ALBERT

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: Nishimura, T. Rust Formation Mechanism on Low Alloy Steels after Exposure Test in High Cl− and High SOx Environmen. Materials 2017, 10(2), 199; doi:10.3390/ma10020199. http://www.mdpi.com/1996-1944/10/2/199 --- Chang, T.; Odnevall Wallinder, I.; de la Fuente, D.; Chico, B.; Morcillo, M.; Welter, J.; Leygraf, C. Analysis of Historic Copper Patinas. Influence of Inclusions on Patina Uniformity. Materials 2017, 10(3), 298; doi:10.3390/ma10030298. http://www.mdpi.com/1996-1944/10/3/298 --- Na, O.; Cai, X.; Xi, Y. Corrosion Prediction with Parallel Finite Element Modeling for Coupled Hygro-Chemo Transport into Concrete under Chloride-Rich Environment. Materials 2017, 10(4), 350; doi:10.3390/ma10040350. http://www.mdpi.com/1996-1944/10/4/350 --- Kreislova, K.; Knotkova, D. The Results of 45 Years of Atmospheric Corrosion Study in the Czech Republic. Materials 2017, 10(4), 394; doi:10.3390/ma10040394. http://www.mdpi.com/1996-1944/10/4/394 --- Alcántara, J.; Fuente, D.; Chico, B.; Simancas, J.; Díaz, I.; Morcillo, M. Marine Atmospheric Corrosion of Carbon Steel: A Review. Materials 2017, 10(4), 406; doi:10.3390/ma10040406. http://www.mdpi.com/1996-1944/10/4/406 --- Hosseinpour, S.; Johnson, M. Vibrational Spectroscopy in Studies of Atmospheric Corrosion. Materials 2017, 10(4), 413; doi:10.3390/ma10040413. http://www.mdpi.com/1996-1944/10/4/413 --- Panchenko, Y.; Marshakov, A. Prediction of First-Year Corrosion Losses of Carbon Steel and Zinc in Continental Regions. Materials 2017, 10(4), 422; doi:10.3390/ma10040422. http://www.mdpi.com/1996-1944/10/4/422 --- Chico, B.; de la Fuente, D.; Díaz, I.; Simancas, J.; Morcillo, M. Annual Atmospheric Corrosion of Carbon Steel Worldwide. An Integration of ISOCORRAG, ICP/UNECE and MICAT Databases. Materials 2017, 10(6), 601; doi:10.3390/ma10060601. http://www.mdpi.com/1996-1944/10/6/601 --- Bouchar, M.; Dillmann, P.; Neff, D. New Insights in the Long-Term Atmospheric Corrosion Mechanisms of Low Alloy Steel Reinforcements of Cultural Heritage Buildings. Materials 2017, 10(6), 670; doi:10.3390/ma10060670. http://www.mdpi.com/1996-1944/10/6/670 --- Tidblad, J.; Kreislová, K.; Faller, M.; de la Fuente, D.; Yates, T.; Verney-Carron, A.; Grøntoft, T.; Gordon, A.; Hans, U. ICP Materials Trends in Corrosion, Soiling and Air Pollution (1987–2014). Materials 2017, 10(8), 969; doi:10.3390/ma10080969. http://www.mdpi.com/1996-1944/10/8/969 --- Cole, I. Recent Progress and Required Developments in Atmospheric Corrosion of Galvanised Steel and Zinc. Materials 2017, 10(11), 1288; doi:10.3390/ma10111288. http://www.mdpi.com/1996-1944/10/11/1288

Description / Table of Contents: Over the past few decades, water policies have undergone significant changes in many countries, notably due to the development of national and international political, social, and environmental issues, including globalization, trade liberalization, institutional and legal requirements, changing standards of living, management practices, and technological innovation. Policy changes include both “high level” views about water status and practical instruments, in particular with an emphasis on integrated basin management and economic policy instruments. A relevant part of the water policy literature addresses this topic, mainly as an issue related to environmental conservation. However, water remains a major productive factor, particularly in agriculture. This role is made even more prominent in light of economic crises, increased competition across markets and climate change, as well as fossil energy limitations, which also highlight the water–energy nexus as a key resource issue for future economic viability. The delay, in the past, in recognizing the economic consequences of a limited water supply, and in decoupling economic development from water demand and supply, has resulted in a water-dependent growth model, currently threatened by increasing scarcity and droughts. Consequently, there is now an urgent need for new perspectives for promoting a more sustainable and efficient use of water resources. This calls for, on the one hand, a comprehensive understanding of water efficiency and productivity and, on the other hand, an investigation of the linkages among economic sectors to illustrate trade-offs in water reallocations. In addition, this also points to the need to study the institutional innovations and economic evaluation instruments that are able to better assess policy performance and provide evidence for improved mechanism designs aimed specifically at water efficiency and productivity.

Description / Table of Contents: Frederiks, E.; Stenner, K.; Hobman, E. The Socio-Demographic and Psychological Predictors of Residential Energy Consumption: A Comprehensive Review. Energies 2015, 8(1), 573-609; doi:10.3390/en8010573 --- Sun, W.; He, Y.; Chang, H. Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model. Energies 2015, 8(2), 939-959; doi:10.3390/en8020939 --- Gutierrez-Escolar, A.; Castillo-Martinez, A.; Gomez-Pulido, J.; Gutierrez-Martinez, J.; Stapic, Z.; Medina-Merodio, J. A Study to Improve the Quality of Street Lighting in Spain. Energies 2015, 8(2), 976-994; doi:10.3390/en8020976 --- Chew, K.; Klemeš, J.; Alwi, S.; Manan, Z.; Reverberi, A. Total Site Heat Integration Considering Pressure Drops. Energies 2015, 8(2), 1114-1137; doi:10.3390/en8021114 --- Kim, S.; Shin, K.; Choi, B.; Jo, J.; Cho, S.; Cho, Y. A Study on the Variation of Heating and Cooling Load According to the Use of Horizontal Shading and Venetian Blinds in Office Buildings in Korea. Energies 2015, 8(2), 1487-1504; doi:10.3390/en8021487 --- Sheng, P.; Yang, J.; Shackman, J. Energy’s Shadow Price and Energy Efficiency in China: A Non-Parametric Input Distance Function Analysis. Energies 2015, 8(3), 1975-1989; doi:10.3390/en8031975 --- Benavides, C.; Gonzales, L.; Diaz, M.; Fuentes, R.; García, G.; Palma-Behnke, R.; Ravizza, C. The Impact of a Carbon Tax on the Chilean Electricity Generation Sector. Energies 2015, 8(4), 2674-2700; doi:10.3390/en8042674 --- Li, W.; Li, H.; Sun, S. China’s Low-Carbon Scenario Analysis of CO2 Mitigation Measures towards 2050 Using a Hybrid AIM/CGE Model. Energies 2015, 8(5), 3529-3555; doi:10.3390/en8053529 --- Nasirov, S.; Silva, C.; Agostini, C. Investors’ Perspectives on Barriers to the Deployment of Renewable Energy Sources in Chile. Energies 2015, 8(5), 3794-3814; doi:10.3390/en8053794 --- Deng, X.; Yu, Y.; Liu, Y. Temporal and Spatial Variations in Provincial CO2 Emissions in China from 2005 to 2015 and Assessment of a Reduction Plan. Energies 2015, 8(5), 4549-4571; doi:10.3390/en8054549 --- Klimscheffskij, M.; Van Craenenbroeck, T.; Lehtovaara, M.; Lescot, D.; Tschernutter, A.; Raimundo, C.; Seebach, D.; Timpe, C. Residual Mix Calculation at the Heart of Reliable Electricity Disclosure in Europe—A Case Study on the Effect of the RE-DISS Project. Energies 2015, 8(6), 4667-4696; doi:10.3390/en8064667 --- Ferrara, R. The Smart City and the Green Economy in Europe: A Critical Approach. Energies 2015, 8(6), 4724-4734; doi:10.3390/en8064724 --- Stenner, K.; Nwokora, Z. Current and Future Friends of the Earth: Assessing Cross-National Theories of Environmental Attitudes. Energies 2015, 8(6), 4899-4919; doi:10.3390/en8064899 --- Atlason, R.; Oddsson, G.; Unnthorsson, R. Theorizing for Maintenance Management Improvements: Using Case Studies from the Icelandic Geothermal Sector. Energies 2015, 8(6), 4943-4962; doi:10.3390/en8064943 --- Ellenbeck, S.; Beneking, A.; Ceglarz, A.; Schmidt, P.; Battaglini, A. Security of Supply in European Electricity Markets—Determinants of Investment Decisions and the European Energy Union. Energies 2015, 8(6), 5198-5216; doi:10.3390/en8065198 --- Hasager, C.; Vincent, P.; Badger, J.; Badger, M.; Di Bella, A.; Peña, A.; Husson, R.; Volker, P. Using Satellite SAR to Characterize the Wind Flow around Offshore Wind Farms. Energies 2015, 8(6), 5413-5439; doi:10.3390/en8065413 --- Puigjaner, L.; Pérez-Fortes, M.; Laínez-Aguirre, J. Towards a Carbon-Neutral Energy Sector: Opportunities and Challenges of Coordinated Bioenergy Supply Chains-A PSE Approach. Energies 2015, 8(6), 5613-5660; doi:10.3390/en8065613 --- Thollander, P.; Palm, J. Industrial Energy Management Decision Making for Improved Energy Efficiency—Strategic System Perspectives and Situated Action in Combination. Energies 2015, 8(6), 5694-5703; doi:10.3390/en8065694 --- Jänicke, M. Horizontal and Vertical Reinforcement in Global Climate Governance. Energies 2015, 8(6), 5782-5799; doi:10.3390/en8065782 --- Benavides, C.; Gonzales, L.; Diaz, M.; Fuentes, R.; García, G.; Palma-Behnke, R.; Ravizza, C. Correction: The Impact of a Carbon Tax on the Chilean Electricity Generation Sector. Energies 2015, 8(6), 6247-6248; doi:10.3390/en8066247 --- Wang, W.; Ouyang, W.; Hao, F. A Supply-Chain Analysis Framework for Assessing Densified Biomass Solid Fuel Utilization Policies in China. Energies 2015, 8(7), 7122-7139; doi:10.3390/en8077122 --- Punys, P.; Dumbrauskas, A.; Kasiulis, E.; Vyčienė, G.; Šilinis, L. Flow Regime Changes: From Impounding a Temperate Lowland River to Small Hydropower Operations. Energies 2015, 8(7), 7478-7501; doi:10.3390/en8077478 --- Reid, G.; Wynn, G. The Future of Solar Power in the United Kingdom. Energies 2015, 8(8), 7818-7832; doi:10.3390/en8087818 --- Scott, C.; Sugg, Z. Global Energy Development and Climate-Induced Water Scarcity—Physical Limits, Sectoral Constraints, and Policy Imperatives. Energies 2015, 8(8), 8211-8225; doi:10.3390/en8088211 --- Lilliestam, J.; Patt, A. Barriers, Risks and Policies for Renewables in the Gulf States. Energies 2015, 8(8), 8263-8285; doi:10.3390/en8088263 --- Van Ackere, S.; Van Eetvelde, G.; Schillebeeckx, D.; Papa, E.; Van Wyngene, K.; Vandevelde, L. Wind Resource Mapping Using Landscape Roughness and Spatial Interpolation Methods. Energies 2015, 8(8), 8682-8703; doi:10.3390/en8088682 --- Komendantova, N.; Vocciante, M.; Battaglini, A. Can the BestGrid Process Improve Stakeholder Involvement in Electricity Transmission Projects?. Energies 2015, 8(9), 9407-9433; doi:10.3390/en8099407 --- Kiyar, D.; Wittneben, B. Carbon as Investment Risk—The Influence of Fossil Fuel Divestment on Decision Making at Germany’s Main Power Providers. Energies 2015, 8(9), 9620-9639; doi:10.3390/en8099620 --- Bernardes, L.; Carneiro, J.; Madureira, P.; Brandão, F.; Roque, C. Determination of Priority Study Areas for Coupling CO2 Storage and CH4 Gas Hydrates Recovery in the Portuguese Offshore Area. Energies 2015, 8(9), 10276-10292; doi:10.3390/en80910276 --- Dovì, V.; Battaglini, A. Energy Policy and Climate Change: A Multidisciplinary Approach to a Global Problem. Energies 2015, 8(12), 13473-13480; doi:10.3390/en81212379

Description / Table of Contents: Climate change will bring about significant changes to the capacity of, and the demand on, water resources. The resulting changes include increasing climate variability that is expected to affect hydrologic conditions. The effects of climate variability on various meteorological variables have been extensively observed in many regions around the world. Of these, rainfall is one of the most important variables. Understanding the effects of climate variability on spatial and temporal rainfall characteristics is of special interest to water resource policy makers. Investigating rainfall variability at the regional scale is essential for understanding potential impacts on humans and the natural environment. Atmospheric circulation, topography, land use and other regional features modify global changes to produce unique patterns of change at the regional scale. As the future changes to these water resources cannot be measured in the present, hydrological models are critical in the planning required to adapt our water resource management strategies to future climate conditions. Such models include catchment runoff models, reservoir management models, flood prediction models, groundwater recharge and flow models, and crop water balance models. In water-scarce regions such as Australia, urban water systems are particularly vulnerable to rapid population growth and climate change. In the presence of climate change induced uncertainty, urban water systems need to be more resilient and multi-sourced. Decreasing volumetric rainfall trends have an effect on reservoir yield and operation practices. Severe intensity rainfall events can cause failure of drainage system capacity and subsequent urban flood inundation problems. Policy makers, end users and leading researchers need to work together to develop a consistent approach to interpreting the effects of climate variability and change on water resources.