[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2639":3,"related-2639":99},{"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,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":61,"slug":64,"view_count":22,"doi":65,"paper":66,"created_at":98},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",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":60},[39,48,55],{"code":40,"score":19,"supported":41,"title":42,"abstract":44,"tags":47},"I3",true,[43],"remote sensing",[45,43,46],"遥感","multispectral",[28],{"code":49,"score":20,"supported":41,"title":50,"abstract":52,"tags":54},"H2",[51],"soil",[53,51],"土壤",[],{"code":56,"score":22,"supported":14,"title":57,"abstract":58,"tags":59},"H6",[],[],[27],"农业相关性需要复核",[62,63],"林业信息化 森林土壤 生态修复 证据图谱","林业信息化 森林土壤","林业信息化森林土壤生态修复证据图谱-2639","10.5194\u002Fsoil-12-885-2026",{"doi":65,"openalex_id":67,"authors":68,"venue":10,"cited_by_count":22,"oa_url":89,"card":90,"direction":96,"ingested_from":97},"W7140718837",[69,71,73,75,77,80,83,85,87],{"name":70,"orcid":9},"Maisy Roach-Krajewski",{"name":72,"orcid":9},"Xavier Giroux-Bougard",{"name":74,"orcid":9},"David Paré",{"name":76,"orcid":9},"Catlan Dallaire",{"name":78,"orcid":79},"Luc Guindon","https:\u002F\u002Forcid.org\u002F0000-0002-4346-7351",{"name":81,"orcid":82},"Florian Jordan","https:\u002F\u002Forcid.org\u002F0000-0003-2242-7411",{"name":84,"orcid":9},"Charlotte Norris",{"name":86,"orcid":9},"Kara Webster",{"name":88,"orcid":9},"Jérôme Laganière","https:\u002F\u002Fsoil.copernicus.org\u002Farticles\u002F12\u002F885\u002F2026\u002Fsoil-12-885-2026.pdf",{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"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",{"total":100,"page":22,"page_size":100,"items":101},6,[102,180,255,337,424,479],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":116,"tags":118,"primary_topic":122,"secondary_topics":128,"classification":133,"search_phrases":154,"slug":157,"view_count":158,"doi":159,"paper":160,"created_at":179},5392,"Wetland Loss Associated with Hydro-Agricultural Development in North African Floodplains: Insights from a Historical Cartographic Analysis of the Gharb Plain (Morocco)","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fd18100642","Mediterranean floodplain wetlands are among the most productive ecosystems, yet they are increasingly threatened by agricultural intensification and hydrological modifications. In North Africa, most of these wetlands have been converted to agricultural lands using an extensive ‘drainage–irrigation’ process that has irreversibly degraded them. This study documents this loss through the illustrative example of the Gharb Plain in the northwest of Morocco. The agricultural development process of this plain, initiated in 1927 and only recently completed, is documented by both historical maps and remote sensing data, which we combined to reconstruct the simultaneous wetland decline and agriculture extension. The results reveal a dramatic decline in marshland ecosystems, covering about 88% of the original marshlands in less than one cycle. Several wetlands, including large ones, have entirely disappeared, particularly within the Sebou and Baht river floodplains. To ensure their human occupation, the drainage process and river canalization have been supported by a large program of dam construction upstream of the Gharb Plain. The wetland loss estimate highlights the substantial ecological costs of agricultural intensification and underscores the need for integrated management strategies balancing food production and patrimony conservation. Future research should focus on wetland restoration, ecosystem service assessment, and climate-resilient water management in Mediterranean floodplains.","地中海洪泛平原湿地是最具生产力的生态系统之一，却日益受到农业集约化与水文改造的威胁。在北非，大多数此类湿地已通过大规模的“排水—灌溉”过程被转变为农业用地，并因此遭到不可逆的退化。本研究以摩洛哥西北部加尔卜平原（Gharb Plain）为例，记录了这一丧失过程。该平原的农业开发进程始于1927年，直至近期才告完成，我们结合历史地图与遥感数据，重建了湿地衰退与农业扩张同步发生的过程。结果表明，沼泽生态系统急剧减少，在不到一个周期内约88%的原生沼泽地消失。包括大型湿地在内的多处湿地已完全消失，尤其是在塞布河（Sebou）与巴赫特河（Baht）洪泛平原内。为保障人类占用，排水过程与河流渠化得到了加尔卜平原上游大规模水坝建设计划的支持。湿地丧失的估算凸显了农业集约化所带来的巨大生态代价，并强调需要制定兼顾粮食生产与遗产保护的综合管理策略。未来研究应聚焦于湿地恢复、生态系统服务评估以及地中海洪泛平原的气候韧性水资源管理。","Diversity","2026-10-19T00:00:00Z",76,{"impact":17,"substance":112,"depth":19,"authority":113,"freshness":114,"relevant":22,"comment":115},21,13,8,"该研究结合历史地图与遥感数据，量化了北非洪泛平原湿地因农业开发而丧失的情况，方法扎实、结论明确，对农业生态与土地管理有参考价值，但属区域性案例研究，公共影响有限。",[117],{"name":108,"url":105},[27,28,119,120,121],"灌溉农业","土地利用变化","湿地保护",{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[129],{"code":56,"slug":130,"name":131,"path":132,"parent_code":127},"biodiversity","生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity",{"status":134,"locked":14,"version":35,"method":36,"evidence":135},"classified",{"candidates":136,"agricultural_evidence":41},[137,143,149],{"code":123,"score":13,"supported":41,"title":138,"abstract":139,"tags":142},[],[140,141,124],"灌溉","水资源",[119],{"code":56,"score":21,"supported":41,"title":144,"abstract":145,"tags":148},[],[146,147],"生态系统服务","ecosystem service",[120,121,27],{"code":40,"score":150,"supported":41,"title":151,"abstract":152,"tags":153},7,[],[45,43],[28],[155,156],"Gharb平原 湿地 遥感","摩洛哥 湿地 灌溉","Gharb平原湿地遥感-5392",0,"10.3390\u002Fd18100642",{"doi":159,"openalex_id":161,"authors":162,"venue":108,"cited_by_count":158,"oa_url":105,"card":174,"direction":94,"ingested_from":97},"W7222966485",[163,166,168,171],{"name":164,"orcid":165},"Mohamed Dakki","https:\u002F\u002Forcid.org\u002F0000-0002-8042-3972",{"name":167,"orcid":9},"Said El Falah",{"name":169,"orcid":170},"Ibrahim Makrane","https:\u002F\u002Forcid.org\u002F0009-0002-9201-206X",{"name":172,"orcid":173},"Ismail Mansouri","https:\u002F\u002Forcid.org\u002F0000-0002-3679-6050",{"tldr":175,"method":176,"finding":177,"direction":94,"opportunity":178},"用历史地图与遥感重建摩洛哥Gharb平原1927年以来的湿地丧失与农业扩张过程。","历史地图与遥感数据结合，重建湿地退缩与农田扩张的时空过程。","不到一个开发周期内约88%的原生沼泽消失，大型湿地完全丧失。","可延伸至地中海洪泛平原湿地恢复优先区识别与生态系统服务权衡研究。","2026-10-20T23:30:19.135602Z",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":9,"source_name":186,"source_url":183,"published_at":187,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":188,"score_detail":189,"sources":192,"tags":194,"primary_topic":9,"secondary_topics":198,"classification":199,"search_phrases":216,"slug":219,"view_count":158,"doi":220,"paper":221,"created_at":254},5176,"Spatial Variability and Influencing Factors of Soil P and K in a High-Altitude Mining Area: A Case Study in Southwest China","https:\u002F\u002Fdoi.org\u002F10.15244\u002Fpjoes\u002F224833","The spatial variation and influencing factors of various forms of P and K in soil from a high-altitude mining area were assessed using a combination of traditional statistical and geostatistical methods. The primary factors affecting the distributions of P and K were identified using correlation analysis. The mean total phosphorus (TP), available phosphorus (AP), inorganic phosphorus (IP), total potassium (TK), quick-release potassium (AK), and slow-release potassium (SAK) were 1280.78 mg kg-1, 8.99 mg kg-1, 554.04 mg kg-1, 15358.60 mg kg-1, 215.14 mg kg-1, and 1381.70 mg kg-1, respectively. The coefficients of variation ranged from 44.79% to 80.89%, indicating that all parameters exhibited strong variation intensity. The block-to-base ratio of semivariance varied from 0.00% to 24.53%, indicating strong spatial autocorrelation. Moran’s I analysis revealed significant spatial clustering for TP and TK. However, the spatial distributions of AP, IP, and SAK may be random. Soil pH was negatively correlated with TP and AP, positively correlated with IP, and not correlated with TK, AK, or SAK. SOM was positively correlated with TP and AP and significantly negatively correlated with IP and TK, but not with AK or SAK. Cation exchange capacity was significantly positively correlated with TP, but significantly negatively correlated with TK. Except for IP, there was no significant correlation between soil particle composition and the various forms of P and K. Elevation and aspect had no effect on the distribution of various forms of P and K. NDVI was significantly positively correlated with TP and AP and negatively correlated with IP and TK. Mining activities can strongly affect the availability of P and K. These results have practical significance for soil management and ecological restoration in high-altitude mining areas.","采用传统统计学与地统计学相结合的方法，评估了高海拔矿区土壤中各形态磷（P）和钾（K）的空间变异特征及其影响因素。通过相关分析确定了影响磷、钾分布的主要因素。全磷（TP）、速效磷（AP）、无机磷（IP）、全钾（TK）、速效钾（AK）和缓效钾（SAK）的平均含量分别为1280.78 mg kg-1、8.99 mg kg-1、554.04 mg kg-1、15358.60 mg kg-1、215.14 mg kg-1和1381.70 mg kg-1。变异系数范围为44.79%至80.89%，表明所有参数均表现出强变异强度。半方差块基比在0.00%至24.53%之间，表明存在强烈的空间自相关。Moran's I分析显示TP和TK具有显著的空间聚集性，而AP、IP和SAK的空间分布可能呈随机性。土壤pH与TP和AP呈负相关，与IP呈正相关，与TK、AK和SAK无相关性。土壤有机质（SOM）与TP和AP呈正相关，与IP和TK呈显著负相关，与AK和SAK无相关性。阳离子交换量与TP呈显著正相关，与TK呈显著负相关。除IP外，土壤颗粒组成与各形态磷、钾之间无显著相关性。海拔和坡向对各形态磷、钾的分布无影响。归一化植被指数（NDVI）与TP和AP呈显著正相关，与IP和TK呈负相关。采矿活动可强烈影响磷和钾的有效性。上述结果对高海拔矿区的土壤管理和生态恢复具有实际意义。","Polish Journal of Environmental Studies","2026-10-15T00:00:00Z",60,{"impact":114,"substance":19,"depth":20,"authority":190,"freshness":114,"relevant":22,"comment":191},12,"该研究基于实测数据，揭示了高海拔矿区土壤磷钾的空间变异规律及其影响因素，对矿区土壤管理和生态修复有参考价值，但属于区域性案例研究，公共影响有限。",[193],{"name":186,"url":183},[27,28,195,196,197],"矿区生态","土壤养分","空间变异",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":200},{"candidates":201,"agricultural_evidence":14,"reason":60},[202,207,212],{"code":49,"score":203,"supported":41,"title":204,"abstract":205,"tags":206},15,[51],[53,51],[196],{"code":56,"score":208,"supported":14,"title":209,"abstract":210,"tags":211},2,[],[],[27,195],{"code":40,"score":22,"supported":14,"title":213,"abstract":214,"tags":215},[],[],[28],[217,218],"西南高海拔矿区 土壤磷钾","矿区土壤 空间变异","西南高海拔矿区土壤磷钾-5176","10.15244\u002Fpjoes\u002F224833",{"doi":220,"openalex_id":222,"authors":223,"venue":186,"cited_by_count":158,"oa_url":248,"card":249,"direction":94,"ingested_from":97},"W7222017864",[224,227,230,233,236,239,242,245],{"name":225,"orcid":226},"Xiyao Feng","https:\u002F\u002Forcid.org\u002F0009-0009-1482-4784",{"name":228,"orcid":229},"Yu Chen","https:\u002F\u002Forcid.org\u002F0009-0007-6975-327X",{"name":231,"orcid":232},"Zhongyou Song","https:\u002F\u002Forcid.org\u002F0009-0001-0791-3457",{"name":234,"orcid":235},"Pan Gao","https:\u002F\u002Forcid.org\u002F0009-0000-0039-4562",{"name":237,"orcid":238},"Pengfei Chen","https:\u002F\u002Forcid.org\u002F0009-0000-8394-5672",{"name":240,"orcid":241},"Fabin Yan","https:\u002F\u002Forcid.org\u002F0009-0000-0496-7532",{"name":243,"orcid":244},"Pei Wang","https:\u002F\u002Forcid.org\u002F0009-0001-8997-421X",{"name":246,"orcid":247},"Pan Zhou","https:\u002F\u002Forcid.org\u002F0009-0003-8001-6626","https:\u002F\u002Fwww.pjoes.com\u002Fpdf-224833-149266?filename=Spatial-Variability-and-I.pdf",{"tldr":250,"method":251,"finding":252,"direction":94,"opportunity":253},"评估高海拔矿区土壤磷钾各形态的空间变异及其影响因素。","传统统计与地统计结合，辅以相关分析和Moran's I。","磷钾各形态变异强、空间自相关显著，pH、有机质、NDVI和采矿活动是主要影响因素。","可结合多时相遥感与机器学习，量化采矿扰动下土壤养分空间格局的动态演变与生态修复响应。","2026-10-17T23:30:30.161837Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":260,"content":9,"source_name":261,"source_url":258,"published_at":262,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":263,"sources":266,"tags":268,"primary_topic":9,"secondary_topics":272,"classification":273,"search_phrases":299,"slug":302,"view_count":22,"doi":303,"paper":304,"created_at":336},4595,"Nationwide deadwood mapping reveals rising mountain forests vulnerability","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.isprsjprs.2026.09.041","Forest mortality is increasing globally under climate change, making detailed, large-scale monitoring essential for understanding ecosystem responses and guiding adaptive forest management. Here, we present a spatio-temporal assessment of standing deadwood in Switzerland from 2018 to 2023, derived from centimeter-scale high-resolution aerial imagery. We reveal a consistent upslope concentration of standing deadwood, with highest shares occurring around mid to high elevations (∼1,500 m), despite declining forest cover, and relative increases of up to 43% in overlapping survey areas following the 2018 drought. Maximum temperature anomalies and conifer dominance were the strongest predictors of standing deadwood. The consistent accumulation of standing deadwood at higher elevations suggests increasing vulnerability of mountain forests, with implications for carbon storage, biodiversity, and disturbance susceptibility under ongoing climate change. These patterns highlight the need to address rising forest mortality as a key component of climate-adaptive forest management. Furthermore, our results demonstrate the potential of high-resolution remote sensing for large-scale forest mortality monitoring. Our methods offer a reproducible and transferable framework for identifying vulnerability hotspots and supporting climate-adapted forest management.","在气候变化背景下，全球森林死亡率不断上升，因此开展详细、大规模监测对于理解生态系统响应和指导适应性森林管理至关重要。本文基于厘米级高分辨率航空影像，对2018年至2023年瑞士的立枯木进行了时空评估。我们揭示了立枯木沿海拔向上集中分布的一致规律，尽管森林覆盖度下降，其占比仍在中高海拔（约1，500 m）附近最高，且在2018年干旱后的重叠调查区域中相对增幅可达43%。最高温度异常和针叶树优势度是立枯木最强的预测因子。立枯木在高海拔地区持续累积，表明山地森林脆弱性日益增加，这对碳储存、生物多样性以及持续气候变化下的干扰易感性具有重要意义。这些格局凸显了将不断上升的森林死亡率作为气候适应性森林管理关键组成部分加以应对的必要性。此外，我们的结果证明了高分辨率遥感在大规模森林死亡率监测方面的潜力。我们的方法为识别脆弱性热点和支持气候适应性森林管理提供了一个可重复、可迁移的框架。","ISPRS Journal of Photogrammetry and Remote Sensing","2026-10-08T00:00:00Z",{"impact":19,"substance":18,"depth":264,"authority":20,"freshness":100,"relevant":22,"comment":265},19,"基于厘米级航空影像的全国尺度立枯木制图，方法可迁移，对山地森林脆弱性与气候适应性经营有实质参考价值。",[267],{"name":261,"url":258},[269,28,270,30,271],"气候变化","森林碳汇","高分影像",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":274},{"candidates":275,"agricultural_evidence":14,"reason":60},[276,285,289,294],{"code":277,"score":13,"supported":41,"title":278,"abstract":279,"tags":284},"H7",[],[269,280,281,282,283],"气候适应","干旱","climate change","drought",[269],{"code":40,"score":150,"supported":41,"title":286,"abstract":287,"tags":288},[],[45,43],[28],{"code":56,"score":100,"supported":41,"title":290,"abstract":291,"tags":293},[],[292,130],"生物多样性",[],{"code":295,"score":22,"supported":14,"title":296,"abstract":297,"tags":298},"H4",[],[],[270],[300,301],"瑞士 立枯木 遥感监测","森林死亡 高分辨率航空影像","瑞士立枯木遥感监测-4595","10.1016\u002Fj.isprsjprs.2026.09.041",{"doi":303,"openalex_id":305,"authors":306,"venue":261,"cited_by_count":158,"oa_url":330,"card":331,"direction":94,"ingested_from":97},"W7220997028",[307,310,313,316,319,322,325,328],{"name":308,"orcid":309},"Luca Ferrari","https:\u002F\u002Forcid.org\u002F0000-0002-8957-4830",{"name":311,"orcid":312},"Lars T. Waser‬","https:\u002F\u002Forcid.org\u002F0000-0002-2609-9147",{"name":314,"orcid":315},"Achilleas Psomas","https:\u002F\u002Forcid.org\u002F0009-0008-9966-4988",{"name":317,"orcid":318},"Clemens Mosig","https:\u002F\u002Forcid.org\u002F0009-0003-8662-7698",{"name":320,"orcid":321},"Teja Kattenborn","https:\u002F\u002Forcid.org\u002F0000-0001-7381-3828",{"name":323,"orcid":324},"Christian Ginzler","https:\u002F\u002Forcid.org\u002F0000-0001-6365-2151",{"name":326,"orcid":327},"Verena C. Griess","https:\u002F\u002Forcid.org\u002F0000-0002-3856-3736",{"name":329,"orcid":9},"Mirela Beloiu","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0924271626004892\u002Fpdf",{"tldr":332,"method":333,"finding":334,"direction":94,"opportunity":335},"基于厘米级航空影像绘制瑞士2018-2023年枯立木分布图，揭示山区森林脆弱性上升。","厘米级高分辨率航空影像，时空分析与预测因子建模。","枯立木集中在中高海拔约1500米，2018年干旱后增加达43%，高温异常和针叶树优势是主因。","可迁移该高分辨率遥感框架至其他山区，结合多源数据监测森林死亡动态与碳汇变化。","2026-10-10T23:30:29.803110Z",{"id":338,"title":339,"url":340,"summary":341,"summary_zh":342,"content":9,"source_name":343,"source_url":340,"published_at":344,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":345,"sources":347,"tags":349,"primary_topic":353,"secondary_topics":356,"classification":367,"search_phrases":391,"slug":394,"view_count":208,"doi":395,"paper":396,"created_at":423},4172,"Machine learning reveals distinct drivers of soil organic carbon across the mountain-oasis-desert system of northwest China","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13021-026-00522-5","The mountain-oasis-desert system (MODS) is a distinctive landscape pattern shaped by interactions between geomorphological configurations and hydrological processes in arid regions. However, most soil organic carbon (SOC) mapping studies treat arid landscapes as homogeneous units, leaving limited quantitative evidence on how SOC patterns and environmental associations differ among MODS subsystems. As a critical ecosystem highly sensitive to climate change and human activities, understanding these responses is essential for regional soil carbon management and ecological restoration. In this study, we developed an ecosystem-based modeling framework in the Hexi Region (HXR) to construct separate SOC prediction models for different subsystems, aiming to investigate the spatiotemporal dynamics of SOC stocks and identify their key driving factors across MODS. The results show that incorporating remote sensing (RS) variables significantly improved model performance, increasing the mean R 2 from 0.51 to 0.58. Among the tested models, XGBoost achieved the best performance, providing an effective tool for SOC mapping in arid regions. Shapley additive explanations (SHAP) analysis of the best-performing XGBoost models identified distinct leading predictors across the MODS, including surface shortwave radiation (SRAD) in mountain and desert ecosystems and the difference vegetation index (DVI) in oasis ecosystems. Furthermore, the partial least squares path model (PLS-PM) revealed contrasting influences of climate change and human activities across ecological subsystems. Climatic factors exerted the strongest influence in the mountainous subsystem, whereas in the oasis subsystem intensive human management became the dominant driver of soil carbon cycling. From 2001 to 2023, SOC stocks in oasis regions increased significantly at a rate of 0.17 t C ha⁻ 1 year⁻ 1 , potentially associated with land-use change and agricultural management. Approximately 19.3% of the study area experienced significant SOC stocks declines. By resolving subsystem-specific SOC patterns and environmental associations, this study advances ecosystem-aware SOC assessment in arid regions. The resulting framework also provides a basis for differentiated carbon management across MODS.","山地-绿洲-荒漠系统（MODS）是干旱区地貌格局与水文过程相互作用形成的独特景观格局。然而，大多数土壤有机碳（SOC）制图研究将干旱区景观视为均质单元，关于MODS各子系统间SOC格局及其环境关联差异的定量证据仍然有限。作为对气候变化和人类活动高度敏感的关键生态系统，理解这些响应对于区域土壤碳管理和生态恢复至关重要。本研究在河西地区（HXR）构建了基于生态系统的建模框架，针对不同子系统分别建立SOC预测模型，旨在探究MODS中SOC储量的时空动态并识别其关键驱动因子。结果表明，引入遥感（RS）变量显著提升了模型性能，平均R²从0.51提高至0.58。在测试的模型中，XGBoost表现最佳，为干旱区SOC制图提供了有效工具。对最优XGBoost模型的SHAP（Shapley加法解释）分析识别出MODS各子系统的不同主导预测因子，其中山地和荒漠生态系统为地表短波辐射（SRAD），绿洲生态系统为差值植被指数（DVI）。此外，偏最小二乘路径模型（PLS-PM）揭示了气候变化和人类活动在各生态子系统中的差异化影响。气候因子在山地子系统中影响最强，而在绿洲子系统中，集约化人类管理成为土壤碳循环的主导驱动因素。2001年至2023年，绿洲区域SOC储量以0.17 t C ha⁻¹ year⁻¹的速率显著增加，这可能与土地利用变化和农业管理有关。约19.3%的研究区域经历了SOC储量的显著下降。通过解析子系统特定的SOC格局及其环境关联，本研究推进了干旱区生态系统感知型SOC评估，所构建的框架也为MODS差异化碳管理提供了依据。","Carbon Balance and Management","2026-10-03T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":346},"基于XGBoost与SHAP的干旱区SOC分异驱动研究，方法新颖、数据翔实，对区域碳管理与生态修复有参考价值。",[348],{"name":343,"url":340},[350,351,27,28,352],"机器学习","土壤有机碳","干旱区农业",{"code":49,"slug":51,"name":354,"path":355,"parent_code":127},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[357,361],{"code":277,"slug":358,"name":359,"path":360,"parent_code":127},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":362,"slug":363,"name":364,"path":365,"parent_code":366},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":134,"locked":14,"version":35,"method":36,"evidence":368},{"candidates":369,"agricultural_evidence":41},[370,374,378,383,387],{"code":49,"score":203,"supported":41,"title":371,"abstract":372,"tags":373},[51],[53,51],[351],{"code":277,"score":13,"supported":41,"title":375,"abstract":376,"tags":377},[],[269,281,282],[352],{"code":362,"score":21,"supported":41,"title":379,"abstract":381,"tags":382},[380],"machine learning",[],[350],{"code":40,"score":150,"supported":41,"title":384,"abstract":385,"tags":386},[],[45,43],[28],{"code":56,"score":22,"supported":14,"title":388,"abstract":389,"tags":390},[],[],[27],[392,393],"河西地区 土壤有机碳 机器学习","山地-绿洲-荒漠 遥感 碳储量","河西地区土壤有机碳机器学习-4172","10.1186\u002Fs13021-026-00522-5",{"doi":395,"openalex_id":397,"authors":398,"venue":343,"cited_by_count":158,"oa_url":340,"card":418,"direction":94,"ingested_from":97},"W7219075005",[399,401,404,406,409,411,414,416],{"name":400,"orcid":9},"Y. Chen",{"name":402,"orcid":403},"Haiyang Xi","https:\u002F\u002Forcid.org\u002F0000-0003-3158-8161",{"name":405,"orcid":9},"Meng Zhu",{"name":407,"orcid":408},"Wenju Cheng","https:\u002F\u002Forcid.org\u002F0000-0002-1822-5668",{"name":410,"orcid":9},"Yulian Hao",{"name":412,"orcid":413},"Linbo Qu","https:\u002F\u002Forcid.org\u002F0009-0007-4669-8407",{"name":415,"orcid":9},"Zhehui Wang",{"name":417,"orcid":9},"Bin Wang",{"tldr":419,"method":420,"finding":421,"direction":94,"opportunity":422},"构建基于生态系统的机器学习框架，预测西北荒漠-绿洲-山地系统土壤有机碳并识别各子系统驱动因子。","XGBoost、SHAP、PLS-PM，结合遥感变量与2001-2023年多源数","遥感变量提升模型R²至0.58；山地和沙漠由辐射主导，绿洲由人类管理主导，绿洲碳储量年增0.17 t","可延伸研究绿洲农业管理措施对土壤碳的长期影响，并开发子系统自适应的碳监测与决策模型。","2026-10-04T23:30:24.631460Z",{"id":425,"title":426,"url":427,"summary":428,"summary_zh":429,"content":9,"source_name":430,"source_url":427,"published_at":431,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":432,"sources":434,"tags":436,"primary_topic":9,"secondary_topics":439,"classification":440,"search_phrases":456,"slug":459,"view_count":208,"doi":460,"paper":461,"created_at":478},3791,"Topographic heterogeneity regulates nonlinear vegetation responses to climate and human activities in China’s ecological transition zone","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fffgc.2026.1948232","Ecological transition zones, identified as climate-sensitive areas and biodiversity hotspots, are essential for maintaining regional ecological balance through their vegetation dynamics. However, the impact of topographic heterogeneity on the nonlinear responses of vegetation to climate change and human activities is not well understood. This gap limits our comprehension of ecosystem resilience. This study focuses on the ecological transition zone of the Qinling–Daba Mountains in China. We utilized 30-m resolution Landsat NDVI data from 2000 to 2022, along with climate, human activity, and topographic data. To characterize vegetation dynamics, we employed Theil–Sen trend analysis, the Mann–Kendall test, and the Hurst index. A Bayesian-optimized XGBoost model, combined with SHAP values, was employed to evaluate the relative contributions and response patterns of potential drivers across various vegetation trend zones and topographic gradients. The analysis revealed a significant greening trend, with the normalized difference vegetation index (NDVI) increasing at a rate of 0.0051 yr. −1 ( p \u003C 0.05). Approximately 90.13% of the region displayed an improving trend, while 89.83% indicated a propensity for continued enhancement. The most pronounced vegetation improvements were observed primarily at moderate elevations (1,750–2,250 m), on moderate slopes (15–20°), and on north-facing slopes. Land use and land cover (LULC) (42.5%), elevation (13.2%), population density (8.2%), and precipitation (7.0%) emerged as the most significant contributors to NDVI predictions across the study area. LULC consistently served as the most influential predictor in both regions experiencing significant increases and those facing declines. In contrast, the relative importance and nonlinear response patterns of other factors varied across different trend zones. These findings provide a scientific foundation for targeted ecological restoration and adaptive vegetation management in ecological transition zones.","生态过渡带被认为是气候敏感区和生物多样性热点区，其植被动态对维持区域生态平衡至关重要。然而，地形异质性如何影响植被对气候变化和人类活动的非线性响应，目前尚不明确。这一空白限制了我们对生态系统恢复力的理解。本研究以中国秦岭—大巴山生态过渡带为对象，利用2000—2022年30 m分辨率的Landsat NDVI数据，并结合气候、人类活动和地形数据。为刻画植被动态，我们采用了Theil–Sen趋势分析、Mann–Kendall检验和Hurst指数。采用贝叶斯优化的XGBoost模型，并结合SHAP值，评估了不同植被趋势区和地形梯度上潜在驱动因子的相对贡献及响应模式。分析揭示出显著的绿化趋势，归一化植被指数（NDVI）以0.0051 yr⁻¹的速率增加（p \u003C 0.05）。约90.13%的区域呈改善趋势，89.83%的区域表现出持续增强的倾向。植被改善最显著的区域主要出现在中等海拔（1,750–2,250 m）、中等坡度（15–20°）和北向坡。土地利用与土地覆盖（LULC）（42.5%）、海拔（13.2%）、人口密度（8.2%）和降水（7.0%）是整个研究区NDVI预测中最重要的贡献因子。在显著增加区和下降区，LULC始终是最具影响力的预测因子。相比之下，其他因素的相对重要性和非线性响应模式在不同趋势区存在差异。这些发现为生态过渡带的针对性生态恢复和适应性植被管理提供了科学依据。","Frontiers in Forests and Global Change","2026-09-28T00:00:00Z",{"impact":20,"substance":18,"depth":19,"authority":113,"freshness":21,"relevant":22,"comment":433},"基于30米Landsat NDVI与XGBoost-SHAP量化地形对植被非线性响应的研究，方法新颖、数据扎实，对生态过渡带精准修复有参考价值，但属学术论文，公共传播价值有限。",[435],{"name":430,"url":427},[269,27,437,28,438],"NDVI","植被动态",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":441},{"candidates":442,"agricultural_evidence":14,"reason":60},[443,447,451],{"code":56,"score":150,"supported":41,"title":444,"abstract":445,"tags":446},[],[292,130],[27],{"code":277,"score":150,"supported":41,"title":448,"abstract":449,"tags":450},[],[269,282],[269],{"code":40,"score":452,"supported":14,"title":453,"abstract":454,"tags":455},3,[],[],[437,438,28],[457,458],"秦岭大巴山 生态过渡带 植被","NDVI 地形异质性 植被响应","秦岭大巴山生态过渡带植被-3791","10.3389\u002Fffgc.2026.1948232",{"doi":460,"openalex_id":462,"authors":463,"venue":430,"cited_by_count":158,"oa_url":472,"card":473,"direction":94,"ingested_from":97},"W7214562314",[464,466,468,470],{"name":465,"orcid":9},"Xinwen Su",{"name":467,"orcid":9},"Di Wen",{"name":469,"orcid":9},"Jun Li",{"name":471,"orcid":9},"Peng Dailiang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fforests-and-global-change\u002Farticles\u002F10.3389\u002Fffgc.2026.1948232\u002Fpdf",{"tldr":474,"method":475,"finding":476,"direction":94,"opportunity":477},"研究秦岭-大巴山生态过渡带植被非线性响应气候与人类活动，并揭示地形异质性的调控作用。","用2000-2022年30米Landsat NDVI、气候、人类活动和地形数据，","区域显著绿化，NDVI增速0.0051\u002F年；土地利用、海拔、人口密度和降水是主要驱动因子，地形梯度调","可探究其他生态过渡带中地形异质性对植被非线性响应的普适性，并耦合多源遥感与可解释机器学习优化生态修复。","2026-09-29T23:30:28.608664Z",{"id":480,"title":481,"url":482,"summary":483,"summary_zh":484,"content":9,"source_name":485,"source_url":482,"published_at":486,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":158,"score_detail":487,"sources":489,"tags":491,"primary_topic":494,"secondary_topics":498,"classification":499,"search_phrases":520,"slug":523,"view_count":208,"doi":9,"paper":524,"created_at":537},3547,"Multi-source heterogeneous data integration for urban resilience: a systematic review and evidence map","https:\u002F\u002Fosf.io\u002F58p4z","Amendment recorded 23 September 2026 — change in the number of included studies from 166 to 162. During full-text data extraction, five of the 166 reports that had passed full-text eligibility assessment were flagged by the extractor as uncertain because the extracted data-source field contained only one category. Each was re-examined against the pre-specified nine-category data taxonomy and its counting rules, which are unchanged from the registered protocol. Four reports were found to draw their entire empirical material from a single data category and were therefore reclassified as ineligible: two studies whose material came from remote sensing alone and from a social media corpus alone (exclusion code E2, a study describing itself in multi-source terms while using a single category), one study whose material came from a textual corpus alone (E2), and one study whose only empirical source was a statistical yearbook despite announcing multi-source data in its title (E3, single statistical source only). The fifth report was retained: its data-category coding was corrected from one category to two, because it drew on site-investigation measurements and on statutory seismic zoning data, which are separate acquisition mechanisms under the registered taxonomy. The number of included studies is therefore 162. No eligibility criterion, taxonomy, counting rule, exclusion code or outcome was changed. The reclassification applied the registered criteria to information that was only available once the full texts were read at the extraction stage, and it is recorded here so that the discrepancy between the registered expectation and the reported figure is visible rather than silent. Record-level decisions for all four reclassified reports, and the corrected coding of the fifth, are provided in the review's supplementary materials. ### Review question Urban resilience research increasingly describes itself as drawing on multi-source or heterogeneous urban data, and attaches a methodological claim to that description: that combining sources whose errors and coverage gaps are independent yields assessments more robust than any single source supports. This review examines whether the published evidence bears that claim out. It asks three questions. First, which categories of urban data are combined in resilience research, in what numbers and in which combinations. Second, at which point in the analytical chain integration occurs, and which forms of heterogeneity between sources are explicitly handled. Third, which aspects of the resilience process the resulting evidence covers, and which remain unexamined. ### Searches Web of Science Core Collection and Scopus, searched on 15 September 2026. The search combines a resilience concept block with a data and smart-technology concept block, applied to titles, abstracts and keywords, limited to English-language journal articles and reviews published between 2010 and 2025. Two supplementary methods are used and reported separately: backward citation chasing of the thirty most closely related review articles, and an exploratory search, executed on 19 September 2026, for the social-ecological-technological systems literature, a conceptual stream whose terminology the main search string does not cover. Full search strings as executed, with hit counts and dates, are reported in the supplementary materials. ### Types of study to be included Peer-reviewed journal articles and review articles reporting empirical analysis of urban systems. Conference papers, conference reviews, book chapters, editorials, notes, errata, retracted publications and articles in press are excluded. ### Condition or domain being studied Urban resilience, understood as the capacity of an urban system to resist, absorb, adapt to, recover from and learn from disturbance. Disturbances include flooding, storms and typhoons, earthquakes, heat, fire, drought, pandemics, infrastructure outages and cascading failures, and explicitly multi-hazard settings. ### Participants \u002F population The unit of analysis is an urban system or a sub-unit of one: a city, a city cluster, a district, a neighbourhood, or an urban infrastructure network embedded in a city. Studies whose unit of analysis is a single building in isolation, a rural settlement, an agricultural landscape, a national economy or a non-spatial organisation are excluded. ### Intervention \u002F exposure Not an intervention review. The exposure of interest is the analytical practice of combining heterogeneous categories of urban data. Eligibility requires that a study empirically uses, or systematically discusses, at least two heterogeneous categories of urban data, counted against a nine-category taxonomy fixed before screening: remote sensing imagery; points of interest and map data; mobile trajectory and signalling data; social media and crowdsourced data; internet-of-things and sensor networks; statistical, census and survey data; infrastructure network topology; meteorological, hydrological and hazard data; and textual, policy and documentary sources. Three counting rules apply. Two sources belonging to the same category count as one category. Several variables derived from one acquisition mechanism count as one category. A category counts only where the study actually analyses it. ### Comparator None. This is an evidence map of study characteristics rather than a comparison of interventions. ### Primary outcomes The categories of urban data used and their number per study; the level at which integration occurs, classified as data level, feature level or decision level according to the point in the analytical chain at which the sources are brought together; and the forms of heterogeneity for which an explicit handling procedure is reported, including coordinate registration, spatio-temporal granularity alignment, scale transformation, semantic heterogeneity and missing values. ### Secondary outcomes The conception of resilience adopted or left unstated; the resilience dimensions and process phases covered; the assessment method used; the enabling technology and any named interoperability standard; the presence of a benchmark or external validation; the availability of data and code; and the spatial scale and disturbance type addressed. ### Data extraction and management Titles and abstracts are screened independently by two reviewers against a written decision manual specifying the eligibility criteria, the data taxonomy with its counting rules, and a hierarchy of ten mutually exclusive exclusion codes with a fixed priority order. Agreement is quantified with Cohen's kappa and disagreements are referred to a third reviewer who adjudicates each record against the same manual and records a basis and a rationale. Full texts are obtained for all records advancing from screening. Data are extracted by one reviewer for all included studies, and independently re-extracted by a second reviewer for a random twenty per cent sample. Agreement is computed and reported for every extracted field, using the Jaccard index for multi-valued fields, Cohen's kappa for single-valued categorical fields and a two-way intraclass correlation coefficient for ordinal and count fields. Fields that do not meet the reliability threshold are reported descriptively and are not used to support comparative claims. The complete extraction form, codebook and extracted matrix are provided as supplementary materials. ### Risk of bias \u002F quality assessment Because the included studies are methodologically heterogeneous and largely non-experimental, established risk-of-bias instruments do not apply. Each study is appraised on three items rated low, moderate or high: the adequacy of the methodological description, the transparency of the data provenance, and the extent to which the conclusions are supported by the evidence presented. The appraisal is applied by the extractor and independently verified on the twenty per cent sample. ### Strategy for data synthesis Narrative synthesis with an evidence map. Meta-analysis is not appropriate because the included studies address different disturbances, use different designs and report outcomes on non-commensurable constructs. Results are presented as counts and proportions of studies, a co-occurrence matrix of data categories, and a cross-tabulation of data category against resilience phase. Reporting follows PRISMA 2020, and a completed checklist is provided. ### Analysis of subgroups or subsets Distribution of the level of integration by publication year, by number of data categories and by assessment method. Coverage of resilience phases by data category. Any temporal comparison is accompanied by a test for trend, and differences that do not reach significance are reported as such rather than described as trends. ### Language and dissemination English-language literature only. Findings will be submitted to a peer-reviewed journal. All search strategies, screening records, calibration and reliability records, the extraction codebook and the full extracted data matrix will be made openly available alongside the publication.","修订记录于2026年9月23日——纳入研究数量由166项变更为162项。在全文数据提取阶段，166篇通过全文资格评估的报告中有5篇被提取者标记为不确定，原因是提取的数据来源字段仅包含一个类别。每篇报告均依据预先设定的九类别数据分类体系及其计数规则重新审查，该分类体系与计数规则与注册方案一致，未作变更。其中4篇报告被发现其全部实证材料均来自单一数据类别，因此被重新归类为不符合纳入标准：两项研究的材料分别仅来自遥感数据和仅来自社交媒体语料库（排除代码E2，即研究以多来源术语自我描述但实际使用单一类别），一项研究的材料仅来自文本语料库（E2），另一项研究的唯一实证来源为统计年鉴，尽管其标题宣称使用多来源数据（E3，仅单一统计来源）。第5篇报告予以保留：其数据类别编码从一个类别更正为两个类别，因为该研究依据了现场调查测量数据和法定地震区划数据，这两者在注册分类体系下属于不同的获取机制。因此，纳入研究数量为162项。任何纳入标准、分类体系、计数规则、排除代码或结局指标均未更改。重新归类是将注册标准应用于仅在提取阶段阅读全文后才能获得的信息，此处予以记录，以使注册预期与报告数字之间的差异可见而非被隐匿。所有4篇重新归类报告的记录层面决定，以及第5篇的更正编码，均提供于本综述的补充材料中。 ### 综述问题 城市韧性研究日益声称自身依据多来源或异质性城市数据，并将一项方法论主张附于该描述之上：即结合误差与覆盖缺口相互独立的来源，所得到的评估比任何单一来源所支持的评估更为稳健。本综述检验已发表证据是否支持该主张。它提出三个问题。第一，韧性研究中结合了哪些类别的城市数据，数量如何，以何种组合方式结合。第二，整合发生在分析链条的哪一环节，以及来源之间哪些形式的异质性","OSF Preprints (OSF Preprints)","2026-09-23T00:00:00Z",{"impact":158,"substance":158,"depth":158,"authority":158,"freshness":158,"relevant":158,"comment":488},"该文为城市韧性领域多源异构数据整合的系统综述修订说明，属城市研究与数据科学范畴，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[490],{"name":485,"url":482},[28,31,492,493],"多源数据融合","城市韧性",{"code":40,"slug":495,"name":496,"path":497,"parent_code":366},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":134,"locked":14,"version":35,"method":36,"evidence":500},{"candidates":501,"agricultural_evidence":41},[502,506,510,515],{"code":40,"score":150,"supported":41,"title":503,"abstract":504,"tags":505},[],[45,43],[28],{"code":277,"score":452,"supported":41,"title":507,"abstract":508,"tags":509},[],[283],[],{"code":511,"score":22,"supported":14,"title":512,"abstract":513,"tags":514},"I5",[],[],[492],{"code":516,"score":22,"supported":14,"title":517,"abstract":518,"tags":519},"I7",[],[],[31],[521,522],"城市韧性 多源数据 系统综述","多源数据融合 城市韧性 证据图谱 遥感监测","城市韧性多源数据系统综述-3547",{"doi":9,"openalex_id":525,"authors":526,"venue":485,"cited_by_count":158,"oa_url":9,"card":531,"direction":96,"ingested_from":97},"W7213802657",[527,529],{"name":528,"orcid":9},"ZHUANGHAO SI",{"name":530,"orcid":9},"Jindan Zhang",{"tldr":532,"method":533,"finding":534,"direction":535,"opportunity":536},"系统综述城市韧性研究中多源异构数据整合的证据，最终纳入162项研究。","系统综述与证据图谱，基于九类数据分类法对全文进行数据源编码。","多数研究自称多源，实则仅用单一数据类别，真正整合多源证据有限。","数字乡村与农业信息化","可借鉴其数据分类与整合环节编码框架，研究农业多源数据整合的真实性与稳健性。","2026-09-26T23:30:12.828664Z"]