[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1537":3,"related-1537":90},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":22,"classification":23,"search_phrases":66,"slug":67,"view_count":68,"doi":69,"paper":70,"created_at":89},1537,"Mapping and Monitoring Seasonal Wetland Dynamics of Koonthankulam Ramsar Site Using Sentinel-2 Imagery and Random Forest Classification on Google Earth Engine","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fajgr\u002F2026\u002Fv9i3456","Wetlands provide indispensable ecological, hydrological and socio-economic services, yet remain among the most rapidly altered ecosystems worldwide due to encroaching agriculture, urbanisation and hydrological disruption. This study presents a cloud-based, multi-temporal assessment of Koonthankulam, a Ramsar-designated wetland and Important Bird and Biodiversity Area in Tirunelveli district, Tamil Nadu, India, using Sentinel-2 Multispectral Instrument imagery processed on the Google Earth Engine (GEE) platform. Cloud-free composites were generated for the summer and monsoon seasons of three reference years (2017, 2020 and 2023) within a 1 km buffer drawn outward from the officially notified Ramsar boundary, and a supervised Random Forest classifier was used to delineate four land use\u002Fland cover (LULC) classes: water, vegetation, barren land and settlement (built-up structures were subsumed within the settlement class). In parallel, the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI) were computed to characterise seasonal and interannual dynamics of vegetation vigour, built-up expansion and surface water extent. The results reveal a pronounced seasonal contrast, with vegetated cover consistently higher in the summer composites (peaking near 25 sq. km in 2017) than in the monsoon composites, reflecting the dominance of irrigated paddy cultivation around the tank during the dry months. Settlement area expanded steadily across the study period, nearly quadrupling between 2017 and 2023 (an average annual increase of approximately 24%), while barren land cover remained comparatively stable. Surface water extent fluctuated between approximately 8 and 15 sq. km across seasons and years, sustained in part by canal releases from the Manimuthar and Nanguneri systems during January and February. These findings demonstrate the utility of freely available Sentinel-2 data and cloud-based machine learning for low-cost, repeatable monitoring of small Ramsar wetlands, and provide an evidence base for habitat management and conservation planning at Koonthankulam.","湿地提供了不可或缺的生态、水文和社会经济服务，然而由于农业侵占、城市化和水文干扰，它们仍是全球范围内变化最迅速的生态系统之一。本研究利用在谷歌地球引擎（GEE）平台上处理的Sentinel-2多光谱仪影像，对印度泰米尔纳德邦蒂鲁内尔维利区的库恩坦库拉姆湿地——一处拉姆萨尔指定湿地及重要鸟类与生物多样性区域——进行了基于云的多时相评估。研究以官方公布的拉姆萨尔边界向外1公里缓冲区为范围，生成了三个参考年份（2017年、2020年和2023年）夏季和季风季节的无云影像合成，并采用监督随机森林分类器划分了四种土地利用\u002F土地覆盖（LULC）类别：水体、植被、裸地和建设用地（建成结构归入建设用地类别）。同时，计算了归一化植被指数（NDVI）、归一化建成指数（NDBI）和归一化水体指数（NDWI），以表征植被活力、建成区扩张和地表水范围的季节及年际动态。结果显示明显的季节对比，夏季合成影像中的植被覆盖始终高于季风季节（2017年峰值接近25平方公里），反映了旱季水库周边以灌溉稻田为主的格局。建设用地在研究期间持续扩张，2017年至2023年间几乎增长了四倍（年均增长率约为24%），而裸地覆盖相对稳定。地表水范围在不同季节和年份间波动于约8至15平方公里之间，部分由1月和2月马尼穆塔尔和南古内里系统的渠道放水维持。这些发现证明了免费获取的Sentinel-2数据和基于云的机器学习在低成本、可重复监测小型拉姆萨尔湿地方面的实用性，并为库恩坦库拉姆的栖息地管理和保护规划提供了证据基础。",null,"Asian Journal of Geographical Research","2026-09-02T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究印度湿地，与三农、农业信息化无关。",[19],{"name":10,"url":6},[21],"湿地监测",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":27},"needs_review","2026-10-22.2","rules",{"candidates":28,"agricultural_evidence":32,"reason":65},[29,38,45,52,59],{"code":30,"score":31,"supported":32,"title":33,"abstract":34,"tags":37},"A1",6,true,[],[35,36],"稻田","paddy",[],{"code":39,"score":31,"supported":32,"title":40,"abstract":41,"tags":44},"H6",[],[42,43],"生物多样性","biodiversity",[],{"code":46,"score":31,"supported":32,"title":47,"abstract":48,"tags":51},"I2",[],[49,50],"机器学习","machine learning",[],{"code":53,"score":54,"supported":32,"title":55,"abstract":56,"tags":58},"H3",3,[],[57],"灌溉",[],{"code":60,"score":54,"supported":32,"title":61,"abstract":62,"tags":64},"I3",[],[63],"multispectral",[],"多个产业证据相当，需要确认主归属",[],"MappingandMonitoringSeasonalWetlandDynam-1537",1,"10.9734\u002Fajgr\u002F2026\u002Fv9i3456",{"doi":69,"openalex_id":71,"authors":72,"venue":10,"cited_by_count":15,"oa_url":6,"card":81,"direction":87,"ingested_from":88},"W7204995021",[73,75,78],{"name":74,"orcid":9},"A. R. Varsha",{"name":76,"orcid":77},"Mylswamy Mahendiran","https:\u002F\u002Forcid.org\u002F0000-0002-9938-5742",{"name":79,"orcid":80},"R. Sakthivel","https:\u002F\u002Forcid.org\u002F0000-0002-4634-6336",{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"利用Sentinel-2影像和随机森林在GEE上监测印度Koonthankulam湿地的季节动态。","Sentinel-2影像、GEE平台、随机森林分类、NDVI\u002FNDBI\u002FNDWI","湿地植被夏季高于季风季，建设用地2017-2023年增长近四倍，水面波动8-15平方公里。","农业遥感与作物表型","可扩展至其他小型湿地，结合高分辨率影像和深度学习提高分类精度，并评估湿地变化对农业生态的影响。","农业人工智能与决策模型","openalex","2026-09-03T23:30:51.823258Z",{"total":15,"page":68,"page_size":68,"items":91},[]]