[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2776":3},{"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":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":56},2776,"Mapping coastal forest retreat using convolutional neural networks and different satellite imagery","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0357346","Coastal forests are increasingly threatened by saturated soil and elevated salinity levels resulting from sea level rise, saltwater intrusion, and storm surges. In response to rising salinization and flooding, healthy coastal forests that rely on freshwater (both wetland forests and low-elevation upland forests) are transitioning into landscapes dominated by dead or dying trees, known as ghost forests. Situated among salt-tolerant shrubs and grasses, ghost forests eventually become marshes or open water. Here, our main objective was to quantify the dynamics and pathways of these forest landscape conversions, as well as the factors contributing to the changes, which is vital for understanding the progression of coastal ecosystem degradation and forecasting future changes. We focused first on identifying the best method to track forest landscape change by exploring the role of multiple remote sensing indices (i.e., multispectral, bi-seasonal, topographical, and phenological metrics) in enhancing the performance of deep learning models (convolutional neural networks, CNNs) for land cover classification in the coastal plain of North Carolina using surface reflectance of Landsat 8 and Sentinel-2 images. Then, we used the best available data (Landsat 8) to understand long-term change and identify patterns of land cover change from 1985 to 2021. Our study reveals that incorporating phenology and topographical indices enhances the separability of the ghost forests class from all other vegetation classes. In our assessment, the higher-resolution Sentinel-2 data (F1 Score = 96.3) outperformed Landsat images (F1 score = 93.4) for the 2021 co-available year. However, Landsat remains an important tool used due to its long-term data record. Therefore, we used Landsat to determine that 21% of forests were lost between 1985 and 2021, and that the rate of loss is increasing. Between 2010 and 2021, 23,876 ha of forest were converted to marsh, ghost forest, and shrub, which is 1.5 times higher than the 16,968 ha lost between 1985 and 2010. These conversions from forest to ghost forest and marshes were driven primarily by proximity to the channel, salinity, and the increasing rate of relative sea level rise (RSLR), which are the key environmental drivers of observed changes. By quantifying these changes, we highlight regions most vulnerable to environmental stressors, providing a basis for targeted conservation strategies.","沿海森林正日益受到海平面上升、盐水入侵和风暴潮导致的土壤饱和与盐度升高的威胁。为应对不断加剧的盐渍化和洪水，依赖淡水的健康沿海森林（包括湿地森林和低海拔高地森林）正在转变为以死亡或濒死树木为主的景观，即“幽灵森林”（ghost forests）。幽灵森林与耐盐灌木和草本植物交错分布，最终演变为沼泽或开阔水域。本研究的主要目标是量化这些森林景观转换的动态过程和路径，以及促成这些变化的因素，这对于理解沿海生态系统退化的进程和预测未来变化至关重要。我们首先聚焦于确定追踪森林景观变化的最佳方法，通过探索多种遥感指数（即多光谱、双季节、地形和物候指标）在提升深度学习模型（卷积神经网络，CNNs）土地覆盖分类性能中的作用，研究区域为北卡罗来纳州沿海平原，使用Landsat 8和Sentinel-2影像的地表反射率数据。随后，我们使用最佳可用数据（Landsat 8）来理解长期变化并识别1985年至2021年的土地覆盖变化模式。研究表明，纳入物候和地形指数可增强幽灵森林类别与所有其他植被类别的可分性。在我们的评估中，对于2021年两星共存的年份，较高分辨率的Sentinel-2数据（F1分数 = 96.3）优于Landsat影像（F1分数 = 93.4）。然而，Landsat因其长期数据记录仍是一个重要的使用工具。因此，我们使用Landsat确定1985年至2021年间21%的森林已经消失，且消失速率正在加快。2010年至2021年间，23,876公顷森林转换为沼泽、幽灵森林和灌木，比1985年至2010年间损失的16,968公顷高出1.5倍。这些从森林到幽灵森林和沼泽的转换主要由距河道距离、盐度和相对海平面上升（RSLR）速率的增加所驱动，这些是观测到的变化的关键环境驱动因素。通过量化这些变化，我们突出了最易受环境胁迫影响的区域，为有针对性的保护策略提供了依据。",null,"PLoS ONE","2026-09-15T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,23,14,9,1,"发表于PLoS ONE的研究用CNN结合Landsat与Sentinel-2影像量化海岸森林退化为幽灵森林的过程，方法新颖、数据跨度36年，对沿海生态保护与遥感应用有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"深度学习","遥感监测","海岸防护林","土地覆盖变化","生态退化",0,"10.1371\u002Fjournal.pone.0357346",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":47,"card":48,"direction":54,"ingested_from":55},"W7213246924",[36,39,42,44],{"name":37,"orcid":38},"Tawakalitu Titilayo Tajudeen","https:\u002F\u002Forcid.org\u002F0000-0003-2405-134X",{"name":40,"orcid":41},"Marcelo Ardón","https:\u002F\u002Forcid.org\u002F0000-0001-7275-2672",{"name":43,"orcid":9},"Mirela Tulbure",{"name":45,"orcid":46},"Katherine L. Martin","https:\u002F\u002Forcid.org\u002F0000-0001-6020-9250","https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle\u002Ffile?id=10.1371\u002Fjournal.pone.0357346&type=printable",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"用CNN结合多源遥感指数监测1985-2021年北卡海岸森林退化与幽灵林扩张。","Landsat 8与Sentinel-2影像，多光谱、物候、地形指数，卷积神经网","物候与地形指数提升幽灵林识别；1985-2021年21%森林消失且速率加快，近河道、盐度和海平面上升","农业遥感与作物表型","可迁移该CNN框架到其他海岸带，融合时序Sentinel数据与海平面上升情景预测幽灵林未来扩张。","智慧农业 \u002F 农业物联网","openalex","2026-09-17T23:30:15.391843Z"]