[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4495":3,"related-4495":111},{"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,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":86,"slug":89,"view_count":21,"doi":90,"paper":91,"created_at":110},4495,"A review of forest innovation: Technologies and sustainable practices for environmental conservation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nxsust.2026.100546","Forests play a crucial role in climate regulation, biodiversity conservation, ecosystem service provision, and sustainable socio-economic development. However, increasing deforestation, climate change, habitat fragmentation, and unsustainable resource exploitation continue to threaten forest ecosystems worldwide. The objective of this review is to determine the ecological, economic, and governance conditions under which specific forest innovations translate into measurable sustainability outcomes, and to synthesize these conditions into coherent, evidence-based pathways applicable across different regional contexts. Framed through a social-ecological systems (SES) and resilience lens, this review critically examines recent advances in forest innovation by integrating sustainable forest management practices with emerging technological developments, including precision forestry, remote sensing, geographic information systems (GIS), LiDAR, unmanned aerial vehicles (UAVs), biotechnology, artificial intelligence (AI), digital forestry, biomass utilization, and forest-based bioeconomy approaches. Unlike conventional narrative reviews, this study applies a common five criterion evaluation framework comprising ecological effectiveness, technological maturity, economic feasibility, governance requirements, and regional applicability to comparatively evaluates the strengths, limitations, implementation challenges of these innovations across diverse ecological and socio-economic contexts, thereby moving beyond the largely descriptive, single-domain coverage of prior forest technology and forest governance reviews to an explicitly cross-domain, comparative synthesis. The review further explores the contributions of community-based forest management, climate-smart forestry, carbon management strategies, ecosystem services, green infrastructure, and innovative forest-derived products toward achieving long-term environmental sustainability. The analysis demonstrates that technological innovations alone cannot ensure sustainable forest management unless supported by effective governance, equitable community participation, adequate financial investment, and adaptive policy frameworks. The review identifies critical knowledge gaps related to technology adoption, institutional capacity, climate resilience, and long-term monitoring, and derives four specific, evidence-anchored integrated pathways that combine ecological, technological, economic, and governance innovations. This review provides a comprehensive, deliberately broad-scope synthesis of contemporary forest innovation and offers practical recommendations for researchers, policymakers, and forest managers working toward resilient forest ecosystems and the achievement of the United Nations Sustainable Development Goals, particularly SDG 15 (Life on Land).","森林在气候调节、生物多样性保护、生态系统服务供给以及可持续社会经济发展中发挥着至关重要的作用。然而，日益加剧的森林砍伐、气候变化、栖息地破碎化以及不可持续的资源开发持续威胁着全球森林生态系统。本综述旨在明确特定森林创新在何种生态、经济与治理条件下能够转化为可衡量的可持续性成果，并将这些条件综合为适用于不同区域背景的连贯且基于证据的路径。本综述以社会-生态系统（SES）和韧性为理论框架，通过将可持续森林管理实践与新兴技术发展相结合，批判性地审视了森林创新的最新进展，这些技术包括精准林业、遥感、地理信息系统（GIS）、激光雷达（LiDAR）、无人机（UAV）、生物技术、人工智能（AI）、数字林业、生物质利用以及基于森林的生物经济方法。与传统叙述性综述不同，本研究采用统一的五标准评价框架，包括生态有效性、技术成熟度、经济可行性、治理要求和区域适用性，对上述创新在不同生态和社会经济背景下的优势、局限及实施挑战进行比较评估，从而超越以往森林技术和森林治理综述 largely 描述性、单一领域的覆盖范围，实现明确的跨领域比较综合。本综述进一步探讨了社区森林管理、气候智慧型林业、碳管理策略、生态系统服务、绿色基础设施以及创新性林产品对实现长期环境可持续性的贡献。分析表明，除非得到有效治理、公平的社区参与、充足的财政投资和适应性政策框架的支持，否则仅靠技术创新无法确保可持续森林管理。本综述识别了与技术采用、机构能力、气候韧性和长期监测相关的关键知识空白，并推导出四条具体的、以证据为锚点的综合路径，这些路径融合了生态、技术、经济和治理创新。Th",null,"Next Sustainability","2026-10-07T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,21,13,7,1,"该综述系统整合精准林业、遥感、AI等技术与治理条件，提出四条证据支撑路径，对智慧林业与数字乡村建设有较高参考价值，但属综述类论文、时效性一般，适合进入主题聚合而非头条精选。",[24],{"name":10,"url":6},[26,27,28,29,30,31],"人工智能","智慧林业","遥感监测","森林碳汇","数字林业","可持续经营",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":85},[39,50,58,66,73,80],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":49},"H6",9,true,[],[45,46,47,48],"生物多样性","生态系统服务","biodiversity","ecosystem service",[],{"code":51,"score":41,"supported":42,"title":52,"abstract":53,"tags":57},"H7",[],[54,55,56],"气候变化","climate change","climate resilience",[],{"code":59,"score":41,"supported":42,"title":60,"abstract":61,"tags":65},"I4",[],[62,63,64],"地理信息","GIS","geographic information",[],{"code":67,"score":20,"supported":42,"title":68,"abstract":69,"tags":72},"I3",[],[70,71],"遥感","remote sensing",[28],{"code":74,"score":75,"supported":42,"title":76,"abstract":77,"tags":79},"I2",4,[],[78],"artificial intelligence",[26],{"code":81,"score":21,"supported":14,"title":82,"abstract":83,"tags":84},"H4",[],[],[29],"农业相关性需要复核",[87,88],"森林创新 遥感 LiDAR 无人机","数字林业 森林碳汇 可持续经营","森林创新遥感LiDAR无人机-4495","10.1016\u002Fj.nxsust.2026.100546",{"doi":90,"openalex_id":92,"authors":93,"venue":10,"cited_by_count":100,"oa_url":101,"card":102,"direction":108,"ingested_from":109},"W7220854619",[94,97],{"name":95,"orcid":96},"Shivani Garg","https:\u002F\u002Forcid.org\u002F0000-0001-7879-7177",{"name":98,"orcid":99},"Pooja Arora","https:\u002F\u002Forcid.org\u002F0000-0002-4860-4714",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2949823626003016\u002Fpdf",{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"综述森林创新技术，用五标准框架比较其可持续性成效与实施条件。","社会生态系统与韧性视角，五标准评估框架，综合遥感、AI、数字林业等。","技术创新需治理、社区参与、投资和适应性政策配合才能实现可持续森林管理。","农业遥感与作物表型","可研究遥感\u002FAI在森林碳汇监测中的技术采纳障碍与制度适配路径。","智慧农业 \u002F 农业物联网","openalex","2026-10-09T23:30:10.090218Z",{"total":112,"page":21,"page_size":112,"items":113},6,[114,194,280,360,422,484],{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":9,"source_name":120,"source_url":117,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":123,"sources":128,"tags":130,"primary_topic":9,"secondary_topics":133,"classification":134,"search_phrases":156,"slug":159,"view_count":21,"doi":160,"paper":161,"created_at":193},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",79,{"impact":17,"substance":124,"depth":125,"authority":126,"freshness":112,"relevant":21,"comment":127},22,19,14,"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。",[129],{"name":120,"url":117},[54,28,29,131,132],"林业信息化","高分影像",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":135},{"candidates":136,"agricultural_evidence":14,"reason":85},[137,144,148,152],{"code":51,"score":13,"supported":42,"title":138,"abstract":139,"tags":143},[],[54,140,141,55,142],"气候适应","干旱","drought",[54],{"code":67,"score":20,"supported":42,"title":145,"abstract":146,"tags":147},[],[70,71],[28],{"code":40,"score":112,"supported":42,"title":149,"abstract":150,"tags":151},[],[45,47],[],{"code":81,"score":21,"supported":14,"title":153,"abstract":154,"tags":155},[],[],[29],[157,158],"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":160,"openalex_id":162,"authors":163,"venue":120,"cited_by_count":100,"oa_url":187,"card":188,"direction":106,"ingested_from":109},"W7220997028",[164,167,170,173,176,179,182,185],{"name":165,"orcid":166},"Luca Ferrari","https:\u002F\u002Forcid.org\u002F0000-0002-8957-4830",{"name":168,"orcid":169},"Lars T. Waser‬","https:\u002F\u002Forcid.org\u002F0000-0002-2609-9147",{"name":171,"orcid":172},"Achilleas Psomas","https:\u002F\u002Forcid.org\u002F0009-0008-9966-4988",{"name":174,"orcid":175},"Clemens Mosig","https:\u002F\u002Forcid.org\u002F0009-0003-8662-7698",{"name":177,"orcid":178},"Teja Kattenborn","https:\u002F\u002Forcid.org\u002F0000-0001-7381-3828",{"name":180,"orcid":181},"Christian Ginzler","https:\u002F\u002Forcid.org\u002F0000-0001-6365-2151",{"name":183,"orcid":184},"Verena C. Griess","https:\u002F\u002Forcid.org\u002F0000-0002-3856-3736",{"name":186,"orcid":9},"Mirela Beloiu","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0924271626004892\u002Fpdf",{"tldr":189,"method":190,"finding":191,"direction":106,"opportunity":192},"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":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":9,"source_name":200,"source_url":197,"published_at":201,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":202,"score_detail":203,"sources":207,"tags":209,"primary_topic":9,"secondary_topics":212,"classification":213,"search_phrases":236,"slug":239,"view_count":21,"doi":240,"paper":241,"created_at":279},4579,"Drought responses of a Norway spruce forest on drained peat soil: combining sap-flow sensors, eddy-covariance, meteorological, soil and UAV data","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-23-7091-2026","Abstract. The summer of 2021 brought a severe drought to southern Finland. We explored how a drained boreal peatland forest responds to drought by combining a wide range of in situ monitoring and Unmanned Aerial Vehicle (UAV) remote sensing. A dataset combining eddy-covariance (EC) fluxes, sap-flow sensors, UAV mapping, soil and weather data was collected. Spruce stand reaction to drought is examined at sub-daily to seasonal time scales, and its temporal and spatial features are identified separately for a recently thinned Continuous Cover Forestry (CCF) block and a control block. Sap flow data showed that the CCF-harvested block shows greater tree resilience to high vapour pressure deficit (VPD) than the control block, likely due to the higher soil water availability during the rainless and hot period. At the same time, both the control and CCF harvest blocks had notably reduced net ecosystem exchange (NEE) and increased Bowen ratio, particularly on high VPD days. The UAV surveys indicated that trees in the CCF block tend to have higher canopy temperatures and lower Normalized Difference Vegetation Index (NDVI) than in the control block, implying their possible susceptibility to more extreme drought.","摘要：2021年夏季，芬兰南部遭遇严重干旱。我们结合多种原位监测与无人机（UAV）遥感手段，探讨了排水泥炭地森林对干旱的响应。研究收集了涡度协方差（EC）通量、液流传感器、无人机测绘、土壤及气象数据。我们在亚日到季节尺度上考察了云杉林分对干旱的响应，并分别针对近期疏伐的连续覆盖林业（CCF）样地和对照样地识别其时间和空间特征。液流数据显示，CCF采伐样地中的树木对高饱和水汽压差（VPD）的恢复力强于对照样地，这可能归因于无雨高温期间较高的土壤水分有效性。同时，对照样地和CCF采伐样地的净生态系统交换（NEE）均显著降低，波文比升高，尤其在高VPD日更为明显。无人机调查表明，CCF样地中的树木冠层温度较高，归一化植被指数（NDVI）较低，暗示其可能更易受到更极端干旱的影响。","Biogeosciences","2026-10-09T00:00:00Z",74,{"impact":204,"substance":124,"depth":17,"authority":126,"freshness":205,"relevant":21,"comment":206},12,8,"多源遥感与地面通量观测结合的干旱响应研究，方法新颖、数据扎实，对林业碳汇与智慧林业监测有参考价值。",[208],{"name":200,"url":197},[54,210,28,211,29],"无人机遥感","土壤水分",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":214},{"candidates":215,"agricultural_evidence":14,"reason":85},[216,224,228,232],{"code":217,"score":218,"supported":42,"title":219,"abstract":221,"tags":223},"H2",15,[220],"soil",[222,220],"土壤",[211],{"code":51,"score":218,"supported":42,"title":225,"abstract":226,"tags":227},[142],[141,142],[54],{"code":67,"score":205,"supported":42,"title":229,"abstract":230,"tags":231},[],[70,71],[210,28],{"code":81,"score":21,"supported":14,"title":233,"abstract":234,"tags":235},[],[],[29],[237,238],"挪威云杉 泥炭地 干旱","UAV 涡度相关 云杉","挪威云杉泥炭地干旱-4579","10.5194\u002Fbg-23-7091-2026",{"doi":240,"openalex_id":242,"authors":243,"venue":200,"cited_by_count":100,"oa_url":197,"card":274,"direction":106,"ingested_from":109},"W7127903348",[244,247,250,253,256,259,261,264,266,269,271],{"name":245,"orcid":246},"Pavel Alekseychik","https:\u002F\u002Forcid.org\u002F0000-0002-4081-3917",{"name":248,"orcid":249},"Mikko Peltoniemi","https:\u002F\u002Forcid.org\u002F0000-0003-2028-6969",{"name":251,"orcid":252},"Raisa Mäkipää","https:\u002F\u002Forcid.org\u002F0000-0003-3146-4425",{"name":254,"orcid":255},"Ville Tuominen","https:\u002F\u002Forcid.org\u002F0000-0002-0612-128X",{"name":257,"orcid":258},"Tuomas Laurila","https:\u002F\u002Forcid.org\u002F0000-0002-1967-0624",{"name":260,"orcid":9},"Hamlyn G. Jones",{"name":262,"orcid":263},"Mitro Müller","https:\u002F\u002Forcid.org\u002F0000-0003-0456-9432",{"name":265,"orcid":9},"Evgeny Lopatin",{"name":267,"orcid":268},"Helena Rautakoski","https:\u002F\u002Forcid.org\u002F0000-0002-5297-286X",{"name":270,"orcid":9},"Timo Vesala",{"name":272,"orcid":273},"Samuli Launiainen","https:\u002F\u002Forcid.org\u002F0000-0001-6611-6573",{"tldr":275,"method":276,"finding":277,"direction":106,"opportunity":278},"结合液流、涡度协方差与无人机数据，研究芬兰排水泥炭云杉林对2021年干旱的响应。","液流传感器、涡度协方差、气象土壤及无人机多源数据，对比间伐与对照样地。","间伐样地树木对高VPD更具恢复力，但冠层温度更高、NDVI更低，可能更易受极端干旱影响。","可探究间伐后水分可利用性改善与冠层胁迫信号矛盾背后的机制，并融合多源遥感构建干旱脆弱性预警指标。","2026-10-10T23:30:20.055428Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":9,"source_name":286,"source_url":283,"published_at":287,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":288,"sources":290,"tags":292,"primary_topic":9,"secondary_topics":295,"classification":296,"search_phrases":311,"slug":314,"view_count":315,"doi":316,"paper":317,"created_at":359},4438,"Integrating Remote Sensing and Ground-Based Inventories to Map and Validate Old-Growthness across Italian Forests","https:\u002F\u002Fdoi.org\u002F10.36253\u002Fifm-1250","Mature and Old-Growth-like Forests (MOGF) are critical for biodiversity conservation and climate regulation, yet they remain rare and fragmented across Europe. This study aims to ecologically validate a high-resolution (30 m) predictive map of MOGF probability across Italy by integrating it with independent ground-based data from the 2015 National Forest Inventory (INFC2015). Utilizing a multi-source approach, we analyzed 6993 inventory sampling plots across diverse forest types and environmental gradients. Statistical analysis confirmed that higher MOGF probabilities strongly correlate with indicators of advanced stand development, including older age classes (> 120 years), multilayered vertical structures, and high canopy cover (> 80%). MOGF probability is also significantly higher within strictly protected areas (e.g., National Park Zone A) and topographically remote sites characterized by high altitudes and steep slopes. Quantitative correlations also link MOGF probability to an increase in above-ground biomass and deadwood. While the study successfully validates the spatial patterns of forest maturity, limitations include the current lack of national-scale 3D LiDAR data for deadwood modeling and the lack of microhabitat indicators in the INFC2015. However, the anticipated inclusion of these metrics in future national frameworks will further refine the monitoring of these ecosystems.","成熟林与类原始林（Mature and Old-Growth-like Forests, MOGF）对生物多样性保护和气候调节至关重要，但在欧洲范围内仍属稀有且呈破碎化分布。本研究旨在通过将意大利全国MOGF概率高分辨率（30 m）预测图与2015年全国森林资源清查（INFC2015）的独立地面数据相结合，对该预测图进行生态学验证。采用多源数据方法，我们分析了覆盖多种森林类型和环境梯度的6993个清查样地。统计分析证实，较高的MOGF概率与林分发育高级阶段的指标密切相关，包括较老的龄级（>120年）、多层垂直结构以及高郁闭度（>80%）。MOGF概率在严格保护区（如国家公园A区）以及高海拔和陡坡等地形偏远区域也显著更高。定量相关性还将MOGF概率与地上生物量和枯死木的增加联系起来。尽管本研究成功验证了森林成熟度的空间格局，但仍存在局限性，包括目前缺乏用于枯死木建模的全国尺度三维LiDAR数据，以及INFC2015中缺乏微生境指标。然而，预计这些指标将被纳入未来的国家框架，从而进一步完善对这些生态系统的监测。","L’Italia forestale e montana","2026-10-06T00:00:00Z",{"impact":17,"substance":124,"depth":17,"authority":19,"freshness":205,"relevant":21,"comment":289},"意大利全国尺度遥感与地面清查融合验证老龄林制图，方法可迁移，对森林碳汇与生物多样性监测有参考价值。",[291],{"name":286,"url":283},[45,28,29,293,294],"森林资源清查","古树老林",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":297},{"candidates":298,"agricultural_evidence":14,"reason":85},[299,303,307],{"code":67,"score":41,"supported":42,"title":300,"abstract":301,"tags":302},[71],[],[28],{"code":40,"score":20,"supported":42,"title":304,"abstract":305,"tags":306},[],[45,47],[45],{"code":81,"score":21,"supported":14,"title":308,"abstract":309,"tags":310},[],[],[29],[312,313],"意大利 国家森林清查 遥感","老龄林 遥感 生物量","意大利国家森林清查遥感-4438",2,"10.36253\u002Fifm-1250",{"doi":316,"openalex_id":318,"authors":319,"venue":286,"cited_by_count":100,"oa_url":353,"card":354,"direction":106,"ingested_from":109},"W7220657040",[320,323,326,329,331,333,336,339,342,345,347,350],{"name":321,"orcid":322},"Costanza Borghi","https:\u002F\u002Forcid.org\u002F0000-0002-5334-7548",{"name":324,"orcid":325},"Francesco Solano","https:\u002F\u002Forcid.org\u002F0000-0002-2457-5517",{"name":327,"orcid":328},"Giovanni D’Amico","https:\u002F\u002Forcid.org\u002F0000-0002-2341-3268",{"name":330,"orcid":9},"Titomanlio Pepe",{"name":332,"orcid":9},"Mattia Niccoli",{"name":334,"orcid":335},"Elia Vangi","https:\u002F\u002Forcid.org\u002F0000-0002-9772-2258",{"name":337,"orcid":338},"Francesco Parisi","https:\u002F\u002Forcid.org\u002F0000-0002-1914-7331",{"name":340,"orcid":341},"Davide Travaglini","https:\u002F\u002Forcid.org\u002F0000-0003-0706-2653",{"name":343,"orcid":344},"Giuseppe Modica","https:\u002F\u002Forcid.org\u002F0000-0002-0388-0256",{"name":346,"orcid":9},"Giancarlo Papitto",{"name":348,"orcid":349},"Gianluca Piovesan","https:\u002F\u002Forcid.org\u002F0000-0002-3214-0839",{"name":351,"orcid":352},"Gherardo Chirici","https:\u002F\u002Forcid.org\u002F0000-0002-0669-5726","https:\u002F\u002Fitaliaforestalemontana.it\u002Findex.php\u002Fifm\u002Farticle\u002Fdownload\u002F1250\u002F1223",{"tldr":355,"method":356,"finding":357,"direction":106,"opportunity":358},"用意大利森林调查数据验证了30米分辨率的老龄林概率遥感制图。","整合遥感预测图与2015年全国森林清查6993个样地做统计验证。","高老龄林概率与老林龄、多层结构、高郁闭度、保护区及陡坡高地显著相关。","可引入LiDAR三维数据与微生境指标，提升老龄林及枯木碳储监测精度。","2026-10-08T23:30:30.762484Z",{"id":361,"title":362,"url":363,"summary":364,"summary_zh":365,"content":9,"source_name":366,"source_url":363,"published_at":367,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":368,"sources":370,"tags":372,"primary_topic":9,"secondary_topics":375,"classification":376,"search_phrases":398,"slug":401,"view_count":315,"doi":402,"paper":403,"created_at":421},2671,"Burn severity, vegetation degradation, and carbon storage loss in the Belemedik forest fire: implications for protected area management and post-fire restoration","https:\u002F\u002Fdoi.org\u002F10.46309\u002Fbiodicon.2026.2000016","Purpose: This study evaluates the ecological impacts of the 9 September 2020 forest fire in the Kaleboynu area of Eskikonacık Neighborhood, Pozantı District, Adana Province, on the Çakıt Stream micro-watershed, which partially overlaps with Belemedik Nature Park. Using an integrated remote sensing approach, it assesses fire-induced changes in burn severity, vegetation structure, and carbon storage capacity to support post-fire restoration planning in a protected Mediterranean ecosystem.Methods: The study was conducted within the 9,783.06-ha Çakıt Stream micro-watershed. Cloud-free Sentinel-2A MSI Level-2A imagery acquired before (05.09.2020) and after the fire (15.09.2020) was used to calculate the Normalized Burn Ratio (NBR), Differenced NBR (dNBR), and Normalized Difference Vegetation Index (NDVI). Carbon storage was estimated using a nonlinear regression model relating NDVI to pixel-level carbon content. Spatial analyses were performed in ArcGIS 10.8.Findings: dNBR analysis showed that 92.49% (9,049.16 ha) of the watershed remained unburned or unchanged, while 725.92 ha was affected to varying degrees. NDVI results revealed losses of 330.65 ha of tree cover and 96.09 ha of shrub\u002Fgrassland vegetation, accompanied by a 426.74 ha increase in bare surfaces. Total carbon storage declined from 105,465.71 tC to 105,450.25 tC, representing a loss of 15.46 tC (56.69 t CO₂e), primarily concentrated on forested slopes surrounding the Çakıt Stream valley.Conclusions: Although the wildfire affected only a limited portion of the watershed, it caused substantial biomass losses, disrupted vegetation continuity, and reduced carbon sequestration capacity. The concentration of damage on steep slopes indicates increased risks of erosion and surface runoff. Erosion control, restoration with pioneer and fire-adapted native species, and long-term remote sensing monitoring are recommended to support ecosystem recovery, restore habitat connectivity, and sustain ecosystem services.","目的：本研究评估2020年9月9日发生在阿达纳省波赞特ı区埃斯基科纳吉克街区卡莱博伊努地区的森林火灾对恰克特溪微流域（该流域与贝莱梅迪克自然公园部分重叠）的生态影响。研究采用综合遥感方法，评估火灾引起的火烧严重程度、植被结构和碳储量变化，以支持这一受保护地中海生态系统的火后恢复规划。方法：研究在面积9，783.06公顷的恰克特溪微流域内开展。利用火灾前（2020年9月5日）和火灾后（2020年9月15日）获取的无云Sentinel-2A MSI Level-2A影像，计算归一化燃烧比率（NBR）、差值归一化燃烧比率（dNBR）和归一化植被指数（NDVI）。碳储量采用将NDVI与像元级碳含量关联的非线性回归模型进行估算。空间分析在ArcGIS 10.8中完成。结果：dNBR分析表明，流域92.49%（9，049.16公顷）的区域未过火或未发生变化，725.92公顷受到不同程度影响。NDVI结果显示，乔木覆盖损失330.65公顷，灌木\u002F草地植被损失96.09公顷，同时裸露地表增加426.74公顷。总碳储量从105，465.71吨碳下降至105，450.25吨碳，损失15.46吨碳（56.69吨二氧化碳当量），主要集中在恰克特溪谷周围的森林坡面上。结论：尽管此次野火仅影响流域的有限部分，但造成了大量生物量损失，破坏了植被连续性，并降低了碳固存能力。损害集中在陡坡上，表明侵蚀和地表径流风险增加。建议采取侵蚀控制、利用先锋树种和适应火灾的本地物种进行恢复，以及长期遥感监测，以支持生态系统恢复、恢复栖息地连通性并维持生态系统服务。","Biological Diversity and Conservation","2026-09-15T00:00:00Z",{"impact":100,"substance":100,"depth":100,"authority":100,"freshness":100,"relevant":100,"comment":369},"该研究聚焦土耳其地中海保护区火烧严重度与碳储量损失评估，属生态遥感领域，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[371],{"name":366,"url":363},[373,28,29,374],"生态修复","自然保护地",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":377},{"candidates":378,"agricultural_evidence":14,"reason":85},[379,383,388,392],{"code":67,"score":20,"supported":42,"title":380,"abstract":381,"tags":382},[],[70,71],[28],{"code":81,"score":75,"supported":42,"title":384,"abstract":385,"tags":387},[],[386],"carbon sequestration",[29],{"code":40,"score":75,"supported":42,"title":389,"abstract":390,"tags":391},[],[46],[373],{"code":59,"score":393,"supported":42,"title":394,"abstract":395,"tags":397},3,[],[396],"空间分析",[],[399,400],"自然保护地 森林碳汇 生态修复 遥感监测","自然保护地 森林碳汇","自然保护地森林碳汇生态修复遥感监测-2671","10.46309\u002Fbiodicon.2026.2000016",{"doi":402,"openalex_id":404,"authors":405,"venue":366,"cited_by_count":100,"oa_url":415,"card":416,"direction":106,"ingested_from":109},"W7213299850",[406,409,412],{"name":407,"orcid":408},"Nuriye Ebru Yıldız","https:\u002F\u002Forcid.org\u002F0000-0002-3508-4895",{"name":410,"orcid":411},"Barış Kahveci","https:\u002F\u002Forcid.org\u002F0000-0002-8508-1748",{"name":413,"orcid":414},"Tülay Ezer","https:\u002F\u002Forcid.org\u002F0000-0002-6485-5505","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6248142",{"tldr":417,"method":418,"finding":419,"direction":106,"opportunity":420},"用哨兵2号影像评估2020年土耳其Belemedik林火后的火烧严重度、植被退化与碳储量损失。","Sentinel-2A影像计算NBR\u002FdNBR\u002FNDVI，非线性回归估算碳储量，","仅7.5%流域过火，但损失330.65公顷林地，碳储量减少15.46吨碳，集中于陡坡。","可结合多时相遥感与生态模型，量化火后恢复轨迹及保护区分区管理对碳汇的长期影响。","2026-09-16T23:30:30.571646Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":9,"source_name":428,"source_url":425,"published_at":429,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":430,"score_detail":431,"sources":435,"tags":437,"primary_topic":9,"secondary_topics":441,"classification":442,"search_phrases":464,"slug":467,"view_count":21,"doi":468,"paper":469,"created_at":483},2174,"Optical Remote Sensing and Machine Learning Enable Scalable Carbon Mapping in Himalayan Forests Despite Spectral Saturation Challenges","https:\u002F\u002Fdoi.org\u002F10.32942\u002Fx29d5f","Accurate aboveground biomass (AGB) estimation is critical for climate change mitigation and carbon accounting in mountain regions, yet spatially explicit mapping remains limited by reliance on conventional field methods and underexplored machine learning approaches in the Himalaya. Bhutan, a carbon-negative nation maintaining 70% forest cover, faces urgent demand to develop robust monitoring, reporting, and verification (MRV) systems for carbon market participation under the Paris Agreement Article 6. This study developed a random forest (RF) model that integrates Sentinel-2A multispectral imagery, nine vegetation indices, topographic variables, and field-measured AGB from 99 plots across 244.6 hectares in the Kyentshen Community Forest. Using recursive feature elimination, 10 key predictors were identified from 27 candidate variables: Band 11, SLAVI, Band 12, WDRVI, NDVI, elevation, NDWI, GNDVI, band ratio (r3), and Band 9. The optimized RF model achieved R² = 0.34 and RMSE = 67.98 ± 19.79 t\u002Fha on training data (Ntree = 500, Mtry = 3). However, test-set performance declined substantially (R² = 0.42, RMSE = 86.14 t\u002Fha), indicating overfitting and reflecting limitations of spectral saturation in high-biomass stands (>300 t\u002Fha). A Wilcoxon signed-rank test revealed systematic overestimation (predicted median: 118.36 t\u002Fha vs. observed: 105.15 t\u002Fha; p = 0.03). Despite moderate accuracy, this study demonstrates that freely available Sentinel-2 data combined with machine learning offers a cost-effective, scalable alternative to traditional field mapping for rapid AGB assessment across Bhutan's temperate forests. Vegetation indices, particularly SLAVI and WDRVI, proved superior to raw spectral bands, offering improved sensitivity to dense canopies. The spatial-transect methodology and 10-predictor framework provide a replicable foundation for advancing Bhutan's forest carbon accounting systems. We recommend integrating synthetic aperture radar (SAR) or LiDAR to overcome optical saturation, enabling higher accuracy for robust MRV compliance and carbon credit monetization. This work directly supports the maintenance of Bhutan's carbon-neutral status and establishes the technical baseline for scaling AGB monitoring across mountain regions where engineered infrastructure capacity is limited, but climate action urgency is paramount.","准确估算地上生物量（AGB）对于山区减缓气候变化和碳核算至关重要，但空间显式制图仍受限于对传统野外方法的依赖以及喜马拉雅地区机器学习方法研究不足。不丹是一个维持70%森林覆盖率的负碳国家，迫切需要建立稳健的监测、报告与核查（MRV）体系，以参与《巴黎协定》第六条下的碳市场。本研究开发了一个随机森林（RF）模型，整合了Sentinel-2A多光谱影像、9个植被指数、地形变量以及Kyentshen社区森林244.6公顷范围内99个样地的实测AGB数据。通过递归特征消除法，从27个候选变量中筛选出10个关键预测因子：Band 11、SLAVI、Band 12、WDRVI、NDVI、海拔、NDWI、GNDVI、波段比值（r3）和Band 9。优化后的RF模型在训练数据上达到R² = 0.34，RMSE = 67.98 ± 19.79 t\u002Fha（Ntree = 500，Mtry = 3）。然而，测试集性能显著下降（R² = 0.42，RMSE = 86.14 t\u002Fha），表明存在过拟合，并反映了高生物量林分（>300 t\u002Fha）中光谱饱和的局限性。Wilcoxon符号秩检验揭示了系统性高估（预测中位数：118.36 t\u002Fha vs. 观测值：105.15 t\u002Fha；p = 0.03）。尽管精度中等，本研究表明，免费可获取的Sentinel-2数据结合机器学习为不丹温带森林的快速AGB评估提供了一种成本效益高、可扩展的传统野外制图替代方案。植被指数，特别是SLAVI和WDRVI，被证明优于原始光谱波段，对密集冠层具有更高的敏感性。空间样带方法和10预测因子框架为推进不丹森林碳核算体系提供了可复制的基础。我们建议整合合成孔径雷达（SAR）或激光雷达（LiDAR）以克服光学饱和，从而实现更高精度，满足稳健的MRV合规和碳信用货币化需求。本研究直接支持不丹维持碳中和地位，并为在工程基础设施能力有限但气候行动紧迫性极高的山区推广AGB监测建立了技术基线。","OpenAlex","2026-09-09T00:00:00Z",70,{"impact":204,"substance":432,"depth":433,"authority":19,"freshness":205,"relevant":21,"comment":434},20,17,"研究用Sentinel-2与随机森林实现山地森林生物量制图，方法可复现但精度中等，对农业遥感与碳汇监测有参考价值。",[436],{"name":428,"url":425},[438,28,439,29,440],"农业人工智能","碳核算","生物量估算",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":443},{"candidates":444,"agricultural_evidence":14,"reason":85},[445,451,456,460],{"code":74,"score":218,"supported":42,"title":446,"abstract":448,"tags":450},[447],"machine learning",[449,447],"机器学习",[438],{"code":67,"score":19,"supported":42,"title":452,"abstract":453,"tags":455},[71],[454],"multispectral",[440,28],{"code":51,"score":112,"supported":42,"title":457,"abstract":458,"tags":459},[],[54,55],[],{"code":81,"score":21,"supported":14,"title":461,"abstract":462,"tags":463},[],[],[29],[465,466],"农业人工智能 生物量估算 森林碳汇 遥感监测","农业人工智能 生物量估算","农业人工智能生物量估算森林碳汇遥感监测-2174","10.32942\u002Fx29d5f",{"doi":468,"openalex_id":470,"authors":471,"venue":9,"cited_by_count":100,"oa_url":477,"card":478,"direction":106,"ingested_from":109},"W7212083800",[472,474],{"name":473,"orcid":9},"Namgay Wangchuk",{"name":475,"orcid":476},"Yonten Dorji","https:\u002F\u002Forcid.org\u002F0000-0002-6508-9582","https:\u002F\u002Fecoevorxiv.org\u002Frepository\u002Fobject\u002F14866\u002Fdownload\u002F25843\u002F",{"tldr":479,"method":480,"finding":481,"direction":106,"opportunity":482},"用Sentinel-2与随机森林估算不丹喜马拉雅森林生物量，实现可扩展碳制图。","Sentinel-2多光谱、9个植被指数、地形因子与99个样地实测AGB，随机森","模型精度中等且高生物量区存在光谱饱和，SLAVI与WDRVI优于原始波段。","融合SAR或LiDAR克服光学饱和，提升山地森林碳储量估算与MRV精度。","2026-09-11T23:30:32.934604Z",{"id":485,"title":486,"url":487,"summary":488,"summary_zh":489,"content":9,"source_name":490,"source_url":487,"published_at":491,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":492,"score_detail":493,"sources":495,"tags":497,"primary_topic":9,"secondary_topics":502,"classification":503,"search_phrases":529,"slug":532,"view_count":100,"doi":533,"paper":534,"created_at":558},5577,"Assessing Land Use and Land Cover Change in the Jamanxim River Region (Brazilian Amazon) from 2015 to 2025 Using CBERS-4\u002FPAN Imagery","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i4.269068","Land use and land cover (LULC) changes in the Amazon are among the main drivers of biodiversity loss, ecosystem degradation, and the intensification of climate change. This study aimed to analyze the multitemporal dynamics of LULC around the Jamanxim River, Pará, Brazilian Amazon, between 2015 and 2025, using high-resolution imagery from the CBERS-4\u002FPAN satellite. The study area covers 492.74 km², including part of the Jamanxim National Forest, a conservation unit under strong pressure from agricultural expansion and logging activities. The methodology included geometric correction of the images, Gram–Schmidt pansharpening, and supervised classification with the Random Forest algorithm, validated with historical imagery from Google Earth Pro. Thematic maps of land use and cover were generated, and performance metrics such as overall accuracy, omission and commission errors, and F1-scores were assessed. The results indicated a significant reduction in forest cover, from 412.28 km² in 2015 to 355.47 km² in 2025 (–13.78%). Non-vegetated areas increased by 132.47%, while agricultural areas expanded by 54.97%. The forest and water body classes showed high accuracy (F1-score > 0.95), while confusion persisted between agricultural and bare soil classes. Overall accuracy remained above 90% across all years, with the highest performance recorded in 2020 (96.31%). These results highlight the potential of CBERS-4\u002FPAN imagery for monitoring multitemporal changes in tropical forest landscapes. At the same time, they reveal the intensification of deforestation and the growing threats to conservation in the Amazon, reinforcing the importance of remote sensing for environmental management and policy support.","亚马逊地区的土地利用与土地覆盖（LULC）变化是生物多样性丧失、生态系统退化以及气候变化加剧的主要驱动因素之一。本研究旨在利用CBERS-4\u002FPAN卫星的高分辨率影像，分析2015年至2025年间巴西亚马孙州雅曼辛河（Jamanxim River）周边土地利用与土地覆盖的多时相动态。研究区面积为492.74 km²，包括雅曼辛国家森林（Jamanxim National Forest）的一部分，该保护区正面临农业扩张和采伐活动的强烈压力。研究方法包括影像几何校正、Gram–Schmidt全色锐化以及基于随机森林（Random Forest）算法的监督分类，并利用Google Earth Pro的历史影像进行验证。研究生成了土地利用与覆盖专题图，并评估了总体精度、漏分误差与错分误差以及F1分数等性能指标。结果表明，森林覆盖显著减少，从2015年的412.28 km²降至2025年的355.47 km²（–13.78%）。无植被区域增加了132.47%，农业区域扩张了54.97%。森林和水体类别表现出较高精度（F1分数 > 0.95），而农业类别与裸土类别之间仍存在混淆。各年份总体精度均保持在90%以上，其中2020年性能最高（96.31%）。这些结果凸显了CBERS-4\u002FPAN影像在监测热带森林景观多时相变化方面的潜力，同时揭示了亚马逊地区森林砍伐的加剧以及保护面临的日益增长的威胁，进一步强调了遥感在环境管理和政策支持中的重要性。","Journal of Hyperspectral Remote Sensing","2026-10-20T00:00:00Z",78,{"impact":17,"substance":124,"depth":433,"authority":204,"freshness":41,"relevant":21,"comment":494},"该研究利用CBERS-4\u002FPAN影像量化亚马逊保护区十年间森林减少13.78%，方法规范、结论可靠，对遥感监测与生态保护有参考价值，但地域性强、与国内三农直接关联有限。",[496],{"name":490,"url":487},[28,498,499,500,501],"土地利用","森林保护","亚马逊","CBERS-4",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":504},{"candidates":505,"agricultural_evidence":42,"reason":528},[506,510,514,518,522],{"code":40,"score":20,"supported":42,"title":507,"abstract":508,"tags":509},[],[45,47],[498],{"code":67,"score":20,"supported":42,"title":511,"abstract":512,"tags":513},[],[70,71],[28],{"code":51,"score":112,"supported":42,"title":515,"abstract":516,"tags":517},[],[54,55],[],{"code":217,"score":393,"supported":42,"title":519,"abstract":520,"tags":521},[],[220],[],{"code":523,"score":393,"supported":42,"title":524,"abstract":525,"tags":527},"I7",[],[526],"研究方法",[],"多个产业证据相当，需要确认主归属",[530,531],"CBERS-4 亚马逊 土地利用","Jamanxim 森林覆盖 遥感","CBERS-4亚马逊土地利用-5577","10.29150\u002Fjhrs.v16i4.269068",{"doi":533,"openalex_id":535,"authors":536,"venue":490,"cited_by_count":100,"oa_url":487,"card":553,"direction":106,"ingested_from":109},"W7223141286",[537,540,542,545,548,551],{"name":538,"orcid":539},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":541,"orcid":9},"Gabriel Henrique Santos Silva",{"name":543,"orcid":544},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":546,"orcid":547},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":549,"orcid":550},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":552,"orcid":9},"Sylvana Melo dos Santos",{"tldr":554,"method":555,"finding":556,"direction":106,"opportunity":557},"用CBERS-4\u002FPAN影像分析亚马逊Jamanxim河流域2015-2025年土地利用变化。","几何校正、Gram-Schmidt融合、随机森林监督分类，Google Eart","森林覆盖减少13.78%，农业扩张54.97%，裸地增132.47%，总体精度超90%。","可探索中巴地球资源卫星在热带森林退化监测中的业务化应用与多源数据融合。","2026-10-22T11:20:42.901761Z"]