ALBERT

Description: Lake ice is a significant component of the cryosphere due to its large spatial coverage in high-latitude regions during the winter months. The Laurentian Great Lakes are the world’s largest supply of freshwater and their ice cover has a major impact on regional weather and climate, ship navigation, and public safety. Ice experts at the Canadian Ice Service (CIS) have been manually producing operational Great Lakes image analysis charts based on visual interpretation of the synthetic aperture radar (SAR) images. In that regard, we have investigated the performance of the semi-automated segmentation algorithm “glocal” Iterative Region Growing with Semantics (IRGS) for lake ice classification using dual polarized RADARSAT-2 imagery acquired over Lake Erie. Analysis of various case studies indicated that the “glocal” IRGS algorithm could provide a reliable ice-water classification using dual polarized images with a high overall accuracy of 90.4%. However, lake ice types that are based on stage of development were not effectively identified due to the ambiguous relation between backscatter and ice types. The slight improvement of using dual-pol as opposed to single-pol images for ice-water discrimination was also demonstrated.

Description: This paper aims to investigate the implementation flexibility of multiperiod rail line design in a linear monocentric city. Three alternatives (fast-tracking, deferring, and do-nothing-alternative (DNA) of a candidate rail line project) are examined, based on an in-depth uncertainties analysis of the demand side for this candidate rail line project. Conditions for the three alternatives of fast-tracking, deferring, and DNA are analytically explored and an illustrative example is given to demonstrate the application of the proposed models. Insightful findings are reported on the interrelationship between the rail line length and spatial and temporal correlation of population distribution as well as the implication of the correlation in practice. Sensitivity analyses are carried out in several scenarios in another numerical example to show the proposed conditions of three alternatives.

Description: To reduce the inventory cost and ensure product quality while meeting the diverse demands of customers, manufacturers yield products in batches. However, the raw materials required for manufacturing need to be obtained from suppliers in advance, making it necessary to understand beforehand how to best structure the pickup routes so as to reduce the cost of picking up and stocking while also ensuring the supply of raw materials required for each batch of production. To reduce the transportation and inventory costs, therefore, this paper establishes a mixed integer programming model for the joint optimization of multibatch production and vehicle routing problems involving a pickup. Following this, a two-stage hybrid heuristic algorithm is proposed to solve this model. In the first stage, an integrated algorithm, combining the Clarke-Wright (CW) algorithm and the Record to Record (RTR) travel algorithm, was used to solve vehicle routing problem. In the second stage, the Particle Swarm Optimization (PSO) algorithm was used to allocate vehicles to each production batch. Multiple sets of numerical experiments were then performed to validate the effectiveness of the proposed model and the performance efficiency of the two-stage hybrid heuristic algorithm.

Description: Drivers consecutively direct their gaze to various areas to select relevant information from the traffic environment. The rate of crash risk increases with different off-road glance durations in different driving scenarios. This paper proposed an approach to identify current driving scenarios and predict driver’s eyes-off-road durations using Hidden Markov Model (HMM). A moving base driving simulator study with 26 participants driving in three driving scenarios (urban, rural, and motorway) was conducted. Three different fixed occlusion durations (0-s, 1-s, and 2-s) were applied to quantify eyes-off-road durations. Participants could initiate each occlusion for certain duration by pressing a microswitch on a finger. They were instructed to occlude their vision as often as possible while still driving safely. Drivers’ visual behavior and occlusion behavior were captured and analyzed based on manually frame by frame coding. Visual behaviors in terms of glance duration and glance location in time series were used as input to train HMMs. The results showed that current driving scenarios could be identified ideally using glance location sequences, the accuracy achieving up to 89.3%. And motorway was relatively distinguishable easily with over 90% accuracy. Moreover, HMM-based algorithms that fed up with both glance duration and glance location sequences resulted in a highest accuracy of 92.7% in driver’s eyes-off-road durations prediction. And higher accuracy achieved in longer eyes-off-road durations prediction. It indicates that time series of glance allocations could be used to predict driving behavior and indentify driving environment. The developed models in this study could contribute to the development of scenario sensitive visual inattention prewarning system.

Description: Sustainable supply chain network design has attracted great attention of academia and industry in recent years. Baijiu is one of the world’s oldest distilled alcoholic beverages and plays a significant role in Chinese culture and Chinese people’s daily life. As the production and consumption of Baijiu have a significant influence on the economic, environmental, and social performance of supply chain management, sustainable supply chain network design decisions are critical to the long-term success of the industry. In concert with the rapidly growing Chinese economy, there is a growing demand for a sustainable Baijiu industry. Therefore, this paper constructs and optimizes a network decision-support model for a sustainable Baijiu industry network design. To achieve this, the Baijiu supply chain is examined and a model is proposed for a design that encompasses economic (costs), environmental (carbon emissions), and social (local employment and regional per capita GDP) dimensions. R language programming is then applied to solve the model. A case example indicated that S1 was the optimal decision for reducing costs, S2 was the optimal solution for minimizing carbon emissions, and S3 was the best for maximizing the social impact. Considering the situation of the Baijiu industry and the focal enterprise, it was concluded that S1 would be the best solution for the case company. And the results verified the effectiveness of the framework. This paper develops a systematic and effective approach that decision-makers can use to conduct sustainable network design for Baijiu enterprises.

Description: Land use significantly influences the planet’s land surface and associated biogeochemical processes. With fierce conflict between various land uses, it is important to project the land system process to support decision-making. Lack of insight into scale differences of land use change (LUC) increased uncertainties in previous studies. To quantify the differences in LUCs within an elevation gradient, in this study, a novel model, the stratified land use change simulation model (SLUCS), was developed by using an elevation-based stratification strategy. This model consists of four modules. First, an elevation-based stratification module to develop a quantitative method for generating stratifications using elevation and land-use characteristics. Second, a non-spatial land-use demand module to forecast the overall land use area and make zoning constraints to simulate LUCs. Third, a stratified suitability estimation module that uses the stratified logistic regression method to reveal the regional relationship of the driving factors with LUCs at different stratifications. Finally, a spatial allocation of the land-use module, which projects a spatially explicit LUC. The SLUCS model was applied and tested in the Guizhou and Guangxi Karst Mountainous Region. Results validated the effectiveness of the model, and further demonstrated an improved spatial consistency with the reference, a higher accuracy assessment, and a better simulation performance in conversion areas than the traditional method. Three scenarios from 2015 to 2030 with different land-use priorities were designed and projected. Each scenario presented the same LUC trends, but with different magnitudes, including the rapid expansion of built-up land, the restoration of forest and water, and the loss of farmland and grassland. Priority of the socioeconomic development and ecological protection of the scenarios forecasted a sharper increase in the built-up land and in forests than the historical extrapolation scenario. The SLUCS model visually projected the LUC trajectory and competition between land uses, which suggests specific tradeoffs among management strategies to support sustainable land uses.

Description: In the hydrological cycle, evapotranspiration (ET) transfers moisture from the land surface to the atmosphere and is sensitive to disturbances such as wildfires. Ground-based pre- and post-fire measurements of ET are often unavailable, limiting the potential to understand the extent of wildfire impacts on the hydrological cycle. This research estimated both pre- and post-fire ET using remotely sensed variables and support vector machine (SVM) methods. Input variables (land surface temperature, modified soil-adjusted vegetation index, normalized difference moisture index, normalized burn ratio, precipitation, potential evapotranspiration, albedo and vegetation types) were used to train and develop 56 combinations that yielded 33 unique SVM models to predict actual ET. The models were trained to predict a spatial ET, the Operational Simplified Surface Energy Balance (SSEBop), for the 2003 Coyote Fire in San Diego, California (USA). The optimal SVM model, SVM-ET6, required six input variables and predicted ET for fifteen years with a root-mean-square error (RMSE) of 8.43 mm/month and a R2 of 0.89. The developed model was transferred and applied to the 2003 Old Fire in San Bernardino, California (USA), where a watershed balance approach was used to validate SVM-ET6 predictions. The annual water balance for ten out of fifteen years was within ±20% of the predicted values. This work demonstrated machine learning as a viable method to create a remotely-sensed estimate with wide applicability for regions with sparse data observations and information. This innovative work demonstrated the potential benefit for land and forest managers to understand and analyze the hydrological cycle of watersheds that experience acute disturbances based on this developed predictive ET model.

Description: The shadow-mapping and ray-tracing algorithms are the two popular approaches used in visibility handling for multi-view based texture reconstruction. Visibility testing based on the two algorithms needs a user-defined bias to reduce computation error. However, a constant bias does not work for every part of a geometry. Therefore, the accuracy of the two algorithms is limited. In this paper, we propose a high-precision graphics pipeline-based visibility classification (GPVC) method without introducing a bias. The method consists of two stages. In the first stage, a shader-based rendering is designed in the fixed graphics pipeline to generate initial visibility maps (IVMs). In the second stage, two algorithms, namely, lazy-projection coverage correction (LPCC) and hierarchical iterative vertex-edge-region sampling (HIVERS), are proposed to classify visible primitives into fully visible or partially visible primitives. The proposed method can be easily implemented in the graphics pipeline to achieve parallel acceleration. With respect to efficiency, the proposed method outperforms the bias-based methods. With respect to accuracy, the proposed method can theoretically reach a value of 100%. Compared with available libraries and software, the textured model based on our method is smoother with less distortion and dislocation.

Description: We mapped native, endemic, and introduced (i.e., exotic) tree species counts, relative basal areas of functional groups, species basal areas, and forest biomass from forest inventory data, satellite imagery, and environmental data for Puerto Rico and the Virgin Islands. Imagery included time series of Landsat composites and Moderate Resolution Imaging Spectroradiometer (MODIS)-based phenology. Environmental data included climate, land-cover, geology, topography, and road distances. Large-scale deforestation and subsequent forest regrowth are clear in the resulting maps decades after large-scale transition back to forest. Stand age, climate, geology, topography, road/urban locations, and protection are clearly influential. Unprotected forests on more accessible or arable lands are younger and have more introduced species and deciduous and nitrogen-fixing basal areas, fewer endemic species, and less biomass. Exotic species are widespread—except in the oldest, most remote forests on the least arable lands, where shade-tolerant exotics may persist. Although the maps have large uncertainty, their patterns of biomass, tree species diversity, and functional traits suggest that for a given geoclimate, forest age is a core proxy for forest biomass, species counts, nitrogen-fixing status, and leaf longevity. Geoclimate indicates hard-leaved species commonness. Until global wall-to-wall remote sensing data from specialized sensors are available, maps from multispectral image time series and other predictor data should help with running ecosystem models and as sustainable development indicators. Forest attribute models trained with a tree species ordination and mapped with nearest neighbor substitution (Phenological Gradient Nearest Neighbor method, PGNN) yielded larger correlation coefficients for observed vs. mapped tree species basal areas than Cubist regression tree models trained separately on each species. In contrast, Cubist regression tree models of forest structural and functional attributes yielded larger such correlation coefficients than the ordination-trained PGNN models.

Description: In this study the interaction energies for single-stranded DNA and double-stranded DNA molecules with a lipid bilayer are investigated. The 6-12 Lennard-Jones potential and continuous approximation are used to derive analytical expressions for these interaction energies. Assuming that there is a circular gap in the lipid bilayer, we determine the relationship of the molecular interaction energy, including the circular gap radius and the perpendicular distance of the single-stranded DNA and double-stranded DNA molecules from the gap. For both single-stranded and double-stranded DNA molecules, the relationship between the minimum energy location and the hole radius is calculated; in the case of the double-stranded DNA molecule, we assume that the helical phase angle is equal to . By minimizing the total interaction energies, the results demonstrate that the single-stranded DNA and double-stranded DNA molecules move through a lipid bilayer when the gap radius 10 Å and 13.8 Å, respectively. The results present in this project can be leveraged to understand the interactions between cell-penetrating peptides and biomembranes, which may improve gene and drug delivery.

Description: Displacement monitoring of large bridges is an important source of information concerning their health state. In this paper, a procedure based on satellite Persistent Scatterer Interferometry (PSI) data is presented to assess bridge health. The proposed approach periodically assesses the displacements of a bridge in order to detect abnormal displacements at any position of the bridge. To demonstrate its performances, the displacement characteristics of two bridges, the Nanjing-Dashengguan High-speed Railway Bridge (NDHRB, 1272 m long) and the Nanjing-Yangtze River Bridge (NYRB, 1576-m long), are studied. For this purpose, two independent Sentinel-1 SAR datasets were used, covering a two-year period with 75 and 66 images, respectively, providing very similar results. During the observed period, the two bridges underwent no actual displacements: thermal dilation displacements were dominant. For NDHRB, the total thermal dilation parameter from the PSI analysis was computed using the two different datasets; the difference of the two computations was 0.09 mm/°C, which, assuming a temperature variation of 30 °C, corresponds to a discrepancy of 2.7 mm over the total bridge length. From the total thermal dilation parameters, the coefficients of thermal expansion (CTE) were calculated, which were 11.26 × 10−6/°C and 11.19 × 10−6/°C, respectively. These values match the bridge metal properties. For NYRB, the estimated CTE was 10.46 × 10−6/°C, which also matches the bridge metal properties (11.26 × 10−6/°C). Based on a statistical analysis of the PSI topographic errors of NDHRB, pixels on the bridge deck were selected, and displacement models covering the entire NDHRB were established using the two track datasets; the model was validated on the six piers with an absolute mean error of 0.25 mm/°C. Finally, the health state of NDHRB was evaluated with four more images using the estimated models, and no abnormal displacements were found.

Description: The Cushing Hub in Oklahoma, one of the largest oil storage facilities in the world, is federally designated as critical national infrastructure. In 2014, the formerly aseismic city of Cushing experienced a Mw 4.0 and 4.3 induced earthquake sequence due to wastewater injection. Since then, an M4+ earthquake sequence has occurred annually (October 2014, September 2015, November 2016). Thus far, damage to critical infrastructure has been minimal; however, a larger earthquake could pose significant risk to the Cushing Hub. In addition to inducing earthquakes, wastewater injection also threatens the Cushing Hub through gradual surface uplift. To characterize the impact of wastewater injection on critical infrastructure, we use Differential Interferometric Synthetic Aperture Radar (DInSAR), a satellite radar technique, to observe ground surface displacement in Cushing before and during the induced Mw 5.0 event. Here, we process interferograms of Single Look Complex (SLC) radar data from the European Space Agency (ESA) Sentinel-1A satellite. The preearthquake interferograms are used to create a time series of cumulative surface displacement, while the coseismic interferograms are used to invert for earthquake source characteristics. The time series of surface displacement reveals 4–5.5 cm of uplift across Cushing over 17 months. The coseismic interferogram inversion suggests that the 2016 Mw 5.0 earthquake is shallower than estimated from seismic inversions alone. This shallower source depth should be taken into account in future hazard assessments for regional infrastructure. In addition, monitoring of surface deformation near wastewater injection wells can be used to characterize the subsurface dynamics and implement measures to mitigate damage to critical installations.

Description: Accurate acquisition of forest structural parameters, which is essential for the parameterization of forest growth models and understanding forest ecosystems, is also crucial for forest inventories and sustainable forest management. In this study, simultaneously acquired airborne full-waveform (FWF) LiDAR and hyperspectral data were used to predict forest structural parameters in subtropical forests of southeast China. The pulse amplitude and waveform shape of airborne FWF LiDAR data were calibrated using a physical process-driven and a voxel-based approach, respectively. Different suites of FWF LiDAR and hyperspectral metrics, i.e., point cloud (derived from LiDAR-waveforms) metrics (DPC), full-waveform (geometric and radiometric features) metrics (FW) and hyperspectral (original reflectance bands, vegetation indices and statistical indices) metrics (HS), were extracted and assessed using correlation analysis and principal component analysis (PCA). The selected metrics of DPC, FW and HS were used to fit regression models individually and in combination to predict diameter at breast height (DBH), Lorey’s mean height (HL), stem number (N), basal area (G), volume (V) and above ground biomass (AGB), and the capability of the predictive models and synergetic effects of metrics were assessed using leave-one-out cross validation. The results showed that: among the metrics selected from three groups divided by the PCA analysis, twelve DPC, eight FW and ten HS were highly correlated with the first and second principal component (r 〉 0.7); most of the metrics selected from DPC, FW and HS had weak relationships between each other (r 〈 0.7); the prediction of HL had a relatively higher accuracy (Adjusted-R2 = 0.88, relative RMSE = 10.68%), followed by the prediction of AGB (Adjusted-R2 = 0.84, relative RMSE = 15.14%), and the prediction of V had a relatively lower accuracy (Adjusted-R2 = 0.81, relative RMSE = 16.37%); and the models including only DPC had the capability to predict forest structural parameters with relatively high accuracies (Adjusted-R2 = 0.52–0.81, relative RMSE = 15.70–40.87%) whereas the usage of DPC and FW resulted in higher accuracies (Adjusted-R2 = 0.62–0.87, relative RMSE = 11.01–31.30%). Moreover, the integration of DPC, FW and HS can further improve the accuracies of forest structural parameters prediction (Adjusted-R2 = 0.68–0.88, relative RMSE = 10.68–28.67%).

Description: With the availability to high-accuracy a priori zenith wet delay (ZWD) data, the positioning efficiency of the precise point positioning (PPP) processing can be effectively improved, including accelerating the convergence time and improving the positioning precision, in ground-based Global Navigation Satellite System (GNSS) technology. Considering the limitations existing in the state-of-the-art ZWD models, this paper established and evaluated a new in-situ meteorological observation-based grid model for estimating ZWD named GridZWD using the radiosonde data and the European Centre for Medium-Range Weather Forecasts (ECWMF) data. The results show that ZWD has a strong correlation with the meteorological parameter water vapor pressure in continental and high-latitude regions. The root of mean square error (RMS) of 24.6 mm and 36.0 mm are achievable by the GridZWD model when evaluated with the ECWMF data and the radiosonde data, respectively. An accuracy improvement of approximately 10%~30% compared with the state-of-the-art models (e.g., the Saastamoinen, Hopfield and GPT2w models) can be found for the new built model.

Description: Kernel-based home range models are widely-used to estimate animal habitats and develop conservation strategies. They provide a probabilistic measure of animal space use instead of assuming the uniform utilization within an outside boundary. However, this type of models estimates the home ranges from animal relocations, and the inadequate locational data often prevents scientists from applying them in long-term and large-scale research. In this paper, we propose an end-to-end deep learning framework to simulate kernel home range models. We use the conditional adversarial network as a supervised model to learn the home range mapping from time-series remote sensing imagery. Our approach enables scientists to eliminate the persistent dependence on locational data in home range analysis. In experiments, we illustrate our approach by mapping the home ranges of Bar-headed Geese in Qinghai Lake area. The proposed framework outperforms all baselines in both qualitative and quantitative evaluations, achieving visually recognizable results and high mapping accuracy. The experiment also shows that learning the mapping between images is a more effective way to map such complex targets than traditional pixel-based schemes.

Description: Image registration is a core technology of many different image processing areas and is widely used in the remote sensing community. The accuracy of image registration largely determines the effect of subsequent applications. In recent years, phase correlation-based image registration has drawn much attention because of its high accuracy and efficiency as well as its robustness to gray difference and even slight changes in content. Many researchers have reported that the phase correlation method can acquire a sub-pixel accuracy of 1 / 10 or even 1 / 100 . However, its performance is acquired only in the case of translation, which limits the scope of the application of the method. However, there are few reports on the estimation of scales and angles based on the phase correlation method. To take advantage of the high accuracy property and other merits of phase correlation-based image registration and extend it to estimate the similarity transform, we proposed a novel algorithm, the Multilayer Polar Fourier Transform (MPFT), which uses a fast and accurate polar Fourier transform with different scaling factors to calculate the log-polar Fourier transform. The structure of the polar grids of MPFT is more similar to the one of the log-polar grid. In particular, for rotation estimation only, the polar grid of MPFT is the calculation grid. To validate its effectiveness and high accuracy in estimating angles and scales, both qualitative and quantitative experiments were carried out. The quantitative experiments included a numerical simulation as well as synthetic and real data experiments. The experimental results showed that the proposed method, MPFT, performs better than the existing phase correlation-based similarity transform estimation methods, the Pseudo-polar Fourier Transform (PPFT) and the Multilayer Fractional Fourier Transform method (MLFFT), and the classical feature-based registration method, Scale-Invariant Feature Transform (SIFT), and its variant, ms-SIFT.

Description: Various classification methods have been developed to extract meaningful information from Airborne Laser Scanner (ALS) point clouds. However, the accuracy and the computational efficiency of the existing methods need to be improved, especially for the analysis of large datasets (e.g., at regional or national levels). In this paper, we present a novel deep learning approach to ground classification for Digital Terrain Model (DTM) extraction as well as for multi-class land-cover classification, delivering highly accurate classification results in a computationally efficient manner. Considering the top–down acquisition angle of ALS data, the point cloud is initially projected on the horizontal plane and converted into a multi-dimensional image. Then, classification techniques based on Fully Convolutional Networks (FCN) with dilated kernels are designed to perform pixel-wise image classification. Finally, labels are transferred from pixels to the original ALS points. We also designed a Multi-Scale FCN (MS-FCN) architecture to minimize the loss of information during the point-to-image conversion. In the ground classification experiment, we compared our method to a Convolutional Neural Network (CNN)-based method and LAStools software. We obtained a lower total error on both the International Society for Photogrammetry and Remote Sensing (ISPRS) filter test benchmark dataset and AHN-3 dataset in the Netherlands. In the multi-class classification experiment, our method resulted in higher precision and recall values compared to the traditional machine learning technique using Random Forest (RF); it accurately detected small buildings. The FCN achieved precision and recall values of 0.93 and 0.94 when RF obtained 0.91 and 0.92, respectively. Moreover, our strategy significantly improved the computational efficiency of state-of-the-art CNN-based methods, reducing the point-to-image conversion time from 47 h to 36 min in our experiments on the ISPRS filter test dataset. Misclassification errors remained in situations that were not included in the training dataset, such as large buildings and bridges, or contained noisy measurements.

Description: The evaporation of water from land into the atmosphere is a key component of the hydrological cycle. Accurate estimates of this flux are essential for proper water management and irrigation scheduling. However, continuous and qualitative information on land evaporation is currently not available at the required spatio-temporal scales for agricultural applications and regional-scale water management. Here, we apply the Global Land Evaporation Amsterdam Model (GLEAM) at 100 m spatial resolution and daily time steps to provide estimates of land evaporation over The Netherlands, Flanders, and western Germany for the period 2013–2017. By making extensive use of microwave-based geophysical observations, we are able to provide data under all weather conditions. The soil moisture estimates from GLEAM at high resolution compare well with in situ measurements of surface soil moisture, resulting in a median temporal correlation coefficient of 0.76 across 29 sites. Estimates of terrestrial evaporation are also evaluated using in situ eddy-covariance measurements from five sites, and compared to estimates from the coarse-scale GLEAM v3.2b, land evaporation from the Satellite Application Facility on Land Surface Analysis (LSA-SAF), and reference grass evaporation based on Makkink’s equation. All datasets compare similarly with in situ measurements and differences in the temporal statistics are small, with correlation coefficients against in situ data ranging from 0.65 to 0.95, depending on the site. Evaporation estimates from GLEAM-HR are typically bounded by the high values of the Makkink evaporation and the low values from LSA-SAF. While GLEAM-HR and LSA-SAF show the highest spatial detail, their geographical patterns diverge strongly due to differences in model assumptions, model parameterizations, and forcing data. The separate consideration of rainfall interception loss by tall vegetation in GLEAM-HR is a key cause of this divergence: while LSA-SAF reports maximum annual evaporation volumes in the Green Heart of The Netherlands, an area dominated by shrubs and grasses, GLEAM-HR shows its maximum in the national parks of the Veluwe and Heuvelrug, both densely-forested regions where rainfall interception loss is a dominant process. The pioneering dataset presented here is unique in that it provides observational-based estimates at high resolution under all weather conditions, and represents a viable alternative to traditional visible and infrared models to retrieve evaporation at field scales.

Description: Data from NASA’s Soil Moisture Active Passive Mission (SMAP) and from the California Cooperative Oceanic Fisheries Investigations (CalCOFI) were used to examine the freshening that occurred during 2015–2016 in the Southern California Current System. Overall, the freshening was found to be related to the 2014–2016 Northeast Pacific Warm Anomaly. The primary goal was to determine the feasibility of using SMAP data to observe the surface salinity signal associated with the warming and its coastal impact. As a first step, direct comparisons were done with salinity from the CalCOFI data at one-meter depth. During 2015, SMAP was saltier than CalCOFI by 0.5 Practical Salinity Units (PSU), but biases were reduced to 〈0.1 PSU during 2016. South of 33°N, and nearer to the coast where upwelling dominates, SMAP was fresher in 2015 by almost 0.2 PSU. CalCOFI showed freshening of 0.1 PSU. North of 33°N, SMAP and CalCOFI saw significant freshening in 2016, SMAP by 0.4 PSU and CalCOFI by 0.2 PSU. Differences between SMAP and CalCOFI are consistent with the increased stratification in 2015 and changes in the mixed layer depth. SMAP observed freshening that reached the Baja California Coast.

Description: This work presents an overview of the potential of microwave indices obtained from multi-frequency/polarization radiometry in detecting the characteristics of land surfaces, in particular soil covered by vegetation or snow and agricultural bare soils. Experimental results obtained with ground-based radiometers on different types of natural surfaces by the Microwave Remote Sensing Group of IFAC-CNR starting from ‘80s, are summarized and interpreted by means of theoretical models. It has been pointed out that, with respect to single frequency/polarization observations, microwave indices revealed a higher sensitivity to some significant parameters, which characterize the hydrological cycle, namely: soil moisture, vegetation biomass and snow depth or snow water equivalent. Electromagnetic models have then been used for simulating brightness temperature and microwave indices from land surfaces. As per vegetation covered soils, the well-known tau-omega (τ-ω) model based on the radiative transfer theory has been used, whereas terrestrial snow cover has been simulated using a multi-layer dense-medium radiative transfer model (DMRT). On the basis of these results, operational inversion algorithms for the retrieval of those hydrological quantities have been successfully implemented using multi-channel data from the microwave radiometric sensors operating from satellite.

Description: Rapidly developing droughts, including flash droughts, have frequently occurred throughout East Asia in recent years, causing significant damage to agricultural ecosystems. Although many drought monitoring and warning systems have been developed in recent decades, the short-term prediction of droughts (within 10 days) is still challenging. This study has developed drought prediction models for a short-period of time (one pentad) using remote-sensing data and climate variability indices over East Asia (20°–50°N, 90°–150°E) through random forest machine learning. Satellite-based drought indices were calculated using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture, Tropical Rainfall Measuring Mission (TRMM) precipitation, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and normalized difference vegetation index (NDVI). The real-time multivariate (RMM) Madden–Julian oscillation (MJO) indices were used because the MJO is a short timescale climate variability and has important implications for droughts in East Asia. The validation results show that those drought prediction models with the MJO variables (r ~ 0.7 on average) outperformed the original models without the MJO variables (r ~ 0.4 on average). The predicted drought index maps showed similar spatial distribution to actual drought index maps. In particular, the MJO-based models captured sudden changes in drought conditions well, from normal/wet to dry or dry to normal/wet. Since the developed models can produce drought prediction maps at high resolution (5 km) for a very short timescale (one pentad), they are expected to provide decision makers with more accurate information on rapidly changing drought conditions.

Description: To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distribution histograms. An arithmetic frequency-mean feature is then defined from the red, green and blue band histogram. Secondly, bin-to-bin distance is adapted to measure the change magnitude between the pairwise objects of bi-temporal images. The change magnitude image (CMI) of the bi-temporal images can be generated through object-by-object. Finally, the classical binary method Otsu is used to divide the CMI to a binary change detection map. Experimental results based on two real datasets with different land-cover change scenes demonstrate the effectiveness of the proposed MOHD approach in detecting land-cover change compared with three widely used existing approaches.

Description: We analyze ten years (2008–2017) of ground-based observations of the Aerosol Optical Depth (AOD) in the atmosphere of Kuwait City, in Middle East. The measurements were conducted with a CIMEL sun-sky photometer, at various wavelengths. The daily average AOD at 500 nm (AOD500) is 0.45, while the mean Ångström coefficient (AE), calculated from the pair of wavelengths 440 and 870 nm, is 0.61. The observed high AOD500 values (0.75–2.91), are due to regional sand and dust storm events, which are affecting Kuwait with a mean annual frequency of almost 20 days/year. The long-term record analysis of AOD500 and AE, shows a downward and upward tendency respectively, something which could be attributed to the continuous expansion and industrialization of the main city of Kuwait, in combination with the simultaneous increase of soil moisture over the area. By utilizing back trajectories of air masses for up to 4 days, we assessed the influence of various regions to the aerosol load over Kuwait. The high aerosol loads during spring, are attributed to the dominance of coarse particles from Saudi Arabia (AOD500 0.56–0.74), a source area that contributes the 56% to the mean annual AOD500. Other dust sources affecting significantly Kuwait originated from the regions of Iraq and Iran with contribution of 21%.

Description: The imaging issue of a rotating maneuvering target with a large angle and a high translational speed has been a challenging problem in the area of inverse synthetic aperture radar (ISAR) autofocus imaging, in particular when the target has both radial and angular accelerations. In this paper, on the basis of the phase retrieval algorithm and the Gabor wavelet transform (GWT), we propose a new method for phase error correction. The approach first performs the range compression on ISAR raw data to obtain range profiles, and then carries out the GWT transform as the time-frequency analysis tool for the rotational motion compensation (RMC) requirement. The time-varying terms, caused by rotational motion in the Doppler frequency shift, are able to be eliminated at the selected time frame. Furthermore, the processed backscattered signal is transformed to the one in the frequency domain while applying the phase retrieval to run the translational motion compensation (TMC). Phase retrieval plays an important role in range tracking, because the ISAR echo module is not affected by both radial velocity and the acceleration of the target. Finally, after the removal of both the rotational and translational motion errors, the time-invariant Doppler shift is generated, and radar returned signals from the same scatterer are always kept in the same range cell. Therefore, the unwanted motion effects can be removed by applying this approach to have an autofocused ISAR image of the maneuvering target. Furthermore, the method does not need to estimate any motion parameters of the maneuvering target, which has proven to be very effective for an ideal range–Doppler processing. Experimental and simulation results verify the feasibility of this approach.

Description: The urban green cover in high-spatial resolution (HR) remote sensing images have obvious multiscale characteristics, it is thus not possible to properly segment all features using a single segmentation scale because over-segmentation or under-segmentation often occurs. In this study, an unsupervised cross-scale optimization method specifically for urban green cover segmentation is proposed. A global optimal segmentation is first selected from multiscale segmentation results by using an optimization indicator. The regions in the global optimal segmentation are then isolated into under- and fine-segmentation parts. The under-segmentation regions are further locally refined by using the same indicator as that in global optimization. Finally, the fine-segmentation part and the refined under-segmentation part are combined to obtain the final cross-scale optimized result. The green cover objects can be segmented at their specific optimal segmentation scales in the optimized segmentation result to reduce both under- and over-segmentation errors. Experimental results on two test HR datasets verify the effectiveness of the proposed method.

Description: Surface albedo is a critical parameter in surface energy balance, and albedo change is an important driver of changes in local climate. In this study, we developed a workflow for landscape albedo estimation using images acquired with a consumer-grade camera on board unmanned aerial vehicles (UAVs). Flight experiments were conducted at two sites in Connecticut, USA and the UAV-derived albedo was compared with the albedo obtained from a Landsat image acquired at about the same time as the UAV experiments. We find that the UAV estimate of the visibleband albedo of an urban playground (0.037 ± 0.063, mean ± standard deviation of pixel values) under clear sky conditions agrees reasonably well with the estimates based on the Landsat image (0.047 ± 0.012). However, because the cameras could only measure reflectance in three visible bands (blue, green, and red), the agreement is poor for shortwave albedo. We suggest that the deployment of a camera that is capable of detecting reflectance at a near-infrared waveband should improve the accuracy of the shortwave albedo estimation.

Description: Satellite synthetic aperture radar (SAR) interferometry (InSAR) is a powerful technology to monitor slow ground surface movements. However, the extraction and interpretation of information from big sets of InSAR measurements is a complex and demanding task. In this paper, a new method is presented for automatically detecting potential instability risks affecting buildings and infrastructures, by searching for anomalies in the persistent scatterer (PS) deformations, either in the spatial or in the temporal dimensions. In the spatial dimension, in order to reduce the dataset size and improve data reliability, we utilize a hierarchical clustering method to obtain convergence points that are more trustworthy. Then, we detect deformations characterized by large values and spatial inhomogeneity. In the temporal dimension, we use a signal processing method to decompose the input into two main components: regular periodic deformations and piecewise linear deformations. After removing the periodic component, the velocity variation in each identified temporal partition is analyzed to detect anomalous velocity trends and accelerations. The method has been tested on different sites in China, based on InSAR measurements from COSMO-SkyMed data. The results, verified with in-field surveys, confirm the potential of the method for the automatic detection of deformation anomalies that could cause building or infrastructure stability problems.

Description: Using surface temperature as a signature of the surface energy balance is a way to quantify the spatial distribution of evapotranspiration and water stress. In this work, we used the new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) based on the Two Sources Energy Balance (TSEB) model rationale which solves the surface energy balance equations for the soil and the canopy. SPARSE can be used (i) to retrieve soil and vegetation stress levels from known surface temperature and (ii) to predict transpiration, soil evaporation, and surface temperature for given stress levels. The main innovative feature of SPARSE is that it allows to bound each retrieved individual flux component (evaporation and transpiration) by its corresponding potential level deduced from running the model in prescribed potential conditions, i.e., a maximum limit if the surface water availability is not limiting. The main objective of the paper is to assess the SPARSE model predictions of water stress and evapotranspiration components for its two proposed versions (the “patch” and “layer” resistances network) over 20 in situ data sets encompassing distinct vegetation and climate. Over a large range of leaf area index values and for contrasting vegetation stress levels, SPARSE showed good retrieval performances of evapotranspiration and sensible heat fluxes. For cereals, the layer version provided better latent heat flux estimates than the patch version while both models showed similar performances for sparse crops and forest ecosystems. The bounded layer version of SPARSE provided the best estimates of latent heat flux over different sites and climates. Broad tendencies of observed and retrieved stress intensities were well reproduced with a reasonable difference obtained for most of the points located within a confidence interval of 0.2. The synchronous dynamics of observed and retrieved estimates underlined that the SPARSE retrieved water stress estimates from Thermal Infra-Red data were relevant tools for stress detection.

Description: Short-term traffic flow forecasting is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting remains a challenging task. In order to improve the accuracy of short-term traffic flow forecasting, a short-term traffic flow forecasting method based on LSSVM model optimized by GA-PSO hybrid algorithm is put forward. Firstly, the LSSVM model is constructed with combined kernel function. Then the GA-PSO hybrid optimization algorithm is designed to optimize the kernel function parameters efficiently and effectively. Finally, case validation is carried out using inductive loop data collected from the north-south viaduct in Shanghai. The experimental results demonstrate that the proposed GA-PSO-LSSVM model is superior to comparative method.

Description: The aim of this paper is to study and investigate the optimal control strategy of a discrete mathematical model of smoking with specific saturated incidence rate. The population that we are going to study is divided into five compartments: potential smokers, light smokers, heavy smokers, temporary quitters of smoking, and permanent quitters of smoking. Our objective is to find the best strategy to reduce the number of light smokers, heavy smokers, and temporary quitters of smoking. We use three control strategies which are awareness programs through media and education, treatment, and psychological support with follow-up. Pontryagins maximum principle in discrete time is used to characterize the optimal controls. The numerical simulation is carried out using MATLAB. Consequently, the obtained results confirm the performance of the optimization strategy.

Description: The characteristics of the improved Atomic Frequency Standard (AFS) operated on the latest BeiDou-3 experimental satellites are analyzed from day-of-year (DOY) 254 to 281, of the year 2017, considering the following three aspects: stability, periodicity, and prediction precision. The two-step method of Precise Orbit Determination (POD) is used to obtain the precise clock offsets. We presented the stability of such new clocks and studied the influence of the uneven distribution of the ground stations on the stability performance of the clock. The results show that the orbit influence on the Medium Earth Orbit (MEO) clock offsets is the largest of three satellite types, especially from 3 × 10 3 s to 8.64 × 10 4 s. Considering this orbit influence, the analysis shows that the Passive Hydrogen Maser (PHM) clock carried on C32 is approximately 2.6 × 10 − 14 at an interval of 10 4 , and has the best stability for any averaging intervals among the BeiDou satellite clocks, which currently achieves a level comparable to that of the PHM clock of Galileo, and the rubidium (Rb) clocks of Global Positioning System (GPS) Block IIF. The stability of the improved Rb AFS on BeiDou-3 is also superior to that of BeiDou-2 from 3 × 10 2 s to 3 × 10 3 s, and comparable to that of Rb AFS on the Galileo. Moreover, the periodicity of the PHM clock and the improved Rb clock are presented. For the PHM clock, the amplitudes are obviously reduced, while the new Rb clocks did not show a visible improvement, which will need further analysis in the future. As expected, the precision of the short-term clock prediction is improved because of the better characteristics of AFS. The Root Mean Square (RMS) of 1-h clock prediction is less than 0.16 ns.

Description: The spatiotemporal distribution pattern of the surface temperatures of urban forest canopies (STUFC) is influenced by many environmental factors, and the identification of interactions between these factors can improve simulations and predictions of spatial patterns of urban cool islands. This quantitative research uses an integrated method that combines remote sensing, ground surveys, and spatial statistical models to elucidate the mechanisms that influence the STUFC and considers the interaction of multiple environmental factors. This case study uses Jinjiang, China as a representative of a city experiencing rapid urbanization. We build up a multisource database (forest inventory, digital elevation models, population, and remote sensing imagery) on a uniform coordinate system to support research into the interactions that influence the STUFC. Landsat-5/8 Thermal Mapper images and meteorological data were used to retrieve the temporal and spatial distributions of land surface temperature. Ground observations, which included the forest management planning inventory and population density data, provided the factors that determine the STUFC spatial distribution on an urban scale. The use of a spatial statistical model (GeogDetector model) reveals the interaction mechanisms of STUFC. Although different environmental factors exert different influences on STUFC, in two periods with different hot spots and cold spots, the patch area and dominant tree species proved to be the main factors contributing to STUFC. The interaction between multiple environmental factors increased the STUFC, both linearly and nonlinearly. Strong interactions tended to occur between elevation and dominant species and were prevalent in either hot or cold spots in different years. In conclusion, the combining of multidisciplinary methods (e.g., remote sensing images, ground observations, and spatial statistical models) helps reveal the mechanism of STUFC on an urban scale.

Description: State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is usually discarded in the analysis, although it gives insight about indoor spaces and the topological relations between them. In this research, the trajectory is used in conjunction with the point cloud to subdivide the indoor space into stories, staircases, doorways, and rooms. Analyzing the scanner trajectory as a standalone dataset is used to identify the staircases and to separate the stories. Also, the doors that are traversed by the operator during the scanning are identified by processing only the interesting spots of the point cloud with the help of the trajectory. Semantic information like different space labels is assigned to the trajectory based on the detected doors. Finally, the point cloud is semantically enriched by transferring the labels from the annotated trajectory to the full point cloud. Four real-world datasets with a total of seven stories are used to evaluate the proposed methods. The evaluation items are the total number of correctly detected rooms, doors, and staircases.

Description: This work presents calibration results for the altimeter of Sentinel-3A Surface Topography Mission as determined at the Permanent Facility for Altimetry Calibration in west Crete, Greece. The facility has been providing calibration services for more than 15 years for all past (i.e., Envisat, Jason-1, Jason-2, SARAL/AltiKa, HY-2A) and current (i.e., Sentinel-3A, Sentinel-3B, Jason-3) satellite altimeters. The groundtrack of the Pass No.14 of Sentinel-3A ascends west of the Gavdos island and continues north to the transponder site on the mountains of west Crete. This pass has been calibrated using three independent techniques activated at various sites in the region: (1) the transponder approach for its range bias, (2) the sea-surface method for the estimation of altimeter bias for its sea-surface heights, and (c) the cross-over analysis for inspecting height observations with respect to Jason-3. The other Pass No.335 of Sentinel-3A descends from southwest of Crete to south and intersects the Gavdos calibration site. Additionally, calibration values for this descending pass are presented, applying sea-surface calibration and crossover analysis. An uncertainty analysis for the altimeter biases derived by the transponder and by sea-surface calibrations is also introduced following the new standard of Fiducial Reference Measurements.

Description: The mining waste of open pit mines is usually piled-up in dump sites, making a man-made hill more than tens of meters high. Because of the loose structure of the dump sites, landslides or debris flow may occur after heavy rainfall, threatening local lives and properties. Therefore, dump stability analysis is crucial for ensuring local safety. In this paper, a collaborative stability analysis based on multiple remote sensing technologies was innovatively conducted at the Xudonggou dump of the Anqian iron mine. A small baseline subset (SBAS) analysis was used to derive the spatial and temporal distributions of displacements in the line-of sight (LOS) over the whole study area. The deformation in LOS is translated to the slope direction based on an assumption that displacements only occur parallel to the slope surface. Infrared Thermography (IRT) technology was used to detect weak aquifer layers located at the toe of possible landslide bodies. Then, numerical simulations based on the limit equilibrium method were conducted to calculate the factor of safety for three profiles located on the dump site. The results, emerging from multiple remote sensing technologies, were very consistent and, eventually, the landslide hazard zone of the Xudonggou dump site was outlined.

Description: Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been preferred to alternative image-based approaches, such as optical flow, for camera-based surface flow velocity estimate. In this work, we introduce a novel optical flow scheme, optical tracking velocimetry (OTV), that entails automated feature detection, tracking through the differential sparse Lucas-Kanade algorithm, and then a posteriori filtering to retain only realistic trajectories that pertain to the transit of actual objects in the field of view. The method requires minimal input on the flow direction and camera orientation. Tested on two image data sets collected in diverse natural conditions, the approach proved suitable for rapid and accurate surface flow velocity estimations. Five different feature detectors were compared and the features from accelerated segment test (FAST) resulted in the best balance between the number of features identified and successfully tracked as well as computational efficiency. OTV was relatively insensitive to reduced image resolution but was impacted by acquisition frequencies lower than 7–8 Hz. Compared to traditional correlation-based techniques, OTV was less affected by noise and surface seeding. In addition, the scheme is foreseen to be applicable to real-time gauge-cam implementations.

Description: Numerous studies have documented the effects of irrigation on local, regional, and global climate. However, most studies focused on the cooling effect of irrigated dryland in semiarid or arid regions. In our study, we focused on irrigated paddy fields in humid regions at mid to high latitudes and estimated the effects of paddy field expansion from rain-fed farmland on local temperatures based on remote sensing and observational data. Our results revealed much significant near-surface cooling in spring (May and June) rather than summer (July and August) and autumn (September), which was −2.03 K, −0.73 K and −1.08 K respectively. Non-radiative mechanisms dominated the local temperature response to paddy field expansion from rain-fed farmland in the Sanjiang Plain. The contributions from the changes to the combined effects of the non-radiative process were 123.6%, 95.5%, and 66.9% for spring (May and June), summer (July and August), and autumn (September), respectively. Due to the seasonal changes of the biogeophysical properties for rain-fed farmland and paddy fields during the growing season, the local surface temperature responses, as well as their contributions, showed great seasonal variability. Our results showed that the cooling effect was particularly obvious during the dry spring instead of the warm, wet summer, and indicated that more attention should be paid to the seasonal differences of these effects, especially in a region with a relatively humid climate and distinct seasonal variations.

Description: Vertical urban growth in the form of urban volume or building height is increasingly being seen as a significant indicator and constituent of the urban environment. Although high-resolution digital surface models can provide valuable information, various places lack access to such resources. The objective of this study is to explore the feasibility of using open digital surface models (DSMs), such as the AW3D30, ASTER, and SRTM datasets, for extracting digital building height models (DBHs) and comparing their accuracy. A multidirectional processing and slope-dependent filtering approach for DBH extraction was used. Yangon was chosen as the study location since it represents a rapidly developing Asian city where urban changes can be observed during the acquisition period of the aforementioned open DSM datasets (2001–2011). The effect of resolution degradation on the accuracy of the coarse AW3D30 DBH with respect to the high-resolution AW3D5 DBH was also examined. It is concluded that AW3D30 is the most suitable open DSM for DBH generation and for observing buildings taller than 9 m. Furthermore, the AW3D30 DBH, ASTER DBH, and SRTM DBH are suitable for observing vertical changes in urban structures.

Description: Monitoring the development of vegetation height through time provides a key indicator of crop health and overall condition. Traditional manual approaches for monitoring crop height are generally time consuming, labor intensive and impractical for large-scale operations. Dynamic crop heights collected through the season allow for the identification of within-field problems at critical stages of the growth cycle, providing a mechanism for remedial action to be taken against end of season yield losses. With advances in unmanned aerial vehicle (UAV) technologies, routine monitoring of height is now feasible at any time throughout the growth cycle. To demonstrate this capability, five digital surface maps (DSM) were reconstructed from high-resolution RGB imagery collected over a field of maize during the course of a single growing season. The UAV retrievals were compared against LiDAR scans for the purpose of evaluating the derived point clouds capacity to capture ground surface variability and spatially variable crop height. A strong correlation was observed between structure-from-motion (SfM) derived heights and pixel-to-pixel comparison against LiDAR scan data for the intra-season bare-ground surface (R2 = 0.77 − 0.99, rRMSE = 0.44% − 0.85%), while there was reasonable agreement between canopy comparisons (R2 = 0.57 − 0.65, rRMSE = 37% − 50%). To examine the effect of resolution on retrieval accuracy and processing time, an evaluation of several ground sampling distances (GSD) was also performed. Our results indicate that a 10 cm resolution retrieval delivers a reliable product that provides a compromise between computational cost and spatial fidelity. Overall, UAV retrievals were able to accurately reproduce the observed spatial variability of crop heights within the maize field through the growing season and provide a valuable source of information with which to inform precision agricultural management in an operational context.

Description: Precipitation measurements provide crucial information for hydrometeorological applications. In regions where typical precipitation measurement gauges are sparse, gridded products aim to provide alternative data sources. This study examines the performance of NASA’s Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement Mission (IMERG, GPM) satellite precipitation dataset in capturing the spatio-temporal variability of weather events compared to the German weather radar dataset RADOLAN RW. Besides quantity, also timing of rainfall is of very high importance when modeling or monitoring the hydrologic cycle. Therefore, detection metrics are evaluated along with standard statistical measures to test both datasets. Using indices like “probability of detection” allows a binary evaluation showing the basic categorical accordance of the radar and satellite data. Furthermore, a pixel-by-pixel comparison is performed to assess the ability to represent the spatial variability of rainfall and precipitation quantity. All calculations are additionally carried out for seasonal subsets of the data to assess potentially different behavior due to differences in precipitation schemes. The results indicate significant differences between the datasets. Overall, GPM IMERG overestimates the quantity of precipitation compared to RADOLAN, especially in the winter season. Moreover, shortcomings in detection performance arise in this season with significant erroneously-detected, yet also missed precipitation events compared to the weather radar data. Additionally, along secondary mountain ranges and the Alps, topographically-induced precipitation is not represented in GPM data, which generally shows a lack of spatial variability in rainfall and snowfall estimates due to lower resolution.

Description: A high-resolution three-dimensional (3D) image reconstruction method for a spinning target is proposed in this paper and the anisotropy is overcome by fusing different observation information acquired from the radar network. The proposed method will reconstruct the 3D scattering distribution, and the mapping of the reconstructed 3D image onto the imaging plane is identical to the two-dimensional (2D) imaging result. At first, the range compression and inverse radon transform is employed to produce the 2D image of the spinning target. In addition, the process of mapping the spinning target onto the imaging plane is analyzed and the mapping formulas which are to map the point onto the 2D image plane are derived. After the micro-Doppler signature about which every reconstructed point in 2D imaging result is extracted by the Radon transform, the extended Hough transform is adopted to calculate an important parameter about the micro-Doppler signature, and the 3D image reconstruction model for the spinning target is constructed based on the radar network. Finally, the algorithm for solving the reconstruction model is proposed and the 3D image of the spinning target is obtained. Some simulation results are given to illustrate the effectiveness of the proposed method, and results show that the mean square error (MSE) relatively holds a steady trend when the signal-to-noise ratio (SNR) is higher than −10 dB and the MSE of the reconstructed 3D target image is less than 0.15 when SNR is at the level of −10 dB.

Description: Forests play a key role in terrestrial ecosystems, and the variables extracted from single trees can be used in various fields and applications for evaluating forest production and assessing forest ecosystem services. In this study, we developed an automated hierarchical single-tree segmentation approach based on the high density three-dimensional (3D) Unmanned Aerial Vehicle (UAV) point clouds. First, this approach obtains normalized non-ground UAV points in data preprocessing; then, a voxel-based mean shift algorithm is used to roughly classify the non-ground UAV points into well-detected and under-segmentation clusters. Moreover, potential tree apices for each under-segmentation cluster are obtained with regard to profile shape curves and finally input to the normalized cut segmentation (NCut) algorithm to segment iteratively the under-segmentation cluster into single trees. We evaluated the proposed method using datasets acquired by a Velodyne 16E LiDAR system mounted on a multi-rotor UAV. The results showed that the proposed method achieves the average correctness, completeness, and overall accuracy of 0.90, 0.88, and 0.89, respectively, in delineating single trees. Comparative analysis demonstrated that our method provided a promising solution to reliable and robust segmentation of single trees from UAV LiDAR data with high point cloud density.

Description: Railroads companies conduct regular inspections of their tracks to maintain and update the geographic data for railway management. Traditional railroad inspection methods, such as onsite inspections and semi-automated analysis of imagery and video data, are time consuming and ineffective. This study presents an automated effective method to detect tracks on the basis of their physical shape, geometrical properties, and reflection intensity feature. This study aims to investigate the feasibility of fast extraction of railroad using onboard Velodyne puck data collected by mobile laser scanning (MLS) system. Results show that the proposed method can be executed rapidly on an i5 computer with at least 10 Hz. The MLS system used in this study comprises a Velodyne puck/onboard GNSS receiver/inertial measurement unit. The range accuracy of Velodyne puck equipment is 2 cm, which fulfills the need of precise mapping. Notably, positioning STD is lower than 4 cm in most areas. Experiments are also undertaken to evaluate the timing of the proposed method. Experimental results indicate that the proposed method can extract 3D tracks in real-time and correctly recognize pairs of tracks. Accuracy, precision, and sensitivity of total test area are 99.68%, 97.55%, and 66.55%, respectively. Results suggest that in a multi-track area, close collaboration between MLS platforms mounted on several trains is required.

Description: Aerial laser scanning or photogrammetric point clouds are often noisy at building boundaries. In order to produce regularized polygons from such noisy point clouds, this study proposes a hierarchical regularization method for the boundary points. Beginning with detected planar structures from raw point clouds, two stages of regularization are employed. In the first stage, the boundary points of an individual plane are consolidated locally by shifting them along their refined normal vector to resist noise, and then grouped into piecewise smooth segments. In the second stage, global regularities among different segments from different planes are softly enforced through a labeling process, in which the same label represents parallel or orthogonal segments. This is formulated as a Markov random field and solved efficiently via graph cut. The performance of the proposed method is evaluated for extracting 2D footprints and 3D polygons of buildings in metropolitan area. The results reveal that the proposed method is superior to the state-of-art methods both qualitatively and quantitatively in compactness. The simplified polygons could fit the original boundary points with an average residuals of 0.2 m, and in the meantime reduce up to 90% complexities of the edges. The satisfactory performances of the proposed method show a promising potential for 3D reconstruction of polygonal models from noisy point clouds.

Description: Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.

Description: In this paper, persistent scatterer interferometry and Synthetic Aperture Radar (SAR) tomography have been applied to Sentinel-1 data for urban monitoring. The paper analyses the applicability of SAR tomography to Sentinel-1 data, which is not granted, due to the reduced range and azimuth resolutions and the low resolution in elevation. In a first part of the paper, two implementations of the two techniques are described. In the experimental part, the two techniques are used in parallel to process the same Sentinel-1 data over two test areas. An intercomparison of the results from persistent scatterer interferometry and SAR tomography is carried out, comparing the main parameters estimated by the two techniques. Finally, the paper addresses the complementarity of the two techniques, and in particular it assesses the increase of measurement density that can be achieved by adding the double scatterers from SAR tomography to the persistent scatterer interferometry measurements.

Description: To achieve high-quality surface solar radiation (SSR) data for climate monitoring and analysis, the two satellite-derived monthly SSR datasets of CM SAF CLARA-A2 and SARAH-E have been validated against a homogenized ground-based dataset covering 59 stations across China for 1993–2015 and 1999–2015, respectively. The satellite products overestimate surface solar irradiance by 10.0 W m−2 in CLARA-A2 and 7.5 W m−2 in SARAH-E on average. A strong urbanization effect has been noted behind the large positive bias in China. The bias decreased after 2004, possibly linked to a weakened attenuating effect of aerosols on radiation in China. Both satellite datasets can reproduce the monthly anomalies of SSR, indicated by a significant correlation around 0.8. Due to the neglection of temporal aerosol variability in the satellite algorithms, the discrepancy between the satellite-estimated and ground-observed SSR trends slightly increases in 1999–2015 as compared to 1993–2015. The seasonal performance of the satellite products shows a better accuracy during warm than cold seasons. With respect to the spatial performance, the effects from anthropogenic aerosols, dust aerosols and high elevation and snow-covered surfaces should be well considered in the satellite SSR retrievals to further improve the performance in the eastern, northwestern and southwestern parts of China, respectively.

Description: Understanding forest cover changes is especially important in highly threatened and understudied tropical dry forest landscapes. This research uses Landsat images and a Random Forest classifier (RF) to map old-growth, secondary, and plantation forests and to evaluate changes in their coverage in Ecuador. We used 46 Landsat-derived predictors from the dry and wet seasons to map these forest types and to evaluate the importance of having seasonal variables in classifications. Initial RF models grouped old-growth and secondary forest as a single class because of a lack of secondary forest training data. The model accuracy was improved slightly from 92.8% for the wet season and 94.6% for the dry season to 95% overall by including variables from both seasons. Derived land cover maps indicate that the remaining forest in the landscape occurs mostly along the coastline in a matrix of pastureland, with less than 10% of the landscape covered by plantation forests. To obtain secondary forest training data and evaluate changes in forest cover, we conducted a change analysis between the 1990 and 2015 images. The results indicated that half of the forests present in 1990 were cleared during the 25-year study period and highlighted areas of forest regrowth. We used these areas to extract secondary forest training data and then re-classified the landscape with secondary forest as a class. Classification accuracies decreased with more forest classes, but having data from both seasons resulted in higher accuracy (87.9%) compared to having data from only the wet (85.8%) or dry (82.9%) seasons. The produced cover maps classified the majority of previously identified forest areas as secondary, but these areas likely correspond to forest regrowth and to degraded forests that structurally resemble secondary forests. Among the few areas classified as old-growth forests are known reserves. This research provides evidence of the importance of using bi-seasonal Landsat data to classify forest types and contributes to understanding changes in forest cover of tropical dry forests.

Description: Images obtained from satellites are of an increasing resolution. [...]

Description: Over the 15 years of the Gravity Recovery and Climate Experiment (GRACE) mission, various data processing approaches were developed to derive time-series of global gravity fields based on sensor observations acquired from the two spacecrafts. In this paper, we compare GRACE-based mass anomalies provided by various processing groups against Global Navigation Satellite System (GNSS) station coordinate time-series and in-situ observations of ocean bottom pressure. In addition to the conventional GRACE-based global geopotential models from the main processing centers, we focus particularly on combined gravity field solutions generated within the Horizon2020 project European Gravity Service for Improved Emergency Management (EGSIEM). Although two validation techniques are fully independent from each other, it is demonstrated that they confirm each other to a large extent. Through the validation, we show that the EGSIEM combined long-term monthly solutions are comparable to CSR RL05 and ITSG2016, and better than the other three considered GRACE monthly solutions AIUB RL02, GFZ RL05a, and JPL RL05.1. Depending on the GNSS products, up to 25.6% mean Weighted Root-Mean-Square (WRMS) reduction is obtained when comparing GRACE to the ITRF2014 residuals over 236 GNSS stations. In addition, we also observe remarkable agreement at the annual period between GNSS and GRACE with up to 73% median WRMS reduction when comparing GRACE to the 312 EGSIEM-reprocessed GNSS time series. While the correspondence between GRACE and ocean bottom pressure data is overall much smaller due to lower signal to noise ratio over the oceans than over the continents, up to 50% agreement is found between them in some regions. The results fully confirm the conclusions found using GNSS.

Description: The transition from a command to a market economy resulted in widespread cropland abandonment across the former Soviet Union during the 1990s. Spatial patterns and determinants of abandonment are comparatively well understood for European Russia, but have not yet been assessed for the vast grain belt of Western Siberia, situated in the Eurasian forest steppe. This is unfortunate, as land-use change in Western Siberia is of global significance: Fertile black earth soils and vast mires store large amounts of organic carbon, and both undisturbed and traditional cultural landscapes harbor threatened biodiversity. We compared Landsat images from ca. 1990 (before the break-up of the Soviet Union) and ca. 2015 (current situation) with a supervised classification to estimate the extent and spatial distribution of abandoned cropland. We used logistic regression models to reveal important determinants of cropland abandonment. Ca. 135,000 ha classified as cropland around 1990 were classified as grassland around 2015. This suggests that ca. 20% of all cropland remain abandoned ca. 25 years after the end of the Soviet Union. Abandonment occurred mostly at poorly drained sites. The likelihood of cropland abandonment increased with decreasing soil quality, and increasing distance to medium-sized settlements, roads and railroads. We conclude that soil suitability, access to transport infrastructure and availability of workforce are key determinants of cropland abandonment in Western Siberia.

Description: Long-term precipitation estimates with both finer spatial resolution and better quality are vital and highly needed in various related fields. Numerous downscaling algorithms have been investigated based on the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), to obtain precipitation data with finer resolution (~1 km). However, this research was restricted by the time span of the TMPA dataset, as the starting time of TMPA was 1998. In this study, a new methodological framework incorporating wavelet coherence and Cubist was proposed to retrospectively obtain downscaled precipitation estimates (DS) over the Tibetan Plateau (TP), based on TMPA and ground observations, in 1990s. The correlations and similarities of precipitation patterns between the target years, from 1990 to 1999, and reference years, from 2000 to 2013, were firstly determined using wavelet coherence based on ground observations. Following this, the TMPA data in the reference years were regarded as the reference in the corresponding target years, which were adopted to be downscaled using Cubist models and land surface variables, to obtain the DS in the target years. We found that the DS showed continuous trends, which corresponded well with the ground observations. Additionally, the performances of the DS were better than those of the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over the TP. Therefore, this methodological framework has great potential for obtaining precipitation estimates for the period of the 1990s for which TMPA data is inaccessible.

Description: The application of pest management involves two thresholds when the chemical control and biological control are adopted, respectively. Our purpose is to provide an appropriate balance between the chemical control and biological control. Therefore, a Smith predator-prey system for integrated pest management is established in this paper. In this model, the intensity of implementation of biological control and chemical control depends linearly on the selected control level (threshold). Firstly, the existence and uniqueness of the order-one periodic solution (i.e., OOPS) are proved by means of the subsequent function method to confirm the feasibility of the biological and chemical control strategy of pest management. Secondly, the stability of system is proved by the limit method of the successor points’ sequences and the analogue of the Poincaré criterion. Moreover, an optimization strategy is formulated to reduce the total cost and obtain the best level of pest control. Finally, the numerical simulation of a specific model is performed.

Description: Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as the lack of available handy processing tools and the technical intricacy of waveform processing. This study introduces a new product named the Hyper Point Cloud (HPC), derived from FW LiDAR data, and explores its potential applications, such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which shows promise as a solution to the constraints of using FW LiDAR data. The results of the HPC present a new direction for handling FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has higher maximum intensity (MAXI) and mean intensity (MI) than the vegetation region, while having lower total intensity (TI) and number of intensities (NI) at a given grid cell. Our analysis of intensity distribution contours at the individual tree level exhibit similar patterns, indicating that the MAXI and MI decrease from the tree crown center to the tree boundary, while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR-derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives for the use of FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.

Description: Accurate 3D reconstruction/modelling from unmanned aerial vehicle (UAV)-based imagery has become the key prerequisite in various applications. Although current commercial software has automated the process of image-based reconstruction, a transparent system, which can be incorporated with different user-defined constraints, is still preferred by the photogrammetric research community. In this regard, this paper presents a transparent framework for the automated aerial triangulation of UAV images. The proposed framework is conducted in three steps. In the first step, two approaches, which take advantage of prior information regarding the flight trajectory, are implemented for reliable relative orientation recovery. Then, initial recovery of image exterior orientation parameters (EOPs) is achieved through either an incremental or global approach. Finally, a global bundle adjustment involving Ground Control Points (GCPs) and check points is carried out to refine all estimated parameters in the defined mapping coordinate system. Four real image datasets, which are acquired by two different UAV platforms, have been utilized to evaluate the feasibility of the proposed framework. In addition, a comparative analysis between the proposed framework and the existing commercial software is performed. The derived experimental results demonstrate the superior performance of the proposed framework in providing an accurate 3D model, especially when dealing with acquired UAV images containing repetitive pattern and significant image distortions.

Description: Aerial images are widely used for building detection. However, the performance of building detection methods based on aerial images alone is typically poorer than that of building detection methods using both LiDAR and image data. To overcome these limitations, we present a framework for detecting and regularizing the boundary of individual buildings using a feature-level-fusion strategy based on features from dense image matching (DIM) point clouds, orthophoto and original aerial images. The proposed framework is divided into three stages. In the first stage, the features from the original aerial image and DIM points are fused to detect buildings and obtain the so-called blob of an individual building. Then, a feature-level fusion strategy is applied to match the straight-line segments from original aerial images so that the matched straight-line segment can be used in the later stage. Finally, a new footprint generation algorithm is proposed to generate the building footprint by combining the matched straight-line segments and the boundary of the blob of the individual building. The performance of our framework is evaluated on a vertical aerial image dataset (Vaihingen) and two oblique aerial image datasets (Potsdam and Lunen). The experimental results reveal 89% to 96% per-area completeness with accuracy above almost 93%. Relative to six existing methods, our proposed method not only is more robust but also can obtain a similar performance to the methods based on LiDAR and images.

Description: The next generation of synthetic aperture radar (SAR) systems could foresee satellite missions based on a geosynchronous orbit (GEO SAR). These systems are able to provide radar images with an unprecedented combination of spatial (≤1 km) and temporal (≤12 h) resolutions. This paper investigates the GEO SAR potentialities for soil moisture (SM) mapping finalized to hydrological applications, and defines the best compromise, in terms of image spatio-temporal resolution, for SM monitoring. A synthetic soil moisture–data assimilation (SM-DA) experiment was thus set up to evaluate the impact of the hydrological assimilation of different GEO SAR-like SM products, characterized by diverse spatio-temporal resolutions. The experiment was also designed to understand if GEO SAR-like SM maps could provide an added value with respect to SM products retrieved from SAR images acquired from satellites flying on a quasi-polar orbit, like Sentinel-1 (POLAR SAR). Findings showed that GEO SAR systems provide a valuable contribution for hydrological applications, especially if the possibility to generate many sub-daily observations is sacrificed in favor of higher spatial resolution. In the experiment, it was found that the assimilation of two GEO SAR-like observations a day, with a spatial resolution of 100 m, maximized the performances of the hydrological predictions, for both streamflow and SM state forecasts. Such improvements of the model performances were found to be 45% higher than the ones obtained by assimilating POLAR SAR-like SM maps.

Description: An airborne gravity survey was carried out to fill gaps in the gravity data for the mountainous areas of Taiwan. However, the downward continuation error of airborne gravity data is a major issue, especially in regions with complex terrain, such as Taiwan. The root mean square (RMS) of the difference between the downward continuation values and land gravity was approximately 20 mGal. To improve the results of downward continuation we investigated the inverse Poisson’s integral, the semi-parametric method combined with regularization (SPR) and the least-squares collocation (LSC) in this paper. The numerically simulated experiments are conducted in the Tibetan Plateau, which is also a mountainous area. The results show that as a valuable supplement to the inverse Poisson’s integral, the SPR is a useful approach to estimate systematic errors and to suppress random errors. While the LSC approach generates the best results in the Tibetan Plateau in terms of the RMS of the downward continuation errors. Thus, the LSC approach with a terrain correction (TC) is applied to the downward continuation of real airborne gravity data in Taiwan. The statistical results show that the RMS of the differences between the downward continuation values and land gravity data reduced to 11.7 mGal, which shows that an improvement of 40% is obtained.

Description: We describe an approach (ESMAP; Evaporation–Soil Moisture Active Passive) to estimate direct evaporation from soil, Esoil, by combining remotely-sensed soil drying rates with model calculations of the vertical fluxes in and out of the surface soil layer. Improved knowledge of Esoil can serve as a constraint in how total evapotranspiration is partitioned. The soil drying rates used here are based on SMAP data, but the method could be applied to data from other sensors. We present results corresponding to ten SMAP pixels in North America to evaluate the method. The ESMAP method was applied to intervals between successive SMAP overpasses with limited precipitation (〈2 mm) to avoid uncertainty associated with precipitation, infiltration, and runoff. We used the Hydrus 1-D model to calculate the flux of water across the bottom boundary of the 0 to 50 mm soil layer sensed by SMAP, qbot. During dry intervals, qbot typically transfers water upwards into the surface soil layer from below, usually 〈0.5 mm day−1. Based on a standard formulation, transpiration from the surface soil layer, ET_s, is usually 〈 0.1 mm day−1, and, thus, generally not an important flux. Soil drying rates (converted to equivalent water thickness) are typically between 0 and 1 mm day−1. Evaporation is almost always greater than soil drying rates because qbot is typically a source of water to the surface soil and ET_s is negligible. Evaporation is typically between 0 and 1.5 mm day−1, with the highest values following rainfall. Soil evaporation summed over SMAP overpass intervals with precipitation 〈2 mm (60% of days) accounts for 15% of total precipitation. If evaporation rates are similar during overpasses with substantial precipitation, then the total evaporation flux would account for ~25% of precipitation. ESMAP could be used over spatially continuous domains to provide constraints on Esoil, but model-based Esoil would be required during intervals with substantial precipitation.

Description: The pricing problem of a kind of European vulnerable option was studied. The mixed fractional Brownian motion and the jump process were used to characterize the evolution of stock prices. The closed-form solution to European option pricing was obtained by applying martingale measure transformation method. At the end of this paper, some numerical experiments were adopted to compare the new pricing formula introduced in this paper with the classical Black-Scholes pricing formula. The result showed that the new pricing formula conformed to the actual financial market. In fact, the option value is positively correlated with the underlying asset price and the company’s asset price and the jump process has significant influence on the value of option.

Description: Direct chain enterprises (DCEs) face a decision-making issue as to how to allocate and supply their products to their stores for sales with the minimum losses and maximum profits for the manufacturers. This paper presents a single-cycle optimal allocation model for DCEs under the given total production amount and conditional value at risk loss. The optimal strategy for production allocation and supply is derived. Subsequently, an approximate algorithm for solving the optimal total production amount is presented. The optimal allocation and supply strategy, the minimum total production amount, the minimum allocation strategy, and the discount pricing strategy are obtained for the single cycle. Finally, with the sales data of a food DCE, numerical results corroborate that adopting different production and supply strategies reduces the risk of expected losses and increases the expected return. It is of an important theoretical significance in guiding the production and operation of direct chain enterprises.

Description: The seasonal variability of sea surface salinity anomalies (SSSAs) in the Indian Ocean is investigated for its role in the South Asian Summer Monsoon. We have observed an elongated spatial-feature of the positive SSSAs in the southwestern Indian Ocean before the onset of the South Asian Summer Monsoon (SASM) by using both the Aquarius satellite and the Argo float datasets. The maximum variable areas of SSSAs in the Indian Ocean are along (60 ° E–80 ° E) and symmetrical to the equator, divided into the southern and northern parts. Further, we have found that the annual variability of SSSAs changes earlier than that of sea surface temperature anomalies (SSTAs) in the corresponding areas, due to the change of wind stress and freshwater flux. The change of barrier layer thickness (BLT) anomalies is in phase with that of SSSAs in the southwestern Indian Ocean, which helps to sustain the warming water by prohibiting upwelling. Due to the time delay of SSSAs change between the northern and southern parts, SSSAs, therefore, take part in the seasonal process of the SASM via promoting the SSTAs gradient for the cross-equator currents.

Description: Gaofen-3 (GF-3), the first Chinese spaceborne synthetic aperture radar (SAR) in C-band for civil applications, was launched on August 2016. Some studies have examined the use of GF-3 SAR data for ocean and coastal observations, but these studies generally focus on one particular application. As GF-3 has been in operation over two years, it is essential to evaluate its performance in ocean observation, a primary goal of the GF-3 launch. In this paper, we offer an overview demonstrating the capabilities of GF-3 SAR in ocean and coastal observations by presenting several representative cases, i.e., the monitoring of intertidal flats, offshore tidal turbulent wakes and oceanic internal waves, to highlight the GF-3’s full polarimetry, high spatial resolution and wide-swath imaging advantages. Moreover, we also present a detailed analysis of the use of GF-3 quad-polarization data for sea surface wind retrievals and wave mode data for sea surface wave retrievals. The case studies and statistical analysis suggest that GF-3 has good ocean and coastal monitoring capabilities, though further improvements are possible, particularly in radiometric calibration and stable image quality.

Description: Ship detection plays an important role in automatic remote sensing image interpretation. The scale difference, large aspect ratio of ship, complex remote sensing image background and ship dense parking scene make the detection task difficult. To handle the challenging problems above, we propose a ship rotation detection model based on a Feature Fusion Pyramid Network and deep reinforcement learning (FFPN-RL) in this paper. The detection network can efficiently generate the inclined rectangular box for ship. First, we propose the Feature Fusion Pyramid Network (FFPN) that strengthens the reuse of different scales features, and FFPN can extract the low level location and high level semantic information that has an important impact on multi-scale ship detection and precise location of dense parking ships. Second, in order to get accurate ship angle information, we apply deep reinforcement learning to the inclined ship detection task for the first time. In addition, we put forward prior policy guidance and a long-term training method to train an angle prediction agent constructed through a dueling structure Q network, which is able to iteratively and accurately obtain the ship angle. In addition, we design soft rotation non-maximum suppression to reduce the missed ship detection while suppressing the redundant detection boxes. We carry out detailed experiments on the remote sensing ship image dataset, and the experiments validate that our FFPN-RL ship detection model has efficient detection performance.

Description: Hydromorphological data for many estuaries worldwide is scarce and usually limited to offshore tidal amplitude and remotely-sensed imagery. In many projects, information about morphology and intertidal area is needed to assess the effects of human interventions and rising sea-level on the natural depth distribution and on changing habitats. Habitat area depends on the spatial pattern of intertidal area, inundation time, peak flow velocities and salinity. While numerical models can reproduce these spatial patterns fairly well, their data need and computational costs are high and for each case a new model must be developed. Here, we present a Python tool that includes a comprehensive set of relations that predicts the hydrodynamics, bed elevation and the patterns of channels and bars in mere seconds. Predictions are based on a combination of empirical relations derived from natural estuaries, including a novel predictor for cross-sectional depth distributions, which is dependent on the along-channel width profile. Flow velocity, an important habitat characteristic, is calculated with a new correlation between depth below high water level and peak tidal flow velocity, which was based on spatial numerical modelling. Salinity is calculated from estuarine geometry and flow conditions. The tool only requires an along-channel width profile and tidal amplitude, making it useful for quick assessments, for example of potential habitat in ecology, when only remotely-sensed imagery is available.

Description: Current methods for estimating the surface temperature of sea and lake ice—the ice surface temperature (IST)—utilize two satellite imager thermal bands (11 and 12 μm) at moderate spatial resolution. These “split-window” or dual-band methods have been shown to have low biases and uncertainties. A single-band algorithm would be useful for satellite imagers that have only the 11 μm band at high resolution, such as the Visible Infrared Imaging Radiometer Suite (VIIRS), or that do not have a fully functional 12 μm band, such as the Thermal Infrared Sensor onboard the Landsat 8. This study presents a method for single-band IST retrievals, and validation of the retrievals using IST measurements from an airborne infrared radiation pyrometer during the NASA IceBridge campaign in the Arctic. Results show that IST with a single thermal band from the VIIRS has comparable performance to IST with the VIIRS dual-band (split-window) method, with a bias of 0.22 K and root-mean-square error of 1.03 K.

Description: Himalayan glaciers, in general, are shrinking and glacial lakes are evolving and growing rapidly in number and size as a result of climate change. This study presents the latest remote sensing-based inventory (2017) of glacial lakes (size ≥0.0036 km2) across the Nepal Himalaya using optical satellite data. Furthermore, this study traces the decadal glacial lake dynamics from 1977 to 2017 in the Nepal Himalaya. The decadal mapping of glacial lakes (both glacial-fed and nonglacial-fed) across the Nepal Himalaya reveals an increase in the number and area of lakes from 1977 to 2017, with 606 (55.53 ± 16.52 km2), 1137 (64.56 ± 11.64 km2), 1228 (68.87 ± 12.18 km2), 1489 (74.2 ± 14.22 km2), and 1541 (80.95 ± 15.25 km2) glacial lakes being mapped in 1977, 1987, 1997, 2007, and 2017, respectively. Glacial lakes show heterogeneous rates of expansion in different river basins and elevation zones of Nepal, with apparent decadal emergences and disappearances. Overall, the glacial lakes exhibited ~25% expansion of surface areas from 1987 to 2017. For the period from 1987 to 2017, proglacial lakes with ice contact, among others, exhibited the highest incremental changes in terms of number (181%) and surface area (82%). The continuous amplified mass loss of glaciers, as reported in Central Himalaya, is expected to accompany glacial lake expansion in the future, increasing the risk of glacial lake outburst floods (GLOFs). We emphasize that the rapidly increasing glacial lakes in the Nepal Himalaya can pose potential GLOF threats to downstream population and infrastructure.

Description: Biochemical properties retrieved from remote sensing data are crucial sources of information for many applications. However, leaf and canopy scattering processes must be accounted for to reliably estimate information on canopy biochemistry, carbon-cycle processes and energy exchange. A coupled leaf-canopy model based on spectral invariants theory has been proposed, that uses the so-called Directional Area Scattering Factor (DASF) to correct hyperspectral remote sensing data for canopy structural effects. In this study, the reliability of DASF to decouple canopy structure and biochemistry was empirically tested using simulated reflectance spectra modelled using a Monte Carlo Ray Tracing (MCRT) radiative transfer model. This approach allows all canopy and radiative properties to be specified a priori. Simulations were performed under idealised conditions of directional-hemispherical reflectance, isotropic Lambertian leaf reflectance and transmittance and sufficiently dense (high LAI) canopies with black soil where the impact of canopy background is negligible, and also departures from these conditions. It was shown that both DASF and total canopy scattering could be accurately extracted under idealised conditions using information from both the full 400–2500 nm spectral interval and the 710–790 nm interval alone, even given no prior knowledge of leaf optical properties. Departures from these idealised conditions: varying view geometry, bi-directional reflectance, LAI and soil effects, were tested. We demonstrate that total canopy scattering could be retrieved under conditions of varying view geometry and bi-directional reflectance, but LAI and soil effects were shown to reduce the accuracy with which the scattering can be modelled using the DASF approach. We show that canopy architecture, either homogeneous or heterogeneous 3D arrangements of canopy scattering elements, has important influences over DASF and consequently the accuracy of retrieval of total canopy scattering. Finally, although DASF and total canopy scattering could be retrieved to within 2.4% of the modelled total canopy scattering signal given no prior knowledge of leaf optical properties, spectral invariant parameters were not accurately retrieved from the simulated signal. This has important consequences since these parameters are quite widely used in canopy reflectance modelling and have the potential to help derive new, more accurate canopy biophysical information. Understanding and quantifying the limitations of the DASF approach as we have done here, is an important step in allowing the wider use of these methods for decoupling canopy structure and biochemistry.

Description: Rice (Oryza sativa L.) farmers in Mediterranean regions usually apply organic or mineral fertilizers before seeding that are supplemented with mineral nitrogen (N) later in the season. In general, the midseason N is applied without consideration of the actual crop N status, which may lead to over-fertilization and associated environmental problems. Thus, the purpose of this study was to design and evaluate a N recommendation approach using aerial images for Mediterranean paddy rice systems. A two-year rice field experiment was established in northeastern Spain, with different rates of pig slurry (PS) and mineral N fertilizer. Multispectral aerial images were taken at the rice booting stage, and several vegetation indices (VIs) were calculated. The VIs showed strong relationships with yield and the relations significantly differed between the PS and mineral fertilization treatments. The strongest relations with yield were obtained with gMCARINIR, proposed in this study, (R2 = 0.67), GNDVI (R2 = 0.64) and MCARINIR (R2 = 0.64), indicating the importance of including the green band information. The N recommendation approach generated using the VIs information showed a high success (87.5%) in the preliminary evaluation. The economic and environmental analysis showed that this approach provides a useful tool when compared to the usual farmer practices.

Description: Fires associated with the expansion of cattle ranching and agriculture have become a problem in the Amazon biome, causing severe environmental damages. Remote sensing techniques have been widely used in fire monitoring on the extensive Amazon forest, but accurate automated fire detection needs improvements. The popular Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64 product still has high omission errors in the region. This research aimed to evaluate MODIS time series spectral indices for mapping burned areas in the municipality of Novo Progresso (State of Pará) and to determine their accuracy in the different types of land use/land cover during the period 2000–2014. The burned area mapping from 8-day composite products, compared the following data: near-infrared (NIR) band; spectral indices (Burnt Area Index (BAIM), Global Environmental Monitoring Index (GEMI), Mid Infrared Burn Index (MIRBI), Normalized Burn Ratio (NBR), variation of Normalized Burn Ratio (NBR2), and Normalized Difference Vegetation Index (NDVI)); and the seasonal difference of spectral indices. Moreover, we compared the time series normalization methods per pixel (zero-mean normalization and Z-score) and the seasonal difference between consecutive years. Threshold-value determination for the fire occurrences was obtained from the comparison of MODIS series with visual image classification of Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) data using the overall accuracy. The best result considered the following factors: NIR band and zero-mean normalization, obtaining the overall accuracy of 98.99%, commission errors of 32.41%, and omission errors of 31.64%. The proposed method presented better results in burned area detection in the natural fields (Campinarana) with an overall accuracy value of 99.25%, commission errors of 9.71%, and omission errors of 27.60%, as well as pasture, with overall accuracy value of 99.19%, commission errors of 27.60%, and omission errors of 34.76%. Forest areas had a lower accuracy, with an overall accuracy of 98.62%, commission errors of 23.40%, and omission errors of 49.62%. The best performance of the burned area detection in the pastures is relevant because the deforested areas are responsible for more than 70% of fire events. The results of the proposed method were better than the burned area products (MCD45, MCD64, and FIRE-CCI), but still presented limitations in the identification of burn events in the savanna formations and secondary vegetation.

Description: We investigate the Mw 6.5 Norcia (Central Italy) earthquake by exploiting seismological data, DInSAR measurements, and a numerical modelling approach. In particular, we first retrieve the vertical component (uplift and subsidence) of the displacements affecting the hangingwall and the footwall blocks of the seismogenic faults identified, at depth, through the hypocenters distribution analysis. To do this, we combine the DInSAR measurements obtained from coseismic SAR data pairs collected by the ALOS-2 sensor from ascending and descending orbits. The achieved vertical deformation map displays three main deformation patterns: (i) a major subsidence that reaches the maximum value of about 98 cm near the epicentral zones nearby the town of Norcia; (ii) two smaller uplift lobes that affect both the hangingwall (reaching maximum values of about 14 cm) and the footwall blocks (reaching maximum values of about 10 cm). Starting from this evidence, we compute the rock volumes affected by uplift and subsidence phenomena, highlighting that those involved by the retrieved subsidence are characterized by significantly higher deformation values than those affected by uplift (about 14 times). In order to provide a possible interpretation of this volumetric asymmetry, we extend our analysis by applying a 2D numerical modelling approach based on the finite element method, implemented in a structural-mechanic framework, and exploiting the available geological and seismological data, and the ground deformation measurements retrieved from the multi-orbit ALOS-2 DInSAR analysis. In this case, we consider two different scenarios: the first one based on a single SW-dipping fault, the latter on a main SW-dipping fault and an antithetic zone. In this context, the model characterized by the occurrence of an antithetic zone presents the retrieved best fit coseismic surface deformation pattern. This result allows us to interpret the subsidence and uplift phenomena caused by the Mw 6.5 Norcia earthquake as the result of the gravitational sliding of the hangingwall along the main fault plane and the frictional force acting in the opposite direction, consistently with the double couple fault plane mechanism.

Description: Floods are considered one of the most disastrous hazards all over the world and cause serious casualties and property damage. Therefore, the assessment and regionalization of flood disasters are becoming increasingly important and urgent. To predict the probability of a flood, an essential step is to map flood susceptibility. The main objective of this work is to investigate the use a novel hybrid technique by integrating multi-criteria decision analysis and geographic information system to evaluate flood susceptibility mapping (FSM), which is constructed by ensemble of decision making trial and evaluation laboratory (DEMATEL), analytic network process, weighted linear combinations (WLC) and interval rough numbers (IRN) techniques in the case study at Shangyou County, China. Specifically, we improve the DEMATEL method by applying IRN to determine connections in the network structure based on criteria and to accept imprecisions during collective decision making. The application of IRN can eliminate the necessity of additional information to define uncertain number intervals. Therefore, the quality of the existing data during collective decision making and experts’ perceptions that are expressed through an aggregation matrix can be retained. In this work, eleven conditioning factors associated with flooding were considered and historical flood locations were randomly divided into the training (70% of the total) and validation (30%) sets. The flood susceptibility map validates a satisfactory consistency between the flood-susceptible areas and the spatial distribution of the previous flood events. The accuracy of the map was evaluated by using objective measures of receiver operating characteristic (ROC) curve and area under the curve (AUC). The AUC values of the proposed method coupling with the WLC fuzzy technique for aggregation and flood susceptibility index are 0.988 and 0.964, respectively, which proves that the WLC fuzzy method is more effective for FSM in the study area. The proposed method can be helpful in predicting accurate flood occurrence locations with similar geographic environments and can be effectively used for flood management and prevention.

Description: Earthquakes are reported to be preceded by anomalous increases in satellite-recorded thermal emissions, but published results are often contradicting and/or limited to short periods and areas around the earthquake. We apply a methodology that allows to detect subtle, localized spatio-temporal fluctuations in hyper-temporal, geostationary-based land surface temperature (LST) data. We study 10 areas worldwide, covering 20 large (Mw 〉 5.5) and shallow (〈35 km) land-based earthquakes. We compare years and locations with and without earthquake, and we statistically evaluate our findings with respect to distance from epicentra and temporal coincidence with earthquakes. We detect anomalies throughout the duration of all datasets, at various distances from the earthquake, and in years with and without earthquake alike. We find no distinct repeated patterns in the case of earthquakes that happen in the same region in different years. We conclude that earthquakes do not have a significant effect on detected LST anomalies.

Description: The authors wish to make the following corrections to this paper [...]

Description: Direct assessment of forest regeneration from remote sensing data is a previously little-explored problem. This is due to several factors which complicate object detection of small trees in the understory. Most existing studies are based on airborne laser scanning (ALS) data, which often has insufficient point densities in the understory forest layers. The present study uses plot-based terrestrial laser scanning (TLS) and the survey design was similar to traditional forest inventory practices. Furthermore, a framework of methods was developed to solve the difficulties of detecting understory trees for quantifying regeneration in temperate montane forest. Regeneration is of special importance in our montane study area, since large parts are declared as protection forest against alpine natural hazards. Close to nature forest structures were tackled by separating 3D tree stem detection from overall tree segmentation. In support, techniques from 3D mathematical morphology, Hough transformation and state-of-the-art machine learning were applied. The methodical framework consisted of four major steps. These were the extraction of the tree stems, the estimation of the stem diameters at breast height (DBH), the image segmentation into individual trees and finally, the separation of two groups of regeneration. All methods were fully automated and utilized volumetric 3D image information which was derived from the original point cloud. The total amount of regeneration was split into established regeneration, consisting of trees with a height 〉 130 cm in combination with a DBH 〈 12 cm and unestablished regeneration, consisting of trees with a height 〈 130 cm. Validation was carried out against field-based expert estimates of percentage ground cover, differentiating seven classes that were similar to those used by forest inventory. The mean absolute error (MAE) of our method for established regeneration was 1.11 classes and for unestablished regeneration only 0.27 classes. Considering the metrical distances between the class centres, the MAE amounted 8.08% for established regeneration and 2.23% for unestablished regeneration.

Description: With the increasing temporal resolution of space-borne SAR, large amounts of intensity data are now available for continues land observations. Previous researches proved the effectiveness of multitemporal SAR in land classification, but the characterizations of temporal information were still inadequate. In this paper, we proposed a crop classification scheme, which made full use of multitemporal SAR backscattering responses. In this method, the temporal intensity models were established by the K-means clustering method. The intensity vectors were treated as input features, and the mean intensity vectors of cluster centers were regarded as the temporal models. The temporal models summarized the backscatter evolutions of crops and were utilized as the criterion for crop discrimination. The spectral similarity value (SSV) measure was introduced from hyperspectral image processing for temporal model matching. The unlabeled pixel was assigned to the class to which the temporal model with the highest similarity belonged. Two sets of Sentinel-1 SAR time-series data were used to illustrate the effectiveness of the proposed method. The comparison between SSV and other measures demonstrated the superiority of SSV in temporal model matching. Compared with the decision tree (DT) and naive Bayes (NB) classifiers, the proposed method achieved the best overall accuracies in both VH and VV bands. For most crops, it either obtained the best accuracies or achieved comparable accuracies to the best ones, which illustrated the effectiveness of the proposed method.

Description: Due to the availability of observations and the effectiveness of bias correction, it is still a challenge to assimilate data from the polar orbit satellites into a limited-area and frequently updated model. This study assessed the initial application of satellite radiance data from multiple platforms in the Rapid-refresh Multi-scale Analysis and Prediction System (RMAPS). Satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) were used. Two 12-day retrospective runs were conducted to evaluate the impact of assimilating satellite radiance data on 0–24 h forecasts using RMAPS. The forecasts, initialized from analyses with and without satellite radiance data, were verified against observations. The results showed that satellite radiance data from AMSU-A and MHS had a positive impact on the initial conditions and the forecasts of RMAPS, even over the relatively data-rich area of North China. Compared to the control run that only assimilated conventional observations, an improvement of about 36.8% can be obtained for the temperature bias between 300 hPa and 850 hPa and 0.65% for the average RMSE. Satellite radiance observations from 1200 UTC contribute relatively significantly (77.8%) to the bias improvement of the initial temperature field. For the wind at 10 m, the bias and root-mean-square error (RMSE) both had a reduction for the 0–12 h forecast range. An improvement can be also found for the skill score of the 3-h accumulated rainfall below 10.0 mm in the first 12 h of the forecast range. There was a slight improvement in the skill score of the 6-h accumulated rainfall above 50 mm over North China, with a 20.7% improvement for the first 12 h of the forecast. The inclusion of satellite radiance observations was found to be beneficial for the initial temperature, which consequently improved the forecast skill of the 0–12 h range in the RMAPS.

Description: Pedestrian walking speeds (PWS) can be used as a “body speedometer” to reveal health status information of pedestrians and positioning indoors with other locating methods. This paper proposes a pose awareness solution for estimating pedestrian walking speeds using the sensors built in smartphones. The smartphone usage pose is identified by using a machine learning approach based on data from multiple sensors. The data are then coupled tightly with an adaptive step detection solution to estimate the pedestrian walking speed. Field tests were carried out to verify the advantages of the proposed algorithms compared to existing solutions. The test results demonstrated that the features extracted from the data of the smartphone built-in sensors clearly reveal the characteristics of the pose pattern, with overall accuracy of 98.85% and a kappa statistic of 98.46%. The proposed walking speed estimation solution, running in real-time on a commercial smartphone, performed well, with a mean absolute error of 0.061 m/s, under a challenging walking process combining various usage poses including texting, calling, swinging, and in-pocket modes.

Description: Disaster risk reduction (DRR) is a high priority on the agenda of main stakeholders involved in sustainable development, and earth observation (EO) can provide useful, timely, and economical information in this context. This short communication outlines the European Space Agency’s (ESA) specific initiative to promote the use and uptake of satellite data in the global development community: Earth Observation for Sustainable Development (EO4SD). One activity area under EO4SD is devoted to disaster risk reduction (EO4SD DRR). Within this project, a team of European companies and institutions are tasked to develop EO services for supporting the implementation of DRR in International Financial Institutions’ (IFI) projects. Integration of satellite-borne data and ancillary data to generate insight and actionable information is thereby considered a key factor for improved decision-making. To understand and fully account for the essential user requirements (IFI and client states), engagement with technical leaders is crucial. Fit-for-purpose use of data and comprehensive capacity building eventually ensure scalability and long-term transferability. Future perspectives of EO4SD and DRR regarding mainstreaming are also highlighted.

Description: Global Navigation Satellite System Reflectometry (GNSS-R) is of great significance for the extraction and research of precise information of sea surface topography. Improving measurement accuracy is necessary for realizing spaceborne GNSS-R sea surface altimetry application. The main error source of GNSS-R distance measurement is the error of the specular reflection point positioning, which directly affects the sea surface altimetry accuracy on the reference datum. There is an elevation error of several tens of meters between the reflection reference surface used by the existing specular reflection point geometric positioning methods and the sea surface elevation, which is importantly influenced by the earth’s gravity field. Therefore, the gravity field reflection reference surface correction is the key to improving the specular reflection point positioning accuracy. In this study, based on the correction of the GNSS-R reflection reference surface, research on improving the positioning accuracy of the specular reflection point is carried out. Firstly, in order to reduce the positioning error caused by the elevation difference between the reflection reference surface and the sea surface, the gravity field reflection reference surface correction method (GFRRSCM) which corrects the reflection reference surface from the WGS-84 ellipsoid to geoid is proposed, and the positioning accuracy is improved by 25.15 m. Secondly, the normal projection reflection reference surface correction method (NPRRSCM) is proposed to correct the specular reflection point determined by the GFRRSCM from the reflection reference plane of the radial to that of the normal. Additionally, in the process of solving the spatial geometric relationship of the reflection path, the approximate substitution error is reduced by directly solving the normal projection on the plane, and the positioning accuracy is further improved by 13.05 m towards the normal. Thirdly, based on the gravity field normal projection reflection reference surface combination correction method (GF-NPRRSCCM), the specular reflection point positioning accuracy is synthetically improved by 28.66 m.

Description: Since 1995, Margalefidinium polykrikoides blooms have occurred frequently in the waters around the Korean peninsula. In the South Sea of Korea (SSK), large-scale M. polykrikoides blooms form offshore and are often transported to the coast, where they gradually accumulate. The objective of this study was to investigate the synergistic effect of multi-sensor data for identifying M. polykrikoides blooms in the SSK from July 2018 to August 2018. We found that the Spectral Shape values calculated from in situ spectra and M. polykrikoides cell abundances in the SSK were highly correlated. Comparing red tide spectra from near-coincident multi-sensor data, remote-sensing reflectance (Rrs) spectra were similar to the spectra of in situ measurements from blue to green wavelengths. Rrs true-color composite images and Spectral Shape images of each sensor showed a clear pattern of M. polykrikoides patches, although there were some limitations for detecting red tide patches in coastal areas. We confirmed the complementarity of red tide data extracted from each sensor using an integrated red tide map. Statistical assessment showed that the sensitivity of red tide detection increased when multi-sensor data were used rather than single-sensor data. These results provide useful information for the application of multi-sensor for red tide detection.

Description: (1) Background: Echinococcus multilocularis (Em), a highly pathogenic parasitic tapeworm, is responsible for a significant burden of human disease. In this study, optical and time-series Synthetic Aperture Radar (SAR) data is used synergistically to model key land cover characteristics driving the spatial distributions of two small mammal intermediate host species, Ellobius tancrei and Microtus gregalis, which facilitate Em transmission in a highly endemic area of Kyrgyzstan. (2) Methods: A series of land cover maps are derived from (a) single-date Landsat Operational Land Imager (OLI) imagery, (b) time-series Sentinel-1 SAR data, and (c) Landsat OLI and time-series Sentinel-1 SAR data in combination. Small mammal distributions are analyzed in relation to the surrounding land cover class coverage using random forests, before being applied predictively over broader areas. A comparison of models derived from the three land cover maps are made, assessing their potential for use in cloud-prone areas. (3) Results: Classification accuracies demonstrated the combined OLI-SAR classification to be of highest accuracy, with the single-date OLI and time-series SAR derived classifications of equivalent quality. Random forest analysis identified statistically significant positive relationships between E. tancrei density and agricultural land, and between M. gregalis density and water and bushes. Predictive application of random forest models identified hotspots of high relative density of E. tancrei and M. gregalis across the broader study area. (4) Conclusions: This offers valuable information to improve the targeting of limited-resource disease control activities to disrupt disease transmission in this area. Time-series SAR derived land cover maps are shown to be of equivalent quality to those generated from single-date optical imagery, which enables application of these methods in cloud-affected areas where, previously, this was not possible due to the sparsity of cloud-free optical imagery.

Description: This paper assesses the potential of Synthetic Aperture Radar (SAR) in the C and L bands to penetrate into the canopy cover of wheat, maize and grasslands. For wheat and grasslands, the sensitivity of the C and L bands to in situ surface soil moisture (SSM) was first studied according to three levels of the Normalized Difference Vegetation Index (NDVI 〈 0.4, 0.4 〈 NDVI 〈 0.7, and NDVI 〉 0.7). Next, the temporal evolution of the SAR signal in the C and L bands was analyzed according to SSM and the NDVI. For wheat and grasslands, the results showed that the L-band in HH polarization penetrates the canopy even when the canopy is well-developed (NDVI 〉 0.7), whereas the penetration of the C-band into the canopy is limited for an NDVI 〈 0.7. For an NDVI less than 0.7, the sensitivity of the radar signal to SSM is approximately 0.27 dB/vol.% for the L-band in HH polarization and approximately 0.12 dB/vol.% for the C-band (in both VV and VH polarizations). For highly developed wheat and grassland cover (NDVI 〉 0.7), the sensitivity of the L-band in HH polarization to SSM is approximately 0.19 dB/vol.%, whereas as the C-band is insensitive to SSM. For maize, only the temporal evolution of the C-band according to SSM and the NDVI was studied because the swath of SAR images in the L-band did not cover the maize plots. The results showed that the C-band in VV polarization is able to penetrate the maize canopy even when the canopy is well developed (NDVI 〉 0.7) due to high-order scattering along the soil-vegetation pathway that contains a soil contribution. According to results obtained in this paper, the L-band would penetrate a well-developed maize cover since the penetration depth of the L-band is greater than that of the C-band.

Description: The authors wish to make the following corrections to this paper [...]

Description: Dimensionality Reduction (DR) models are of significance to extract low-dimensional features for Hyperspectral Images (HSIs) data analysis where there exist lots of noisy and redundant spectral features. Among many DR techniques, the Graph-Embedding Discriminant Analysis framework has demonstrated its effectiveness for HSI feature reduction. Based on this framework, many representation based models are developed to learn the similarity graphs, but most of these methods ignore the spatial information, resulting in unsatisfactory performance of DR models. In this paper, we firstly propose a novel supervised DR algorithm termed Spatial-aware Collaborative Graph for Discriminant Analysis (SaCGDA) by introducing a simple but efficient spatial constraint into Collaborative Graph-based Discriminate Analysis (CGDA) which is inspired by recently developed Spatial-aware Collaborative Representation (SaCR). In order to make the representation of samples on the data manifold smoother, i.e., similar pixels share similar representations, we further add the spectral Laplacian regularization and propose the Laplacian regularized SaCGDA (LapSaCGDA), where the two spectral and spatial constraints can exploit the intrinsic geometric structures embedded in HSIs efficiently. Experiments on three HSIs data sets verify that the proposed SaCGDA and LapSaCGDA outperform other state-of-the-art methods.

Description: Remote sensing fractional snow cover area (fSCA) has been increasingly used to get an improved estimate of the spatiotemporal distribution of snow water equivalent (SWE) through reanalysis using different data assimilation (DA) schemes. Although the effective assimilation period of fSCA is well recognized in previous studies, little attention has been given to explicitly account for the relative significance of measurements in constraining model parameters and states. Timing of the more informative period varies both spatially and temporally in response to various climatic and physiographic factors. Here we use an automatic detection approach to locate the critical points in the time axis where the mean snow cover changes and where the melt-out period starts. The assimilation period was partitioned into three timing windows based on these critical points. A fuzzy coefficient was introduced in two ensemble-based DA schemes to take into account for the variability in informational value of fSCA observations with time. One of the DA schemes used in this study was the particle batch smoother (Pbs). The main challenge in Pbs and other Bayesian-based DA schemes is, that most of the weights are carried by few ensemble members. Thus, we also considered an alternative DA scheme based on the limits of acceptability concept (LoA) and certain hydrologic signatures and it has yielded an encouraging result. An improved estimate of SWE was also obtained in most of the analysis years as a result of introducing the fuzzy coefficients in both DA schemes. The most significant improvement was obtained in the correlation coefficient between the predicted and observed SWE values (site-averaged); with an increase by 8% and 16% after introducing the fuzzy coefficient in Pbs and LoA, respectively.

Description: The carbon cycle of the terrestrial biosphere plays a vital role in controlling the global carbon balance and, consequently, climate change. Reliably modeled CO2 fluxes between the terrestrial biosphere and the atmosphere are necessary in projections of policy strategies aiming at constraining carbon emissions and of future climate change. In this study, SMOS (Soil Moisture and Ocean Salinity) L3 soil moisture and JRC-TIP FAPAR (Joint Research Centre—Two-stream Inversion Package Fraction of Absorbed Photosynthetically Active Radiation) data with respective original resolutions at 10 sites were used to constrain the process-based terrestrial biosphere model, BETHY (Biosphere, Energy Transfer and Hydrology), using the carbon cycle data assimilation system (CCDAS). We find that simultaneous assimilation of these two datasets jointly at all 10 sites yields a set of model parameters that achieve the best model performance in terms of independent observations of carbon fluxes as well as soil moisture. Assimilation in a single-site mode or using only a single dataset tends to over-adjust related parameters and deteriorates the model performance of a number of processes. The optimized parameter set derived from multi-site assimilation with soil moisture and FAPAR also improves, when applied at global scale simulations, the model-data fit against atmospheric CO2. This study demonstrates the potential of satellite-derived soil moisture and FAPAR when assimilated simultaneously in a model of the terrestrial carbon cycle to constrain terrestrial carbon fluxes. It furthermore shows that assimilation of soil moisture data helps to identity structural problems in the underlying model, i.e., missing management processes at sites covered by crops and grasslands.

Description: A digital surface model (DSM) is an important geospatial infrastructure used in various fields. In this paper, we deal with how to improve the quality of DSMs generated from stereo image matching. During stereo image matching, there are outliers due to mismatches, and non-matching regions due to match failure. Such outliers and non-matching regions have to be corrected accurately and efficiently for high-quality DSM generation. This process has been performed by applying a local distribution model, such as inverse distance weight (IDW), or by forming a triangulated irregular network (TIN). However, if the area of non-matching regions is large, it is not trivial to interpolate elevation values using neighboring cells. In this study, we proposed a new DSM interpolation method using a 3D mesh model, which is more robust to outliers and large holes. We compared mesh-based DSM with IDW-based DSM and analyzed the characteristics of each. The accuracy of the mesh-based DSM was a 2.80 m root mean square error (RMSE), while that for the IDW-based DSM was 3.22 m. While the mesh-based DSM successfully removed empty grid cells and outliers, the IDW-based DSM had sharper object boundaries. Because of the nature of surface reconstruction, object boundaries appeared smoother on the mesh-based DSM. We further propose a method of integrating the two DSMs. The integrated DSM maintains the sharpness of object boundaries without significant accuracy degradation. The contribution of this paper is the use of 3D mesh models (which have mainly been used for 3D visualization) for efficient removal of outliers and non-matching regions without a priori knowledge of surface types.

Description: Among grapevine diseases affecting European vineyards, Flavescence dorée (FD) and Grapevine Trunk Diseases (GTD) are considered the most relevant challenges for viticulture because of the damage they cause to vineyards. Unmanned Aerial Vehicle (UAV) multispectral imagery could be a powerful tool for the automatic detection of symptomatic vines. However, one major difficulty is to discriminate different kinds of diseases leading to similar leaves discoloration as it is the case with FD and GTD for red vine cultivars. The objective of this paper is to evaluate the potentiality of UAV multispectral imagery to separate: symptomatic vines including FD and GTD (Esca and black dead arm) from asymptomatic vines (Case 1) and FD vines from GTD ones (Case 2). The study sites are localized in the Gaillac and Minervois wine production regions (south of France). A set of seven vineyards covering five different red cultivars was studied. Field work was carried out between August and September 2016. In total, 218 asymptomatic vines, 502 FD vines and 199 GTD vines were located with a centimetric precision GPS. UAV multispectral images were acquired with a MicaSense RedEdge® sensor and were processed to ultimately obtain surface reflectance mosaics at 0.10 m ground spatial resolution. In this study, the potentiality of 24 variables (5 spectral bands, 15 vegetation indices and 4 biophysical parameters) are tested. The vegetation indices are selected for their potentiality to detect abnormal vegetation behavior in relation to stress or diseases. Among the biophysical parameters selected, three are directly linked to the leaf pigments content (chlorophyll, carotenoid and anthocyanin). The first step consisted in evaluating the performance of the 24 variables to separate symptomatic vine vegetation (FD or/and GTD) from asymptomatic vine vegetation using the performance indicators from the Receiver Operator Characteristic (ROC) Curve method (i.e., Area Under Curve or AUC, sensibility and specificity). The second step consisted in mapping the symptomatic vines (FD and/or GTD) at the scale of the field using the optimal threshold resulting from the ROC curve. Ultimately, the error between the level of infection predicted by the selected variables (proportion of symptomatic pixels by vine) and observed in the field (proportion of symptomatic leaves by vine) is calculated. The same methodology is applied to the three levels of analysis: by vineyard, by cultivar (Gamay, Fer Servadou) and by berry color (all red cultivars). At the vineyard and cultivar levels, the best variables selected varies. The AUC of the best vegetation indices and biophysical parameters varies from 0.84 to 0.95 for Case 1 and 0.74 to 0.90 for Case 2. At the berry color level, no variable is efficient in discriminating FD vines from GTD ones (Case 2). For Case 1, the best vegetation indices and biophysical parameter are Red Green Index (RGI)/ Green-Red Vegetation Index (GRVI) (based on the green and red spectral bands) and Car (linked to carotenoid content). These variables are more effective in mapping vines with a level of infection greater than 50%. However, at the scale of the field, we observe misclassified pixels linked to the presence of mixed pixels (shade, bare soil, inter-row vegetation and vine vegetation) and other factors of abnormal coloration (e.g., apoplectic vines).

Description: Warming of the Arctic leads to a decrease in sea ice, and the decrease of sea ice, in turn, results in warming of the Arctic again. Several microwave sensors have provided continuously updated sea ice data for over 30 years. Many studies have been conducted to investigate the relationships between the satellite-derived sea ice concentration (SIC) of the Arctic and climatic factors associated with the accelerated warming. However, linear equations using the general circulation model (GCM) data, with low spatial resolution, cannot sufficiently cope with the problem of complexity or non-linearity. Time-series techniques are effective for one-step-ahead forecasting, but are not appropriate for future prediction for about ten or twenty years because of increasing uncertainty when forecasting multiple steps ahead. This paper describes a new approach to near-future prediction of Arctic SIC by employing a deep learning method with multi-model ensemble. We used the regional climate model (RCM) data provided in higher resolution, instead of GCM. The RCM ensemble was produced by Bayesian model averaging (BMA) to minimize the uncertainty which can arise from a single RCM. The accuracies of RCM variables were much improved by the BMA2 method, which took into consideration temporal and spatial variations to minimize the uncertainty of individual RCMs. A deep neural network (DNN) method was used to deal with the non-linear relationships between SIC and climate variables, and to provide a near-future prediction for the forthcoming 10 to 20 years. We adjusted the DNN model for optimized SIC prediction by adopting best-fitted layer structure, loss function, optimizer algorithm, and activation function. The accuracy was much improved when the DNN model was combined with BMA2 ensemble, showing the correlation coefficient of 0.888. This study provides a viable option for monitoring Arctic sea ice change of the near future.

Description: Moving target detection plays a primary and pivotal role in avionics visual analysis, which aims to completely and accurately detect moving objects from complex backgrounds. However, due to the relatively small sizes of targets in aerial video, many deep networks that achieve success in normal size object detection are usually accompanied by a high rate of false alarms and missed detections. To address this problem, we propose a novel visual detail augmented mapping approach for small aerial target detection. Concretely, we first present a multi-cue foreground segmentation algorithm including motion and grayscale information to extract potential regions. Then, based on the visual detail augmented mapping approach, the regions that might contain moving targets are magnified to multi-resolution to obtain detailed target information and rearranged into new foreground space for visual enhancement. Thus, original small targets are mapped to a more efficient foreground augmented map which is favorable for accurate detection. Finally, driven by the success of deep detection network, small moving targets can be well detected from aerial video. Experiments extensively demonstrate that the proposed method achieves success in small aerial target detection without changing the structure of the deep network. In addition, compared with the-state-of-art object detection algorithms, it performs favorably with high efficiency and robustness.

Description: We evaluated the feasibility of using aerial photo-based office methods rather than field-collected data to validate Landsat-based change detection products in national parks in Washington State. Landscape change was performed using LandTrendr algorithm. The resulting change patches were labeled in the office using aerial imagery and a random sample of patches was visited in the field by experienced analysts. Comparison of the two labels and associated confidence shows that the magnitude or severity of the change is a strong indicator of whether field assessment is warranted, and that confusion about patches with lower magnitude changes is not always resolved with a field visit. Our work demonstrates that validation of Landsat-derived landscape change patches can be done using office based tools such as aerial imagery, and that such methods provide an adequate validation for most change types, thus reducing the need for expensive field visits.

Description: Being an important economic crop that contributes 35% of the total consumption of vegetable oil, remote sensing-based quantitative detection of oil palm trees has long been a key research direction for both agriculture and environmental purposes. While existing methods already demonstrate satisfactory effectiveness for small regions, performing the detection for a large region with satisfactory accuracy is still challenging. In this study, we proposed a two-stage convolutional neural network (TS-CNN)-based oil palm detection method using high-resolution satellite images (i.e. Quickbird) in a large-scale study area of Malaysia. The TS-CNN consists of one CNN for land cover classification and one CNN for object classification. The two CNNs were trained and optimized independently based on 20,000 samples collected through human interpretation. For the large-scale oil palm detection for an area of 55 km2, we proposed an effective workflow that consists of an overlapping partitioning method for large-scale image division, a multi-scale sliding window method for oil palm coordinate prediction, and a minimum distance filter method for post-processing. Our proposed approach achieves a much higher average F1-score of 94.99% in our study area compared with existing oil palm detection methods (87.95%, 81.80%, 80.61%, and 78.35% for single-stage CNN, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN), respectively), and much fewer confusions with other vegetation and buildings in the whole image detection results.

Description: Grasslands, which represent around 40% of the terrestrial area, are mostly located in arid and semi-arid zones. Semiarid ecosystems in Africa have been identified as being particularly vulnerable to the impacts of increased human pressure on land, as well as enhanced climate variability. Grasslands are indeed very responsive to variations in precipitation. This study evaluates the sensitivity of the grassland ecosystem to precipitation variability in space and time, by identifying the factors controlling this response, based on monthly precipitation data from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) data from the Multi-angle Imaging SpectroRadiometer-High Resolution (MISR-HR) datasets, used as proxy for productivity, at 60 grassland sites in South Africa. Our results show that MISR-HR products adequately capture the spatial and temporal variability in productivity at scales that are relevant to this study, and they are therefore a good tool to study climate change impacts on ecosystem at small spatial scales over large spatial and temporal domains. We show that combining several determinants and accounting for legacies improves our ability to understand patterns, identify areas of vulnerability, and predict the future of grassland productivity. Mean annual precipitation is a good predictor of mean grassland productivity. The grasslands with a mean annual rainfall above about 530 mm have a different functional response to those receiving less than that amount of rain, on average. On the more arid and less fertile soils, large inter-annual variability reduces productivity. Our study suggests that grasslands on the more marginal soils are the most vulnerable to climate change.

Description: The aim of this report to solve the open problem suggested by Chen et al. We study the graph entropy with ABC edge weights and present bounds of it for connected graphs, regular graphs, complete bipartite graphs, chemical graphs, tree, unicyclic graphs, and star graphs. Moreover, we compute the graph entropy for some families of dendrimers.

Description: Investigating mangrove species composition is a basic and important topic in wetland management and conservation. This study aims to explore the potential of close-range hyperspectral imaging with a snapshot hyperspectral sensor for identifying mangrove species under field conditions. Specifically, we assessed the data pre-processing and transformation, waveband selection and machine-learning techniques to develop an optimal classification scheme for eight mangrove species in Qi’ao Island of Zhuhai, Guangdong, China. After data pre-processing and transformation, five spectral datasets, which included the reflectance spectra R and its first-order derivative d(R), the logarithm of the reflectance spectra log(R) and its first-order derivative d[log(R)], and hyperspectral vegetation indices (VIs), were used as the input data for each classifier. Consequently, three waveband selection methods, including the stepwise discriminant analysis (SDA), correlation-based feature selection (CFS), and successive projections algorithm (SPA) were used to reduce dimensionality and select the effective wavebands for identifying mangrove species. Furthermore, we evaluated the performance of mangrove species classification using four classifiers, including linear discriminant analysis (LDA), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). Application of the four considered classifiers on the reflectance spectra of all wavebands yielded overall classification accuracies of the eight mangrove species higher than 80%, with SVM having the highest accuracy of 93.54% (Kappa = 0.9256). Using the selected wavebands derived from SPA, the accuracy of SVM reached 93.13% (Kappa = 0.9208). The addition of hyperspectral VIs and d[log(R)] spectral datasets further improves the accuracies to 93.54% (Kappa = 0.9253) and 96.46% (Kappa = 0.9591), respectively. These results suggest that it is highly effective to apply field close-range snapshot hyperspectral images and machine-learning classifiers to classify mangrove species.

Description: A river island is a shaped sediment accumulation body with its top above the water’s surface in crooked or branching streams. In this paper, four river islands in Yangzhong City in the lower reaches of the Yangtze River were studied. The spatio-temporal evolution information of the islands was quantitatively extracted using the threshold value method, binarization model, and cluster analysis, based on Thematic Mapper (TM) and Enhanced Thematic Mapper+ (ETM+) images of the Landsat satellite series from 1985 to 2015. The variation mechanism and influencing factors were analyzed using an unstructured-grid, Finite-Volume Coastal Ocean Model (FVCOM) hydrodynamic numerical simulation, as well as the water-sediment data measured by hydrological stations. The annual average total area of these islands was 251,224.46 m2 during 1985–2015, and the total area first increased during 1985–2000 and decreased later during 2000–2015. Generally, the total area increased during these 30 years. Taipingzhou island had the largest area and the biggest changing rate, Xishadao island had the smallest area, and Zhongxinsha island had the smallest changing rate. The river islands’ area change was influenced by river runoff, sediment discharge, and precipitation, and sediment discharge proved to be the most significant natural factor in island evolution. River island evolution was also found to be affected by both runoff and oceanic tide. The difference in flow-field caused silting up in the Leigongdao Island and the head of Taipingzhou Island, and a serious reduction in the middle and tail of Taipingzhou Island. The method used in this paper has good applicability to river islands in other rivers around the world.

Description: This study assessed accuracies of MCD43A3, Global Land-Surface Satellite (GLASS) and forthcoming Multi-source Data Synergized Quantitative Remote Sensing Production system (MuSyQ) albedos using ground observations and Huan Jing (HJ) data over the Heihe River Basin. MCD43A3 and MuSyQ albedos show similar high accuracies with identical root mean square errors (RMSE). Nevertheless, MuSyQ albedo is better correlated with ground measurements when sufficient valid observations are available or snow-free. The opposite happens when less than seven valid observations are available. GLASS albedo presents a larger RMSE than MCD43A3 and MuSyQ albedos in comparison with ground measurements. Over surfaces with smaller seasonal variations, MCD43A3 and MuSyQ albedos show smaller RMSEs than GLASS albedo in comparison with HJ albedo. However, for surfaces with larger temporal variations, both RMSEs and R2 of GLASS albedo are comparable with MCD43A3 and MuSyQ. Generally, MCD43A3 and MuSyQ albedos featured the same RMSEs of 0.034 and similar R2 (0.920 and 0.903, respectively), which are better than GLASS albedo (RMSE = 0.043, R2 = 0.787). However, when it comes to comparison with aggregated HJ albedo, MuSyQ and GLASS albedos are with lower RMSEs of 0.027 and 0.032 and higher R2 of 0.900 and 0.898 respectively than MCD43A3 (RMSE = 0.038, R2 = 0.836). Despite the limited geographic region of the study area, they still provide an important insight into the accuracies of three albedo products.

Description: Dual-frequency GNSS data processing is currently one of the most useful techniques for sounding the ionosphere. Hence, this work was aimed at the evaluation of ground-based GNSS data for the continuous monitoring of polar patches in both hemispheres. In this contribution, we proposed to use epoch-wise relative STEC values in order to detect these structures. The applied indicator is defined as a difference between an undifferenced geometry-free linear combination of GNSS signals and the background ionospheric variations, which were assessed with an iterative algorithm of four-degree polynomial fitting. The occurrence of patches during the St. Patrick geomagnetic storm was performed for validation purposes. The first part of the work confirmed the applicability of the relative STEC values for such investigations. On the other hand, it also revealed the limitations related to the inhomogeneous distribution of stations, which may affect the results in both hemispheres. This was confirmed with a preliminary cross-evaluation of GNSS and in situ SWARM datasets. Apart from the periods with a well-established coincidence, the opposite situation, when both methods indicated different parts of the polar ionosphere, was also observed. The second part of this contribution depicted the feasibility of continuous patch detection for both regions, and thus the interhemispheric comparison of the analyzed structures. It has demonstrated the strong disproportion between patches in the northern and southern hemispheres. This discrepancy seems to be related to the different amount of plasma propagating from the dusk sector, which is justified by the values of relative STEC at mid-latitudes. The observed structures are also strongly dependent on the orientation of the interplanetary magnetic field.

Description: The highly accurate multiresolution leaf area index (LAI) is an important parameter for carbon cycle simulation for bamboo forests at different scales. However, current LAI products have discontinuous resolution with 1 km mostly, that makes it difficult to accurately quantify the spatiotemporal evolution of carbon cycle at different resolutions. Thus, this study used MODIS LAI product (MOD15A2) and MODIS reflectance data (MOD09Q1) of Moso bamboo forest (MBF) from 2015, and it adopted a hierarchical Bayesian network (HBN) algorithm coupled with a dynamic LAI model and the PROSAIL model to obtain high-precision LAI data at multiresolution (i.e., 1000, 500, and 250 m). The results showed the LAIs assimilated using the HBN at the three resolutions corresponded with the actual growth trend of the MBF and correlated significantly with the observed LAI with a determination coefficient (R2) value of 〉0.80. The highest-precision assimilated LAI was obtained at 1000-m resolution with R2 values of 0.91. The LAI assimilated using the HBN algorithm achieved better accuracy than the MODIS LAI with increases in the R2 value of 2.7 times and decreases in the root mean square error of 87.8%. Therefore, the HBN algorithm applied in this study can effectively obtain highly accurate multiresolution LAI time series data for bamboo forest.