[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3016":3,"related-3016":79},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":78},3016,"Afforestation enhances ecosystem stability in the farming-pastoral ecotone of northern China, but its effects are constrained by water availability","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjpe\u002Frtag232","Abstract The farming-pastoral ecotone of northern China (FPENC), a typical semi-agricultural and semi-pastoral ecologically fragile zone, has long faced controversy over afforestation due to its water-limited environment, raising concerns about how afforestation affects this fragile ecosystem. In this study, based on Google Earth Engine and a Random Forest model, we derive afforestation distribution (1985-2020), and further investigated ecosystem stability under afforestation from the perspectives of short-term responses and long-term dynamic changes. The results showed: (1) By 2020, 67,700 km2 had been afforested in FPENC (6.20% of its area). Afforestation survival strongly depended on precipitation, with relatively low retention rates (40%-70%) in the zones with \u003C 400 mm. (2) At short-term timescales, afforestation mitigated ecosystem state deviations caused by extreme drought events and improved surrounding ecosystems within a 150 m buffer. (3) Over long-term timescales, NDVI across the FPENC exhibited a significant increasing trend over the past four decades, accompanied by an overall enhancement in ecosystem recovery capacity. In zones with > 400 mm annual precipitation and with forest cover exceeding 40%, ecosystem recovery capacity increased significantly, showing there might be a potential linkage among afforestation, precipitation, and ecosystem recovery processes. This study revealed despite water scarcity and low tree survival, afforestation under suitable precipitation can effectively enhance regional ecosystem stability.","中国北方农牧交错带（FPENC）是典型的半农半牧生态脆弱区，由于环境水分受限，长期以来在造林问题上存在争议，引发了人们对造林如何影响这一脆弱生态系统的担忧。本研究基于Google Earth Engine和随机森林模型，反演了1985—2020年造林分布，并进一步从短期响应和长期动态变化两个角度研究了造林条件下的生态系统稳定性。结果表明：（1）截至2020年，FPENC已造林67,700 km²，占其面积的6.20%。造林存活强烈依赖降水，在降水量小于400 mm的区域，保存率相对较低（40%—70%）。（2）在短期时间尺度上，造林缓解了极端干旱事件引起的生态系统状态偏离，并改善了150 m缓冲区内周边生态系统。（3）在长期时间尺度上，过去四十年FPENC的NDVI呈显著增加趋势，同时生态系统恢复能力总体增强。在年降水量大于400 mm且森林覆盖率超过40%的区域，生态系统恢复能力显著提高，表明造林、降水与生态系统恢复过程之间可能存在潜在联系。本研究揭示，尽管存在水分短缺和树木存活率低的问题，但在适宜降水条件下，造林能够有效增强区域生态系统稳定性。",null,"Journal of Plant Ecology","2026-09-17T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"基于遥感与随机森林量化北方农牧交错带造林成效及水分约束，数据规模大、结论有新意，对生态修复与农业信息化有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"生态修复","遥感监测","造林","农牧交错带","水资源约束",[32,33],"北方农牧交错带 造林 水资源","Google Earth Engine 造林 遥感","北方农牧交错带造林水资源-3016",0,"10.1093\u002Fjpe\u002Frtag232",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":70,"card":71,"direction":75,"ingested_from":77},"W7213445880",[40,43,46,48,50,52,54,56,58,60,63,65,68],{"name":41,"orcid":42},"Yuchao Luo","https:\u002F\u002Forcid.org\u002F0000-0003-0063-3505",{"name":44,"orcid":45},"Haile Zhao","https:\u002F\u002Forcid.org\u002F0000-0002-5870-062X",{"name":47,"orcid":9},"Qianhe Wang",{"name":49,"orcid":9},"Yi Zhou",{"name":51,"orcid":9},"Xin Chen",{"name":53,"orcid":9},"Yuling Jin",{"name":55,"orcid":9},"Xingjie Yin",{"name":57,"orcid":9},"Guoliang Zhang",{"name":59,"orcid":9},"Haorui Sun",{"name":61,"orcid":62},"Jun Bai","https:\u002F\u002Forcid.org\u002F0000-0002-1408-4271",{"name":64,"orcid":9},"Huiyao Shi",{"name":66,"orcid":67},"Zhihua Pan","https:\u002F\u002Forcid.org\u002F0000-0002-8187-1574",{"name":69,"orcid":9},"Pingli An","https:\u002F\u002Facademic.oup.com\u002Fjpe\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fjpe\u002Frtag232\u002F71179175\u002Frtag232.pdf",{"tldr":72,"method":73,"finding":74,"direction":75,"opportunity":76},"基于GEE和随机森林反演1985-2020年北方农牧交错带造林分布，评估其对生态系统稳定性的影响。","Google Earth Engine遥感数据与随机森林模型，分析造林分布及ND","造林可缓解干旱影响并提升恢复力，但存活率与效果受降水制约，400mm为关键阈值。","农业遥感与作物表型","可探究不同降水梯度下造林-水分-恢复力耦合机制，优化生态修复的水资源约束阈值。","openalex","2026-09-20T23:30:22.592517Z",{"total":80,"page":21,"page_size":80,"items":81},6,[82,121,175,220,253,299],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":87,"content":9,"source_name":88,"source_url":85,"published_at":89,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":35,"score_detail":90,"sources":92,"tags":94,"search_phrases":97,"slug":100,"view_count":35,"doi":101,"paper":102,"created_at":120},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":35,"substance":35,"depth":35,"authority":35,"freshness":35,"relevant":35,"comment":91},"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。",[93],{"name":88,"url":85},[26,27,95,96],"森林碳汇","自然保护地",[98,99],"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":101,"openalex_id":103,"authors":104,"venue":88,"cited_by_count":35,"oa_url":114,"card":115,"direction":75,"ingested_from":77},"W7213299850",[105,108,111],{"name":106,"orcid":107},"Nuriye Ebru Yıldız","https:\u002F\u002Forcid.org\u002F0000-0002-3508-4895",{"name":109,"orcid":110},"Barış Kahveci","https:\u002F\u002Forcid.org\u002F0000-0002-8508-1748",{"name":112,"orcid":113},"Tülay Ezer","https:\u002F\u002Forcid.org\u002F0000-0002-6485-5505","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6248142",{"tldr":116,"method":117,"finding":118,"direction":75,"opportunity":119},"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":122,"title":123,"url":124,"summary":125,"summary_zh":126,"content":9,"source_name":127,"source_url":124,"published_at":89,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":128,"score_detail":129,"sources":133,"tags":135,"search_phrases":139,"slug":142,"view_count":35,"doi":143,"paper":144,"created_at":174},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。","SOIL",79,{"impact":130,"substance":18,"depth":17,"authority":19,"freshness":131,"relevant":21,"comment":132},16,9,"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。",[134],{"name":127,"url":124},[26,27,136,137,138],"森林土壤","林业信息化","证据图谱",[140,141],"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":143,"openalex_id":145,"authors":146,"venue":127,"cited_by_count":21,"oa_url":167,"card":168,"direction":173,"ingested_from":77},"W7140718837",[147,149,151,153,155,158,161,163,165],{"name":148,"orcid":9},"Maisy Roach-Krajewski",{"name":150,"orcid":9},"Xavier Giroux-Bougard",{"name":152,"orcid":9},"David Paré",{"name":154,"orcid":9},"Catlan Dallaire",{"name":156,"orcid":157},"Luc Guindon","https:\u002F\u002Forcid.org\u002F0000-0002-4346-7351",{"name":159,"orcid":160},"Florian Jordan","https:\u002F\u002Forcid.org\u002F0000-0003-2242-7411",{"name":162,"orcid":9},"Charlotte Norris",{"name":164,"orcid":9},"Kara Webster",{"name":166,"orcid":9},"Jérôme Laganière","https:\u002F\u002Fsoil.copernicus.org\u002Farticles\u002F12\u002F885\u002F2026\u002Fsoil-12-885-2026.pdf",{"tldr":169,"method":170,"finding":171,"direction":75,"opportunity":172},"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。","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","2026-09-16T23:30:09.927061Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":9,"source_name":181,"source_url":178,"published_at":182,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":183,"score_detail":184,"sources":188,"tags":190,"search_phrases":194,"slug":197,"view_count":35,"doi":198,"paper":199,"created_at":219},2063,"Higher vegetation diversity and stronger forest structure occur in managed zones despite widespread land cover degradation in Ise Forest Reserve Nigeria","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44415-026-00129-1","The forest ecosystem of Ise Forest Reserve in Nigeria is threatened by land-use conversion and timber exploitation. However, part of the enclave was managed under the Southwest\u002FNiger Delta Forest Project, described as the “managed zone”. This study investigated spatial differences in vegetation condition and temporal patterns of land-cover degradation by integrating systematic field sampling, remote sensing and geographic information technologies. Eleven 25 × 25 m plots were laid along 200 m transects during a field sampling exercise carried out in disturbed and managed zones and were used to test differences in species diversity and structural attributes. In total, 27 species (17 families) were recorded, with Ricinodendron heudelotii achieving the highest frequency ( n = 55) and Diospyros lotus exhibiting the most significant mean height (12.20 ± 0.00 m), canopy cover (18.60 ± 0.00 m), and diameter at breast height, DBH (138.13 ± 0.00 cm). Significant differences ( p \u003C 0.001) in height, DBH, and canopy cover were observed between the disturbed and managed zones. Vegetation diversity was higher in the managed zone than in the disturbed zone. The values of the Shannon–Wiener, Simpson concentration, and Margalef richness indices were higher in the managed zone than in the disturbed zone. The lower rates of farmland expansion and secondary forest loss in the managed zone indicated spatial differences in the severity of land degradation. Since data on the area’s pre-intervention stage for direct comparison were not available, the differences between the managed and disturbed zones could not be attributed solely to the 2021 conservation project and remain subject to review in a long-term evaluation.","尼日利亚伊势森林保护区的森林生态系统正受到土地利用转换和木材采伐的威胁。然而，该飞地的一部分在“西南\u002F尼日尔三角洲森林项目”下得到管理，被称为“管理区”。本研究通过整合系统野外采样、遥感与地理信息技术，探讨了植被状况的空间差异以及土地覆盖退化的时间格局。在受干扰区和管理区开展的野外采样中，沿200 m样带设置了11个25 × 25 m样方，用于检验物种多样性和结构属性的差异。共记录27个物种（17科），其中非洲樱桃（Ricinodendron heudelotii）出现频率最高（n = 55），而君迁子（Diospyros lotus）的平均高度（12.20 ± 0.00 m）、冠层盖度（18.60 ± 0.00 m）和胸径（DBH，138.13 ± 0.00 cm）最大。受干扰区与管理区在高度、胸径和冠层盖度方面存在显著差异（p \u003C 0.001）。管理区的植被多样性高于受干扰区。Shannon–Wiener指数、Simpson集中度指数和Margalef丰富度指数在管理区均高于受干扰区。管理区农田扩张和次生林丧失速率较低，表明土地退化严重程度存在空间差异。由于缺乏该地区干预前阶段的数据以进行直接比较，管理区与受干扰区之间的差异不能完全归因于2021年的保护项目，仍有待长期评估加以审视。","Discover Forests","2026-09-08T00:00:00Z",61,{"impact":20,"substance":17,"depth":185,"authority":186,"freshness":20,"relevant":21,"comment":187},15,12,"尼日利亚保护区管理成效的实证研究，方法扎实但地域性强、与国内三农信息化关联有限，可作为遥感生态监测案例参考。",[189],{"name":181,"url":178},[26,191,192,27,193],"生物多样性","土地退化","森林保护",[195,196],"生物多样性 土地退化 森林保护 生态修复","生物多样性 土地退化","生物多样性土地退化森林保护生态修复-2063","10.1007\u002Fs44415-026-00129-1",{"doi":198,"openalex_id":200,"authors":201,"venue":181,"cited_by_count":35,"oa_url":213,"card":214,"direction":75,"ingested_from":77},"W7211968416",[202,205,207,209,211],{"name":203,"orcid":204},"Oluwatobi Emmanuel Olaniyi","https:\u002F\u002Forcid.org\u002F0000-0002-1421-9068",{"name":206,"orcid":9},"Elijah Ogunsakin",{"name":208,"orcid":9},"Babafemi Ogunjemite",{"name":210,"orcid":9},"Kawiyu Rafiu",{"name":212,"orcid":9},"Ebere Anozie","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44415-026-00129-1.pdf",{"tldr":215,"method":216,"finding":217,"direction":75,"opportunity":218},"研究尼日利亚Ise森林保护区管理区与干扰区的植被多样性和结构差异及土地覆盖退化。","野外样方调查、遥感与GIS结合，比较管理区和干扰区植被指标。","管理区植被多样性和森林结构显著优于干扰区，农田扩张和次生林损失更低。","可开展长期监测评估保护项目效果，结合多时相遥感量化管理措施对植被恢复的因果影响。","2026-09-10T23:30:30.245193Z",{"id":221,"title":222,"url":223,"summary":224,"summary_zh":225,"content":9,"source_name":226,"source_url":223,"published_at":227,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":35,"score_detail":228,"sources":230,"tags":232,"search_phrases":236,"slug":239,"view_count":35,"doi":240,"paper":241,"created_at":252},3015,"Forecasting Urbanization Dynamics On İstanbul’s European Side Using Deep Learning And Extrapolation Techniques","https:\u002F\u002Fdoi.org\u002F10.2478\u002Fjlecol-2026-0037","Abstract Rapid urbanization in metropolitan regions poses significant environmental, social, and infrastructural challenges, necessitating advanced analytical approaches to monitor and predict urban growth. This study investigates the spatio-temporal dynamics of urbanization on the European side of Istanbul from 2013 to 2024 using Landsat 8 imagery and a deep learning (DL)–based Land Cover Classification model integrated within ArcGIS Pro. The U-Net–based pre-trained model generated 15-class Land Use\u002FLand Cover (LULC) maps, which were validated against the Urban Atlas dataset, resulting in high classification accuracies for forest and water classes (PA: 0.84–0.94; UA: 0.87–0.87) and an overall binary urban\u002Fnon-urban accuracy of 87 %, confirming the robustness of the employed DL approach. Spatio-temporal analyses of LULC data were conducted using both Ordinary Least Squares (OLS) and nonlinear regression functions to examine urban growth trends and project future development for 2025, 2026, and 2027. The results indicate a strong linear increase in urbanized areas across most districts, with total developed area on the European side projected to reach approximately 807 km² by 2027, representing a nearly 50% increase compared to 2013. These findings highlight the significant pressure of urban expansion on natural and agricultural lands and emphasize the need for informed planning strategies. By integrating remote sensing, deep learning, and predictive modeling, this study provides actionable insights for sustainable urban development, offering a replicable framework for monitoring rapid urbanization and supporting policy decisions to mitigate environmental and socio-spatial impacts in rapidly growing metropolitan regions.","摘要 大都市区域的快速城市化带来了显著的环境、社会和基础设施挑战，亟需先进的分析方法来监测和预测城市增长。本研究利用Landsat 8影像和集成于ArcGIS Pro中的基于深度学习（DL）的土地覆盖分类模型，研究了2013年至2024年伊斯坦布尔欧洲一侧城市化的时空动态。基于U-Net的预训练模型生成了15类土地利用\u002F土地覆盖（LULC）地图，并依据Urban Atlas数据集进行了验证，森林和水体类别的分类精度较高（生产者精度PA：0.84–0.94；用户精度UA：0.87–0.87），城市\u002F非城市二分类总体精度达87%，证实了所采用深度学习方法稳健可靠。研究采用普通最小二乘法（OLS）和非线性回归函数对LULC数据进行时空分析，以考察城市增长趋势并预测2025年、2026年和2027年的未来发展。结果表明，大多数区域的城市化面积呈显著线性增长，预计到2027年欧洲一侧的总建成区面积将达到约807 km²，较2013年增长近50%。这些发现凸显了城市扩张对自然和农业用地的巨大压力，并强调了科学规划策略的必要性。通过整合遥感、深度学习和预测建模，本研究为可持续城市发展提供了可操作的见解，为监测快速城市化提供了一个可复制的框架，并支持旨在缓解快速增长的都市区域中环境和社会空间影响的政策决策。","Journal of Landscape Ecology","2026-09-19T00:00:00Z",{"impact":35,"substance":35,"depth":35,"authority":35,"freshness":35,"relevant":35,"comment":229},"研究伊斯坦布尔城市扩张与土地覆盖预测，属城市遥感与景观生态领域，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。",[231],{"name":226,"url":223},[233,27,234,235],"深度学习","土地利用","城市扩张",[237,238],"伊斯坦布尔 城市扩张 遥感","U-Net 土地覆盖分类","伊斯坦布尔城市扩张遥感-3015","10.2478\u002Fjlecol-2026-0037",{"doi":240,"openalex_id":242,"authors":243,"venue":226,"cited_by_count":35,"oa_url":223,"card":247,"direction":75,"ingested_from":77},"W7213670913",[244],{"name":245,"orcid":246},"Gizem Dinç","https:\u002F\u002Forcid.org\u002F0000-0003-2406-604X",{"tldr":248,"method":249,"finding":250,"direction":75,"opportunity":251},"用Landsat 8影像和U-Net深度学习模型分析伊斯坦布尔欧洲侧2013-2024年城市化动态并","Landsat 8影像、ArcGIS Pro中U-Net预训练模型生成15类LU","城市面积呈强线性增长，2027年预计达807 km²，较2013年增长近50%，挤压自然与农业用地。","可借鉴该遥感+深度学习+外推框架，研究快速城市化对城郊农业用地与耕地保护的时空影响。","2026-09-20T23:30:21.335156Z",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":258,"content":9,"source_name":259,"source_url":256,"published_at":227,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":266,"tags":268,"search_phrases":273,"slug":276,"view_count":35,"doi":277,"paper":278,"created_at":298},3013,"AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41348-026-01352-w","AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration。Journal of Plant Diseases and Protection","人工智能与遥感在真菌及卵菌病害监测中的应用：现状与生物学整合。《植物病害与保护杂志》","Journal of Plant Diseases and Protection",77,{"impact":17,"substance":262,"depth":263,"authority":264,"freshness":131,"relevant":21,"comment":265},20,17,13,"核心期刊综述，系统梳理AI与遥感在真菌及卵菌病害监测中的进展与生物学整合路径，对智慧农业植保方向有参考价值。",[267],{"name":259,"url":256},[269,270,271,27,272],"智慧农业","农业人工智能","植物病害","病害预警",[274,275],"AI 遥感 真菌病害 监测","植物病害 遥感 预警","AI遥感真菌病害监测-3013","10.1007\u002Fs41348-026-01352-w",{"doi":277,"openalex_id":279,"authors":280,"venue":259,"cited_by_count":35,"oa_url":9,"card":293,"direction":75,"ingested_from":77},"W7213649225",[281,283,285,287,290],{"name":282,"orcid":9},"Biju Vadakkemukadiyil Chellappan",{"name":284,"orcid":9},"C. L. Biji",{"name":286,"orcid":9},"Vanshika Arun Meda",{"name":288,"orcid":289},"Sajad Ali","https:\u002F\u002Forcid.org\u002F0000-0002-3230-1436",{"name":291,"orcid":292},"Sherif Mohamed El‐Ganainy","https:\u002F\u002Forcid.org\u002F0000-0001-5226-4604",{"tldr":294,"method":295,"finding":296,"direction":75,"opportunity":297},"综述AI与遥感在真菌及卵菌病害监测中的现状，强调生物信息整合。","文献综述，整合AI、遥感与病原生物学数据。","AI与遥感结合可提升病害监测，但需融入病原生物学机制。","可研究将病原生活史与遥感时序特征耦合的病害预警模型。","2026-09-20T23:30:21.177583Z",{"id":300,"title":301,"url":302,"summary":303,"summary_zh":304,"content":9,"source_name":305,"source_url":302,"published_at":227,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":306,"score_detail":307,"sources":309,"tags":311,"search_phrases":316,"slug":319,"view_count":35,"doi":320,"paper":321,"created_at":344},3012,"Topographic and economic drivers of agricultural–ecological coupling in Gansu Province: spatial differentiation and threshold effects","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecolind.2026.115463","Improved understanding of agricultural–ecological coupling is critical for sustainable development in semi-arid regions. This study integrated the Remote Sensing Ecological Index (RSEI), Agricultural Green Development Index (AGDI), Coupling Coordination Degree (CCD) model, and XGBoost–SHAP to examine the spatiotemporal evolution and nonlinear drivers of agricultural–ecological coupling in Gansu Province from 2010 to 2023 at the county scale. A four-quadrant framework was applied to identify differentiated management types. Results showed that farmland ecological quality exhibited a stepped north–south pattern, improving during 2010–2018 but declining after 2019. Agricultural green development displayed a “high southeast, low northwest” pattern, increasing by 0.0036 yr −1 ( p \u003C 0.01). The coupling coordination degree increased from 0.57 to 0.63, with significant spatial clustering (Moran's I = 0.438–0.528, p \u003C 0.001), characterized by stable “high–high” clusters in southern Gansu and “low–low” clusters in northern Gansu. Relief amplitude (40.9%) and GDP per capita (36.1%) jointly explained 77.0% of coordination variation. Elevation above 2000 m constrained coordination, whereas GDP per capita between 15,000 and 30,000 CNY promoted green transformation, with diminishing effects beyond 40,000 CNY. Severe drought conditions (SPEI \u003C −1.5) caused agricultural–ecological imbalance. Counties were classified into four management types (H H, L–H, L–L, and H–L), requiring differentiated governance strategies. This study provides quantifiable thresholds for improving agricultural–ecological coupling in semi-arid regions.","深入理解农业—生态耦合关系对于半干旱地区的可持续发展至关重要。本研究集成遥感生态指数（RSEI）、农业绿色发展指数（AGDI）、耦合协调度（CCD）模型以及XGBoost–SHAP方法，在县域尺度上考察了2010—2023年甘肃省农业—生态耦合的时空演变与非线性驱动因素，并运用四象限框架识别差异化治理类型。结果表明：耕地生态质量呈南北阶梯状格局，2010—2018年持续改善，2019年后有所下降。农业绿色发展呈“东南高、西北低”的格局，年均增长0.0036（p \u003C 0.01）。耦合协调度由0.57升至0.63，具有显著空间集聚特征（Moran's I = 0.438–0.528，p \u003C 0.001），表现为陇南稳定的“高—高”集聚和陇北的“低—低”集聚。地形起伏度（40.9%）与人均GDP（36.1%）共同解释了77.0%的协调度变异。海拔2000 m以上对协调度形成约束，人均GDP在15000～30000元之间促进绿色转型，超过40000元后效应递减。严重干旱条件（SPEI \u003C −1.5）导致农业—生态失衡。各县域被划分为四种管理类型（H–H、L–H、L–L和H–L），需采取差异化治理策略。本研究为半干旱地区提升农业—生态耦合水平提供了可量化的阈值依据。","Ecological Indicators",81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":131,"relevant":21,"comment":308},"基于RSEI、AGDI与XGBoost-SHAP的县域尺度农业生态耦合研究，方法新颖、阈值结论可量化，对半干旱区差异化治理有参考价值。",[310],{"name":305,"url":302},[312,27,313,314,315],"农业绿色发展","甘肃农业","农业生态耦合","县域治理",[317,318],"甘肃 农业生态耦合 遥感","RSEI AGDI 耦合协调度","甘肃农业生态耦合遥感-3012","10.1016\u002Fj.ecolind.2026.115463",{"doi":320,"openalex_id":322,"authors":323,"venue":305,"cited_by_count":35,"oa_url":302,"card":339,"direction":75,"ingested_from":77},"W7213602184",[324,326,329,331,334,336],{"name":325,"orcid":9},"Baopeng Xie",{"name":327,"orcid":328},"Yongqiang Wang","https:\u002F\u002Forcid.org\u002F0009-0009-5289-2403",{"name":330,"orcid":9},"Ying Chen",{"name":332,"orcid":333},"Tingting Pei","https:\u002F\u002Forcid.org\u002F0000-0002-2789-4919",{"name":335,"orcid":9},"Wen Wang",{"name":337,"orcid":338},"Jinlong Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-1161-5460",{"tldr":340,"method":341,"finding":342,"direction":75,"opportunity":343},"基于县域遥感与统计指标，揭示甘肃农业-生态耦合的时空分异及地形经济阈值。","RSEI、AGDI、耦合协调度模型与XGBoost-SHAP，2010–2023","地形起伏与人均GDP解释77%耦合差异，海拔2000米以上及严重干旱抑制协调。","可探索多尺度耦合阈值与干旱预警结合，构建差异化治理的智能决策支持系统。","2026-09-20T23:30:21.067452Z"]