ALBERT

Description / Table of Contents: The sustainability of irrigation and drainage in the face of many variants and constraints like availability of water as a resource, ecological balance, socio-cultural impacts, and climate change effects lies in the strategies adopted and systems emplaced. It has always remained a challenge for the users of irrigation waters to maintain sustainability in quality and quantity. This book aims ?to explore frontiers of knowledge in coining sustainable strategies and systems direly needed in managing the quality and quantity of water required for crop irrigation, surface and root zone drainage and flood management using available tools of research and development?. Eminent authors and their colleagues possessing varied professional backgrounds and expertise have dealt with these issues concerning the strategies and systems of irrigation and drainage. This book will prove to be beneficial for crop growers, agricultural engineers, water resource managers, academicians and graduate students alike.

Description / Table of Contents: In recent years, the topic of 3D reconstruction and modeling of complex architectures from remotely acquired multiple data sources has been of growing interest. This “democratization” of 3D modeling processes and the large availability of data is, however, not always followed by reliable, affordable and powerful tools for realizing photo-realistic, metric, re-usable and semantic-aware 3D products. This should be a motivation to research, design, develop and validate novel easy-to-use, ease-to-learn and a low-cost framework for 3D modeling and further understanding of virtual environments using multiple data sources, so that the whole 3D modeling community has access to an affordable, transferable, functional and usable framework of methods and tools. This challenge causes several problems that should be addressed: from improving and testing the technical capabilities of new capturing devices, to the solution of problems resultant from large image blocks, from delivering Building Information Modeling (BIM) standards in order to provide new management approaches to replacing existing visualization tools with new working experiences such as Virtual and Augmented Reality or game-engine technology.

Description / Table of Contents: Accurate and timely information of crop growth and conditions is critical for precision farming, crop management, crop yield estimation, crop disaster early warning and mitigation, agricultural production planning, crop commodity trading, and food security decision support. Recent advances in imaging and non-imaging sensor technologies, remote sensing platforms, and satellite data availability have provided new opportunities and challenges, and have resulted in many new investigations and much progress in crop growth monitoring. Ground-based millimeter-level very high spatial resolution hyperspectral imaging, which is acquired from sensors, such as ImSpectorV10E (SpecIm, Spectral Imaging Ltd., Finland) and HySpec VNIR-1600 (Norsk Elektro Optikk, Norway), enables us to discern the within-canopy and within-leaf variation in crop conditions in target fields. Affordable low-weight multispectral/hyperspectral sensors on unmanned aerial systems (UAVs) and/or regular aircrafts have significantly improved the efficiency and effectiveness in monitoring within- and between-field variations in crop growth. The recent very-high-resolution satellite imagery, acquired typically in sub-meter to 5 meter resolution, such as WorldView-2, Pleiades-1, IKONOS, and RapidEye, has brought us into a new phase of remote sensing for precision crop management over large farming areas. The freely available satellite data from sensors, such as MODIS, NPP VIIRS, and Landsat, have greatly facilitated large scale (i.e., regional or even global level) crop growth monitoring.

Description / Table of Contents: Chen, K.; Zamora, N.; Babeyko, A.; Li, X.; Ge, M. Precise Positioning of BDS, BDS/GPS: Implications for Tsunami Early Warning in South China Sea. Remote Sensing 2015, 7(12), 15955-15968; doi:10.3390/rs71215814 --- Cianflone, G.; Tolomei, C.; Brunori, C.; Dominici, R. InSAR Time Series Analysis of Natural and Anthropogenic Coastal Plain Subsidence: The Case of Sibari (Southern Italy). Remote Sensing 2015, 7(12), 16004-16023; doi:10.3390/rs71215812 --- Kropáček, J.; Vařilová, Z.; Baroň, I.; Bhattacharya, A.; Eberle, J.; Hochschild, V. Remote Sensing for Characterisation and Kinematic Analysis of Large Slope Failures: Debre Sina Landslide, Main Ethiopian Rift Escarpment. Remote Sensing 2015, 7(12), 16183-16203; doi:10.3390/rs71215821 --- Pacheco-Martínez, J.; Cabral-Cano, E.; Wdowinski, S.; Hernández-Marín, M.; Ortiz-Lozano, J.; Zermeño-de-León, M. Application of InSAR and Gravimetry for Land Subsidence Hazard Zoning in Aguascalientes, Mexico. Remote Sensing 2015, 7(12), 17035-17050; doi:10.3390/rs71215868 --- Al-Rawabdeh, A.; He, F.; Moussa, A.; El-Sheimy, N.; Habib, A. Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition. Remote Sensing 2016, 8(2), 95; doi:10.3390/rs8020095 --- Zhai, W.; Shen, H.; Huang, C.; Pei, W. Building Earthquake Damage Information Extraction from a Single Post-Earthquake PolSAR Image. Remote Sensing 2016, 8(3), 171; doi:10.3390/rs8030171 --- Jiang, Y.; Liao, M.; Zhou, Z.; Shi, X.; Zhang, L.; Balz, T. Landslide Deformation Analysis by Coupling Deformation Time Series from SAR Data with Hydrological Factors through Data Assimilation. Remote Sensing 2016, 8(3), 179; doi:10.3390/rs8030179 --- He, M.; Zhu, Q.; Du, Z.; Hu, H.; Ding, Y.; Chen, M. A 3D Shape Descriptor Based on Contour Clusters for Damaged Roof Detection Using Airborne LiDAR Point Clouds. Remote Sensing 2016, 8(3), 189; doi:10.3390/rs8030189 --- Hu, J.; Wang, Q.; Li, Z.; Zhao, R.; Sun, Q. Investigating the Ground Deformation and Source Model of the Yangbajing Geothermal Field in Tibet, China with the WLS InSAR Technique. Remote Sensing 2016, 8(3), 191; doi:10.3390/rs8030191 --- Hsieh, Y.; Chan, Y.; Hu, J. Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan. Remote Sensing 2016, 8(3), 199; doi:10.3390/rs8030199 --- Zhu, S.; Xu, C.; Wen, Y.; Liu, Y. Interseismic Deformation of the Altyn Tagh Fault Determined by Interferometric Synthetic Aperture Radar (InSAR) Measurements. Remote Sensing 2016, 8(3), 233; doi:10.3390/rs8030233 --- Vetrivel, A.; Gerke, M.; Kerle, N.; Vosselman, G. Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach. Remote Sensing 2016, 8(3), 231; doi:10.3390/rs8030231 --- Bardi, F.; Raspini, F.; Ciampalini, A.; Kristensen, L.; Rouyet, L.; Lauknes, T.; Frauenfelder, R.; Casagli, N. Space-Borne and Ground-Based InSAR Data Integration: The Åknes Test Site. Remote Sensing 2016, 8(3), 237; doi:10.3390/rs8030237 --- Liu, P.; Li, Q.; Li, Z.; Hoey, T.; Liu, G.; Wang, C.; Hu, Z.; Zhou, Z.; Singleton, A. Anatomy of Subsidence in Tianjin from Time Series InSAR. Remote Sensing 2016, 8(3), 266; doi:10.3390/rs8030266 --- Ma, Y.; Chen, F.; Liu, J.; He, Y.; Duan, J.; Li, X. An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing. Remote Sensing 2016, 8(4), 272; doi:10.3390/rs8040272 --- Yang, C.; Zhang, Q.; Xu, Q.; Zhao, C.; Peng, J.; Ji, L. Complex Deformation Monitoring over the Linfen–Yuncheng Basin (China) with Time Series InSAR Technology. Remote Sensing 2016, 8(4), 284; doi:10.3390/rs8040284 --- Watanabe, M.; Thapa, R.; Shimada, M. Pi-SAR-L2 Observation of the Landslide Caused by Typhoon Wipha on Izu Oshima Island. Remote Sensing 2016, 8(4), 282; doi:10.3390/rs8040282 --- Plank, S.; Twele, A.; Martinis, S. Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data. Remote Sensing 2016, 8(4), 307; doi:10.3390/rs8040307 --- Solaro, G.; De Novellis, V.; Castaldo, R.; De Luca, C.; Lanari, R.; Manunta, M.; Casu, F. Coseismic Fault Model of Mw 8.3 2015 Illapel Earthquake (Chile) Retrieved from Multi-Orbit Sentinel1-A DInSAR Measurements. Remote Sensing 2016, 8(4), 323; doi:10.3390/rs8040323 --- Bai, L.; Jiang, L.; Wang, H.; Sun, Q. Spatiotemporal Characterization of Land Subsidence and Uplift (2009–2010) over Wuhan in Central China Revealed by TerraSAR-X InSAR Analysis. Remote Sensing 2016, 8(4), 350; doi:10.3390/rs8040350 --- Xu, B.; Li, Z.; Feng, G.; Zhang, Z.; Wang, Q.; Hu, J.; Chen, X. Continent-Wide 2-D Co-Seismic Deformation of the 2015 Mw 8.3 Illapel, Chile Earthquake Derived from Sentinel-1A Data: Correction of Azimuth Co-Registration Error. Remote Sensing 2016, 8(5), 376; doi:10.3390/rs8050376 --- Chen, M.; Tomás, R.; Li, Z.; Motagh, M.; Li, T.; Hu, L.; Gong, H.; Li, X.; Yu, J.; Gong, X. Imaging Land Subsidence Induced by Groundwater Extraction in Beijing (China) Using Satellite Radar Interferometry. Remote Sensing 2016, 8(6), 468; doi:10.3390/rs8060468 --- Ji, L.; Xu, J.; Zhao, Q.; Yang, C. Source Parameters of the 2003–2004 Bange Earthquake Sequence, Central Tibet, China, Estimated from InSAR Data. Remote Sensing 2016, 8(6), 516; doi:10.3390/rs8060516 --- Li, Y.; Jiang, W.; Zhang, J.; Luo, Y. Space Geodetic Observations and Modeling of 2016 Mw 5.9 Menyuan Earthquake: Implications on Seismogenic Tectonic Motion. Remote Sensing 2016, 8(6), 519; doi:10.3390/rs8060519 --- Trasatti, E.; Tolomei, C.; Pezzo, G.; Atzori, S.; Salvi, S. Deformation and Related Slip Due to the 2011 Van Earthquake (Turkey) Sequence Imaged by SAR Data and Numerical Modeling. Remote Sensing 2016, 8(6), 532; doi:10.3390/rs8060532 --- Wang, C.; Mao, X.; Wang, Q. Landslide Displacement Monitoring by a Fully Polarimetric SAR Offset Tracking Method. Remote Sensing 2016, 8(8), 624; doi:10.3390/rs8080624 --- Liu, Y.; Xu, C.; Li, Z.; Wen, Y.; Chen, J.; Li, Z. Time-Dependent Afterslip of the 2009 Mw 6.3 Dachaidan Earthquake (China) and Viscosity beneath the Qaidam Basin Inferred from Postseismic Deformation Observations. Remote Sensing 2016, 8(8), 649; doi:10.3390/rs8080649 --- Xu, B.; Feng, G.; Li, Z.; Wang, Q.; Wang, C.; Xie, R. Coastal Subsidence Monitoring Associated with Land Reclamation Using the Point Target Based SBAS-InSAR Method: A Case Study of Shenzhen, China. Remote Sensing 2016, 8(8), 652; doi:10.3390/rs8080652 --- Sun, L.; Muller, J. Evaluation of the Use of Sub-Pixel Offset Tracking Techniques to Monitor Landslides in Densely Vegetated Steeply Sloped Areas. Remote Sensing 2016, 8(8), 659; doi:10.3390/rs8080659 --- De Novellis, V.; Castaldo, R.; Lollino, P.; Manunta, M.; Tizzani, P. Advanced Three-Dimensional Finite Element Modeling of a Slow Landslide through the Exploitation of DInSAR Measurements and in Situ Surveys. Remote Sensing 2016, 8(8), 670; doi:10.3390/rs8080670 --- Zhang, Y.; Wu, H.; Kang, Y.; Zhu, C. Ground Subsidence in the Beijing-Tianjin-Hebei Region from 1992 to 2014 Revealed by Multiple SAR Stacks. Remote Sensing 2016, 8(8), 675; doi:10.3390/rs8080675 --- Zhou, G.; Yue, T.; Shi, Y.; Zhang, R.; Huang, J. Second-Order Polynomial Equation-Based Block Adjustment for Orthorectification of DISP Imagery. Remote Sensing 2016, 8(8), 680; doi:10.3390/rs8080680 --- Bonì, R.; Pilla, G.; Meisina, C. Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis. Remote Sensing 2016, 8(8), 686; doi:10.3390/rs8080686 --- Xie, S.; Duan, J.; Liu, S.; Dai, Q.; Liu, W.; Ma, Y.; Guo, R.; Ma, C. Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake. Remote Sensing 2016, 8(9), 759; doi:10.3390/rs8090759 --- Fernández, T.; Pérez, J.; Cardenal, J.; Gómez, J.; Colomo, C.; Delgado, J. Analysis of Landslide Evolution Affecting Olive Groves Using UAV and Photogrammetric Techniques. Remote Sensing 2016, 8(10), 837; doi:10.3390/rs8100837 --- Cignetti, M.; Manconi, A.; Manunta, M.; Giordan, D.; De Luca, C.; Allasia, P.; Ardizzone, F. Taking Advantage of the ESA G-POD Service to Study Ground Deformation Processes in High Mountain Areas: A Valle d’Aosta Case Study, Northern Italy. Remote Sensing 2016, 8(10), 852; doi:10.3390/rs8100852 --- Cooner, A.; Shao, Y.; Campbell, J. Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake. Remote Sensing 2016, 8(10), 868; doi:10.3390/rs8100868 --- Zhou, W.; Li, S.; Zhou, Z.; Chang, X. InSAR Observation and Numerical Modeling of the Earth-Dam Displacement of Shuibuya Dam (China). Remote Sensing 2016, 8(10), 877; doi:10.3390/rs8100877 --- Qu, T.; Lu, P.; Liu, C.; Wu, H.; Shao, X.; Wan, H.; Li, N.; Li, R. Hybrid-SAR Technique: Joint Analysis Using Phase-Based and Amplitude-Based Methods for the Xishancun Giant Landslide Monitoring. Remote Sensing 2016, 8(10), 874; doi:10.3390/rs8100874 --- Gong, L.; Wang, C.; Wu, F.; Zhang, J.; Zhang, H.; Li, Q. Earthquake-Induced Building Damage Detection with Post-Event Sub-Meter VHR TerraSAR-X Staring Spotlight Imagery. Remote Sensing 2016, 8(11), 887; doi:10.3390/rs8110887 --- Ding, C.; Feng, G.; Li, Z.; Shan, X.; Du, Y.; Wang, H. Spatio-Temporal Error Sources Analysis and Accuracy Improvement in Landsat 8 Image Ground Displacement Measurements. Remote Sensing 2016, 8(11), 937; doi:10.3390/rs8110937 --- Ma, C.; Cheng, X.; Yang, Y.; Zhang, X.; Guo, Z.; Zou, Y. Investigation on Mining Subsidence Based on Multi-Temporal InSAR and Time-Series Analysis of the Small Baseline Subset—Case Study of Working Faces 22201-1/2 in Bu’ertai Mine, Shendong Coalfield, China. Remote Sensing 2016, 8(11), 951; doi:10.3390/rs8110951 --- Caló, F.; Notti, D.; Galve, J.; Abdikan, S.; Görüm, T.; Pepe, A.; Balik Şanli, F. DInSAR-Based Detection of Land Subsidence and Correlation with Groundwater Depletion in Konya Plain, Turkey. Remote Sensing 2017, 9(1), 83; doi:10.3390/rs9010083 --- Tomás, R.; Li, Z. Earth Observations for Geohazards: Present and Future Challenges. Remote Sensing 2017, 9(3), 194; doi:10.3390/rs9030194

Description / Table of Contents: Chen, K.; Zamora, N.; Babeyko, A.; Li, X.; Ge, M. Precise Positioning of BDS, BDS/GPS: Implications for Tsunami Early Warning in South China Sea. Remote Sensing 2015, 7(12), 15955-15968; doi:10.3390/rs71215814 --- Cianflone, G.; Tolomei, C.; Brunori, C.; Dominici, R. InSAR Time Series Analysis of Natural and Anthropogenic Coastal Plain Subsidence: The Case of Sibari (Southern Italy). Remote Sensing 2015, 7(12), 16004-16023; doi:10.3390/rs71215812 --- Kropáček, J.; Vařilová, Z.; Baroň, I.; Bhattacharya, A.; Eberle, J.; Hochschild, V. Remote Sensing for Characterisation and Kinematic Analysis of Large Slope Failures: Debre Sina Landslide, Main Ethiopian Rift Escarpment. Remote Sensing 2015, 7(12), 16183-16203; doi:10.3390/rs71215821 --- Pacheco-Martínez, J.; Cabral-Cano, E.; Wdowinski, S.; Hernández-Marín, M.; Ortiz-Lozano, J.; Zermeño-de-León, M. Application of InSAR and Gravimetry for Land Subsidence Hazard Zoning in Aguascalientes, Mexico. Remote Sensing 2015, 7(12), 17035-17050; doi:10.3390/rs71215868 --- Al-Rawabdeh, A.; He, F.; Moussa, A.; El-Sheimy, N.; Habib, A. Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition. Remote Sensing 2016, 8(2), 95; doi:10.3390/rs8020095 --- Zhai, W.; Shen, H.; Huang, C.; Pei, W. Building Earthquake Damage Information Extraction from a Single Post-Earthquake PolSAR Image. Remote Sensing 2016, 8(3), 171; doi:10.3390/rs8030171 --- Jiang, Y.; Liao, M.; Zhou, Z.; Shi, X.; Zhang, L.; Balz, T. Landslide Deformation Analysis by Coupling Deformation Time Series from SAR Data with Hydrological Factors through Data Assimilation. Remote Sensing 2016, 8(3), 179; doi:10.3390/rs8030179 --- He, M.; Zhu, Q.; Du, Z.; Hu, H.; Ding, Y.; Chen, M. A 3D Shape Descriptor Based on Contour Clusters for Damaged Roof Detection Using Airborne LiDAR Point Clouds. Remote Sensing 2016, 8(3), 189; doi:10.3390/rs8030189 --- Hu, J.; Wang, Q.; Li, Z.; Zhao, R.; Sun, Q. Investigating the Ground Deformation and Source Model of the Yangbajing Geothermal Field in Tibet, China with the WLS InSAR Technique. Remote Sensing 2016, 8(3), 191; doi:10.3390/rs8030191 --- Hsieh, Y.; Chan, Y.; Hu, J. Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan. Remote Sensing 2016, 8(3), 199; doi:10.3390/rs8030199 --- Zhu, S.; Xu, C.; Wen, Y.; Liu, Y. Interseismic Deformation of the Altyn Tagh Fault Determined by Interferometric Synthetic Aperture Radar (InSAR) Measurements. Remote Sensing 2016, 8(3), 233; doi:10.3390/rs8030233 --- Vetrivel, A.; Gerke, M.; Kerle, N.; Vosselman, G. Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach. Remote Sensing 2016, 8(3), 231; doi:10.3390/rs8030231 --- Bardi, F.; Raspini, F.; Ciampalini, A.; Kristensen, L.; Rouyet, L.; Lauknes, T.; Frauenfelder, R.; Casagli, N. Space-Borne and Ground-Based InSAR Data Integration: The Åknes Test Site. Remote Sensing 2016, 8(3), 237; doi:10.3390/rs8030237 --- Liu, P.; Li, Q.; Li, Z.; Hoey, T.; Liu, G.; Wang, C.; Hu, Z.; Zhou, Z.; Singleton, A. Anatomy of Subsidence in Tianjin from Time Series InSAR. Remote Sensing 2016, 8(3), 266; doi:10.3390/rs8030266 --- Ma, Y.; Chen, F.; Liu, J.; He, Y.; Duan, J.; Li, X. An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing. Remote Sensing 2016, 8(4), 272; doi:10.3390/rs8040272 --- Yang, C.; Zhang, Q.; Xu, Q.; Zhao, C.; Peng, J.; Ji, L. Complex Deformation Monitoring over the Linfen–Yuncheng Basin (China) with Time Series InSAR Technology. Remote Sensing 2016, 8(4), 284; doi:10.3390/rs8040284 --- Watanabe, M.; Thapa, R.; Shimada, M. Pi-SAR-L2 Observation of the Landslide Caused by Typhoon Wipha on Izu Oshima Island. Remote Sensing 2016, 8(4), 282; doi:10.3390/rs8040282 --- Plank, S.; Twele, A.; Martinis, S. Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data. Remote Sensing 2016, 8(4), 307; doi:10.3390/rs8040307 --- Solaro, G.; De Novellis, V.; Castaldo, R.; De Luca, C.; Lanari, R.; Manunta, M.; Casu, F. Coseismic Fault Model of Mw 8.3 2015 Illapel Earthquake (Chile) Retrieved from Multi-Orbit Sentinel1-A DInSAR Measurements. Remote Sensing 2016, 8(4), 323; doi:10.3390/rs8040323 --- Bai, L.; Jiang, L.; Wang, H.; Sun, Q. Spatiotemporal Characterization of Land Subsidence and Uplift (2009–2010) over Wuhan in Central China Revealed by TerraSAR-X InSAR Analysis. Remote Sensing 2016, 8(4), 350; doi:10.3390/rs8040350 --- Xu, B.; Li, Z.; Feng, G.; Zhang, Z.; Wang, Q.; Hu, J.; Chen, X. Continent-Wide 2-D Co-Seismic Deformation of the 2015 Mw 8.3 Illapel, Chile Earthquake Derived from Sentinel-1A Data: Correction of Azimuth Co-Registration Error. Remote Sensing 2016, 8(5), 376; doi:10.3390/rs8050376 --- Chen, M.; Tomás, R.; Li, Z.; Motagh, M.; Li, T.; Hu, L.; Gong, H.; Li, X.; Yu, J.; Gong, X. Imaging Land Subsidence Induced by Groundwater Extraction in Beijing (China) Using Satellite Radar Interferometry. Remote Sensing 2016, 8(6), 468; doi:10.3390/rs8060468 --- Ji, L.; Xu, J.; Zhao, Q.; Yang, C. Source Parameters of the 2003–2004 Bange Earthquake Sequence, Central Tibet, China, Estimated from InSAR Data. Remote Sensing 2016, 8(6), 516; doi:10.3390/rs8060516 --- Li, Y.; Jiang, W.; Zhang, J.; Luo, Y. Space Geodetic Observations and Modeling of 2016 Mw 5.9 Menyuan Earthquake: Implications on Seismogenic Tectonic Motion. Remote Sensing 2016, 8(6), 519; doi:10.3390/rs8060519 --- Trasatti, E.; Tolomei, C.; Pezzo, G.; Atzori, S.; Salvi, S. Deformation and Related Slip Due to the 2011 Van Earthquake (Turkey) Sequence Imaged by SAR Data and Numerical Modeling. Remote Sensing 2016, 8(6), 532; doi:10.3390/rs8060532 --- Wang, C.; Mao, X.; Wang, Q. Landslide Displacement Monitoring by a Fully Polarimetric SAR Offset Tracking Method. Remote Sensing 2016, 8(8), 624; doi:10.3390/rs8080624 --- Liu, Y.; Xu, C.; Li, Z.; Wen, Y.; Chen, J.; Li, Z. Time-Dependent Afterslip of the 2009 Mw 6.3 Dachaidan Earthquake (China) and Viscosity beneath the Qaidam Basin Inferred from Postseismic Deformation Observations. Remote Sensing 2016, 8(8), 649; doi:10.3390/rs8080649 --- Xu, B.; Feng, G.; Li, Z.; Wang, Q.; Wang, C.; Xie, R. Coastal Subsidence Monitoring Associated with Land Reclamation Using the Point Target Based SBAS-InSAR Method: A Case Study of Shenzhen, China. Remote Sensing 2016, 8(8), 652; doi:10.3390/rs8080652 --- Sun, L.; Muller, J. Evaluation of the Use of Sub-Pixel Offset Tracking Techniques to Monitor Landslides in Densely Vegetated Steeply Sloped Areas. Remote Sensing 2016, 8(8), 659; doi:10.3390/rs8080659 --- De Novellis, V.; Castaldo, R.; Lollino, P.; Manunta, M.; Tizzani, P. Advanced Three-Dimensional Finite Element Modeling of a Slow Landslide through the Exploitation of DInSAR Measurements and in Situ Surveys. Remote Sensing 2016, 8(8), 670; doi:10.3390/rs8080670 --- Zhang, Y.; Wu, H.; Kang, Y.; Zhu, C. Ground Subsidence in the Beijing-Tianjin-Hebei Region from 1992 to 2014 Revealed by Multiple SAR Stacks. Remote Sensing 2016, 8(8), 675; doi:10.3390/rs8080675 --- Zhou, G.; Yue, T.; Shi, Y.; Zhang, R.; Huang, J. Second-Order Polynomial Equation-Based Block Adjustment for Orthorectification of DISP Imagery. Remote Sensing 2016, 8(8), 680; doi:10.3390/rs8080680 --- Bonì, R.; Pilla, G.; Meisina, C. Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis. Remote Sensing 2016, 8(8), 686; doi:10.3390/rs8080686 --- Xie, S.; Duan, J.; Liu, S.; Dai, Q.; Liu, W.; Ma, Y.; Guo, R.; Ma, C. Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake. Remote Sensing 2016, 8(9), 759; doi:10.3390/rs8090759 --- Fernández, T.; Pérez, J.; Cardenal, J.; Gómez, J.; Colomo, C.; Delgado, J. Analysis of Landslide Evolution Affecting Olive Groves Using UAV and Photogrammetric Techniques. Remote Sensing 2016, 8(10), 837; doi:10.3390/rs8100837 --- Cignetti, M.; Manconi, A.; Manunta, M.; Giordan, D.; De Luca, C.; Allasia, P.; Ardizzone, F. Taking Advantage of the ESA G-POD Service to Study Ground Deformation Processes in High Mountain Areas: A Valle d’Aosta Case Study, Northern Italy. Remote Sensing 2016, 8(10), 852; doi:10.3390/rs8100852 --- Cooner, A.; Shao, Y.; Campbell, J. Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake. Remote Sensing 2016, 8(10), 868; doi:10.3390/rs8100868 --- Zhou, W.; Li, S.; Zhou, Z.; Chang, X. InSAR Observation and Numerical Modeling of the Earth-Dam Displacement of Shuibuya Dam (China). Remote Sensing 2016, 8(10), 877; doi:10.3390/rs8100877 --- Qu, T.; Lu, P.; Liu, C.; Wu, H.; Shao, X.; Wan, H.; Li, N.; Li, R. Hybrid-SAR Technique: Joint Analysis Using Phase-Based and Amplitude-Based Methods for the Xishancun Giant Landslide Monitoring. Remote Sensing 2016, 8(10), 874; doi:10.3390/rs8100874 --- Gong, L.; Wang, C.; Wu, F.; Zhang, J.; Zhang, H.; Li, Q. Earthquake-Induced Building Damage Detection with Post-Event Sub-Meter VHR TerraSAR-X Staring Spotlight Imagery. Remote Sensing 2016, 8(11), 887; doi:10.3390/rs8110887 --- Ding, C.; Feng, G.; Li, Z.; Shan, X.; Du, Y.; Wang, H. Spatio-Temporal Error Sources Analysis and Accuracy Improvement in Landsat 8 Image Ground Displacement Measurements. Remote Sensing 2016, 8(11), 937; doi:10.3390/rs8110937 --- Ma, C.; Cheng, X.; Yang, Y.; Zhang, X.; Guo, Z.; Zou, Y. Investigation on Mining Subsidence Based on Multi-Temporal InSAR and Time-Series Analysis of the Small Baseline Subset—Case Study of Working Faces 22201-1/2 in Bu’ertai Mine, Shendong Coalfield, China. Remote Sensing 2016, 8(11), 951; doi:10.3390/rs8110951 --- Caló, F.; Notti, D.; Galve, J.; Abdikan, S.; Görüm, T.; Pepe, A.; Balik Şanli, F. DInSAR-Based Detection of Land Subsidence and Correlation with Groundwater Depletion in Konya Plain, Turkey. Remote Sensing 2017, 9(1), 83; doi:10.3390/rs9010083 --- Tomás, R.; Li, Z. Earth Observations for Geohazards: Present and Future Challenges. Remote Sensing 2017, 9(3), 194; doi:10.3390/rs9030194

Description / Table of Contents: Perea-Moreno, A.; Aguilera-Ureña, M.; Meroño-De Larriva, J.; Manzano-Agugliaro, F. Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses. Water 2016, 8(12), 584; https://doi.org/10.3390/w8120584 --- Frappart, F.; Bourrel, L.; Brodu, N.; Riofrío Salazar, X.; Baup, F.; Darrozes, J.; Pombosa, R. Monitoring of the Spatio-Temporal Dynamics of the Floods in the Guayas Watershed (Ecuadorian Pacific Coast) Using Global Monitoring ENVISAT ASAR Images and Rainfall Data. Water 2017, 9(1), 12; https://doi.org/10.3390/w9010012 --- Li, Y.; Gong, H.; Zhu, L.; Li, X. Measuring Spatiotemporal Features of Land Subsidence, Groundwater Drawdown, and Compressible Layer Thickness in Beijing Plain, China. Water 2017, 9(1), 64; https://doi.org/10.3390/w9010064 --- Yang, F.; Guo, J.; Tan, H.; Wang, J. Automated Extraction of Urban Water Bodies from ZY‐3 Multi‐Spectral Imagery. Water 2017, 9(2), 144; https://doi.org/10.3390/w9020144 --- Lee, J.; Choi, H. Improvements to Runoff Predictions from a Land Surface Model with a Lateral Flow Scheme Using Remote Sensing and In Situ Observations. Water 2017, 9(2), 148; https://doi.org/10.3390/w9020148 --- Sharif, H.; Al-Zahrani, M.; Hassan, A. Physically, Fully-Distributed Hydrologic Simulations Driven by GPM Satellite Rainfall over an Urbanizing Arid Catchment in Saudi Arabia. Water 2017, 9(3), 163; https://doi.org/10.3390/w9030163 --- Wang, X.; Chen, H.; Chen, Y. Large Differences between Glaciers 3D Surface Extents and 2D Planar Areas in Central Tianshan. Water 2017, 9(4), 282; https://doi.org/10.3390/w9040282 --- Wang, R.; Chen, J.; Wang, X. Comparison of IMERG Level-3 and TMPA 3B42V7 in Estimating Typhoon-Related Heavy Rain. Water 2017, 9(4), 276; https://doi.org/10.3390/w9040276 --- Pan, C.; Wang, X.; Liu, L.; Huang, H.; Wang, D. Improvement to the Huff Curve for Design Storms and Urban Flooding Simulations in Guangzhou, China. Water 2017, 9(6), 411; https://doi.org/10.3390/w9060411 --- Ouyang, H.; Shih, S.; Wu, C. Optimal Combinations of Non-Sequential Regressors for ARX-Based Typhoon Inundation Forecast Models Considering Multiple Objectives. Water 2017, 9(7), 519; https://doi.org/10.3390/w9070519 --- Lu, Y.; Song, W.; Lu, J.; Wang, X.; Tan, Y. An Examination of Soil Moisture Estimation Using Ground Penetrating Radar in Desert Steppe. Water 2017, 9(7), 521; https://doi.org/10.3390/w9070521 --- Tekeli, A. Exploring Jeddah Floods by Tropical Rainfall Measuring Mission Analysis. Water 2017, 9(8), 612; https://doi.org/10.3390/w9080612 --- Wang, X.; Xie, H. A Review on Applications of Remote Sensing and Geographic Information Systems (GIS) in Water Resources and Flood Risk Management. Water 2018, 10(5), 608; https://doi.org/10.3390/w10050608

Description / Table of Contents: Editorial - Remote Sensing and Geospatial Technologies in Public Health / ISPRS Int. J. Geo-Inf. 2018, 7(8), 303; https://doi.org/10.3390/ijgi7080303 --- CALPUFF and CAFOs: Air Pollution Modeling and Environmental Justice Analysis in the North Carolina Hog Industry / ISPRS Int. J. Geo-Inf. 2015, 4(1), 150-171; https://doi.org/10.3390/ijgi4010150 --- Analyzing the Correlation between Deer Habitat and the Component of the Risk for Lyme Disease in Eastern Ontario, Canada: A GIS-Based Approach / ISPRS Int. J. Geo-Inf. 2015, 4(1), 105-123; https://doi.org/10.3390/ijgi4010105 --- Geospatial Technology: A Tool to Aid in the Elimination of Malaria in Bangladesh / ISPRS Int. J. Geo-Inf. 2015, 4(1), 47-58; https://doi.org/10.3390/ijgi4010047 --- Examining Personal Air Pollution Exposure, Intake, and Health Danger Zone Using Time Geography and 3D Geovisualization / ISPRS Int. J. Geo-Inf. 2015, 4(1), 32-46; https://doi.org/10.3390/ijgi4010032 --- Use of the NASA Giovanni Data System for Geospatial Public Health Research: Example of Weather-Influenza Connection / ISPRS Int. J. Geo-Inf. 2014, 3(4), 1372-1386; https://doi.org/10.3390/ijgi3041372 --- Mapping Entomological Dengue Risk Levels in Martinique Using High-Resolution Remote-Sensing Environmental Data / ISPRS Int. J. Geo-Inf. 2014, 3(4), 1352-1371; https://doi.org/10.3390/ijgi3041352 --- Improving Inland Water Quality Monitoring through Remote Sensing Techniques / ISPRS Int. J. Geo-Inf. 2014, 3(4), 1234-1255; https://doi.org/10.3390/ijgi3041234 --- Impacts of Scale on Geographic Analysis of Health Data: An Example of Obesity Prevalence / ISPRS Int. J. Geo-Inf. 2014, 3(4), 1198-1210; https://doi.org/10.3390/ijgi3041198 --- Geographical Variation of Incidence of Chronic Obstructive Pulmonary Disease in Manitoba, Canada / ISPRS Int. J. Geo-Inf. 2014, 3(3), 1039-1057; https://doi.org/10.3390/ijgi3031039 --- Holistics 3.0 for Health / ISPRS Int. J. Geo-Inf. 2014, 3(3), 1023-1038; https://doi.org/10.3390/ijgi3031023 --- Dasymetric Mapping and Spatial Modeling of Mosquito Vector Exposure, Chesapeake, Virginia, USA / ISPRS Int. J. Geo-Inf. 2014, 3(3), 891-913; https://doi.org/10.3390/ijgi3030891 --- Modeling Properties of Influenza-Like Illness Peak Events with Crossing Theory / ISPRS Int. J. Geo-Inf. 2014, 3(2), 764-780; https://doi.org/10.3390/ijgi3020764 --- Correlating Remote Sensing Data with the Abundance of Pupae of the Dengue Virus Mosquito Vector, Aedes aegypti, in Central Mexico / ISPRS Int. J. Geo-Inf. 2014, 3(2), 732-749; https://doi.org/10.3390/ijgi3020732 --- Canadian Forest Fires and the Effects of Long-Range Transboundary Air Pollution on Hospitalizations among the Elderly / ISPRS Int. J. Geo-Inf. 2014, 3(2), 713-731; https://doi.org/10.3390/ijgi3020713 --- Nexus of Health and Development: Modelling Crude Birth Rate and Maternal Mortality Ratio Using Nighttime Satellite Images / ISPRS Int. J. Geo-Inf. 2014, 3(2), 693-712; https://doi.org/10.3390/ijgi3020693

Description / Table of Contents: Nowadays, the innovation in space technologies creates a new trend for the Earth observation and monitoring from space. This book contains high quality and compressive work on both microwave and optical remote sensing applications. This book is divided into five sections: (i) remote sensing for biomass estimation, (ii) remote sensing-based glacier studies, (iii) remote sensing for coastal and ocean applications, (iv) sewage leaks and environment disasters, and (v) remote sensing image processing. Each chapter offers an opportunity to expand the knowledge about various remote sensing techniques and persuade researchers to deliver new research novelty for environment studies.

Description / Table of Contents: The success of the Suomi National Polar-orbiting Partnership (NPP) brings us into a new era of global daily Earth observations, ranging from the faintest light of human settlements and air glows to the dramatic events of hurricanes and forest fires, as well as the subtle changes in the planet Earth which we call home. At the heart of all satellite applications, calibration/validation of the measurements and derived products is the key. Satellite product calibration and validation have become increasingly more important and challenging in order to meet the stringent requirements for accurate quantitative data for climate change detection, numerical weather prediction, and environmental intelligence. Validation is required not only for the satellite measurements, but also for all geophysical retrievals, including aerosols, cloud properties, radiation budget, sea surface temperature, ocean color, active fire, albedo, snow and ice, vegetation, as well as nightlights from human settlements. Active validation research includes but not limited to, comparisons with similar products from other satellites, with in situ, aircraft measurements, or observations from other platforms. Validation results not only help users and decision makers but also serve as feedback to calibration, which in turn improves the products. This Special Issue of Remote Sensing aims at exploring recent results in the calibration and validation of the Suomi National Polar-orbiting Partnership satellite (Suomi NPP)/JPSS radiometers.

Description / Table of Contents: Mechanisms of SAR Imaging of Shallow Water Topography of the Subei Bank / Remote Sens. 2017, 9(11), 1203; doi:10.3390/rs9111203 --- Detection of Bivalve Beds on Exposed Intertidal Flats Using Polarimetric SAR Indicators / Remote Sens. 2017, 9(10), 1047; doi:10.3390/rs9101047 --- Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004–2013) RADARSAT Data / Remote Sens. 2017, 9(10), 1041; doi:10.3390/rs9101041 --- Assimilation of Typhoon Wind Field Retrieved from Scatterometer and SAR Based on the Huber Norm Quality Control / Remote Sens. 2017, 9(10), 987; doi:10.3390/rs9100987 --- Performance Analysis of Ocean Surface Topography Altimetry by Ku-Band Near-Nadir Interferometric SAR / Remote Sens. 2017, 9(9), 933; doi:10.3390/rs9090933 --- Satellite Survey of Internal Waves in the Black and Caspian Seas / Remote Sens. 2017, 9(9), 892; doi:10.3390/rs9090892 --- Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection / Remote Sens. 2017, 9(8), 860; doi:10.3390/rs9080860 --- Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation / Remote Sens. 2017, 9(8), 795; doi:10.3390/rs9080795 --- Modulation Model of High Frequency Band Radar Backscatter by the Internal Wave Based on the Third-Order Statistics / Remote Sens. 2017, 9(5), 501; doi:10.3390/rs9050501 --- Ku-Band Sea Surface Radar Backscatter at Low Incidence Angles under Extreme Wind Conditions / Remote Sens. 2017, 9(5), 474; doi:10.3390/rs9050474 --- Doppler Spectrum-Based NRCS Estimation Method for Low-Scattering Areas in Ocean SAR Images / Remote Sens. 2017, 9(3), 219; doi:10.3390/rs9030219 --- An Improved Shape Contexts Based Ship Classification in SAR Images / Remote Sens. 2017, 9(2), 145; doi:10.3390/rs9020145 --- GF-3 SAR Ocean Wind Retrieval: The First View and Preliminary Assessment / Remote Sens. 2017, 9(7), 694; doi:10.3390/rs9070694