ALBERT

Description: IJGI, Vol. 7, Pages 382: Incremental Road Network Generation Based on Vehicle Trajectories ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7100382 Authors: Zhongyi Ni Lijun Xie Tian Xie Binhua Shi Yao Zheng Nowadays, most vehicles are equipped with positioning devices such as GPS which can generate a tremendous amount of trajectory data and upload them to the server in real time. The trajectory data can reveal the shape and evolution of the road network and therefore has an important value for road planning, vehicle navigation, traffic analysis, and so on. In this paper, a road network generation method is proposed based on the incremental learning of vehicle trajectories. Firstly, the input vehicle trajectory data are cleaned by a preprocess module. Then, the original scattered positions are clustered and mapped to the representation points which stand for the feature points of the real roads. After that, the corresponding representation points are connected based on the original connection information of the trajectories. Finally, all representation points are connected by a Delaunay triangulation network and the real road segments are found by a shortest path searching approach between the connected representation point pairs. Experiments show that this method can build the road network from scratch and refine it with the input data continuously. Both the accuracy and timeliness of the extracted road network can continuously be improved with the growth of real-time trajectory data.

Description: IJGI, Vol. 7, Pages 383: Care, Indifference and Anxiety—Attitudes toward Location Data in Everyday Life ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7100383 Authors: Michal Rzeszewski Piotr Luczys Modern mobile devices are replete with advanced sensors that expand the array of possible methods of locating users. This can be used as a tool to gather and use spatial information, but it also brings with it the specter of “geosurveillance” in which the “location” becomes a product in itself. In the realm of software developers, space/place has been reduced and discretized to a set of coordinates, devoid of human experiences and meanings. To function in such digitally augmented realities, people need to adopt specific attitudes, often marked with anxiety. We explored attitudes toward location data collection practices using qualitative questionnaire surveys (n = 280) from Poznan and Edinburgh. The prevailing attitude that we identified is neutral with a strong undertone of resignation—surrendering personal location is viewed as a form of digital currency. A smaller number of people had stronger, emotional views, either very positive or very negative, based on uncritical technological enthusiasm or fear of privacy violation. Such a wide spectrum of attitudes is not only produced by interaction with technology but can also be a result of different values associated with space and place itself. Those attitudes can bring additional bias into spatial datasets that is not related to demographics.

Description: Remote Sensing, Vol. 10, Pages 1529: Tomographic Imaging of Ionospheric Plasma Bubbles Based on GNSS and Radio Occultation Measurements Remote Sensing doi: 10.3390/rs10101529 Authors: Fabricio dos Santos Prol Manuel Hernández-Pajares Marcio Tadeu de Assis Honorato Muella Paulo de Oliveira Camargo Total electron content measurements given by the global navigation satellite system (GNSS) have successfully presented results to capture the signatures of equatorial plasma bubbles. In contrast, the correct reproduction of plasma depletions at electron density level is still a relevant challenge for ionospheric tomographic imaging. In this regard, this work shows the first results of a new tomographic reconstruction technique based on GNSS and radio-occultation data to map the vertical and horizontal distributions of ionospheric plasma bubbles in one of the most challenging conditions of the equatorial region. Twenty-three days from 2013 and 2014 with clear evidence of plasma bubble structures propagating through the Brazilian region were analyzed and compared with simultaneous observations of all-sky images in the 630.0 nm emission line of the atomic oxygen. The mean rate of success of the tomographic method was 37.1%, being more efficient near the magnetic equator, where the dimensions of the structures are larger. Despite some shortcomings of the reconstruction technique, mainly associated with ionospheric scintillations and the weak geometry of the ground-based GNSS receivers, both vertical and horizontal distributions were mapped over more than 30° in latitude, and have been detected in instances where the meteorological conditions disrupted the possibility of analyzing the OI 630 nm emissions. Therefore, the results revealed the proposed tomographic reconstruction as an efficient tool for mapping characteristics of the plasma bubble structures, which may have a special interest in Space Weather, Spatial Geodesy, and Telecommunications.

Description: Remote Sensing, Vol. 10, Pages 1528: Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation Remote Sensing doi: 10.3390/rs10101528 Authors: Liang Han Guijun Yang Haikuan Feng Chengquan Zhou Hao Yang Bo Xu Zhenhai Li Xiaodong Yang Maize (zee mays L.) is one of the most important grain crops in China. Lodging is a natural disaster that can cause significant yield losses and threaten food security. Lodging identification and analysis contributes to evaluate disaster losses and cultivates lodging-resistant maize varieties. In this study, we collected visible and multispectral images with an unmanned aerial vehicle (UAV), and introduce a comprehensive methodology and workflow to extract lodging features from UAV imagery. We use statistical methods to screen several potential feature factors (e.g., texture, canopy structure, spectral characteristics, and terrain), and construct two nomograms (i.e., Model-1 and Model-2) with better validation performance based on selected feature factors. Model-2 was superior to Model-1 in term of its discrimination ability, but had an over-fitting phenomenon when the predicted probability of lodging went from 0.2 to 0.4. The results show that the nomogram could not only predict the occurrence probability of lodging, but also explore the underlying association between maize lodging and the selected feature factors. Compared with spectral features, terrain features, texture features, canopy cover, and genetic background, canopy structural features were more conclusive in discriminating whether maize lodging occurs at the plot scale. Using nomogram analysis, we identified protective factors (i.e., normalized difference vegetation index, NDVI and canopy elevation relief ratio, CRR) and risk factors (i.e., Hcv1) related to maize lodging, and also found a problem of terrain spatial variability that is easily overlooked in lodging-resistant breeding trials.

Description: Remote Sensing, Vol. 10, Pages 1527: Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia Remote Sensing doi: 10.3390/rs10101527 Authors: Dieu Tien Bui Himan Shahabi Ataollah Shirzadi Kamran Chapi Mohsen Alizadeh Wei Chen Ayub Mohammadi Baharin Bin Ahmad Mahdi Panahi Haoyuan Hong Yingying Tian Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments.

Description: Remote Sensing, Vol. 10, Pages 1526: NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies Remote Sensing doi: 10.3390/rs10101526 Authors: Xizhi Zhao Bailang Yu Yan Liu Shenjun Yao Ting Lian Liujia Chen Chengshu Yang Zuoqi Chen Jianping Wu Whereas monthly and annual nighttime light (NTL) composite datasets are being increasingly used to estimate socioeconomic status, use of the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data has been limited for detecting and assessing the impact of short-term disastrous events. This study explores the application of daily NPP-VIIRS DNB data in assessing the impact of three types of natural disasters: earthquakes, floods, and storms. Daily DNB images one month prior to and 10 days after a disastrous event were collected and a Percent of Normal Light (PNL) image was produced as the ratio of the mean DNB radiance of the pre- and post-disaster images. Areas with a PNL value lower than one were considered as being affected by the event. The results were compared with the damaged proxy map and the flood proxy map generated using synthetic aperture radar data as well as the reported power outage rates. Our analyses show that overall NPP-VIIRS DNB daily data are useful for detecting damages and power outages caused by earthquake, storm, and flood events. Cloud coverage was identified as a major limitation in using the DNB daily data; rescue activities, traffic, and socioeconomic status of the areas also affect the use of DNB daily data in assessing the impact of natural disasters. Our findings offer new insight into the use of the daily DNB data and provide a practical guide for researchers and practitioners who may consider using such data in different situations or regions.

Description: Remote Sensing, Vol. 10, Pages 1525: Spatiotemporal Patterns of Vegetation Greenness Change and Associated Climatic and Anthropogenic Drivers on the Tibetan Plateau during 2000–2015 Remote Sensing doi: 10.3390/rs10101525 Authors: Lanhui Li Yili Zhang Linshan Liu Jianshuang Wu Zhaofeng Wang Shicheng Li Huamin Zhang Jiaxing Zu Mingjun Ding Basanta Paudel Alpine vegetation on the Tibetan Plateau (TP) is known to be sensitive to both climate change and anthropogenic disturbance. However, the magnitude and patterns of alpine vegetation dynamics and the driving mechanisms behind their variation on the TP remains under debate. In this study, we used updated MODIS Collection 6 Normalized Difference Vegetation Index (NDVI) from the Terra satellite combined with linear regression and the Break for Additive Season and Trend model to reanalyze the spatiotemporal patterns of vegetation change on the TP during 2000–2015. We then quantified the responses of vegetation variation to climatic and anthropogenic factors by coupling climatic and human footprint datasets. Results show that growing season NDVI (GNDVI) values increased significantly overall (0.0011 year−1, p < 0.01) during 2000–2015 and that 70.37% of vegetated area on the TP (23.47% significantly with p < 0.05) exhibited greening trends with the exception of the southwest TP. However, vegetation greenness experienced trend shifts from greening to browning in half of the ecosystem zones occurred around 2010, likely induced by spatially heterogeneous temporal trends of climate variables. The vegetation changes in the northeastern and southwestern TP were water limited, the mid-eastern TP exhibited strong temperature responses, and the south of TP was driven by a combination of temperature and solar radiation. Furthermore, we found that, to some extent, anthropogenic disturbances offset climate-driven vegetation greening and aggravated vegetation browning induced by water deficit. These findings suggest that the impact of anthropogenic activities on vegetation change might not overwhelm that of climate change at the region scale.

Description: Remote Sensing, Vol. 10, Pages 1524: Using Satellite Remote Sensing to Study the Impact of Climate and Anthropogenic Changes in the Mesopotamian Marshlands, Iraq Remote Sensing doi: 10.3390/rs10101524 Authors: Reyadh Albarakat Venkat Lakshmi Compton J. Tucker The Iraqi Marshes in Southern Iraq are considered one of the most important wetlands in the world. From 1982 to the present, their area has varied between 10,500 km2 and 20,000 km2. The marshes support a variety of plants, such as reeds and papyrus, and are home to many species of birds. These marshes are Al-Hammar, Central or Al-Amarah, and Al-Huwaiza. Freshwater supplies to the marshes come from the Tigris and Euphrates rivers in Iraq and from the Karkha River from Iran. For this analysis, we used the Land Long-Term Data Record Version 5 (LTDR V5) Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) sensor dataset. This dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution to monitor the spatial and temporal variability of vegetation along with other hydrological variables such as land surface temperature, precipitation, and evapotranspiration. In our analysis, we considered three time periods: 1982–1992; 1993–2003; and 2004–2017 due to anthropogenic activities and climate changes. Furthermore, we examined the relationships between various water cycle variables through the investigation of vegetation and water coverage changes, and studied the impacts of climate change and anthropogenic activities on the Iraqi Marshes and considered additional ground observations along with the satellite datasets. Statistical analyses over the last 36 years show significant deterioration in the vegetation: 68.78%, 98.73, and 83.71% of the green biomass has declined for Al-Hammar, The Central marshes, and Al-Huwaiza, respectively. The AVHRR and Landsat images illustrate a decrease in water and vegetation coverage, which in turn has led to an increase in barren lands. Unfortunately, statistical analyses show that marshland degradation is mainly induced by human actions. The shrinkage in water supplies taken by Iraq’s local neighbors (i.e., Turkey, Syria, and Iran) has had a sharp impact on water levels. The annual discharge of the Tigris declined from ~2500–3000 m3/s to ~500 m3/s, and the annual discharge of the Euphrates River declined from ~1500 m3/s to less than 500 m3/s.

Description: Remote Sensing, Vol. 10, Pages 1522: Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia Remote Sensing doi: 10.3390/rs10101522 Authors: Gina Leonita Monika Kuffer Richard Sliuzas Claudio Persello The survey-based slum mapping (SBSM) program conducted by the Indonesian government to reach the national target of “cities without slums” by 2019 shows mapping inconsistencies due to several reasons, e.g., the dependency on the surveyor’s experiences and the complexity of the slum indicators set. By relying on such inconsistent maps, it will be difficult to monitor the national slum upgrading program’s progress. Remote sensing imagery combined with machine learning algorithms could support the reduction of these inconsistencies. This study evaluates the performance of two machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF), for slum mapping in support of the slum mapping campaign in Bandung, Indonesia. Recognizing the complexity in differentiating slum and formal areas in Indonesia, the study used a combination of spectral, contextual, and morphological features. In addition, sequential feature selection (SFS) combined with the Hilbert–Schmidt independence criterion (HSIC) was used to select significant features for classifying slums. Overall, the highest accuracy (88.5%) was achieved by the SVM with SFS using contextual, morphological, and spectral features, which is higher than the estimated accuracy of the SBSM. To evaluate the potential of machine learning-based slum mapping (MLBSM) in support of slum upgrading programs, interviews were conducted with several local and national stakeholders. Results show that local acceptance for a remote sensing-based slum mapping approach varies among stakeholder groups. Therefore, a locally adapted framework is required to combine ground surveys with robust and consistent machine learning methods, for being able to deal with big data, and to allow the rapid extraction of consistent information on the dynamics of slums at a large scale.

Description: Remote Sensing, Vol. 10, Pages 1523: Geocoding Error Correction for InSAR Point Clouds Remote Sensing doi: 10.3390/rs10101523 Authors: Sina Montazeri Fernando Rodríguez González Xiao Xiang Zhu Persistent Scatterer Interferometry (PSI) is an advanced multitemporal InSAR technique that is capable of retrieving the 3D coordinates and the underlying deformation of time-coherent scatterers. Various factors degrade the localization accuracy of PSI point clouds in the geocoding process, which causes problems for interpretation of deformation results and also making it difficult for the point clouds to be compared with or integrated into data from other sensors. In this study, we employ the SAR imaging geodesy method to perform geodetic corrections on SAR timing observations and thus improve the positioning accuracy in the horizontal components. We further utilize geodetic stereo SAR to extract large number of highly precise ground control points (GCP) from SAR images, in order to compensate for the unknown height offset of the PSI point cloud. We demonstrate the applicability of the approach using TerraSAR-X high resolution spotlight images over the city of Berlin, Germany. The corrected results are compared with a reference LiDAR point cloud of Berlin, which confirms the improvement in the geocoding accuracy.