use efficiency (LUE). The procedure of measuring PRI at multiple irradiance-temperature conditions has been designed also for testing accuracy of ΔPRI in estimating LUE. A correction mechanism of subtracting daily measured PRI from early morning PRI has been performed to account for differences in photosynthetic pigments between irradiance-temperature regimes. Introducing ΔPRI, which provided a better estimate of non-photochemical quenching (NPQ) compared to PRI, also improved the accuracy of LUE estimation. Furthermore, ΔPRI was able to detect the effect of drought, which is poorly observable from PRI.

Description: Remote Sensing, Vol. 10, Pages 1200: Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images Remote Sensing doi: 10.3390/rs10081200 Authors: Xin Zhang Bingfang Wu Guillermo E. Ponce-Campos Miao Zhang Sheng Chang Fuyou Tian Rice is a staple food in East Asia and Southeast Asia—an area that accounts for more than half of the world’s population, and 11% of its cultivated land. Studies on rice monitoring can provide direct or indirect information on food security, and water source management. Remote sensing has proven to be the most effective method for the large-scale monitoring of croplands, by using temporary and spectral information. The Google Earth Engine (GEE) is a cloud-based platform providing access to high-performance computing resources for processing extremely large geospatial datasets. In this study, by leveraging the computational power of GEE and a large pool of satellite and other geophysical data (e.g., forest and water extent maps, with high accuracy at 30 m), we generated the first up-to-date rice extent map with crop intensity, at 10 m resolution in the three provinces with the highest rice production in China (the Heilongjiang, Hunan and Guangxi provinces). Optical and synthetic aperture radar (SAR) data were monthly and metric composited to ensure a sufficient amount of up-to-date data without cloud interference. To remove the common confounding noise in the pixel-based classification results at medium to high resolution, we integrated the pixel-based classification (using a random forest classifier) result with the object-based segmentation (using a simple linear iterative clustering (SLIC) method). This integration resulted in the rice planted area data that most closely resembled official statistics. The overall accuracy was approximately 90%, which was validated by ground crop field points. The F scores reached 87.78% in the Heilongjiang Province for monocropped rice, 89.97% and 80.00% in the Hunan Province for mono- and double-cropped rice, respectively, and 88.24% in the Guangxi Province for double-cropped rice.

Description: Symmetry, Vol. 10, Pages 315: Long Time Behavior and Global Dynamics of Simplified Von Karman Plate Without Rotational Inertia Driven by White Noise Symmetry doi: 10.3390/sym10080315 Authors: Huatao Chen Dengqing Cao Jingfei Jiang Xiaoming Fan Without the assumption that the coefficient of weak damping is large enough, the existence of the global random attractors for simplified Von Karman plate without rotational inertia driven by either additive white noise or multiplicative white noise are proved. Instead of the classical splitting method, the techniques to verify the asymptotic compactness rely on stabilization estimation of the system. Furthermore, a clear relationship between in-plane components of the external force that act on the edge of the plate and the expectation of radius of the global random attractors can be obtained from the theoretical results. Based on the relationship between global random attractor and random probability invariant measure, the global dynamics of the plates are analyzed numerically. With increasing the in-plane components of the external force that act on the edge of the plate, global D-bifurcation, secondary global D-bifurcation and complex local dynamical behavior occur in motion of the system. Moreover, increasing the intensity of white noise leads to the dynamical behavior becoming simple. The results on global dynamics reveal that random snap-through which seems to be a complex dynamics intuitively is essentially a simple dynamical behavior.

Description: Symmetry, Vol. 10, Pages 314: Systematic Review of Decision Making Algorithms in Extended Neutrosophic Sets Symmetry doi: 10.3390/sym10080314 Authors: Mohsin Khan Le Hoang Son Mumtaz Ali Hoang Thi Minh Chau Nguyen Thi Nhu Na Florentin Smarandache The Neutrosophic set (NS) has grasped concentration by its ability for handling indeterminate, uncertain, incomplete, and inconsistent information encountered in daily life. Recently, there have been various extensions of the NS, such as single valued neutrosophic sets (SVNSs), Interval neutrosophic sets (INSs), bipolar neutrosophic sets (BNSs), Refined Neutrosophic Sets (RNSs), and triangular fuzzy number neutrosophic set (TFNNs). This paper contains an extended overview of the concept of NS as well as several instances and extensions of this model that have been introduced in the last decade, and have had a significant impact in literature. Theoretical and mathematical properties of NS and their counterparts are discussed in this paper as well. Neutrosophic-set-driven decision making algorithms are also overviewed in detail.

Description: Symmetry, Vol. 10, Pages 311: On p-Adic Fermionic Integrals of q-Bernstein Polynomials Associated with q-Euler Numbers and Polynomials † Symmetry doi: 10.3390/sym10080311 Authors: Lee-Chae Jang Taekyun Kim Dae San Kim Dmitry Victorovich Dolgy We study a q-analogue of Euler numbers and polynomials naturally arising from the p-adic fermionic integrals on Zp and investigate some properties for these numbers and polynomials. Then we will consider p-adic fermionic integrals on Zp of the two variable q-Bernstein polynomials, recently introduced by Kim, and demonstrate that they can be written in terms of the q-analogues of Euler numbers. Further, from such p-adic integrals we will derive some identities for the q-analogues of Euler numbers.

Description: Symmetry, Vol. 10, Pages 312: Connecting Electroweak Symmetry Breaking and Flavor: A Light Dilaton D and a Sequential Heavy Quark Doublet Q Symmetry doi: 10.3390/sym10080312 Authors: Wei-Shu Hou The 125 GeV boson is quite consistent with the Higgs boson of the Standard Model (SM), but there is a challenge from Anderson as to whether this particle is in the Lagrangian. As Large Hadron Collider (LHC) Run 2 enters its final year of running, we ought to reflect and make sure we have gotten everything right. The ATLAS and CMS combined Run 1 analysis claimed a measurement of 5.4σ vector boson fusion (VBF) production which is consistent with SM, which seemingly refutes Anderson. However, to verify the source of electroweak symmetry breaking (EWSB), we caution that VBF measurement is too important for us to be imprudent in any way, and gluon–gluon fusion (ggF) with similar tag jets must be simultaneous measured, which should be achievable in LHC Run 2. The point is to truly test the dilaton possibility—the pseudo-Goldstone boson of scale invariance violation. We illustrate EWSB by dynamical mass generation of a sequential quark doublet (Q) via its ultrastrong Yukawa coupling and argue how this might be consistent with a 125 GeV dilaton, D. The ultraheavy 2mQ≳4–5 TeV scale explains the absence of New Physics so far, while the mass generation mechanism shields us from the UV theory for the strong Yukawa coupling. Collider and flavor physics implications are briefly touched upon. Current Run 2 analyses show correlations between the ggF and VBF measurements, but the newly observed tt¯H production at LHC poses a challenge.

Description: Symmetry, Vol. 10, Pages 305: Evaluation of a Third-Party Logistics (3PL) Provider Using a Rough SWARA–WASPAS Model Based on a New Rough Dombi Agregator Symmetry doi: 10.3390/sym10080305 Authors: Siniša Sremac Željko Stević Dragan Pamučar Miloš Arsić Bojan Matić For companies active in various sectors, the implementation of transport services and other logistics activities has become one of the key factors of efficiency in the total supply chain. Logistics outsourcing is becoming more and more important, and there is an increasing number of third party logistics providers. In this paper, logistics providers were evaluated using the Rough SWARA (Step-Wise Weight Assessment Ratio Analysis) and Rough WASPAS (Weighted Aggregated Sum Product Assessment) models. The significance of the eight criteria on the basis of which evaluation was carried out was determined using the Rough SWARA method. In order to allow for a more precise consensus in group decision-making, the Rough Dombi aggregator was developed in order to determine the initial rough matrix of multi-criteria decision-making. A total of 10 logistics providers dealing with the transport of dangerous goods for chemical industry companies were evaluated using the Rough WASPAS approach. The obtained results demonstrate that the first logistics provider is also the best one, a conclusion confirmed by a sensitivity analysis comprised of three parts. In the first part, parameter ρ was altered through 10 scenarios in which only alternatives four and five change their ranks. In the second part of the sensitivity analysis, a calculation was performed using the following approaches: Rough SAW (Simple Additive Weighting), Rough EDAS (Evaluation Based on Distance from Average Solution), Rough MABAC (MultiAttributive Border Approximation Area Comparison), and Rough TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). They showed a high correlation of ranks determined by applying Spearman’s correlation coefficient in the third part of the sensitivity analysis.

Description: IJGI, Vol. 7, Pages 311: Processing BIM and GIS Models in Practice: Experiences and Recommendations from a GeoBIM Project in The Netherlands ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080311 Authors: Ken Arroyo Ohori Abdoulaye Diakité Thomas Krijnen Hugo Ledoux Jantien Stoter It is widely acknowledged that the integration of BIM and GIS data is a crucial step forward for future 3D city modelling, but most of the research conducted so far has covered only the high-level and semantic aspects of GIS-BIM integration. This paper presents the results of the GeoBIM project, which tackled three integration problems focussing instead on aspects involving geometry processing: (i) the automated processing of complex architectural IFC models; (ii) the integration of existing GIS subsoil data in BIM; and (iii) the georeferencing of BIM models for their use in GIS software. All the problems have been studied using real world models and existing datasets made and used by practitioners in The Netherlands. For each problem, this paper exposes in detail the issues faced, proposed solutions, and recommendations for a more successful integration.

Description: Symmetry, Vol. 10, Pages 318: Detectability Improved Tamper Detection Scheme for Absolute Moment Block Truncation Coding Compressed Images Symmetry doi: 10.3390/sym10080318 Authors: Wien Hong Xiaoyu Zhou Der-Chyuan Lou Xiaoqin Huang Cancan Peng Since digital media is gaining popularity nowadays, people are more concerned about its integrity protection and authentication since tampered media may result in unexpected problems. Considering a better media protection technique, this paper proposes an efficient tamper detection scheme for absolute moment block truncation coding (AMBTC) compressed images. In AMBTC, each image block is represented by two quantization levels (QLs) and a bitmap. Requiring insignificant computation cost, it attracts not only a wide range of application developers, but also a variety of studies to investigate the authentication of its codes. While the existing methods protect the AMBTC codes to a large extent, the leakage of some unprotected codes may be insensitive to intentional tampering. The proposed method fully protects the AMBTC codes by embedding authentication codes (ACs) into QLs. Meanwhile, the most significant bits of QLs are symmetrically perturbed to generate the candidates of ACs. The ACs that cause the minimum distortion are embedded into the least significant bits of QLs to minimize the distortion. When compared with prior works, the experimental results reveal that the proposed method offers a significant sensitivity-of-tamper property while providing a comparable image quality.

Description: Remote Sensing, Vol. 10, Pages 1211: Evaluating Metal Effects on the Reflectance Spectra of Plant Leaves during Different Seasons in Post-Mining Areas, China Remote Sensing doi: 10.3390/rs10081211 Authors: Chao Zhou Shengbo Chen Yuanzhi Zhang Jianhua Zhao Derui Song Dawei Liu This study examined the relationship between the leaf reflectance of different seasons and the concentration of heavy metal elements in leaves, such as Co, Cu, Mo, and Ni in a post-mining area. The reflectance spectra and leaf samples of three typical plants were measured and collected in a whole growth cycle (June, July, August, and September). The Red Edge Position (REP), Readjustment Normalized Difference Vegetation Index (RE-NDVI), and Photochemical Reflectance Index (PRI) were extracted and used to explore its relation with the heavy metals concentrations in leaves between different seasons. The results show that all three Vegetation Indices (VIs) were insensitive indicators for monitoring the metal effects of vegetation in different seasons, which showed similar trends. Based on this, the Continuum Removal Indices (CRIs) were proposed from the continuum removed approach and extended for detecting the effects of heavy metal pollution over a full growth cycle. The relationship between the metal concentrations and CRIs of different plants was respectively analyzed by Stepwise Multiple Linear Regression (SMLR) and Partial Least Squares Regression (PLSR). It is found that a significant correlation exists between the band depth and the concentration of Cu and Ni based on the White birch data sets using the PLSR, resulting in a small deviation from the established relationships. Compared with VIs, the approach of coupling CRIs and multiple regressions was effective for improving the estimation accuracy. The presented study provides a detection model of leaf heavy metals that can be adapted to different growing cycles, even an arbitrary growing cycle.

Description: Symmetry, Vol. 10, Pages 319: CRCM: A New Combined Data Gathering and Energy Charging Model for WRSN Symmetry doi: 10.3390/sym10080319 Authors: Yuhou Wang Ying Dong Shiyuan Li Hao Wu Mengyao Cui With the development of wireless sensor networks (WSNs), the problem about how to increase the lifecycle of the WSNs is always a hot discussion point, and some researchers have devoted to the ‘energy saving’ to decrease the energy consumption of the sensor nodes by different algorithms. However, the fundamental technique is ‘energy acquiring’ for the battery which can solve the limited capacity problem. In this paper, we study the data gathering and energy charging by a mobile charger (MC) at the same time that most energy consumption can be saved by short communication distance. We have named this as the recharging model-combined recharging and collecting data model on-demand (CRCM). Firstly, the hexagon-based (HB) algorithm is proposed to sort all sensor nodes in the region to make data collecting and energy charging work at the same time. Then we consider both residual energy and geographic position (REGP) of the sensor node to calculate the priority of each cluster. Thirdly, the dynamic mobile charger (DMC) algorithm is proposed to calculate the number of MCs to make sure no sensor node will die in each charging queue. Finally, the simulations show that our REGP algorithm is better than Earliest Deadline First (EDF) and Nearest-Job-Next with Preemption (NJNP), and the DMC plays well when the number of sensor nodes increase.

Description: Remote Sensing, Vol. 10, Pages 1234: HOMPC: A Local Feature Descriptor Based on the Combination of Magnitude and Phase Congruency Information for Multi-Sensor Remote Sensing Images Remote Sensing doi: 10.3390/rs10081234 Authors: Zhitao Fu Qianqing Qin Bin Luo Hong Sun Chun Wu Local region description of multi-sensor images remains a challenging task in remote sensing image analysis and applications due to the non-linear radiation variations between images. This paper presents a novel descriptor based on the combination of the magnitude and phase congruency information of local regions to capture the common features of images with non-linear radiation changes. We first propose oriented phase congruency maps (PCMs) and oriented magnitude binary maps (MBMs) using the multi-oriented phase congruency and magnitude information of log-Gabor filters. The two feature vectors are then quickly constructed based on the convolved PCMs and MBMs. Finally, a dense descriptor named the histograms of oriented magnitude and phase congruency (HOMPC) is developed by combining the histograms of oriented phase congruency (HPC) and the histograms of oriented magnitude (HOM) to capture the structure and shape properties of local regions. HOMPC was evaluated with three datasets composed of multi-sensor remote sensing images obtained from unmanned ground vehicle, unmanned aerial vehicle, and satellite platforms. The descriptor performance was evaluated by recall, precision, F1-measure, and area under the precision-recall curve. The experimental results showed the advantages of the HOM and HPC combination and confirmed that HOMPC is far superior to the current state-of-the-art local feature descriptors.

Description: Remote Sensing, Vol. 10, Pages 1236: Progressive Degradation of an Ice Rumple in the Thwaites Ice Shelf, Antarctica, as Observed from High-Resolution Digital Elevation Models Remote Sensing doi: 10.3390/rs10081236 Authors: Seung Hee Kim Duk-jin Kim Hyun-Cheol Kim Ice rumples are locally-grounded features of flowing ice shelves, elevated tens of meters above the surrounding surface. These features may significantly impact the dynamics of ice-shelf grounding lines, which are strongly related to shelf stability. In this study, we used TanDEM-X data to construct high-resolution DEMs of the Thwaites ice shelf in West Antarctica from 2011 to 2013. We also generated surface deformation maps which allowed us to detect and monitor the elevation changes of an ice rumple that appeared sometime between the observations of a grounding line of the Thwaites glacier using Double-Differential Interferometric SAR (DDInSAR) in 1996 and 2011. The observed degradation of the ice rumple during 2011–2013 may be related to a loss of contact with the underlying bathymetry caused by the thinning of the ice shelf. We subsequently used a viscoelastic deformation model with a finite spherical pressure source to reproduce the surface expression of the ice rumple. Global optimization allowed us to fit the model to the observed deformation map, producing reasonable estimates of the ice thickness at the center of the pressure source. Our conclusion is that combining the use of multiple high-resolution DEMs and the simple viscoelastic deformation model is feasible for observing and understanding the transient nature of small ice rumples, with implications for monitoring ice shelf stability.

Description: Remote Sensing, Vol. 10, Pages 1232: Assessing Coastal SMAP Surface Salinity Accuracy and Its Application to Monitoring Gulf of Maine Circulation Dynamics Remote Sensing doi: 10.3390/rs10081232 Authors: Semyon A. Grodsky Douglas Vandemark Hui Feng Monitoring the cold and productive waters of the Gulf of Maine and their interactions with the nearby northwestern (NW) Atlantic shelf is important but challenging. Although remotely sensed sea surface temperature (SST), ocean color, and sea level have become routine, much of the water exchange physics is reflected in salinity fields. The recent invention of satellite salinity sensors, including the Soil Moisture Active Passive (SMAP) radiometer, opens new prospects in regional shelf studies. However, local sea surface salinity (SSS) retrieval is challenging due to both cold SST limiting salinity sensor sensitivity and proximity to land. For the NW Atlantic, our analysis shows that SMAP SSS is subject to an SST-dependent bias that is negative and amplifies in winter and early spring due to the SST-related drop in SMAP sensor sensitivity. On top of that, SMAP SSS is subject to a land contamination bias. The latter bias becomes noticeable and negative when the antenna land contamination factor (LC) exceeds 0.2%, and attains maximum negative values at LC = 0.4%. Coastward of LC = 0.5%, a significant positive land contamination bias in absolute SMAP SSS is evident. SST and land contamination bias components are seasonally dependent due to seasonal changes in SST/winds and terrestrial microwave properties. Fortunately, it is shown that SSS anomalies computed relative to a satellite SSS climatology can effectively remove such seasonal biases along with the real seasonal cycle. SMAP monthly SSS anomalies have sufficient accuracy and applicability to extend nearer to the coasts. They are used to examine the Gulf of Maine water inflow, which displayed important water intrusions in between Georges Banks and Nova Scotia in the winters of 2016/17 and 2017/18. Water intrusion patterns observed by SMAP are generally consistent with independent measurements from the European Soil Moisture Ocean Salinity (SMOS) mission. Circulation dynamics related to the 2016/2017 period and enhanced wind-driven Scotian Shelf transport into the Gulf of Maine are discussed.

Description: Remote Sensing, Vol. 10, Pages 1230: Assisting Flood Disaster Response with Earth Observation Data and Products: A Critical Assessment Remote Sensing doi: 10.3390/rs10081230 Authors: Guy J-P. Schumann G. Robert Brakenridge Albert J. Kettner Rashid Kashif Emily Niebuhr Floods are among the top-ranking natural disasters in terms of annual cost in insured and uninsured losses. Since high-impact events often cover spatial scales that are beyond traditional regional monitoring operations, remote sensing, in particular from satellites, presents an attractive approach. Since the 1970s, there have been many studies in the scientific literature about mapping and monitoring of floods using data from various sensors onboard different satellites. The field has now matured and hence there is a general consensus among space agencies, numerous organizations, scientists, and end-users to strengthen the support that satellite missions can offer, particularly in assisting flood disaster response activities. This has stimulated more research in this area, and significant progress has been achieved in recent years in fostering our understanding of the ways in which remote sensing can support flood monitoring and assist emergency response activities. This paper reviews the products and services that currently exist to deliver actionable information about an ongoing flood disaster to emergency response operations. It also critically discusses requirements, challenges and perspectives for improving operational assistance during flood disaster using satellite remote sensing products.

Description: Symmetry, Vol. 10, Pages 323: Temperature-Dependent s± ↔ s++ Transitions in the Multiband Model for Fe-Based Superconductors with Impurities Symmetry doi: 10.3390/sym10080323 Authors: V. A. Shestakov M. M. Korshunov O. V. Dolgov We study the dependence of the superconducting gaps on both the disorder and the temperature within the two-band model for iron-based materials. In the clean limit, the system is in the s± state with sign-changing gaps. Scattering by nonmagnetic impurities leads to the change of the sign of the smaller gap, resulting in a transition from the s± to the s++ state with the sign-preserving gaps. We show here that the transition is temperature-dependent. Thus, there is a line of s±→s++ transition in the temperature–disorder phase diagram. There is a narrow range of impurity scattering rates, where the disorder-induced s±→s++ transition occurs at low temperatures, but then the low-temperature s++ state transforms back to the s± state at higher temperatures. With increasing impurity scattering rate, the temperature of such s++→s± transition shifts to the critical temperature Tc, and only the s++ state is left for higher amounts of disorder.

Description: Remote Sensing, Vol. 10, Pages 1228: Generic and Automatic Markov Random Field-Based Registration for Multimodal Remote Sensing Image Using Grayscale and Gradient Information Remote Sensing doi: 10.3390/rs10081228 Authors: Li Yan Ziqi Wang Yi Liu Zhiyun Ye The automatic image registration serves as a technical prerequisite for multimodal remote sensing image fusion. Meanwhile, it is also the technical basis for change detection, image stitching and target recognition. The demands of subpixel level registration accuracy can be rarely satisfied with a multimodal image registration method based on feature matching. In light of this, we propose a Generic and automatic Markov Random Field (MRF)-based registration framework of multimodal image using grayscale and gradient information. The proposed approach performs non-rigid registration and formulates an MRF model while grayscale and gradient statistical information of a multimodal image is employed for the evaluation of similarity while the spatial weighting function is optimized simultaneously. Besides, the value space is discretized to improve the convergence speed. The developed automatic approach was validated both qualitatively and quantitatively, demonstrating its potential for a variety of multimodal remote sensing datasets and scenes. As for the registration accuracy, the average target registration error of the proposed framework is less than 1 pixel, while the maximum displacement error is less than 1 pixel. Compared with the polynomial model registration based on manual selection, the registration accuracy has been significantly improved. In the meantime, the proposed approach had the partial applicability for the multimodal image registration of large deformation scenes. It is also proved that the proposed registration framework using grayscale and gradient information outperforms the MRF-based registration using only grayscale information and only gradient information while the proposed registration framework using Gaussian function as spatial weighting function is superior to that using distance inverse weight method.

Description: Remote Sensing, Vol. 10, Pages 1242: Ocean Wave Measurement Using Short-Range K-Band Narrow Beam Continuous Wave Radar Remote Sensing doi: 10.3390/rs10081242 Authors: Jian Cui Ralf Bachmayer Brad deYoung Weimin Huang We describe a technique to measure ocean wave period, height and direction. The technique is based on the characteristics of transmission and backscattering of short-range K-band narrow beam continuous wave radar at the sea surface. The short-range K-band radar transmits and receives continuous signals close to the sea surface at a low-grazing angle. By sensing the motions of a dominant facet at the sea surface that strongly scatters signals back and is located directly in front of the radar, the wave orbital velocity can be measured from the Doppler shift of the received radar signal. The period, height and direction of ocean wave are determined from the relationships among wave orbital velocity, ocean wave characteristics and the Doppler shift. Numerical simulations were performed to validate that the dominant facet exists and ocean waves are measured by sensing its motion. Validation experiments were conducted in a wave tank to verify the feasibility of the proposed ocean wave measurement method. The results of simulations and experiments demonstrate the effectiveness of the short-range K-band narrow beam continuous wave radar for the measurement of ocean waves.

Description: Remote Sensing, Vol. 10, Pages 1241: Troposphere Water Vapour Tomography: A Horizontal Parameterised Approach Remote Sensing doi: 10.3390/rs10081241 Authors: Qingzhi Zhao Yibin Yao Wanqiang Yao Global Navigation Satellite System (GNSS) troposphere tomography has become one of the most cost-effective means to obtain three-dimensional (3-d) image of the tropospheric water vapour field. Traditional methods divide the tomography area into a number of 3-d voxels and assume that the water vapour density at any voxel is a constant during the given period. However, such behaviour breaks the spatial continuity of water vapour density in a horizontal direction and the number of unknown parameters needing to be estimated is very large. This is the focus of the paper, which tries to reconstruct the water vapor field using the tomographic technique without imposing empirical horizontal and vertical constraints. The proposed approach introduces the layered functional model in each layer vertically and only an a priori constraint is imposed for the water vapor information at the location of the radiosonde station. The elevation angle mask of 30° is determined according to the distribution of intersections between the satellite rays and different layers, which avoids the impact of ray bending and the error in slant water vapor (SWV) at low elevation angles on the tomographic result. Additionally, an optimal weighting strategy is applied to the established tomographic model to obtain a reasonable result. The tomographic experiment is performed using Global Positioning System (GPS) data of 12 receivers derived from the Satellite Positioning Reference Station Network (SatRef) in Hong Kong. The quality of the established tomographic model is validated under different weather conditions and compared with the conventional tomography method using 31-day data, respectively. The numerical result shows that the proposed method is applicable and superior to the traditional one. Comparisons of integrated water vapour (IWV) of the proposed method with that derived from radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data show that the root mean square (RMS)/Bias of their differences are 3.2/−0.8 mm and 3.3/−1.7 mm, respectively, while the values of traditional method are 5.1/−3.9 mm and 6.3/−5.9 mm, respectively. Furthermore, the water vapour density profiles are also compared with radiosonde and ECMWF data, and the values of RMS/Bias error for the proposed method are 0.88/0.06 g/m3 and 0.92/−0.08 g/m3, respectively, while the values of the traditional method are 1.33/0.38 g/m3 and 1.59/0.40 g/m3, respectively.

Description: Remote Sensing, Vol. 10, Pages 1237: Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data Remote Sensing doi: 10.3390/rs10081237 Authors: Rebecca Del’ Papa Moreira Scafutto Carlos Roberto de Souza Filho Methane (CH4) display spectral features in several regions of the infrared range (0.75–14 µm), which can be used for the remote mapping of emission sources through the detection of CH4 plumes from natural seeps and leaks. Applications of hyperspectral remote sensing techniques for the detection of CH4 in the near and shortwave infrared (NIR-SWIR: 0.75–3 µm) and longwave infrared (LWIR: 7–14 µm) have been demonstrated in the literature with multiple sensors and scenarios. However, the acquisition and processing of hyperspectral data in the midwave infrared (MWIR: 3–5 µm) for this application is rather scarce. Here, a controlled field experiment was used to evaluate the potential for CH4 plume detection in the MWIR based on hyperspectral data acquired with the SEBASS airborne sensor. For comparison purposes, LWIR data were also acquired simultaneously with the same instrument. The experiment included surface and undersurface emission sources (ground stations), with flow rates ranging between 0.6–40 m3/h. The data collected in both ranges were sequentially processed using the same methodology. The CH4 plume was detected, variably, in both datasets. The gas plume was detected in all LWIR images acquired over nine gas leakage stations. In the MWIR range, the plume was detected in only four stations, wherein 18 m3/h was the lowest flux sensed. We demonstrate that the interference of target reflectance, the low contrast between plume and background and a low signal of the CH4 feature in the MWIR at ambient conditions possibly explain the inferior results observed for this range when compared to LWIR. Furthermore, we show that the acquisition time and weather conditions, including specific limits of temperature, humidity, and wind speed, proved critical for plume detection using daytime MWIR hyperspectral data.

Description: Remote Sensing, Vol. 10, Pages 1238: Refining Land Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very High Resolution Remote Sensing Images Remote Sensing doi: 10.3390/rs10081238 Authors: Guoqing Cui Zhiyong Lv Guangfei Li Jón Atli Benediktsson Yudong Lu Land cover classification that uses very high resolution (VHR) remote sensing images is a topic of considerable interest. Although many classification methods have been developed, the accuracy and usability of classification systems can still be improved. In this paper, a novel post-processing approach based on a dual-adaptive majority voting strategy (D-AMVS) is proposed to improve the performance of initial classification maps. D-AMVS defines a strategy for refining each label of a classified map that is obtained by different classification methods from the same original image, and fusing the different refined classification maps to generate a final classification result. The proposed D-AMVS contains three main blocks. (1) An adaptive region is generated by gradually extending the region around a central pixel based on two predefined parameters (T1 and T2) to utilize the spatial feature of ground targets in a VHR image. (2) For each classified map, the label of the central pixel is refined according to the majority voting rule within the adaptive region. This is defined as adaptive majority voting. Each initial classified map is refined in this manner pixel by pixel. (3) Finally, the refined classified maps are used to generate a final classification map, and the label of the central pixel in the final classification map is determined by applying AMV again. Each entire classified map is scanned and refined pixel by pixel based on the proposed D-AMVS. The accuracies of the proposed D-AMVS approach are investigated with two remote sensing images with high spatial resolutions of 1.0 m and 1.3 m. Compared with the classical majority voting method and a relatively new post-processing method called the general post-classification framework, the proposed D-AMVS can achieve a land cover classification map with less noise and higher classification accuracies.

Description: Remote Sensing, Vol. 10, Pages 929: Intercomparison and Validation of SAR-Based Ice Velocity Measurement Techniques within the Greenland Ice Sheet CCI Project Remote Sensing doi: 10.3390/rs10060929 Authors: John Peter Merryman Boncori Morten Langer Andersen Jørgen Dall Anders Kusk Martijn Kamstra Signe Bech Andersen Noa Bechor Suzanne Bevan Christian Bignami Noel Gourmelen Ian Joughin Hyung-Sup Jung Adrian Luckman Jeremie Mouginot Julia Neelmeijer Eric Rignot Kilian Scharrer Thomas Nagler Bernd Scheuchl Tazio Strozzi Ice velocity is one of the products associated with the Ice Sheets Essential Climate Variable. This paper describes the intercomparison and validation of ice-velocity measurements carried out by several international research groups within the European Space Agency Greenland Ice Sheet Climate Change Initiative project, based on space-borne Synthetic Aperture Radar (SAR) data. The goal of this activity was to survey the best SAR-based measurement and error characterization approaches currently in practice. To this end, four experiments were carried out, related to different processing techniques and scenarios, namely differential SAR interferometry, multi aperture SAR interferometry and offset-tracking of incoherent as well as of partially-coherent data. For each task, participants were provided with common datasets covering areas located on the Greenland ice-sheet margin and asked to provide mean velocity maps, quality characterization and a description of processing algorithms and parameters. The results were then intercompared and validated against GPS data, revealing in several cases significant differences in terms of coverage and accuracy. The algorithmic steps and parameters influencing the coverage, accuracy and spatial resolution of the measurements are discussed in detail for each technique, as well as the consistency between quality parameters and validation results. This allows several recommendations to be formulated, in particular concerning procedures which can reduce the impact of analyst decisions, and which are often found to be the cause of sub-optimal algorithm performance.

Description: Remote Sensing, Vol. 10, Pages 926: Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe Remote Sensing doi: 10.3390/rs10060926 Authors: Najib Djamai Richard Fernandes Sentinel-2 is a constellation of two satellites launched by the European Space Agency (ESA), respectively on 23 June 2015 and 7 March 2017, to map geophysical parameters over land surfaces. ESA provides Level 2 bottom-of-atmosphere reflectance (BOA) products (ESA-L2A) for Europe, with plans for operational global coverage, as well as the Sen2Cor (S2C) offline processor. In this study, aerosol optical thickness (AOT), precipitable water vapour (WVP) and surface reflectance from ESA-L2A products are compared with S2C output when using identical input Level 1 radiance products. Additionally, AOT and WVP are validated against reference measurement. As ESA and S2C share the same input and atmospheric correction algorithm, it was hypothesized that they should show identical validation performance and that differences between products should be negligible in comparison to the uncertainty of retrieved geophysical parameters due to radiometric uncertainty alone. Validation and intercomparison was performed for five clear-sky growing season dates for each of three ESA-L2A tiles selected to span a range of vegetation and topography as well as to be close to the AERONET measurement site. Validation of S2C (ESA) products using AERONET site measurements indicated an overall root mean square error (RMSE) of 0.06 (0.07) and a bias of 0.05 (0.09) for AOT and 0.20 cm (0.22 cm) and the bias was −0.02 cm (−0.10 cm) for WVP. Intercomparison of S2C-L2A and ESA-L2A showed an overall agreement higher than 99% for scene classification (SCL) maps and negligible differences for WVP (RMSE under 0.09 and R2 above 0.99). Larger disagreement was observed for aerosol optical thickness (AOT) (RMSE up to 0.04 and R2 as low as 0.14). For BOA reflectance, disagreement between products depends on vegetation cover density, topography slope and spectral band. The largest differences were observed for red-edge and infrared bands in mountainous vegetated areas (RMSE up to 4.9% reflectance and R2 as low as 0.53). These differences are of similar magnitude to the radiometric calibration requirements for the Sentinel 2 imager. The differences had minimal impact of commonly used vegetation indices (NDVI, NDWI, EVI), but application of the Sentinel Level 2 biophysical processor generally resulted in proportional differences in most derived vegetation parameters. It is recommended that the consistency of ESA and S2C products should be improved by the developers of the ESA and S2C processors.

Description: Remote Sensing, Vol. 10, Pages 870: Low-Frequency Sea Surface Radar Doppler Echo Remote Sensing doi: 10.3390/rs10060870 Authors: Yury Yu. Yurovsky Vladimir N. Kudryavtsev Semyon A. Grodsky Bertrand Chapron The sea surface normalized radar backscatter cross-section (NRCS) and Doppler velocity (DV) exhibit energy at low frequencies (LF) below the surface wave peak. These NRCS and DV variations are coherent and thus may produce a bias in the DV averaged over large footprints, which is important for interpretation of Doppler scatterometer measurements. To understand the origin of LF variations, the platform-borne Ka-band radar measurements with well-pronounced LF variations at frequencies below wave peak (0.19 Hz) are analyzed. These data show that the LF NRCS is coherent with wind speed at 21 m height while the LF DV is not. The NRCS-wind correlation is significant only at frequencies below 0.01 Hz indicating either differences between near-surface wind (affecting radar signal) and 21-m height wind (actually measured) or contributions of other mechanisms of LF radar signal variations. It is shown that non-linearity in NRCS-wave slope Modulation Transfer Function (MTF) and inherent averaging within radar footprint account for NRCS and DV LF variance, with the exception of VV NRCS for which almost half of the LF variance is unexplainable by these mechanisms and perhaps attributable to wind fluctuations. Although the distribution of radar DV is quasi-Gaussian, suggesting virtually little impact of non-linearity, the LF DV variations arise due to footprint averaging of correlated local DV and non-linear NRCS. Numerical simulations demonstrate that MTF non-linearity weakly affects traditional linear MTF estimate (less than 10% for typical MTF magnitudes less than 20). Thus the linear MTF is a good approximation to evaluate the DV averaged over large footprints typical of satellite observations.

Description: Remote Sensing, Vol. 10, Pages 869: The Potential and Challenges of Using Soil Moisture Active Passive (SMAP) Sea Surface Salinity to Monitor Arctic Ocean Freshwater Changes Remote Sensing doi: 10.3390/rs10060869 Authors: Wenqing Tang Simon Yueh Daqing Yang Alexander Fore Akiko Hayashi Tong Lee Severine Fournier Benjamin Holt Sea surface salinity (SSS) links various components of the Arctic freshwater system. SSS responds to freshwater inputs from river discharge, sea ice change, precipitation and evaporation, and oceanic transport through the open straits of the Pacific and Atlantic oceans. However, in situ SSS data in the Arctic Ocean are very sparse and insufficient to depict the large-scale variability to address the critical question of how climate variability and change affect the Arctic Ocean freshwater. The L-band microwave radiometer on board the NASA Soil Moisture Active Passive (SMAP) mission has been providing SSS measurements since April 2015, at approximately 60 km resolution with Arctic Ocean coverage in 1–2 days. With improved land/ice correction, the SMAP SSS algorithm that was developed at the Jet Propulsion Laboratory (JPL) is able to retrieve SSS in ice-free regions 35 km of the coast. SMAP observes a large-scale contrast in salinity between the Atlantic and Pacific sides of the Arctic Ocean, while retrievals within the Arctic Circle vary over time, depending on the sea ice coverage and river runoff. We assess the accuracy of SMAP SSS through comparative analysis with in situ salinity data collected by Argo floats, ships, gliders, and in field campaigns. Results derived from nearly 20,000 pairs of SMAP and in situ data North of 50°N collocated within a 12.5-km radius and daily time window indicate a Root Mean Square Difference (RMSD) less than ~1 psu with a correlation coefficient of 0.82 and a near unity regression slope over the entire range of salinity. In contrast, the Hybrid Coordinate Ocean Model (HYCOM) has a smaller RMSD with Argo. However, there are clear systematic biases in the HYCOM for salinity in the range of 25–30 psu, leading to a regression slope of about 0.5. In the region North of 65°N, the number of collocated samples drops more than 70%, resulting in an RMSD of about 1.2 psu. SMAP SSS in the Kara Sea shows a consistent response to discharge anomalies from the Ob’ and Yenisei rivers between 2015 and 2016, providing an assessment of runoff impact in a region where no in situ salinity data are available for validation. The Kara Sea SSS anomaly observed by SMAP is missing in the HYCOM SSS, which assimilates climatological runoffs without interannual changes. We explored the feasibility of using SMAP SSS to monitor the sea surface salinity variability at the major Arctic Ocean gateways. Results show that although the SMAP SSS is limited to about 1 psu accuracy, many large salinity changes are observable. This may lead to the potential application of satellite SSS in the Arctic monitoring system as a proxy of the upper ocean layer freshwater exchanges with subarctic oceans.

Description: Symmetry, Vol. 10, Pages 210: An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance Symmetry doi: 10.3390/sym10060210 Authors: Wei-zhen Sun Jie-sheng Wang Xian Wei Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm inspired by humpback whale hunting behavior. WOA has many similarities with other swarm intelligence algorithms (PSO, GWO, etc.). WOA’s unique search mechanism enables it to have a strong global search capability while taking into account the strong global search capabilities. In this work, considering the the deficiency of WOA in local search mechanism, combined with the optimization methods of other group intelligent algorithms, perceptual perturbation mechanism is introduced, which makes the agent perform more detailed searches near the local extreme point. At the same time, since the WOA uses a logarithmic spiral curve, the agent cannot fully search all the spaces within its search range, even though the introduction of the perturbation mechanism may still lead to the algorithm falling into a local optimum. Therefore, the equal pitch Archimedes spiral curve is chosen to replace the classic logarithmic spiral curve. In order to fully verify the effect of the search path on the performance of the algorithm, several other spiral curves have been chosen for experimental comparison. By utilizing the 23 benchmark test functions, the simulation results show that WOA (PDWOA) with perceptual perturbation significantly outperforms the standard WOA. Then, based on the PDWOA, the effect of the search path on the performance of the algorithm has been verified. The simulation results show that the equal pitch of the Archimedean spiral curve is best.

Description: Symmetry, Vol. 10, Pages 201: Topological Characterization of the Symmetrical Structure of Bismuth Tri-Iodide Symmetry doi: 10.3390/sym10060201 Authors: Muhammad Imran Muhammad Arfan Ali Sarfaraz Ahmad Muhammad Kamran Siddiqui Abdul Qudair Baig The bismuth tri-iodide ( B i I 3 ) is an inorganic compound. It is the result of the response of bismuth and iodine, which has inspired enthusiasm for subjective inorganic investigation. The topological indices are the numerical invariants of the molecular graph that portray its topology and are normally graph invariants. In 1975, Randic presented, in a bond-added substance, a topological index as a descriptor for portraying subatomic branching. In this paper, we investigate the precious stone structure of bismuth tri-iodide chain and sheet. Moreover, exact formulas of degree-based added-substance topological indices principally the first, second, and hyper Zagreb indices, the general Randic index, the geometric-arithmetic index, the fourth atom-bond connectivity index, and the fifth geometric arithmetic index of the subatomic graph of bismuth tri-iodide for both chain and sheet structures are determined.

Description: Remote Sensing, Vol. 10, Pages 934: Concentric Circle Pooling in Deep Convolutional Networks for Remote Sensing Scene Classification Remote Sensing doi: 10.3390/rs10060934 Authors: Kunlun Qi Qingfeng Guan Chao Yang Feifei Peng Shengyu Shen Huayi Wu Convolutional neural networks (CNNs) have been increasingly used in remote sensing scene classification/recognition. The conventional CNNs are sensitive to the rotation of the image scene, which will inevitably result in the misclassification of remote sensing scene images that belong to the same category. In this work, we equip the networks with a new pooling strategy, “concentric circle pooling”, to alleviate the above problem. The new network structure, called CCP-net can generate a concentric circle-based spatial-rotation-invariant representation of an image, hence improving the classification accuracy. The square kernel is adopted to approximate the circle kernels in concentric circle pooling, which is much more efficient and suitable for CNNs to propagate gradients. We implement the training of the proposed network structure with standard back-propagation, thus CCP-net is an end-to-end trainable CNNs. With these advantages, CCP-net should in general improve CNN-based remote sensing scene classification methods. Experiments using two publicly available remote sensing scene datasets demonstrate that using CCP-net can achieve competitive classification results compared with the state-of-art methods.

Description: Symmetry, Vol. 10, Pages 218: Gauss Map and Its Applications on Ruled Submanifolds in Minkowski Space Symmetry doi: 10.3390/sym10060218 Authors: Sun Jung Young Kim We study ruled submanifolds in Minkowski space in regard to the Gauss map satisfying some partial differential equation. As a generalization of usual cylinders, cones and null scrolls in a three-dimensional Minkowski space, a cylinder over a space curve, a product manifold of a right cone and a k-plane, a product manifold of a hyperbolic cone and a k-plane which look like kinds of cylinders over cones in 3-space, and the generalized B-scroll kind in Minkowski space are characterized with the partial differential equation regarding the Gauss map, where k is a positive integer.

Description: Symmetry, Vol. 10, Pages 217: Decision-Making via Neutrosophic Support Soft Topological Spaces Symmetry doi: 10.3390/sym10060217 Authors: Parimala Mani Karthika Muthusamy Saeid Jafari Florentin Smarandache Udhayakumar Ramalingam The concept of interval neutrosophic sets has been studied and the introduction of a new kind of set in topological spaces called the interval valued neutrosophic support soft set has been suggested. We study some of its basic properties. The main purpose of this paper is to give the optimum solution to decision-making in real life problems the using interval valued neutrosophic support soft set.

Description: IJGI, Vol. 7, Pages 218: A Spatiotemporal Multi-View-Based Learning Method for Short-Term Traffic Forecasting ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060218 Authors: Shifen Cheng Feng Lu Peng Peng Sheng Wu Short-term traffic forecasting plays an important part in intelligent transportation systems. Spatiotemporal k-nearest neighbor models (ST-KNNs) have been widely adopted for short-term traffic forecasting in which spatiotemporal matrices are constructed to describe traffic conditions. The performance of the models is closely related to the spatial dependencies, the temporal dependencies, and the interaction of spatiotemporal dependencies. However, these models use distance functions and correlation coefficients to identify spatial neighbors and measure the temporal interaction by only considering the temporal closeness of traffic, which result in existing ST-KNNs that cannot fully reflect the essential features of road traffic. This study proposes an improved spatiotemporal k-nearest neighbor model for short-term traffic forecasting by utilizing a multi-view learning algorithm named MVL-STKNN that fully considers the spatiotemporal dependencies of traffic data. First, the spatial neighbors for each road segment are automatically determined using cross-correlation under different temporal dependencies. Three spatiotemporal views are built on the constructed spatiotemporal closeness, periodic, and trend matrices to represent spatially heterogeneous traffic states. Second, a spatiotemporal weighting matrix is introduced into the ST-KNN model to recognize similar traffic patterns in the three spatiotemporal views. Finally, the results of traffic pattern recognition under these three spatiotemporal views are aggregated by using a neural network algorithm to describe the interaction of spatiotemporal dependencies. Extensive experiments were conducted using real vehicular-speed datasets collected on city roads and expressways. In comparison with baseline methods, the results show that the MVL-STKNN model greatly improves short-term traffic forecasting by lowering the mean absolute percentage error between 28.24% and 46.86% for the city road dataset and, between 53.80% and 90.29%, for the expressway dataset. The results suggest that multi-view learning merits further attention for traffic-related data mining under such a dynamic and data-intensive environment, which owes to its comprehensive consideration of spatial correlation and heterogeneity as well as temporal fluctuation and regularity in road traffic.

Description: IJGI, Vol. 7, Pages 217: Deep Belief Networks Based Toponym Recognition for Chinese Text ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060217 Authors: Shu Wang Xueying Zhang Peng Ye Mi Du In Geographical Information Systems, geo-coding is used for the task of mapping from implicitly geo-referenced data to explicitly geo-referenced coordinates. At present, an enormous amount of implicitly geo-referenced information is hidden in unstructured text, e.g., Wikipedia, social data and news. Toponym recognition is the foundation of mining this useful geo-referenced information by identifying words as toponyms in text. In this paper, we propose an adapted toponym recognition approach based on deep belief network (DBN) by exploring two key issues: word representation and model interpretation. A Skip-Gram model is used in the word representation process to represent words with contextual information that are ignored by current word representation models. We then determine the core hyper-parameters of the DBN model by illustrating the relationship between the performance and the hyper-parameters, e.g., vector dimensionality, DBN structures and probability thresholds. The experiments evaluate the performance of the Skip-Gram model implemented by the Word2Vec open-source tool, determine stable hyper-parameters and compare our approach with a conditional random field (CRF) based approach. The experimental results show that the DBN model outperforms the CRF model with smaller corpus. When the corpus size is large enough, their statistical metrics become approaching. However, their recognition results express differences and complementarity on different kinds of toponyms. More importantly, combining their results can directly improve the performance of toponym recognition relative to their individual performances. It seems that the scale of the corpus has an obvious effect on the performance of toponym recognition. Generally, there is no adequate tagged corpus on specific toponym recognition tasks, especially in the era of Big Data. In conclusion, we believe that the DBN-based approach is a promising and powerful method to extract geo-referenced information from text in the future.

Description: Symmetry, Vol. 10, Pages 214: Intelligent Prognostics of Degradation Trajectories for Rotating Machinery Based on Asymmetric Penalty Sparse Decomposition Model Symmetry doi: 10.3390/sym10060214 Authors: Qing Li Steven Y. Liang The ability to accurately track the degradation trajectories of rotating machinery components is arguably one of the challenging problems in prognostics and health management (PHM). In this paper, an intelligent prediction approach based on asymmetric penalty sparse decomposition (APSD) algorithm combined with wavelet neural network (WNN) and autoregressive moving average-recursive least squares algorithm (ARMA-RLS) is proposed for degradation prognostics of rotating machinery, taking the accelerated life test of rolling bearings as an example. Specifically, the health indicators time series (e.g., peak-to-peak value and Kurtosis) is firstly decomposed into low frequency component (LFC) and high frequency component (HFC) using the APSD algorithm; meanwhile, the resulting non-convex regularization problem can be efficiently solved using the majorization-minimization (MM) method. In particular, the HFC part corresponds to the stable change around the zero line of health indicators which most extensively occurs; in contrast, the LFC part is essentially related to the evolutionary trend of health indicators. Furthermore, the nonparametric-based method, i.e., WNN, and parametric-based method, i.e., ARMA-RLS, are respectively introduced to predict the LFC and HFC that focus on abrupt degradation regions (e.g., last 100 points). Lastly, the final predicted data could be correspondingly obtained by integrating the predicted LFC and predicted HFC. The proposed methodology is tested using degradation health indicator time series from four rolling bearings. The proposed approach performed favorably when compared to some state-of-the-art benchmarks such as WNN and largest Lyapunov (LLyap) methods.

Description: Symmetry, Vol. 10, Pages 222: Bayer Image Demosaicking Using Eight-Directional Weights Based on the Gradient of Color Difference Symmetry doi: 10.3390/sym10060222 Authors: Yizheng Liu Chengyou Wang Hongming Zhao Jiayang Song Shiyue Chen In this paper, we propose a new demosaicking algorithm which uses eight-directional weights based on the gradient of color difference (EWGCD) for Bayer image demosaicking. To obtain the interpolation of green (G) pixels, the eight-directional G pixel values are first estimated in red (R)/blue (B) pixels. This estimate is used to calculate the color difference in R/B pixels of the Bayer image in diagonal directions. However, in horizontal and vertical directions, the new estimated G pixels are defined to obtain the color difference. The eight-directional weights of estimated G pixels can be obtained by considering the gradient of the color difference and the gradient of the RGB pixels of the Bayer image. Therefore, the eight-directional weighted values and the first estimated G pixel values are combined to obtain the full G image. Compared with six similar algorithms using the same eighteen McMaster images, the results of the experiment demonstrate that the proposed algorithm has a better performance not only in the subjective visual measurement but also in the assessments of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index measurement.

Description: Remote Sensing, Vol. 10, Pages 961: Temporal and Spatial Characteristics of EVI and Its Response to Climatic Factors in Recent 16 years Based on Grey Relational Analysis in Inner Mongolia Autonomous Region, China Remote Sensing doi: 10.3390/rs10060961 Authors: Dong He Guihua Yi Tingbin Zhang Jiaqing Miao Jingji Li Xiaojuan Bie The Inner Mongolia Autonomous Region (IMAR) is a major source of rivers, catchment areas, and ecological barriers in the northeast of China, related to the nation’s ecological security and improvement of the ecological environment. Therefore, studying the response of vegetation to climate change has become an important part of current global change research. Since existing studies lack detailed descriptions of the response of vegetation to different climatic factors using the method of grey correlation analysis based on pixel, the temporal and spatial patterns and trends of enhanced vegetation index (EVI) are analyzed in the growing season in IMAR from 2000 to 2015 based on moderate resolution imaging spectroradiometer (MODIS) EVI data. Combined with the data of air temperature, relative humidity, and precipitation in the study area, the grey relational analysis (GRA) method is used to study the time lag of EVI to climate change, and the study area is finally zoned into different parts according to the driving climatic factors for EVI on the basis of lag analysis. The driving zones quantitatively show the characteristics of temporal and spatial differences in response to different climatic factors for EVI. The results show that: (1) The value of EVI generally features in spatial distribution, increasing from the west to the east and the south to the north. The rate of change is 0.22/10°E from the west to the east, 0.28/10°N from the south to the north; (2) During 2000–2015, the EVI in IMAR showed a slightly upward trend with a growth rate of 0.021/10a. Among them, the areas with slight and significant improvement accounted for 21.1% and 7.5% of the total area respectively, ones with slight and significant degradation being 24.6% and 4.3%; (3) The time lag analysis of climatic factors for EVI indicates that vegetation growth in the study area lags behind air temperature by 1–2 months, relative humidity by 1–2 months, and precipitation by one month respectively; (4) During the growing season, the EVI of precipitation driving zone (21.8%) in IMAR is much larger than that in the air temperature driving zone (8%) and the relative humidity driving zone (11.6%). The growth of vegetation in IMAR generally has the closest relationship with precipitation. The growth of vegetation does not depend on the change of a single climatic factor. Instead, it is the result of the combined action of multiple climatic factors and human activities.

Description: Remote Sensing, Vol. 10, Pages 959: The Temperature Vegetation Dryness Index (TVDI) Based on Bi-Parabolic NDVI-Ts Space and Gradient-Based Structural Similarity (GSSIM) for Long-Term Drought Assessment Across Shaanxi Province, China (2000–2016) Remote Sensing doi: 10.3390/rs10060959 Authors: Ying Liu Hui Yue Traditional NDVI-Ts space is triangular or trapezoidal, but Liu et al. (2015) discovered that the NDVI-Ts space was bi-parabolic when the study area was covered with low biomass vegetation. Moreover, the numerical value of the indicator was considered in most of the study when the drought conditions in the space domain were evaluated. In addition, quantitatively assessing the spatial-temporal changes of the drought was not enough. In this study, first, we used MODIS NDVI and Ts data to reexamine if the NDVI-Ts space with “time” and a single pixel domain is bi-parabolic in the Shaanxi province of China, which is vegetated with low biomass to high biomass. This is compared with the triangular NDVI-Ts space and one of the well-known drought indexes called the temperature-vegetation index (TVX). The results demonstrated that dry and wet edges exhibited a parabolic shape again in scatter plots of Ts and NDVI in the Shaanxi province, which was linear in the triangular NDVI-Ts space. The Temperature Vegetation Dryness Index (TVDIc) was obtained from bi-parabolic NDVI-Ts andTVDIt was obtained from the triangular NDVI-Ts space and TVX were compared with 10-cm depth relative soil moisture. By estimating the 10-cm depth soil moisture, TVDIc was better than TVDIt, which were all apparently better than TVX. Second, combined with MODIS data, the drought conditions of the study area were assessed by TVDIc between 2000 to 2016. Spatially, the drought in the Shaanxi Province between 2000 to 2016 were mainly distributed in the northwest, North Shaanxi, and the North and East Guanzhong plain. The drought area of the Shaanxi province accounted for 31.95% in 2000 and 27.65% in 2016, respectively. Third, we quantitatively evaluated the variation of the drought status by using Gradient-based Structural Similarity (GSSIM) methods. The area of the drought conditions significantly changed and moderately changed at 5.34% and 40.22%, respectively, between 2000 and 2016. Finally, the possible reasons for drought change were discussed. The change of precipitation, temperature, irrigation, destruction or betterment of vegetation, and the enlargement of opening mining, etc., can lead to the variations of drought.

Description: Remote Sensing, Vol. 10, Pages 963: A Seismic Capacity Evaluation Approach for Architectural Heritage Using Finite Element Analysis of Three-Dimensional Model: A Case Study of the Limestone Hall in the Ming Dynasty Remote Sensing doi: 10.3390/rs10060963 Authors: Siliang Chen Shaohua Wang Chen Li Qingwu Hu Hongjun Yang A lot of architectural heritage in China are urgently in need to carry out seismic assessment for further conservation. In this paper, a seismic capacity evaluation approach for architectural heritage using finite element analysis with precision three-dimensional data was proposed. The Limestone Hall of Shaanxi Province was taken as an example. First, low attitude unmanned aerial vehicle photogrammetry and a close-range photogrammetry camera were used to collect multiple view images to obtain the precision three-dimensional current model of the Limestone. Second, the dimensions of internal structures of Limestone Hall are obtained by means of structural analysis; re-establishing the ideal model of Limestone Hall based on the modeling software. Third, a finite element analysis was conducted to find out the natural frequency and seismic stress in various conditions with the 3D model using ANSYS software. Finally, the seismic capacity analysis results were comprehensively evaluated for the risk assessment and simulation. The results showed that for architectural heritage with a multilayer structure, utilizing photogrammetric surveying and mapping, 3D software modeling, finite element software simulation, and seismic evaluation for simulation was feasible where the precision of the modeling and parameters determine the accuracy of the simulation. The precise degree of the three-dimensional model, the accurate degree of parameter measurement and estimation, the setting of component attributes in the finite element model and the strategy of finite element analysis have an important effect on the result of seismic assessment. The main body structure of the Limestone Hall could resist an VII-degree earthquake at most, and the ridge of the second floor could not resist a V-degree earthquake due to unsupported conditions. The maximum deformation of the Limestone Hall during the earthquake occurred in the tabia layer below the second roof.

Description: IJGI, Vol. 7, Pages 222: A Citizen Science Approach for Collecting Toponyms ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060222 Authors: Aji Putra Perdana Frank O. Ostermann The emerging trends and technologies of surveying and mapping potentially enable local experts to contribute and share their local geographical knowledge of place names (toponyms). We can see the increasing numbers of toponyms in digital platforms, such as OpenStreetMap, Facebook Place Editor, Swarm Foursquare, and Google Local Guide. On the other hand, government agencies keep working to produce concise and complete gazetteers. Crowdsourced geographic information and citizen science approaches offer a new paradigm of toponym collection. This paper addresses issues in the advancing toponym practice. First, we systematically examined the current state of toponym collection and handling practice by multiple stakeholders, and we identified a recurring set of problems. Secondly, we developed a citizen science approach, based on a crowdsourcing level of participation, to collect toponyms. Thirdly, we examined the implementation in the context of an Indonesian case study. The results show that public participation in toponym collection is an approach with the potential to solve problems in toponym handling, such as limited human resources, accessibility, and completeness of toponym information. The lessons learnt include the knowledge that the success of this approach depends on the willingness of the government to advance their workflow, the degree of collaboration between stakeholders, and the presence of a communicative approach in introducing and sharing toponym guidelines with the community.

Description: Remote Sensing, Vol. 10, Pages 975: A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment Remote Sensing doi: 10.3390/rs10060975 Authors: Mohsen Alizadeh Ibrahim Ngah Mazlan Hashim Biswajeet Pradhan Amin Beiranvand Pour Vulnerability assessment is one of the prerequisites for risk analysis in disaster management. Vulnerability to earthquakes, especially in urban areas, has increased over the years due to the presence of complex urban structures and rapid development. Urban vulnerability is a result of human behavior which describes the extent of susceptibility or resilience of social, economic, and physical assets to natural disasters. The main aim of this paper is to develop a new hybrid framework using Analytic Network Process (ANP) and Artificial Neural Network (ANN) models for constructing a composite social, economic, environmental, and physical vulnerability index. This index was then applied to Tabriz City, which is a seismic-prone province in the northwestern part of Iran with recurring devastating earthquakes and consequent heavy casualties and damages. A Geographical Information Systems (GIS) analysis was used to identify and evaluate quantitative vulnerability indicators for generating an earthquake vulnerability map. The classified and standardized indicators were subsequently weighed and ranked using an ANP model to construct the training database. Then, standardized maps coupled with the training site maps were presented as input to a Multilayer Perceptron (MLP) neural network for producing an Earthquake Vulnerability Map (EVM). Finally, an EVM was produced for Tabriz City and the level of vulnerability in various zones was obtained. South and southeast regions of Tabriz City indicate low to moderate vulnerability, while some zones of the northeastern tract are under critical vulnerability conditions. Furthermore, the impact of the vulnerability of Tabriz City on population during an earthquake was included in this analysis for risk estimation. A comparison of the result produced by EVM and the Population Vulnerability (PV) of Tabriz City corroborated the validity of the results obtained by ANP-ANN. The findings of this paper are useful for decision-makers and government authorities to obtain a better knowledge of a city’s vulnerability dimensions, and to adopt preparedness strategies in the future for Tabriz City. The developed hybrid framework of ANP and ANN Models can easily be replicated and applied to other urban regions around the world for sustainability and environmental management.

Description: IJGI, Vol. 7, Pages 232: A RSSI/PDR-Based Probabilistic Position Selection Algorithm with NLOS Identification for Indoor Localisation ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060232 Authors: Ke Han Huashuai Xing Zhongliang Deng Yichen Du In recent years, location-based services have been receiving increasing attention because of their great development prospects. Researchers from all over the world have proposed many solutions for indoor positioning over the past several years. However, owing to the dynamic and complex nature of indoor environments, accurately and efficiently localising targets in indoor environments remains a challenging problem. In this paper, we propose a novel indoor positioning algorithm based on the received signal strength indication and pedestrian dead reckoning. In order to enhance the accuracy and reliability of our proposed probabilistic position selection algorithm in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a low-complexity identification approach is proposed to identify the change in the channel situation between NLOS and LOS. Numerical experiment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional methods in mixed LOS and NLOS indoor environments.

Description: IJGI, Vol. 7, Pages 229: Automated Orthorectification of VHR Satellite Images by SIFT-Based RPC Refinement ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060229 Authors: Hakan Kartal Ugur Alganci Elif Sertel Raw remotely sensed images contain geometric distortions and cannot be used directly for map-based applications, accurate locational information extraction or geospatial data integration. A geometric correction process must be conducted to minimize the errors related to distortions and achieve the desired location accuracy before further analysis. A considerable number of images might be needed when working over large areas or in temporal domains in which manual geometric correction requires more labor and time. To overcome these problems, new algorithms have been developed to make the geometric correction process autonomous. The Scale Invariant Feature Transform (SIFT) algorithm is an image matching algorithm used in remote sensing applications that has received attention in recent years. In this study, the effects of the incidence angle, surface topography and land cover (LC) characteristics on SIFT-based automated orthorectification were investigated at three different study sites with different topographic conditions and LC characteristics using Pleiades very high resolution (VHR) images acquired at different incidence angles. The results showed that the location accuracy of the orthorectified images increased with lower incidence angle images. More importantly, the topographic characteristics had no observable impacts on the location accuracy of SIFT-based automated orthorectification, and the results showed that Ground Control Points (GCPs) are mainly concentrated in the “Forest” and “Semi Natural Area” LC classes. A multi-thread code was designed to reduce the automated processing time, and the results showed that the process performed 7 to 16 times faster using an automated approach. Analyses performed on various spectral modes of multispectral data showed that the arithmetic data derived from pan-sharpened multispectral images can be used in automated SIFT-based RPC orthorectification.

Description: Remote Sensing, Vol. 10, Pages 976: Physical Retrieval of Land Surface Emissivity Spectra from Hyper-Spectral Infrared Observations and Validation with In Situ Measurements Remote Sensing doi: 10.3390/rs10060976 Authors: Guido Masiello Carmine Serio Sara Venafra Giuliano Liuzzi Laurent Poutier Frank-M. Göttsche A fully physical retrieval scheme for land surface emissivity spectra is presented, which applies to high spectral resolution infrared observations from satellite sensors. The surface emissivity spectrum is represented with a suitably truncated Principal Component Analysis (PCA) transform and PCA scores are simultaneously retrieved with surface temperature and atmospheric parameters. The retrieval methodology has been developed within the general framework of Optimal Estimation and, in this context, is the first physical scheme based on a PCA representation of the emissivity spectrum. The scheme has been applied to IASI (Infrared Atmospheric Sounder Interferometer) and the retrieved emissivities have been validated with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. It has been found that the retrieved emissivity spectra are independent of background information and in good agreement with in situ observations.

Description: IJGI, Vol. 7, Pages 244: Shaking Maps Based on Cumulative Absolute Velocity and Arias Intensity: The Cases of the Two Strongest Earthquakes of the 2016–2017 Central Italy Seismic Sequence ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070244 Authors: Antonio Costanzo By referring to the two strongest earthquakes of the 2016–2017 Central Italy seismic sequence, this paper presents a procedure to make shaking maps through empirical relationships between macroseismic intensity and ground-motion parameters. Hundreds of waveforms were processed to obtain instrumental ground-motion features which could be correlated with the potential damage intensities. To take into account peak value, frequency, duration, and energy content, which all contribute to damage, cumulative absolute velocity and Arias intensity were used to quantify the features of the ground motion. Once these parameters had been calculated at the recording sites, they were interpolated through geostatistical techniques on the whole struck area. Finally, empirical relationships were used for mapping intensities, i.e., potential effects on the built environment. The results referred to both earthquake scenarios that were analyzed and were also used for assessing the influence of the spatial coverage of the instrumental network. In fact, after the first events, the Italian seismic network was subjected to the addition and thickening of sensors in the epicentral area, especially. The results obtained by models only dependent on ground-motion parameters or even on the epicentral distance were compared with the official ShakeMaps and the observed intensities for assessing their reliability. Finally, some suggestions are proposed to improve the procedure that could be used for rapidly assessing ground shaking and mapping damage potential producing useful information for non-expert audience.

Description: IJGI, Vol. 7, Pages 242: Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070242 Authors: Gaofei Yin Ainong Li Chaoyang Wu Jiyan Wang Qiaoyun Xie Zhengjian Zhang Xi Nan Huaan Jin Jinhu Bian Guangbin Lei The spatially explicit aboveground biomass (AGB) generated through upscaling field measurements is critical for carbon cycle simulation and optimized management of grasslands. However, the spatial gaps that exist in the optical remote sensing data, underutilization of the multispectral data cube and unavailability of uncertainty information hinder the generation of seamless and accurate AGB maps. This study proposes a novel framework to address the above challenges. The proposed framework filled the spatial gaps in the remote sensing data via the consistent adjustment of the climatology to actual observations (CACAO) method. Gaussian process regression (GPR) was used to fully exploit the multispectral data cube and generated the pixelwise uncertainty concurrent with the AGB estimation. A case study in a 100 km × 100 km area located in the Zoige Plateau, China was used to evaluate this framework. The results show that the CACAO method can fill almost all of the gaps, accounting for 93.1% of the study area, with satisfactory accuracy. The generated AGB map from the GPR was characterized by a relatively high accuracy (R2 = 0.64, RMSE = 48.13 g/m2) compared to vegetation index-derived ones, and was accompanied by a corresponding uncertainty map that provides a new source of information on the credibility of each pixel. This study demonstrates the potential of the joint use of gap-filling and machine-learning methods to generate spatially explicit AGB.

Description: Remote Sensing, Vol. 10, Pages 993: Landslide Identification and Monitoring along the Jinsha River Catchment (Wudongde Reservoir Area), China, Using the InSAR Method Remote Sensing doi: 10.3390/rs10070993 Authors: Chaoying Zhao Ya Kang Qin Zhang Zhong Lu Bin Li Landslide identification and monitoring are two significant research aspects for landslide analysis. In addition, landslide mode deduction is key for the prevention of landslide hazards. Surface deformation results with different scales can serve for different landslide analysis. L-band synthetic aperture radar (SAR) data calculated with Interferometric Point Target Analysis (IPTA) are first employed to detect potential landslides at the catchment-scale Wudongde reservoir area. Twenty-two active landslides are identified and mapped over more than 2500 square kilometers. Then, for one typical landslide, Jinpingzi landslide, its spatiotemporal deformation characteristics are analyzed with the small baseline subsets (SBAS) interferometric synthetic aperture radar (InSAR) technique. High-precision surface deformation results are obtained by comparing with in-situ georobot measurements. The spatial deformation pattern reveals the different stabilities among five different sections of Jinpingzi landslide. InSAR results for Section II of Jinpingzi landslide show that this active landslide is controlled by two boundaries and geological structure, and its different landslide deformation magnitudes at different sections on the surface companying with borehole deformation reveals the pull-type landslide mode. Correlation between time series landslide motion and monthly precipitation, soil moisture inverted from SAR intensity images and water level fluctuations suggests that heavy rainfall is the main trigger factor, and the maximum deformation of the landslide was highly consistent with the peak precipitation with a time lag of about 1 to 2 months, which gives us important guidelines to mitigate and prevent this kind of hazard.

Description: Remote Sensing, Vol. 10, Pages 1000: Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing Remote Sensing doi: 10.3390/rs10071000 Authors: Xuedong Li Hongyan Zhang Guangbin Yang Yanling Ding Jianjun Zhao The increasing frequency of fires inhibits the estimation of carbon reserves in boreal forest ecosystems because fires release significant amounts of carbon into the atmosphere through combustion. However, less is known regarding the effects of vegetation succession processes on ecosystem C-flux that follow fires. This paper describes intra- and inter-annual vegetation restoration trajectories via MODIS time-series and Landsat data. The temporal and spatial characteristics of the natural succession were analyzed from 2000 to 2016. Finally, we regressed post-fire MODIS EVI, LST and LSWI values onto GPP and NPP values to identify the main limiting factors during post-fire carbon exchange. The results show immediate variations after the fire event, with EVI and LSWI decreasing by 0.21 and 0.31, respectively, and the LST increasing to 6.89 °C. After this initial variation, subsequent fire-induced variations were significantly smaller; instead, seasonality began governing the change characteristics. The greatest differences in EVI, LST and LSWI were observed in August and September compared to those in other months (0.29, 6.9 and 0.35, respectively), including July, which was the second month after the fire. We estimated the mean EVI recovery periods under different fire intensities (approximately 10, 12 and 16 years): the LST recovery time is one year earlier than that of the EVI. GPP and NPP decreased after the fire by 22–45 g C·m−2·month−1 (30–80%) and 0.13–0.35 kg C·m−2·year−1 (20–60%), respectively. Excluding the winter period, when no photosynthesis occurred, the correlation between the EVI and GPP was the strongest, and the correlation coefficient varied with the burn intensity. When changes in EVI, LST and LSWI after the fire in the boreal forest were more significant, the severity of the fire determined the magnitude of the changes, and the seasonality aggravated these changes. On the other hand, the seasonality is another important factor that affects vegetation restoration and land-surface energy fluxes in boreal forests. The strong correlations between EVI and GPP/NPP reveal that the C-flux can be simply and directly estimated on a per-pixel basis from EVI data, which can be used to accurately estimate land-surface energy fluxes during vegetation restoration and reduce uncertainties in the estimation of forests’ carbon reserves.

Description: Symmetry, Vol. 10, Pages 231: Observers and Their Notion of Spacetime beyond Special Relativity Symmetry doi: 10.3390/sym10070231 Authors: José Manuel Carmona José Luis Cortés José Javier Relancio It is plausible that quantum gravity effects may lead us to a description of Nature beyond the framework of special relativity. In this case, either the relativity principle is broken or it is maintained. These two scenarios (a violation or a deformation of special relativity) are very different, both conceptually and phenomenologically. We discuss some of their implications on the description of events for different observers and the notion of spacetime.

Description: Symmetry, Vol. 10, Pages 233: Symmetries of Differential Equations in Cosmology Symmetry doi: 10.3390/sym10070233 Authors: Michael Tsamparlis Andronikos Paliathanasis The purpose of the current article is to present a brief albeit accurate presentation of the main tools used in the study of symmetries of Lagrange equations for holonomic systems and subsequently to show how these tools are applied in the major models of modern cosmology in order to derive exact solutions and deal with the problem of dark matter/energy. The key role in this approach are the first integrals of the field equations. We start with the Lie point symmetries and the first integrals defined by them, that is, the Hojman integrals. Subsequently, we discuss the Noether point symmetries and the well-known method for deriving the Noether integrals. By means of the Inverse Noether Theorem, we show that, to every Hojman quadratic first integral, it is possible to associate a Noether symmetry whose Noether integral is the original Hojman integral. It is emphasized that the point transformation generating this Noether symmetry need not coincide with the point transformation defining the Lie symmetry which produces the Hojman integral. We discuss the close connection between the Lie point and the Noether point symmetries with the collineations of the metric defined by the kinetic energy of the Lagrangian. In particular, the generators of Noether point symmetries are elements of the homothetic algebra of that metric. The key point in the current study of cosmological models is the introduction of the mini superspace, which is the space that is defined by the physical variables of the model, which is not the spacetime where the model evolves. The metric in the mini superspace is found from the kinematic part of the Lagrangian and we call it the kinetic metric. The rest part of the Lagrangian is the effective potential. We consider coordinate transformations of the original mini superspace metric in order to bring it to a form where we know its collineations, that is, the Killing vectors, the homothetic vector, etc. Then, we write the field equations of the cosmological model and we use the connection of these equations with the collineations of the mini superspace metric to compute the first integrals and subsequently to obtain analytic solutions for various allowable potentials and finally draw conclusions about the problem of dark energy. We consider the ΛCDM cosmological model, the scalar field cosmology, the Brans–Dicke cosmology, the f(R) gravity, the two scalar fields cosmology with interacting scalar fields and the Galilean cosmology. In each case, we present the relevant results in the form of tables for easy reference. Finally, we discuss briefly the higher order symmetries (the contact symmetries) and show how they are applied in the cases of scalar field cosmology and in the f(R) gravity.

Description: Remote Sensing, Vol. 10, Pages 1018: An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators Remote Sensing doi: 10.3390/rs10071018 Authors: Vassiliki Markogianni Dionissios Kalivas George P. Petropoulos Elias Dimitriou In-situ monitoring of lake water quality in synergy with satellite remote sensing represents the latest scientific trend in many water quality monitoring programs worldwide. This study investigated the suitability of the Operational Land Imager (OLI) instrument onboard the Landsat 8 satellite platform in accurately estimating key water quality parameters such as chlorophyll-a and nutrient concentrations. As a case study the largest freshwater body of Greece (Trichonis Lake) was used. Two Landsat 8 images covering the study site were acquired on 30 October 2013 and 30 August 2014 respectively. Near concurrent in-situ observations from two water sampling campaigns were also acquired from 22 stations across the lake under study. In-situ measurements (nutrients and chlorophyll-a concentrations) were statistically correlated with various spectral band combinations derived from the Landsat imagery of year 2014. Subsequently, the most statistically promising predictive models were applied to the satellite image of 2013 and validation was conducted using in-situ data of 2013 as reference. Results showed a relatively variable statistical relationship between the in-situ and reflectances (R logchl-a: 0.58, R NH4+: 0.26, R chl-a: 0.44). Correlation coefficient (R) values reported of up to 0.7 for ammonium concentrations and also up to 0.5 and up to 0.4 for chl-a concentration and chl-a concentrations respectively. These results represent a higher accuracy of Landsat 8 in comparison to its predecessors in the Landsat satellites series, as evidenced in the literature. Our findings suggest that Landsat 8 has a promising capability in estimating water quality components in an oligotrophic freshwater body characterized by a complete absence of any quantitative, temporal and spatial variance, as is the case of Trichonis lake. Yet, even with the presence of a lot of ground information as was the case in our study, a quantitatively accurate estimation of water quality constituents in coastal/inland waters remains a great challenge. The launch of sophisticated spaceborne sensing systems, such as that of Landsat 8, can assist in improving our ability to estimate freshwater lake properties from space.

Description: IJGI, Vol. 7, Pages 251: Optical Satellite Image Geo-Positioning with Weak Convergence Geometry ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070251 Authors: Yang Wu Yongsheng Zhang Donghong Wang Delin Mo High-resolution optical satellites are widely used in environmental monitoring. With the aim to observe the largest possible coverage, the overlapping areas and intersection angles of respective optical satellite images are usually small. However, the conventional bundle adjustment method leads to erroneous results or even failure under conditions of weak geometric convergence. By transforming the traditional stereo adjustment to a planar adjustment and combining it with linear programming (LP) theory, a new method that can solve the bias compensation parameters of all satellite images is proposed in this paper. With the support of freely available open source digital elevation models (DEMs) and sparse ground control points (GCPs), the method can not only ensure the consistent inner precision of all images, but also the absolute geolocation accuracy of the ground points. Tests of the two data sets covering different landscapes validated the effectiveness and feasibility of the method. The results showed that the geo-positioning performance of the method was better in regions of smaller topographic relief or for satellite images with a larger imaging altitude angle. The best accuracy of image geolocation with weak convergence geometry was as high as to 3.693 m in the horizontal direction and 6.510 m in the vertical direction, which is a level of accuracy equal to that of images with good intersection conditions.

Description: Remote Sensing, Vol. 10, Pages 1022: Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network Remote Sensing doi: 10.3390/rs10071022 Authors: Wenmin Qin Lunche Wang Aiwen Lin Ming Zhang Muhammad Bilal Aerosols can absorb and scatter surface solar radiation (SSR), which is called the aerosol radiative forcing effect (ARF). Great efforts have been made for the estimation of the aerosol optical depth (AOD), SSR and ARF using meteorological measurements and satellite observations. However, the accuracy, and spatial and temporal resolutions of these existing AOD, SSR and ARF models should be improved to meet the application requirements, due to the uncertainties and gaps of input parameters. In this study, an optimized back propagation (BP) artificial neural network (Genetic_BP) was developed for improving the estimation of the AOD values. The retrieved AOD values using the Genetic_BP model and meteorological measurements at China Meteorological Administration (CMA) stations were used to calculate SSR and bottom of the atmosphere (BOA) ARF (ARFB) using Yang’s Hybrid model (YHM). The result show that the Genetic_BP could be used for estimating AOD values with high accuracy (R = 0.866 for CASNET (China Aerosol Remote Sensing Network) stations and R = 0.865 for AERONET (Aerosol Robotic Network) stations). The estimated SSR also showed a good agreement with SSR measurements at 96 CMA radiation stations, with RMSE, MAE, R and R2 of 29.27%, 23.77%, 0.948, and 0.899, respectively. The estimated ARFB values are also highly correlated with the AERONET ARFB ones with RMSE, MAE, R and R2 of −35.47%, −25.33%, 0.843, and 0.711, respectively. Finally, the spatial and temporal variations of AOD, SSR, and ARFB values over Mainland China were investigated. Both AOD and SSR values are generally higher in summer than in other seasons. The ARFB are generally stronger in spring and summer than in other seasons. The ranges for the monthly mean AOD, SSR and ARFB values over Mainland China are 0.183–0.333, 10.218–24.196 MJ m−2day−1 and −2.986 to −1.244 MJ m−2day−1, respectively. The Qinghai-Tibetan Plateau has always been an area with the highest SSR, the lowest AOD and the weakest ARFB. In contrast, the Sichuan Basin has always been an area with low SSR, high AOD, and strong ARFB. The newly proposed AOD model may be of vital importance for improving the accuracy and computational efficiency of AOD, SSR and ARFB estimations for solar energy applications, ecological modeling, and energy policy.

Description: Symmetry, Vol. 10, Pages 245: A Forecasting Model Based on Multi-Valued Neutrosophic Sets and Two-Factor, Third-Order Fuzzy Fluctuation Logical Relationships Symmetry doi: 10.3390/sym10070245 Authors: Hongjun Guan Jie He Aiwu Zhao Zongli Dai Shuang Guan Making predictions according to historical values has long been regarded as common practice by many researchers. However, forecasting solely based on historical values could lead to inevitable over-complexity and uncertainty due to the uncertainties inside, and the random influence outside, of the data. Consequently, finding the inherent rules and patterns of a time series by eliminating disturbances without losing important details has long been a research hotspot. In this paper, we propose a novel forecasting model based on multi-valued neutrosophic sets to find fluctuation rules and patterns of a time series. The contributions of the proposed model are: (1) using a multi-valued neutrosophic set (MVNS) to describe the fluctuation patterns of a time series, the model could represent the fluctuation trend of up, equal, and down with degrees of truth, indeterminacy, and falsity which significantly preserve details of the historical values; (2) measuring the similarities of different fluctuation patterns by the Hamming distance could avoid the confusion caused by incomplete information from limited samples; and (3) introducing another related time series as a secondary factor to avoid warp and deviation in inferring inherent rules of historical values, which could lead to more comprehensive rules for further forecasting. To evaluate the performance of the model, we explored the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as the major factor we forecast, and the Dow Jones Index as the secondary factor to facilitate the predicting of the TAIEX. To show the universality of the model, we applied the proposed model to forecast the Shanghai Stock Exchange Composite Index (SHSECI) as well.

Description: Symmetry, Vol. 10, Pages 244: Computing Zagreb Indices and Zagreb Polynomials for Symmetrical Nanotubes Symmetry doi: 10.3390/sym10070244 Authors: Zehui Shao Muhammad Kamran Siddiqui Mehwish Hussain Muhammad Topological indices are numbers related to sub-atomic graphs to allow quantitative structure-movement/property/danger connections. These topological indices correspond to some specific physico-concoction properties such as breaking point, security, strain vitality of chemical compounds. The idea of topological indices were set up in compound graph hypothesis in view of vertex degrees. These indices are valuable in the investigation of mitigating exercises of specific Nanotubes and compound systems. In this paper, we discuss Zagreb types of indices and Zagreb polynomials for a few Nanotubes covered by cycles.

Description: IJGI, Vol. 7, Pages 299: Drift-Aware Monocular Localization Based on a Pre-Constructed Dense 3D Map in Indoor Environments ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080299 Authors: Guanyuan Feng Lin Ma Xuezhi Tan Danyang Qin Recently, monocular localization has attracted increased attention due to its application to indoor navigation and augmented reality. In this paper, a drift-aware monocular localization system that performs global and local localization is presented based on a pre-constructed dense three-dimensional (3D) map. In global localization, a pixel-distance weighted least squares algorithm is investigated for calculating the absolute scale for the epipolar constraint. To reduce the accumulative errors that are caused by the relative position estimation, a map interaction-based drift detection method is introduced in local localization, and the drift distance is computed by the proposed line model-based maximum likelihood estimation sample consensus (MLESAC) algorithm. The line model contains a fitted line segment and some visual feature points, which are used to seek inliers of the estimated feature points for drift detection. Taking advantage of the drift detection method, the monocular localization system switches between the global and local localization modes, which effectively keeps the position errors within an expected range. The performance of the proposed monocular localization system is evaluated on typical indoor scenes, and experimental results show that compared with the existing localization methods, the accuracy improvement rates of the absolute position estimation and the relative position estimation are at least 30.09% and 65.59%, respectively.

Description: IJGI, Vol. 7, Pages 297: The Negative Effects of Alcohol Establishment Size and Proximity on the Frequency of Violent and Disorder Crime across Block Groups of Victoria, British Columbia ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080297 Authors: Jessica L. Fitterer Trisalyn A. Nelson Timothy Stockwell Multiple studies have associated the density of alcohol establishments with crime. What is not well understood is the influence of establishment patron capacity on the magnitude of crime in an area, or how the spacing of liquor primary establishments impacts crime levels. Using a Poisson spatial lag model, we estimated how patron capacity of on-premises licenses and the total number of off-premises licenses were associated with the frequency of violent and disorder crime occurring on Friday and Saturday nights in Victoria, British Columbia. To identify how the distance between bars and pubs was associated with the frequency of crime within 200 m of each establishment, we applied bivariate curve fitting and change detection techniques. Our model explained 76% percent of the variance in crime frequencies. Bars and pubs within block groups, and in neighboring block groups, had a significant positive association (p < 0.05) with the frequency of crime compared to other on-premises licenses (e.g., restaurants, theatres, clubs, hotels), and off-premises liquor stores. For every additional 1111 bar or pub patron seats the crime frequency per block group is expected to double over a 17 month period (factor of 1.0009 per patron seat). Crime frequency significantly dropped (p < 0.05) around (200 m) bars and pubs that are spaced greater than 300 m apart. Our results provide the first evidenced-based information for evaluating the size and spacing of on-premises licenses in Canada.

Description: IJGI, Vol. 7, Pages 298: Grid-Based Crime Prediction Using Geographical Features ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080298 Authors: Ying-Lung Lin Meng-Feng Yen Liang-Chih Yu Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design’s baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.

Description: IJGI, Vol. 7, Pages 306: Satellite-Derived Bathymetry for Improving Canadian Hydrographic Service Charts ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080306 Authors: René Chénier Marc-André Faucher Ryan Ahola Approximately 1000 Canadian Hydrographic Service (CHS) charts cover Canada’s oceans and navigable waters. Many charts use information collected with techniques that predate the more advanced technologies available to Hydrographic Offices (HOs) today. Furthermore, gaps in survey data, particularly in the Canadian Arctic where only 6% of waters are surveyed to modern standards, are also problematic. Through a Canadian Space Agency (CSA) Government Related Initiatives Program (GRIP) project, CHS is exploring remote sensing techniques to assist with the improvement of Canadian navigational charts. Projects exploring optical/Synthetic Aperture Radar (SAR) shoreline extraction and change detection, as well as optical Satellite-Derived Bathymetry (SDB), are currently underway. This paper focuses on SDB extracted from high-resolution optical imagery, highlighting current results as well as the challenges and opportunities CHS will encounter when implementing SDB within its operational chart production process. SDB is of particular interest to CHS due to its ability to supplement depths derived from traditional hydrographic surveys. This is of great importance in shallow and/or remote Canadian waters where achieving wide-area depth coverage through traditional surveys is costly, time-consuming and a safety risk to survey operators. With an accuracy of around 1 m, SDB could be used by CHS to fill gaps in survey data and to provide valuable information in dynamic areas.

Description: IJGI, Vol. 7, Pages 305: Prioritizing Abandoned Mine Lands Rehabilitation: Combining Landscape Connectivity and Pattern Indices with Scenario Analysis Using Land-Use Modeling ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080305 Authors: Liping Zhang Shiwen Zhang Yajie Huang An Xing Zhiqing Zhuo Zhongxiang Sun Zhen Li Meng Cao Yuanfang Huang Connectivity modeling approaches for abandoned mine lands (AML) patches are limited in post-mining landscape restoration, especially where great land use changes might be expected due to large-scale land reclamation. This study presents a novel approach combining AML patch sizes with a proximity index to characterize patch-scaled connectivity for determining the spatial positions of patches with huge sizes and high connectivity. Then this study propose a scenario-based method coupled with landscape-scale metrics for quantifying landscape-scaled connectivity, which aims at exploring the optimal reclamation scheme with the highest connectivity. Using the Mentougou District in Beijing, China, as a case study, this paper confirmed which patches should be reclaimed first to meet the predetermined reclamation numbers; then this paper tested three different reclamation scenarios (i.e., cultivated land-oriented, forest-oriented, and construction land-oriented scenarios) to describe the impact of the different development strategies on landscape connectivity. The research found that the forest-oriented scenario increased connectivity quantitatively, showing an increase in the integral index of connectivity (IIC) and other landscape-scale metrics. Therefore, this paper suggests that future land-use policies should emphasize converting AML into more forest to blend in with the surrounding land-use categories. The findings presented here can contribute to better understanding the quantitative analysis of the connectivity of AML patches at both the patch scale and the landscape scale, thus providing scientific support for AML management in mine-site rehabilitation.

Description: Remote Sensing, Vol. 10, Pages 1205: Examining the Accuracy of GlobCurrent Upper Ocean Velocity Data Products on the Northwestern Atlantic Shelf Remote Sensing doi: 10.3390/rs10081205 Authors: Hui Feng Douglas Vandemark Julia Levin John Wilkin This study provides a regional coastal ocean assessment of global upper ocean current data developed by the GlobCurrent (GC) project. These gridded data synthesize multiple satellite altimeter and wind model inputs to estimate both Geostrophic and Ekman-layer velocities. While the GC product was mostly devised and intended for open ocean studies, the present objective is to assess whether its data quality nearer the coast is suitable for other applications. The key ground truth sources are long-term mean and time series observations on the Northwestern Atlantic (NWA) shelf derived from Acoustic Doppler Current Profilers (ADCP) and high frequency (HF) radar networks in both the Mid-Atlantic Bight (MAB) and the Gulf of Maine (GoM). Results indicate that mean geostrophic currents across the MAB and the offshore GoM agree to roughly 10% in speed and 10 degree in direction with the in situ depth-averaged currents, with correlation levels of 0.5–0.8 at seasonal and longer time scales. Interior GoM comparisons at 5 coastal buoys show much less agreement. One likely source of GoM error is shown to be the GC mean dynamic topography near the coast. Comparison to near-surface MAB HF radar current measurements on the MAB shelf shows significant GC data improvement when including the surface Ekman term. Overall, the study results imply that application of GlobCurrent data may prove useful in coastal seas with broad continental shelves such as the MAB or Scotian shelf, but that large inaccuracies inside the GoM diminish its utility there.

Description: Symmetry, Vol. 10, Pages 302: Fermion Scattering in a CPT-Even Lorentz Violation Quantum Electrodynamics Symmetry doi: 10.3390/sym10080302 Authors: Frederico Santos Manoel Ferreira In this work, we reassess two known processes of Quantum Electrodynamics involving electrons and muons. The photon propagator is modified by a CPT-even Lorentz-violating (LV) tensor, while fermion lines and the vertex interaction are not altered. Using the Feynman rules, the associated cross sections for unpolarized scatterings are evaluated, revealing the usual energy dependence and Lorentz-violating contributions that induce space anisotropy. A possible route to constraining the LV coefficients is presented and the results properly commented.

Description: Symmetry, Vol. 10, Pages 301: Evaluating Water Use Efficiency in China’s Western Provinces Based on a Slacks-Based Measure (SBM)-Undesirable Window Model and a Malmquist Productivity Index Symmetry doi: 10.3390/sym10080301 Authors: Yuyan Luo Lu Yin Yong Qin Zhong Wang Yanfeng Gong This paper evaluated the water use efficiency in 12 Chinese western provinces from 2005 to 2015. Based on data availability and the index selection rationality and the slacks-based measure (SBM)-undesirable Window analysis model and the Malmquist productivity index, the water resource inputs and outputs were measured to analyze water use efficiency. Total investment in fixed assets for the whole society and total water, made up of total agricultural water, total industrial water, and total domestic water, were used as the input indexes, and regional GDP and waste water discharge were the output indexes, with the waste water discharge being regarded as an undesirable output. The data from different years and different provinces in the same period, and data from the same year and the same provinces in different periods were compared in order to derive the water resource efficiency and technical changes over time and space. It was found that the total water resource factor productivity in the 12 provinces grew slowly in the study period, that water resource technical progress positively affected water use efficiency, and that a lack of technical efficiency restricted water use efficiency growth. Several suggestions are given to optimize water use efficiency in the 12 provinces. The research findings and suggestions provide valuable reference for studies in related fields.

Description: IJGI, Vol. 7, Pages 293: Shp2graph: Tools to Convert a Spatial Network into an Igraph Graph in R ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080293 Authors: Binbin Lu Huabo Sun Paul Harris Miaozhong Xu Martin Charlton In this study, we introduce the R package shp2graph, which provides tools to convert a spatial network into an ‘igraph’ graph of the igraphR package. This conversion greatly empowers a spatial network study, as the vast array of graph analytical tools provided in igraph are then readily available to the network analysis, together with the inherent advantages of being within the R statistical computing environment and its vast array of statistical functions. Through three urban road network case studies, the calculation of road network distances with shp2graph and with igraph is demonstrated through four key stages: (i) confirming the connectivity of a spatial network; (ii) integrating points/locations with a network; (iii) converting a network into a graph; and (iv) calculating network distances (and travel times). Throughout, the required R commands are given to provide a useful tutorial on the use of shp2graph.

Description: IJGI, Vol. 7, Pages 292: Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080292 Authors: Bahram Saeidian Mohammad Saadi Mesgari Biswajeet Pradhan Mostafa Ghodousi After an earthquake, it is required to establish temporary relief centers in order to help the victims. Selection of proper sites for these centers has a significant effect on the processes of urban disaster management. In this paper, the location and allocation of relief centers in district 1 of Tehran are carried out using Geospatial Information System (GIS), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model, a simple clustering method and the two meta-heuristic algorithms of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). First, using TOPSIS, the proposed clustering method and GIS analysis tools, sites satisfying initial conditions with adequate distribution in the area are chosen. Then, the selection of proper centers and the allocation of parcels to them are modelled as a location/allocation problem, which is solved using the meta-heuristic optimization algorithms. Also, in this research, PSO and ACO are compared using different criteria. The implementation results show the general adequacy of TOPSIS, the clustering method, and the optimization algorithms. This is an appropriate approach to solve such complex site selection and allocation problems. In view of the assessment results, the PSO finds better answers, converges faster, and shows higher consistency than the ACO.

Description: Symmetry, Vol. 10, Pages 304: No-Reference Image Blur Assessment Based on Response Function of Singular Values Symmetry doi: 10.3390/sym10080304 Authors: Shanqing Zhang Pengcheng Li Xianghua Xu Li Li Ching-Chun Chang Blur is an important factor affecting the image quality. This paper presents an efficient no-reference (NR) image blur assessment method based on a response function of singular values. For an image, the grayscale image is computed to the acquire spatial information. In the meantime, the gradient map is computed to acquire the shape information, and the saliency map can be obtained by using scale-invariant feature transform (SIFT). Then, the grayscale image, the gradient map, and the saliency map are divided into blocks of the same size. The blocks of the gradient map are converted into discrete cosine transform (DCT) coefficients, from which the response function of singular values (RFSV) are generated. The sum of the RFSV are then utilized to characterize the image blur. The variance of the grayscale image and the DCT domain entropy of the gradient map are used to reduce the impact of the image content. The SIFT-dependent weights are calculated in the saliency map, which are assigned to the image blocks. Finally, the blur score is the normalized sum of the RFSV. Extensive experiments are conducted on four synthetic databases and two real blur databases. The experimental results indicate that the blur scores produced by our method are highly correlated with the subjective evaluations. Furthermore, the proposed method is superior to six state-of-the-art methods.

Description: IJGI, Vol. 7, Pages 308: Identifying Modes of Driving Railway Trains from GPS Trajectory Data: An Ensemble Classifier-Based Approach ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080308 Authors: Han Zheng Zanyang Cui Xingchen Zhang Recognizing Modes of Driving Railway Trains (MDRT) can help to solve railway freight transportation problems in driver behavior research, auto-driving system design and capacity utilization optimization. Previous studies have focused on analyses and applications of MDRT, but there is currently no approach to automatically and effectively identify MDRT in the context of big data. In this study, we propose an integrated approach including data preprocessing, feature extraction, classifiers modeling, training and parameter tuning, and model evaluation to infer MDRT using GPS data. The highlights of this study are as follows: First, we propose methods for extracting Driving Segmented Standard Deviation Features (DSSDF) combined with classical features for the purpose of improving identification performances. Second, we find the most suitable classifier for identifying MDRT based on a comparison of performances of K-Nearest Neighbor, Support Vector Machines, AdaBoost, Random Forest, Gradient Boosting Decision Tree, and XGBoost. From the real-data experiment, we conclude that: (i) The ensemble classifier XGBoost produces the best performance with an accuracy of 92.70%; (ii) The group of DSSDF plays an important role in identifying MDRT with an accuracy improvement of 11.2% (using XGBoost). The proposed approach has been applied in capacity utilization optimization and new driver training for the Baoshen Railway.

Description: Remote Sensing, Vol. 10, Pages 1215: Filtering Stems and Branches from Terrestrial Laser Scanning Point Clouds Using Deep 3-D Fully Convolutional Networks Remote Sensing doi: 10.3390/rs10081215 Authors: Zhouxin Xi Chris Hopkinson Laura Chasmer Terrestrial laser scanning (TLS) can produce precise and detailed point clouds of forest environment, thus enabling quantitative structure modeling (QSM) for accurate tree morphology and wood volume allocation. Applying QSM to plot-scale wood delineation is highly dependent on wood visibility from forest scans. A common problem is to filter wood point from noisy leafy points in the crowns and understory. This study proposed a deep 3-D fully convolution network (FCN) to filter both stem and branch points from complex plot scans. To train the 3-D FCN, reference stem and branch points were delineated semi-automatically for 14 sampled areas and three common species. Among seven testing areas, agreements between reference and model prediction, measured by intersection over union (IoU) and overall accuracy (OA), were 0.89 (stem IoU), 0.54 (branch IoU), 0.79 (mean IoU), and 0.94 (OA). Wood filtering results were further incorporated to a plot-scale QSM to extract individual tree forms, isolated wood, and understory wood from three plot scans with visual assessment. The wood filtering experiment provides evidence that deep learning is a powerful tool in 3-D point cloud processing and parsing.

Description: Remote Sensing, Vol. 10, Pages 1208: Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery Remote Sensing doi: 10.3390/rs10081208 Authors: Javier Marcello Francisco Eugenio Javier Martín Ferran Marqués Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.

Description: Remote Sensing, Vol. 10, Pages 1209: Fog and Low Cloud Frequency and Properties from Active-Sensor Satellite Data Remote Sensing doi: 10.3390/rs10081209 Authors: Jan Cermak An analysis of fog and low cloud properties and distribution is performed using satellite-based LiDAR. Recent years have seen great progress in the remote sensing of fog and low clouds using passive satellite-based sensors. On this basis, maps of fog distribution and frequency as well as baseline climatologies have been constructed. However, no information on fog altitude and vertical extent is available in this way, and fog/low cloud below other clouds cannot be detected in most cases. In this study, ten years of observations by the LiDAR aboard the CALIPSO (Cloud-Aerosol LiDAR and Pathfinder Satellite Observations) platform are used to construct a map and statistical evaluations of fog/low cloud distribution and properties. For the purpose of evaluation, a comparison is made to an evaluation of fog/low cloud distribution in Europe, derived from Meteosat measurements using the Satellite-Based Operation Fog Observation Scheme (SOFOS). Both maps show good agreement in spatial patterns in this region with very diverse fog formation mechanisms. It is found that fog/low cloud layers display distinct spatial differences in terms of geometrical thickness and detection accuracy. The number of fog/low cloud instances missed by passive-sensor retrievals due to multi-layer cloud situations is considerable, with clear regional differences.

Description: Remote Sensing, Vol. 10, Pages 1216: UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health Remote Sensing doi: 10.3390/rs10081216 Authors: Jonathan P. Dash Grant D. Pearse Michael S. Watt The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth’s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature Pinus radiata D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas.

Description: Remote Sensing, Vol. 10, Pages 1219: Lidar Studies of Wind Turbulence in the Stable Atmospheric Boundary Layer Remote Sensing doi: 10.3390/rs10081219 Authors: Viktor A. Banakh Igor N. Smalikho The kinetic energy of turbulence, the dissipation rate of turbulent energy, and the integral scale of turbulence in the stable atmospheric boundary layer at the location heights of low-level jets (LLJs) have been measured with a coherent Doppler light detection and ranging (lidar) system. The turbulence is shown to be weak in the central part of LLJs. The kinetic energy of turbulence at the maximum velocity heights of the jet does not exceed 0.1 (m/s)2, while the dissipation rate is about 10−5 m2/s3. On average, the integral scale of turbulence in the central part of the jet is about 100 m, which is two to three times less than the effective vertical size of the LLJ.

Description: Remote Sensing, Vol. 10, Pages 1222: Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data Remote Sensing doi: 10.3390/rs10081222 Authors: Yanjun Wang Qi Chen Lin Liu Xiong Li Arun Kumar Sangaiah Kai Li Power lines classification is important for electric power management and geographical objects extraction using LiDAR (light detection and ranging) point cloud data. Many supervised classification approaches have been introduced for the extraction of features such as ground, trees, and buildings, and several studies have been conducted to evaluate the framework and performance of such supervised classification methods in power lines applications. However, these studies did not systematically investigate all of the relevant factors affecting the classification results, including the segmentation scale, feature selection, classifier variety, and scene complexity. In this study, we examined these factors systematically using airborne laser scanning and mobile laser scanning point cloud data. Our results indicated that random forest and neural network were highly suitable for power lines classification in forest, suburban, and urban areas in terms of the precision, recall, and quality rates of the classification results. In contrast to some previous studies, random forest yielded the best results, while Naïve Bayes was the worst classifier in most cases. Random forest was the more robust classifier with or without feature selection for various LiDAR point cloud data. Furthermore, the classification accuracies were directly related to the selection of the local neighborhood, classifier, and feature set. Finally, it was suggested that random forest should be considered in most cases for power line classification.

Description: Remote Sensing, Vol. 10, Pages 1220: Correction: Shao, Z.; et al. A Benchmark Dataset for Performance Evaluation of Multi-Label Remote Sensing Image Retrieval. Remote Sens. 2018, 10, 964 Remote Sensing doi: 10.3390/rs10081220 Authors: Zhenfeng Shao Ke Yang Weixun Zhou In our paper [1], we presented a dense labeling dataset that can be used for not only single-label and multi-label remote sensing image retrieval but also pixel-based problems such as semantic segmentation.[...]

Description: Remote Sensing, Vol. 10, Pages 1227: Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas Remote Sensing doi: 10.3390/rs10081227 Authors: Dimosthenis Traganos Bharat Aggarwal Dimitris Poursanidis Konstantinos Topouzelis Nektarios Chrysoulakis Peter Reinartz Seagrasses are traversing the epoch of intense anthropogenic impacts that significantly decrease their coverage and invaluable ecosystem services, necessitating accurate and adaptable, global-scale mapping and monitoring solutions. Here, we combine the cloud computing power of Google Earth Engine with the freely available Copernicus Sentinel-2 multispectral image archive, image composition, and machine learning approaches to develop a methodological workflow for large-scale, high spatiotemporal mapping and monitoring of seagrass habitats. The present workflow can be easily tuned to space, time and data input; here, we show its potential, mapping 2510.1 km2 of P. oceanica seagrasses in an area of 40,951 km2 between 0 and 40 m of depth in the Aegean and Ionian Seas (Greek territorial waters) after applying support vector machines to a composite of 1045 Sentinel-2 tiles at 10-m resolution. The overall accuracy of P. oceanica seagrass habitats features an overall accuracy of 72% following validation by an independent field data set to reduce bias. We envision that the introduced flexible, time- and cost-efficient cloud-based chain will provide the crucial seasonal to interannual baseline mapping and monitoring of seagrass ecosystems in global scale, resolving gain and loss trends and assisting coastal conservation, management planning, and ultimately climate change mitigation.

Description: Remote Sensing, Vol. 10, Pages 1229: A CNN-SIFT Hybrid Pedestrian Navigation Method Based on First-Person Vision Remote Sensing doi: 10.3390/rs10081229 Authors: Qi Zhao Boxue Zhang Shuchang Lyu Hong Zhang Daniel Sun Guoqiang Li Wenquan Feng The emergence of new wearable technologies, such as action cameras and smart glasses, has driven the use of the first-person perspective in computer applications. This field is now attracting the attention and investment of researchers aiming to develop methods to process first-person vision (FPV) video. The current approaches present particular combinations of different image features and quantitative methods to accomplish specific objectives, such as object detection, activity recognition, user–machine interaction, etc. FPV-based navigation is necessary in some special areas, where Global Position System (GPS) or other radio-wave strength methods are blocked, and is especially helpful for visually impaired people. In this paper, we propose a hybrid structure with a convolutional neural network (CNN) and local image features to achieve FPV pedestrian navigation. A novel end-to-end trainable global pooling operator, called AlphaMEX, has been designed to improve the scene classification accuracy of CNNs. A scale-invariant feature transform (SIFT)-based tracking algorithm is employed for movement estimation and trajectory tracking of the person through each frame of FPV images. Experimental results demonstrate the effectiveness of the proposed method. The top-1 error rate of the proposed AlphaMEX-ResNet outperforms the original ResNet (k = 12) by 1.7% on the ImageNet dataset. The CNN-SIFT hybrid pedestrian navigation system reaches 0.57 m average absolute error, which is an adequate accuracy for pedestrian navigation. Both positions and movements can be well estimated by the proposed pedestrian navigation algorithm with a single wearable camera.

Description: IJGI, Vol. 7, Pages 318: A Methodology for Planar Representation of Frescoed Oval Domes: Formulation and Testing on Pisa Cathedral ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080318 Authors: Andrea Piemonte Gabriella Caroti Isabel Martínez-Espejo Zaragoza Filippo Fantini Luca Cipriani This paper presents an original methodology for planar development of a frescoed dome with an oval plan. Input data include a rigorous geometric survey, performed with a laser scanner, and a photogrammetry campaign, which associates a high-quality photographic texture to the 3D model. Therefore, the main topics include the development of geometry and, contextually, of the associated textures. In order to overcome the inability to directly develop the surface, an orthographic azimuthal projection is used. Starting from a prerequisite study of building methodology, the dome is divided into sectors and bands, each linked with the maximum acceptable deformations and the actual geometric discontinuities detectable by the analysis of Gaussian curvature. Upon definition of the development model, a custom automation script has been devised for geometry projection. This effectively generates a (u,v) map, associated to the model, which is used for model texturing and provides the planar development of the fresco.

Description: Remote Sensing, Vol. 10, Pages 1244: The Locking Depth of the Cholame Section of the San Andreas Fault from ERS2-Envisat InSAR Remote Sensing doi: 10.3390/rs10081244 Authors: Guillaume Bacques Marcello de Michele Daniel Raucoules Hideo Aochi The Cholame section of the San Andreas Fault (SAF), which has been considered locked since 1857, has been little studied using geodetic methods. In this study, we propose to use Interferometric Synthetic Aperture Radar (InSAR) to contribute to the improvement of the knowledge of this section of the SAF. In particular, the objective of this work is to provide a description of the transition between the Parkfield and Cholame-Carrizo segments further southeast by producing an estimate of the locking depth of the Cholame segment by combining ERS2 (European Remote Sensing) and Envisat Advanced SAR (ASAR) satellites data. Our results indicate that the locking depth between the Parkfield and the Cholame-Carrizo segments deepens to the southeast. We then use these results as a hint to refine the tectonic loading on this section of the SAF.

Description: Remote Sensing, Vol. 10, Pages 1240: Identifying Establishment Year and Pre-Conversion Land Cover of Rubber Plantations on Hainan Island, China Using Landsat Data during 1987–2015 Remote Sensing doi: 10.3390/rs10081240 Authors: Bangqian Chen Xiangming Xiao Zhixiang Wu Tin Yun Weili Kou Huichun Ye Qinghuo Lin Russell Doughty Jinwei Dong Jun Ma Wei Luo Guishui Xie Jianhua Cao Knowing the stand age of rubber tree (Hevea brasiliensis) plantations is vitally important for best management practices, estimations of rubber latex yields, and carbon cycle studies (e.g., biomass, carbon pools, and fluxes). However, the stand age (as estimated from the establishment year of rubber plantation) is not available across large regions. In this study, we analyzed Landsat time series images from 1987–2015 and developed algorithms to identify (1) the establishment year of rubber plantations; and (2) the pre-conversion land cover types, such as old rubber plantations, evergreen forests, and cropland. Exposed soil during plantation establishment and linear increases in canopy closure during non-production periods (rubber seedling to mature plantation) were used to identify the establishment year of rubber plantations. Based on the rubber plantation map for 2015 (overall accuracy = 97%), and 1981 Landsat images since 1987, we mapped the establishment year of rubber plantations on Hainan Island (R2 = 0.85/0.99, and RMSE = 2.34/0.54 years at pixel/plantation scale). The results show that: (1) significant conversion of croplands and old rubber plantations to new rubber plantations has occurred substantially in the northwest and northern regions of Hainan Island since 2000, while old rubber plantations were mainly distributed in the southeastern inland strip; (2) the pattern of rubber plantation expansion since 1987 consisted of fragmented plantations from smallholders, and there was no tendency to expand towards a higher altitude and steep slope regions; (3) the largest land source for new rubber plantations since 1988 was old rubber plantations (1.26 × 105 ha), followed by cropland (0.95 × 105 ha), and evergreen forests (0.68 × 105 ha). The resultant algorithms and maps of establishment year and pre-conversion land cover types are likely to be useful in plantation management, and ecological assessments of rubber plantation expansion in China.

Description: Symmetry, Vol. 10, Pages 327: Different Forms of Triangular Neutrosophic Numbers, De-Neutrosophication Techniques, and their Applications Symmetry doi: 10.3390/sym10080327 Authors: Avishek Chakraborty Sankar Prasad Mondal Ali Ahmadian Norazak Senu Shariful Alam Soheil Salahshour In this paper, we introduce the concept of neutrosophic number from different viewpoints. We define different types of linear and non-linear generalized triangular neutrosophic numbers which are very important for uncertainty theory. We introduced the de-neutrosophication concept for neutrosophic number for triangular neutrosophic numbers. This concept helps us to convert a neutrosophic number into a crisp number. The concepts are followed by two application, namely in imprecise project evaluation review technique and route selection problem.

Description: IJGI, Vol. 7, Pages 320: Exploring Railway Network Dynamics in China from 2008 to 2017 ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080320 Authors: Yaping Huang Shiwei Lu Xiping Yang Zhiyuan Zhao China’s high speed rail (HSR) network has been rapidly constructed and developed during the past 10 years. However, few studies have reported the spatiotemporal changes of railway network structures and how those structures have been affected by the operation of high speed rail systems in different periods. This paper analyzes the evolving network characteristics of China’s railway network during each of the four main stages of HSR development over a 10-year period. These four stages include Stage 1, when no HSR was in place prior to August 2008; Stage 2, when several HSR lines were put into operation between August 2008, and July 2011; Stage 3, when the network skeleton of most main HSR lines was put into place. This covered the period until January 2013. Finally, Stage 4 covers the deep intensification of several new HSR lines and the rapid development of intercity-HSR railway lines between January 2013, and July 2017. This paper presents a detailed analysis of the timetable-based statistical properties of China’s railway network, as well as the spatiotemporal patterns of the more than 2700 stations that have been affected by the opening of HSR lines and the corresponding policy changes. Generally, we find that the distribution of both degrees and strengths are characterized by scale-free patterns. In addition, the decreasing average path length and increasing network clustering coefficient indicate that the small world characteristic is more significant in the evolution of China’s railway network. Correlations between different network indices are explored, in order to further investigate the dynamics of China’s railway system. Overall, our study offers a new approach for assessing the growth and evolution of a real railway network based on train timetables. Our study can also be referenced by policymakers looking to adjust HSR operations and plan future HSR routes.

Description: Remote Sensing, Vol. 10, Pages 1250: Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series Remote Sensing doi: 10.3390/rs10081250 Authors: Alexandre Bouvet Stéphane Mermoz Marie Ballère Thierry Koleck Thuy Le Toan To detect deforestation using Earth Observation (EO) data, widely used methods are based on the detection of temporal changes in the EO measurements within the deforested patches. In this paper, we introduce a new indicator of deforestation obtained from synthetic aperture radar (SAR) images, which relies on a geometric artifact that appears when deforestation happens, in the form of a shadow at the border of the deforested patch. The conditions for the appearance of these shadows are analyzed, as well as the methods that can be employed to exploit them to detect deforestation. The approach involves two steps: (1) detection of new shadows; (2) reconstruction of the deforested patch around the shadows. The launch of Sentinel-1 in 2014 has opened up opportunities for a potential exploitation of this approach in large-scale applications. A deforestation detection method based on this approach was tested in a 600,000 ha site in Peru. A detection rate of more than 95% is obtained for samples larger than 0.4 ha, and the method was found to perform better than the optical-based UMD-GLAD Forest Alert dataset both in terms of spatial and temporal detection. Further work needed to exploit this approach at operational levels is discussed.

Description: Remote Sensing, Vol. 10, Pages 1245: Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign Remote Sensing doi: 10.3390/rs10081245 Authors: Mehrez Zribi Erwan Motte Nicolas Baghdadi Frédéric Baup Sylvia Dayau Pascal Fanise Dominique Guyon Mireille Huc Jean Pierre Wigneron The aim of this study is to analyze the sensitivity of airborne Global Navigation Satellite System Reflectometry (GNSS-R) on soil surface and vegetation cover characteristics in agricultural areas. Airborne polarimetric GNSS-R data were acquired in the context of the GLORI’2015 campaign over two study sites in Southwest France in June and July of 2015. Ground measurements of soil surface parameters (moisture content) and vegetation characteristics (leaf area index (LAI), and vegetation height) were recorded for different types of crops (corn, sunflower, wheat, soybean, vegetable) simultaneously with the airborne GNSS-R measurements. Three GNSS-R observables (apparent reflectivity, the reflected signal-to-noise-ratio (SNR), and the polarimetric ratio (PR)) were found to be well correlated with soil moisture and a major vegetation characteristic (LAI). A tau-omega model was used to explain the dependence of the GNSS-R reflectivity on both the soil moisture and vegetation parameters.

Description: Remote Sensing, Vol. 10, Pages 1249: Canopy Hyperspectral Sensing of Paddy Fields at the Booting Stage and PLS Regression can Assess Grain Yield Remote Sensing doi: 10.3390/rs10081249 Authors: Kensuke Kawamura Hiroshi Ikeura Sengthong Phongchanmaixay Phanthasin Khanthavong Canopy hyperspectral (HS) sensing is a promising tool for estimating rice (Oryza sativa L.) yield. However, the timing of HS measurements is crucial for assessing grain yield prior to harvest because rice growth stages strongly influence the sensitivity to different wavelengths and the evaluation performance. To clarify the optimum growth stage for HS sensing-based yield assessments, the grain yield of paddy fields during the reproductive phase to the ripening phase was evaluated from field HS data in conjunction with iterative stepwise elimination partial least squares (ISE-PLS) regression. The field experiments involved three different transplanting dates (12 July, 26 July, and 9 August) in 2017 for six cultivars with three replicates (n = 3 × 6 × 3 = 54). Field HS measurements were performed on 2 October 2017, during the panicle initiation, booting, and ripening growth stages. The predictive accuracy of ISE-PLS was compared with that of the standard full-spectrum PLS (FS-PLS) via coefficient of determination (R2) values and root mean squared errors of cross-validation (RMSECV), and the robustness was evaluated by the residual predictive deviation (RPD). Compared with the FS-PLS models, the ISE-PLS models exhibited higher R2 values and lower RMSECV values for all data sets. Overall, the highest R2 values and the lowest RMSECV values were obtained from the ISE-PLS model at the booting stage (R2 = 0.873, RMSECV = 22.903); the RPD was >2.4. Selected HS wavebands in the ISE-PLS model were identified in the red-edge (710–740 nm) and near-infrared (830 nm) regions. Overall, these results suggest that the booting stage might be the best time for in-season rice grain assessment and that rice yield could be evaluated accurately from the HS sensing data via the ISE-PLS model.

Description: Remote Sensing, Vol. 10, Pages 1246: Efficient SfM for Oblique UAV Images: From Match Pair Selection to Geometrical Verification Remote Sensing doi: 10.3390/rs10081246 Authors: San Jiang Wanshou Jiang Accurate orientation is required for the applications of UAV (Unmanned Aerial Vehicle) images. In this study, an integrated Structure from Motion (SfM) solution is proposed, which aims to address three issues to ensure the efficient and reliable orientation of oblique UAV images, including match pair selection for large-volume images with large overlap degree, reliable feature matching of images captured from varying directions, and efficient geometrical verification of initial matches. By using four datasets captured with different oblique imaging systems, the proposed SfM solution is comprehensively compared and analyzed. The results demonstrate that linear computational costs can be achieved in feature extraction and matching; although high decrease ratios occur in image pairs, reliable orientation results are still obtained from both the relative and absolute bundle adjustment (BA) tests when compared with other software packages. For the orientation of oblique UAV images, the proposed method can be an efficient and reliable solution.

Description: Symmetry, Vol. 10, Pages 328: Thermodynamic Response of Beams on Winkler Foundation Irradiated by Moving Laser Pulses Symmetry doi: 10.3390/sym10080328 Authors: Yuxin Sun Shoubin Liu Zhangheng Rao Yuhang Li Jialing Yang In this paper, the exact analytical solutions are developed for the thermodynamic behavior of an Euler-Bernoulli beam resting on an elastic foundation and exposed to a time decaying laser pulse that scans over the beam with a uniform velocity. The governing equations, namely the heat conduction equation and the vibration equation are solved using the Green’s function approach. The temporal and special distributions of temperature, deflection, strain, and the energy absorbed by the elastic foundation are calculated. The effects of the laser motion speed, the modulus of elastic foundation reaction, and the laser pulse duration time are studied in detail.

Description: Remote Sensing, Vol. 10, Pages 1253: Automated Cobble Mapping of a Mixed Sand-Cobble Beach Using a Mobile LiDAR System Remote Sensing doi: 10.3390/rs10081253 Authors: Hironori Matsumoto Adam P. Young Cobbles (64–256 mm) are found on beaches throughout the world, influence beach morphology, and can provide shoreline stability. Detailed, frequent, and spatially large-scale quantitative cobble observations at beaches are vital toward a better understanding of sand-cobble beach systems. This study used a truck-mounted mobile terrestrial LiDAR system and a raster-based classification approach to map cobbles automatically. Rasters of LiDAR intensity, intensity deviation, topographic roughness, and slope were utilized for cobble classification. Four machine learning techniques including maximum likelihood, decision tree, support vector machine, and k-nearest neighbors were tested on five raster resolutions ranging from 5–50 cm. The cobble mapping capability varied depending on pixel size, classification technique, surface cobble density, and beach setting. The best performer was a maximum likelihood classification using 20 cm raster resolution. Compared to manual mapping at 15 control sites (size ranging from a few to several hundred square meters), automated mapping errors were <12% (best fit line). This method mapped the spatial location of dense cobble regions more accurately compared to sparse and moderate density cobble areas. The method was applied to a ~40 km section of coast in southern California, and successfully generated temporal and spatial cobble distributions consistent with previous observations.

Description: Remote Sensing, Vol. 10, Pages 1251: Land Cover Change Detection Using Multiple Shape Parameters of Spectral and NDVI Curves Remote Sensing doi: 10.3390/rs10081251 Authors: Boyu Liu Jun Chen Jiage Chen Weiwei Zhang Spectral and NDVI values have been used to calculate the change magnitudes of land cover, but may result in many pseudo-changes because of inter-class variance. Recently, the shape information of spectral or NDVI curves such as direction, angle, gradient, or other mathematical indicators have been used to improve the accuracy of land cover change detection. However, these measurements, in terms of the single shape features, can hardly capture the complete trends of curves affected by the unsynchronized phenology. Therefore, the calculated change magnitudes are indistinct such that changes and no-changes have a low contrast. This problem has prevented traditional change detection methods from achieving a higher accuracy using bi-temporal images or NDVI time series. In this paper, a multiple shape parameters-based change detection method is proposed by combining the spectral correlation operator and the shape features of NDVI temporal curves (phase angle cumulant, baseline cumulant, relative cumulation rate, and zero-crossing rate). The change magnitude is derived by integrating all the inter-annual differences of these shape parameters. The change regions are discriminated by an automated threshold selection method known as histogram concavity analysis. The results showed that the mean differences in the change magnitudes of the proposed method between 2100 changed and 2523 unchanged pixels was 32%, the overall accuracy was approximately 88%, and the kappa coefficient was 0.76. A comparative analysis was conducted with bi-temporal image-based methods and NDVI time series-based methods, and we demonstrate that the proposed method is more effective and robust than traditional methods in achieving high-contrast change magnitudes and accuracy.

Description: Remote Sensing, Vol. 10, Pages 1252: Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping Remote Sensing doi: 10.3390/rs10081252 Authors: Prima Riza Kadavi Chang-Wook Lee Saro Lee The main purpose of this study was to produce landslide susceptibility maps using various ensemble-based machine learning models (i.e., the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models) for the Sacheon-myeon area of South Korea. A landslide inventory map including a total of 762 landslides was compiled based on reports and aerial photograph interpretations. The landslides were randomly separated into two datasets: 70% of landslides were selected for the model establishment and 30% were used for validation purposes. Additionally, 20 landslide condition factors divided into five categories (topographic factors, hydrological factors, soil map, geological map, and forest map) were considered in the landslide susceptibility mapping. The relationships among landslide occurrence and landslide conditioning factors were analyzed and the landslide susceptibility maps were calculated and drawn using the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models. Finally, the maps were validated using the area under the curve (AUC) method. The Multiclass Classifier method had higher prediction accuracy (85.9%) than the Bagging (AUC = 85.4%), LogitBoost (AUC = 84.8%), and AdaBoost (84.0%) methods.