Potential of C-band Sentinel-1 InSAR for ground surface deformation monitoring in the southern boreal forest: An investigation in the Genhe River basin
Chenqi Huang, Lingxiao Wang, Lin Zhao, Shibo Liu, Defu Zou, Guangyue Liu, Guojie Hu, Erji Du, Yao Xiao, Chong Wang, Yuxin Zhang, Yuanwei Wang, Yu Zhang, Zhibin Li
{"title":"Potential of C-band Sentinel-1 InSAR for ground surface deformation monitoring in the southern boreal forest: An investigation in the Genhe River basin","authors":"Chenqi Huang, Lingxiao Wang, Lin Zhao, Shibo Liu, Defu Zou, Guangyue Liu, Guojie Hu, Erji Du, Yao Xiao, Chong Wang, Yuxin Zhang, Yuanwei Wang, Yu Zhang, Zhibin Li","doi":"10.1016/j.jag.2024.104302","DOIUrl":null,"url":null,"abstract":"The boreal forest surrounds the Arctic region and is the most extensive ecosystem on Earth; one-third of its soil is influenced by permafrost and accompanying wetlands. Interferometric Synthetic Aperture Radar (InSAR) technology has been widely utilized to monitor ground surface deformation in Arctic tundra and alpine grassland permafrost environments; however, its application in boreal forest areas is limited due to dense canopy cover and severe interferometric decorrelation. This study investigates the application of C-band InSAR to ground surface deformation monitoring in a southern boreal forest environment at Genhe River watershed in the northern part of the Greater Khingan Mountains, Northeast China. The analysis revealed that freezing-season interferograms have higher interferometric qualities and are more suitable for deformation monitoring. An InSAR pair correction and stacking algorithm was developed for retrieving extensive freezing-season deformation which could maximize the use of low-quality InSAR pairs, and reduce the effects of the snow depth phase and atmospheric distortions. The retrieved multiannual freezing-season deformation ranged from –32.8 mm to 129.1 mm. The uplift regions clearly indicate the extent of low-lying wetlands, which are influenced by frost heave caused by freezing soil water. Additionally, the “subsidence” areas correspond to farmland and evergreen coniferous forest regions in the study area, where liquid water content is higher than in other land cover types, thus resulting in a longer optical path for the radar signal. This study presents the first systematic analysis of applying C-band InSAR to ground surface deformation monitoring in the southern boreal forest environment. The retrieved seasonal deformation and deformation processes have a high potential for identifying wetlands, differentiating between forest types, and providing valuable insights into the hydrothermal conditions and dynamics of the boreal ecosystem.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"22 1","pages":""},"PeriodicalIF":7.5000,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Applied Earth Observation and Geoinformation","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1016/j.jag.2024.104302","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Earth and Planetary Sciences","Score":null,"Total":0}
引用次数: 0
Abstract
The boreal forest surrounds the Arctic region and is the most extensive ecosystem on Earth; one-third of its soil is influenced by permafrost and accompanying wetlands. Interferometric Synthetic Aperture Radar (InSAR) technology has been widely utilized to monitor ground surface deformation in Arctic tundra and alpine grassland permafrost environments; however, its application in boreal forest areas is limited due to dense canopy cover and severe interferometric decorrelation. This study investigates the application of C-band InSAR to ground surface deformation monitoring in a southern boreal forest environment at Genhe River watershed in the northern part of the Greater Khingan Mountains, Northeast China. The analysis revealed that freezing-season interferograms have higher interferometric qualities and are more suitable for deformation monitoring. An InSAR pair correction and stacking algorithm was developed for retrieving extensive freezing-season deformation which could maximize the use of low-quality InSAR pairs, and reduce the effects of the snow depth phase and atmospheric distortions. The retrieved multiannual freezing-season deformation ranged from –32.8 mm to 129.1 mm. The uplift regions clearly indicate the extent of low-lying wetlands, which are influenced by frost heave caused by freezing soil water. Additionally, the “subsidence” areas correspond to farmland and evergreen coniferous forest regions in the study area, where liquid water content is higher than in other land cover types, thus resulting in a longer optical path for the radar signal. This study presents the first systematic analysis of applying C-band InSAR to ground surface deformation monitoring in the southern boreal forest environment. The retrieved seasonal deformation and deformation processes have a high potential for identifying wetlands, differentiating between forest types, and providing valuable insights into the hydrothermal conditions and dynamics of the boreal ecosystem.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.