[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2639":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":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":66},2639,"Post-disturbance soil monitoring in forests using remote sensing: an evidence map","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fsoil-12-885-2026","Forest soils underpin ecosystem resilience and productivity but are increasingly threatened by natural and anthropogenic disturbances. Monitoring post-disturbance soil degradation at operational scales remains challenging in forests, where ground-signal obstruction and reliance on proxy indicators constrain remote sensing (RS) applications. To identify where RS can benefit soil monitoring and inform emerging reporting needs, we developed a structured evidence map of studies assessing post-disturbance forest soil degradation using RS methods. From 4338 records, 72 primary studies were synthesized across disturbance types, biomes, platforms, scales, and indicators. The evidence base is dominated by wildfire and harvesting, reflecting disturbance pathways that produce observable surface impacts. Multispectral satellite data remain the primary tool for mapping post-fire severity and erosion-related indicators, while LiDAR and stereo-photogrammetry are most often used to quantify surface deformation after harvest operations. Indicators tied to subsurface physical, chemical, or biological change remain sparsely represented due to observability limits. Overall, RS is most effective for mapping disturbance footprints, detecting surface-expressed indicators, and stratifying landscapes for targeted field assessment, rather than directly measuring soil properties. This evidence map clarifies the benefits and limits of RS, identifies persistent gaps, and highlights priorities for developing disturbance-aware soil-monitoring frameworks. It also clarifies which soil indicators are most consistently observable with RS and which require complementary approaches. By linking disturbance processes to observable indicators, this synthesis helps identify realistic RS-supported objectives that may inform future reporting frameworks within national forest monitoring and assessment programs.","森林土壤支撑着生态系统的韧性与生产力，却日益受到自然和人为干扰的威胁。在森林中，由于地面信号遮挡以及对代理指标的依赖限制了遥感（RS）应用，在可操作尺度上监测干扰后的土壤退化仍具挑战性。为明确遥感可在何处助力土壤监测并满足新兴的报告需求，我们编制了一份结构化证据图，涵盖利用遥感方法评估干扰后森林土壤退化的研究。从4338条记录中，综合了72项原始研究，涉及干扰类型、生物群系、平台、尺度和指标。证据基础以野火和采伐为主，反映了可产生可观测地表影响的干扰路径。多光谱卫星数据仍是绘制火灾后严重程度和侵蚀相关指标的主要工具，而激光雷达（LiDAR）和立体摄影测量最常用于量化采伐作业后的地表形变。由于可观测性限制，与地下物理、化学或生物变化相关的指标仍鲜有涉及。总体而言，遥感最适用于绘制干扰足迹、探测地表表现的指标，以及为针对性实地评估进行景观分层，而非直接测量土壤属性。该证据图阐明了遥感的优势与局限，识别了持续存在的空白，并指出了开发干扰感知土壤监测框架的优先事项。它还明确了哪些土壤指标最易通过遥感持续观测，哪些需要互补方法。通过将干扰过程与可观测指标相联系，本综合有助于确定现实的遥感支持目标，可为国家级森林监测与评估项目未来的报告框架提供参考。",null,"SOIL","2026-09-15T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,9,1,"系统梳理遥感监测灾后森林土壤退化的证据图谱，方法规范、数据规模可观，对林业遥感与土壤监测应用有实质参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"生态修复","遥感监测","森林土壤","林业信息化","证据图谱",0,"10.5194\u002Fsoil-12-885-2026",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":22,"oa_url":57,"card":58,"direction":64,"ingested_from":65},"W7140718837",[37,39,41,43,45,48,51,53,55],{"name":38,"orcid":9},"Maisy Roach-Krajewski",{"name":40,"orcid":9},"Xavier Giroux-Bougard",{"name":42,"orcid":9},"David Paré",{"name":44,"orcid":9},"Catlan Dallaire",{"name":46,"orcid":47},"Luc Guindon","https:\u002F\u002Forcid.org\u002F0000-0002-4346-7351",{"name":49,"orcid":50},"Florian Jordan","https:\u002F\u002Forcid.org\u002F0000-0003-2242-7411",{"name":52,"orcid":9},"Charlotte Norris",{"name":54,"orcid":9},"Kara Webster",{"name":56,"orcid":9},"Jérôme Laganière","https:\u002F\u002Fsoil.copernicus.org\u002Farticles\u002F12\u002F885\u002F2026\u002Fsoil-12-885-2026.pdf",{"tldr":59,"method":60,"finding":61,"direction":62,"opportunity":63},"用证据图方法系统梳理遥感监测干扰后森林土壤退化的72项研究，明确其能力与局限。","证据图法，从4338条记录筛选72项研究，按干扰、平台、指标等分类。","遥感擅长制图干扰范围与地表指标，难以直接测量土壤理化生物属性。","农业遥感与作物表型","可构建干扰感知的土壤监测框架，并研发地表-地下指标耦合的遥感反演方法。","智慧农业 \u002F 农业物联网","openalex","2026-09-16T23:30:09.927061Z"]