Zhen Xiao , Runkui Li , Mingjun Ding , Panli Cai , Jingxian Guo , Haiyu Fu , Xiaoping Zhang , Xianfeng Song
{"title":"揭示间歇性地表水的隐藏动态:遥感框架","authors":"Zhen Xiao , Runkui Li , Mingjun Ding , Panli Cai , Jingxian Guo , Haiyu Fu , Xiaoping Zhang , Xianfeng Song","doi":"10.1016/j.rse.2024.114285","DOIUrl":null,"url":null,"abstract":"<div><p>Intermittent surface water frequently transitioning between water and land over months and years, plays a crucial and increasingly significant role in both social and ecological systems. However, their vital and dramatic dynamics have mainly remained invisible due to monitoring limitations. We present a new remote sensing framework to capture the long-term monthly dynamics of surface water bodies, applying it to Poyang Lake, the largest freshwater lake in China. This framework employed a random forest classifier on all available Landsat data to identify monthly surface water bodies. Additionally, we developed a Spatial and Temporal Neighborhood Similarity-based Gap Filling method to restore water bodies obscured by clouds and ensure spatial integrity. Furthermore, we introduced an index to quantify the intermittency of surface water bodies on a scale from 0 to 1, allowing for the classification of water bodies into three categories: perennial, wet intermittent, and dry intermittent. Employing this framework, we reconstructed the most complete monthly 30-m surface water dataset for cloudy regions to date, covering April 1986 to September 2023, demonstrating a strong correlation (Spearman's rank correlation coefficient of 0.909) with observed water levels. The results reveal a landscape dominantly composed of intermittent water bodies (91.2%), with a rapidly shrinking trend of perennial water bodies at 1303.58 ha per year. Notably, 162,685 ha (21.9%) of water bodies transitioned toward drier and more intermittent statuses. Dry intermittent water bodies exhibited the most pronounced land-water transitions, with the highest water-to-land (82.5%) and land-to-water (89.9%) proportions among the three categories. By uncovering the hidden dynamics of intermittent surface water, and highlighting its prevalence, expansion, and vulnerability, this framework paves the way for a better understanding of these critical water dynamics across the globe.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unveiling the hidden dynamics of intermittent surface water: A remote sensing framework\",\"authors\":\"Zhen Xiao , Runkui Li , Mingjun Ding , Panli Cai , Jingxian Guo , Haiyu Fu , Xiaoping Zhang , Xianfeng Song\",\"doi\":\"10.1016/j.rse.2024.114285\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Intermittent surface water frequently transitioning between water and land over months and years, plays a crucial and increasingly significant role in both social and ecological systems. However, their vital and dramatic dynamics have mainly remained invisible due to monitoring limitations. We present a new remote sensing framework to capture the long-term monthly dynamics of surface water bodies, applying it to Poyang Lake, the largest freshwater lake in China. This framework employed a random forest classifier on all available Landsat data to identify monthly surface water bodies. Additionally, we developed a Spatial and Temporal Neighborhood Similarity-based Gap Filling method to restore water bodies obscured by clouds and ensure spatial integrity. Furthermore, we introduced an index to quantify the intermittency of surface water bodies on a scale from 0 to 1, allowing for the classification of water bodies into three categories: perennial, wet intermittent, and dry intermittent. Employing this framework, we reconstructed the most complete monthly 30-m surface water dataset for cloudy regions to date, covering April 1986 to September 2023, demonstrating a strong correlation (Spearman's rank correlation coefficient of 0.909) with observed water levels. The results reveal a landscape dominantly composed of intermittent water bodies (91.2%), with a rapidly shrinking trend of perennial water bodies at 1303.58 ha per year. Notably, 162,685 ha (21.9%) of water bodies transitioned toward drier and more intermittent statuses. Dry intermittent water bodies exhibited the most pronounced land-water transitions, with the highest water-to-land (82.5%) and land-to-water (89.9%) proportions among the three categories. By uncovering the hidden dynamics of intermittent surface water, and highlighting its prevalence, expansion, and vulnerability, this framework paves the way for a better understanding of these critical water dynamics across the globe.</p></div>\",\"PeriodicalId\":417,\"journal\":{\"name\":\"Remote Sensing of Environment\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2024-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing of Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0034425724003031\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425724003031","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Unveiling the hidden dynamics of intermittent surface water: A remote sensing framework
Intermittent surface water frequently transitioning between water and land over months and years, plays a crucial and increasingly significant role in both social and ecological systems. However, their vital and dramatic dynamics have mainly remained invisible due to monitoring limitations. We present a new remote sensing framework to capture the long-term monthly dynamics of surface water bodies, applying it to Poyang Lake, the largest freshwater lake in China. This framework employed a random forest classifier on all available Landsat data to identify monthly surface water bodies. Additionally, we developed a Spatial and Temporal Neighborhood Similarity-based Gap Filling method to restore water bodies obscured by clouds and ensure spatial integrity. Furthermore, we introduced an index to quantify the intermittency of surface water bodies on a scale from 0 to 1, allowing for the classification of water bodies into three categories: perennial, wet intermittent, and dry intermittent. Employing this framework, we reconstructed the most complete monthly 30-m surface water dataset for cloudy regions to date, covering April 1986 to September 2023, demonstrating a strong correlation (Spearman's rank correlation coefficient of 0.909) with observed water levels. The results reveal a landscape dominantly composed of intermittent water bodies (91.2%), with a rapidly shrinking trend of perennial water bodies at 1303.58 ha per year. Notably, 162,685 ha (21.9%) of water bodies transitioned toward drier and more intermittent statuses. Dry intermittent water bodies exhibited the most pronounced land-water transitions, with the highest water-to-land (82.5%) and land-to-water (89.9%) proportions among the three categories. By uncovering the hidden dynamics of intermittent surface water, and highlighting its prevalence, expansion, and vulnerability, this framework paves the way for a better understanding of these critical water dynamics across the globe.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.