Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR) is one of the most powerful Earth observation techniques, especially useful for measuring highly detailed ground deformation over large ground areas. M...Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR) is one of the most powerful Earth observation techniques, especially useful for measuring highly detailed ground deformation over large ground areas. Much research has been carried out to apply MT-InSAR to monitor ground and infrastructure deformation in urban areas related to land reclamation, underground construction and groundwater extraction.This paper reviews the progress in the research and identifies challenges in applying the technology, including the inconsistency in coherent point identification when different approaches are used, the reliability issue in parameter estimation, difficulty in accurate geolocation of measured points, the one-dimensional line-of-sight nature of InSAR measurements, the inability of making complete measurements over an area due to geometric distortions, especially the shadowing effects, the challenges in processing large SAR datasets, the decrease of the number of coherent points with the increase of the length of SAR time series, and the difficulty in quality control of MT-InSAR results.展开更多
China has been affected by some of the world’s most serious geological disasters and experiences high economic damage every year.Geohazards occur not only in remote areas but also in highly populated cities.In the fr...China has been affected by some of the world’s most serious geological disasters and experiences high economic damage every year.Geohazards occur not only in remote areas but also in highly populated cities.In the framework of the Dragon-432365 Project,this paper presents the main results and the major conclusions derived from an extensive exploitation of Sentinel-1,ALOS-2(Advanced Land Observing Satellite 2),GF-3(Gao Fen Satellite 3),and latest launched SAR(Synthetic Aperture Radar),together with methods that allow the evaluation of their importance for various geohazards.Therefore,in the scope of this project,the great benefits of recent remote sensing data(wide spatial and temporal coverage)that allow a detailed reconstruction of past displacement events and to monitor currently occurring phenomena are exploited to study different areas and geohazards problems,including:surface deformation of mountain slopes;identification and monitoring of ground movements and subsidence;landslides;ground fissure;and building inclination studies.Suspicious movements detected in the different study areas were cross validated with different SAR sensors and truth data.展开更多
Creation of a spectral signature reflectance data, which aids in the identification of the crops is important in determining size and location crop fields. Therefore, we developed a spectral signature reflectance for ...Creation of a spectral signature reflectance data, which aids in the identification of the crops is important in determining size and location crop fields. Therefore, we developed a spectral signature reflectance for the vegetative stage of the green gram (Vigna. radiata L.) over 5 years (2020, 2018, 2017, 2015, and 2013) for agroecological zone IV and V in Kenya. The years chosen were those whose satellite resolution data was available for the vegetative stage of crop growth in the short rain season (October, November, December (OND)). We used Landsat 8 OLI satellite imagery in this study. Cropping pattern data for the study area were evaluated by calculating the Top of Atmosphere reflectance. Farms geo-referencing, along with field data collection, was undertaken to extract Top of Atmosphere reflectance for bands 2, 3, 4 and 7. We also carried a spectral similarity assessment on the various cropping patterns. The spectral reflectance ranged from 0.07696 - 0.09632, 0.07466 - 0.09467, 0.0704047 - 0.12188,0.19822 - 0.24387, 0.19269 - 0.26900, and 0.11354 - 0.20815 for bands 2, 3, 4, 5, 6, and 7 for green gram, respectively. The results showed a dissimilarity among the various cropping patterns. The lowest dissimilarity index was 0.027 for the maize (Zea mays L.) bean (Phaseolus vulgaris) versus the maize-pigeon pea (Cajanus cajan) crop, while the highest dissimilarity index was 0.443 for the maize bean versus the maize bean and cowpea cropping patterns. High crop dissimilarities experienced across the cropping pattern through these spectral reflectance values confirm that the green gram was potentially identifiable. The results can be used in crop type identification in agroecological lower midland zone IV and V for mung bean management. This study therefore suggests that use of reflectance data in remote sensing of agricultural ecosystems would aid in planning, management, and crop allocation to different ecozones.展开更多
基金The National Natural Science Foundation of China(41774023)The Research Grants Council(RGC)of Hong Kong(PolyU152232/17E,PolyU152164/18E),The Faculty of Construction and Environment(ZZGD)+1 种基金The Research Institute for Sustainable Urban Development(RISUD)(1-BBWB)The TerraSAR-X Science plan(GEO3603)。
文摘Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR) is one of the most powerful Earth observation techniques, especially useful for measuring highly detailed ground deformation over large ground areas. Much research has been carried out to apply MT-InSAR to monitor ground and infrastructure deformation in urban areas related to land reclamation, underground construction and groundwater extraction.This paper reviews the progress in the research and identifies challenges in applying the technology, including the inconsistency in coherent point identification when different approaches are used, the reliability issue in parameter estimation, difficulty in accurate geolocation of measured points, the one-dimensional line-of-sight nature of InSAR measurements, the inability of making complete measurements over an area due to geometric distortions, especially the shadowing effects, the challenges in processing large SAR datasets, the decrease of the number of coherent points with the increase of the length of SAR time series, and the difficulty in quality control of MT-InSAR results.
基金National Natural Science Foundation of China(Nos.41590852,42071453)。
文摘China has been affected by some of the world’s most serious geological disasters and experiences high economic damage every year.Geohazards occur not only in remote areas but also in highly populated cities.In the framework of the Dragon-432365 Project,this paper presents the main results and the major conclusions derived from an extensive exploitation of Sentinel-1,ALOS-2(Advanced Land Observing Satellite 2),GF-3(Gao Fen Satellite 3),and latest launched SAR(Synthetic Aperture Radar),together with methods that allow the evaluation of their importance for various geohazards.Therefore,in the scope of this project,the great benefits of recent remote sensing data(wide spatial and temporal coverage)that allow a detailed reconstruction of past displacement events and to monitor currently occurring phenomena are exploited to study different areas and geohazards problems,including:surface deformation of mountain slopes;identification and monitoring of ground movements and subsidence;landslides;ground fissure;and building inclination studies.Suspicious movements detected in the different study areas were cross validated with different SAR sensors and truth data.
文摘Creation of a spectral signature reflectance data, which aids in the identification of the crops is important in determining size and location crop fields. Therefore, we developed a spectral signature reflectance for the vegetative stage of the green gram (Vigna. radiata L.) over 5 years (2020, 2018, 2017, 2015, and 2013) for agroecological zone IV and V in Kenya. The years chosen were those whose satellite resolution data was available for the vegetative stage of crop growth in the short rain season (October, November, December (OND)). We used Landsat 8 OLI satellite imagery in this study. Cropping pattern data for the study area were evaluated by calculating the Top of Atmosphere reflectance. Farms geo-referencing, along with field data collection, was undertaken to extract Top of Atmosphere reflectance for bands 2, 3, 4 and 7. We also carried a spectral similarity assessment on the various cropping patterns. The spectral reflectance ranged from 0.07696 - 0.09632, 0.07466 - 0.09467, 0.0704047 - 0.12188,0.19822 - 0.24387, 0.19269 - 0.26900, and 0.11354 - 0.20815 for bands 2, 3, 4, 5, 6, and 7 for green gram, respectively. The results showed a dissimilarity among the various cropping patterns. The lowest dissimilarity index was 0.027 for the maize (Zea mays L.) bean (Phaseolus vulgaris) versus the maize-pigeon pea (Cajanus cajan) crop, while the highest dissimilarity index was 0.443 for the maize bean versus the maize bean and cowpea cropping patterns. High crop dissimilarities experienced across the cropping pattern through these spectral reflectance values confirm that the green gram was potentially identifiable. The results can be used in crop type identification in agroecological lower midland zone IV and V for mung bean management. This study therefore suggests that use of reflectance data in remote sensing of agricultural ecosystems would aid in planning, management, and crop allocation to different ecozones.
