[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3848":3,"related-3848":77},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":48,"slug":51,"view_count":52,"doi":53,"paper":54,"created_at":76},3848,"Effects of Burn Severity on Tree Attributes in Mediterranean Mixed Conifer Stands: Evidence from the Gazipaşa 2025 Wildfire","https:\u002F\u002Fdoi.org\u002F10.17475\u002Fkastorman.2047439","Aim of study: Burn severity in Mediterranean forests is increasing. However, the effects of burn severity on tree attributes in steep, elevation‑stratified mixed conifer forests have not been sufficiently studied. This work was conducted to examine the spatial distribution of burn severity in the Gazipaşa 2025 fire using the delta Normalized Burn Ratio (dNBR), to assess its consistency with ground indicators at the tree‑attribute level, and to analyze how tree attributes vary across burn‑severity classes. Area of study: The study area comprises the area affected by wildfire that occurred in Gazipaşa (Antalya, Türkiye) in 2025. Material and Methods: The Gazipaşa 2025 fire was examined by integrating Sentinel‑2 dNBR with measurements of tree attributes. In the field, the following were measured: bole scorch height (BSH), crown damage (CRD), crown base height (CBH), diameter at breast height (DBH), and cambium damage (CD). The field study was conducted across nine plots (three burn‑severity classes × three conifer species: Pinus brutia, Pinus nigra, Abies cilicica; n=90 trees). Main results: Burn‑severity mapping revealed a mosaic dominated by low severity (62.9%), with moderate‑low (27.2%) and moderate‑high (9.8%) classes; high severity was negligible (0.04%). Mean burn severity was highest in mixed P. nigra–A. cilicica stands, followed by pure\u002Fmixed P. brutia stands. The burn severity-CRD relationship was found to be significant (r=0.633, p\u003C0.01), confirming that the burn‑severity mapping was consistent with tree‑level indicators. Tree attributes varied according to burn severity: BSH and CRD increased from low to moderate‑high, whereas CBH did not show significant difference among burn‑severity classes. Nevertheless, CBH was higher in trees with living cambium than in dead trees. Highlights: The integration of field measurements and remote sensing techniques provides validated, scalable metrics for measuring burn severity and identifying tree‑level impacts in Mediterranean mixed conifer forests. This finding contributes to determining how burn severity, across severity classes, affects tree stem and crown damage during wildfires.","研究目的：地中海森林的火灾严重程度正在增加。然而，在陡峭、海拔分层的针叶混交林中，火灾严重程度对树木属性的影响尚未得到充分研究。本研究利用归一化燃烧比差值（dNBR）考察了Gazipaşa 2025年火灾中火灾严重程度的空间分布，评估其与树木属性层面地面指标的一致性，并分析树木属性在不同火灾严重程度等级间的变化。研究区域：研究区域包括2025年土耳其安塔利亚省Gazipaşa地区发生的野火所影响的区域。材料与方法：通过将Sentinel-2 dNBR与树木属性测量相结合，对Gazipaşa 2025年火灾进行了研究。野外测量了以下指标：树干熏黑高度（BSH）、树冠损伤（CRD）、冠基高度（CBH）、胸径（DBH）和形成层损伤（CD）。野外研究在九个样地中进行（三个火灾严重程度等级×三种针叶树：土耳其松（Pinus brutia）、欧洲黑松（Pinus nigra）、西里西亚冷杉（Abies cilicica）；n=90棵树）。主要结果：火灾严重程度制图显示以低严重程度为主的镶嵌格局（62.9%），中低（27.2%）和中高（9.8%）等级次之；高严重程度可忽略不计（0.04%）。平均火灾严重程度在歐洲黑松–西里西亚冷杉混交林中最高，其次为纯\u002F混交土耳其松林。火灾严重程度与CRD的关系显著（r=0.633，p\u003C0.01），证实火灾严重程度制图与树木层面指标一致。树木属性随火灾严重程度而变化：BSH和CRD从低到中高等级递增，而CBH在不同火灾严重程度等级间无显著差异。然而，形成层存活的树木CBH高于死亡树木。研究亮点：野外测量与遥感技术的整合为测量地中海针叶混交林中的火灾严重程度和识别树木层面影响提供了经过验证的、可扩展的指标。这一发现有助于确定火灾严重程度在不同等级间如何影响野火期间的树干和树冠损伤。",null,"Kastamonu University Journal of Forestry Faculty","2026-09-28T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦土耳其地中海混交针叶林火烧严重度与树木属性，属林火遥感生态研究，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。",[19],{"name":10,"url":6},[21,22,23,24],"遥感监测","森林火灾","地中海针叶林","火烧严重度",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":47},[32,41],{"code":33,"score":34,"supported":35,"title":36,"abstract":37,"tags":40},"I3",7,true,[],[38,39],"遥感","remote sensing",[21],{"code":42,"score":43,"supported":14,"title":44,"abstract":45,"tags":46},"H7",1,[],[],[22],"农业相关性需要复核",[49,50],"Gazipaşa 野火","Sentinel-2 dNBR 火烧严重度","Gazipaşa野火-3848",2,"10.17475\u002Fkastorman.2047439",{"doi":53,"openalex_id":55,"authors":56,"venue":10,"cited_by_count":15,"oa_url":68,"card":69,"direction":73,"ingested_from":75},"W7214607422",[57,59,62,65],{"name":58,"orcid":9},"Tuncay Karan",{"name":60,"orcid":61},"Mustafa Avcı","https:\u002F\u002Forcid.org\u002F0000-0001-6704-8947",{"name":63,"orcid":64},"Şükran Oğuzoğlu","https:\u002F\u002Forcid.org\u002F0000-0002-1816-8691",{"name":66,"orcid":67},"Kadir Alperen Coskuner","https:\u002F\u002Forcid.org\u002F0000-0001-5249-1604","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6481208",{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"结合Sentinel-2 dNBR与地面样地数据，评估2025年Gazipaşa野火对地中海混交针叶","Sentinel-2 dNBR遥感制图与9个样地90棵树的树冠、树干损伤实测结合","火烧严重度以低度为主，dNBR与树冠损伤显著相关，BSH和CRD随严重度升高而增加。","农业遥感与作物表型","可拓展多时相遥感与树种级损伤建模，构建火烧后森林恢复力评估与碳汇动态预测框架。","openalex","2026-09-30T23:30:30.776921Z",{"total":78,"page":43,"page_size":78,"items":79},6,[80,148,200,279,363,421],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":85,"content":9,"source_name":86,"source_url":83,"published_at":87,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":88,"score_detail":89,"sources":96,"tags":98,"primary_topic":9,"secondary_topics":102,"classification":103,"search_phrases":124,"slug":127,"view_count":15,"doi":128,"paper":129,"created_at":147},4823,"Comparing Sentinel-2 Spectral Indices for Mapping Forest Fire Damage in Pollino National Park, Italy","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fisprs-archives-xlviii-m-13-2026-1-2026","In recent years, climate change has significantly increased the severity and frequency of wildfires across Europe, with the 2025 season marking an unprecedented peak of one million hectares burned. Rapid and cost-effective mapping of these damaged areas is crucial for environmental recovery, making satellite remote sensing an indispensable tool. This study evaluates and compares the performance of four Sentinel-2 spectral burn indices for mapping forest fire damage within the Pollino National Park (Italy) occurred in September 2025, an area severely affected by the devastating summer fires of 2025. The analyzed indices include the Normalized Burn Ratio (NBR), Normalized Burn Ratio Plus (NBR+), Normalized Burn Ratio Short Wave Infrared (NBR-SWIR), and Normalized Difference Short Wave Infrared (ND-SWIR). An image differencing technique was applied to pre-fire (June 7, 2025) and post-fire (November 14, 2025) datasets, followed by a supervised Maximum Likelihood classification to map burned and unburned classes. Accuracy assessment via confusion matrices showed that NBR and NBR+ delivered the highest performance, achieving an Overall Accuracy (OA) of 0.976 and 0.974, respectively. ND-SWIR performed closely at 0.966, whereas NBR-SWIR yielded a lower OA of 0.873 due to the misclassification of bare soil transitions as burned areas. These findings highlight the critical role of specific SWIR-band combinations in optimizing automated wildfire monitoring systems.","近年来，气候变化显著加剧了欧洲野火的严重程度和发生频率，2025年火灾季烧毁面积达到一百万公顷，创下历史新高。快速且经济高效地绘制这些受损区域对于环境恢复至关重要，使卫星遥感成为不可或缺的工具。本研究评估并比较了四种哨兵2号（Sentinel-2）光谱燃烧指数在绘制2025年9月发生于意大利波利诺国家公园（Pollino National Park）的森林火灾损害中的表现，该地区受2025年夏季毁灭性火灾的严重影响。所分析的指数包括归一化燃烧比率（NBR）、归一化燃烧比率增强版（NBR+）、归一化燃烧比率短波红外（NBR-SWIR）以及归一化差值短波红外（ND-SWIR）。研究对火灾前（2025年6月7日）和火灾后（2025年11月14日）数据集应用了图像差值技术，随后采用监督最大似然分类法绘制燃烧与未燃烧类别。通过混淆矩阵进行的精度评估表明，NBR和NBR+表现最佳，总体精度（OA）分别达到0.976和0.974。ND-SWIR紧随其后，为0.966，而NBR-SWIR由于将裸土转变误分类为燃烧区域，总体精度较低，为0.873。这些发现凸显了特定短波红外波段组合在优化自动化野火监测系统中的关键作用。","˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences\u002FInternational archives of the photogrammetry, remote sensing and spatial information sciences","2026-10-12T00:00:00Z",74,{"impact":90,"substance":91,"depth":92,"authority":93,"freshness":94,"relevant":43,"comment":95},12,22,18,13,9,"方法对比扎实、结论明确，但属区域案例研究，对农业信息化有间接参考价值，可入精选但非必选。",[97],{"name":86,"url":83},[99,21,100,22,101],"Sentinel-2","灾害评估","植被指数",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":104},{"candidates":105,"agricultural_evidence":14,"reason":47},[106,110,117],{"code":33,"score":94,"supported":35,"title":107,"abstract":108,"tags":109},[],[38,39],[99,101,21],{"code":42,"score":111,"supported":35,"title":112,"abstract":113,"tags":116},8,[],[114,115],"气候变化","climate change",[22,100],{"code":118,"score":119,"supported":35,"title":120,"abstract":121,"tags":123},"H2",3,[],[122],"soil",[],[125,126],"Sentinel-2 森林火灾 遥感","Pollino 国家公园 火烧迹地","Sentinel-2森林火灾遥感-4823","10.5194\u002Fisprs-archives-xlviii-m-13-2026-1-2026",{"doi":128,"openalex_id":130,"authors":131,"venue":86,"cited_by_count":15,"oa_url":141,"card":142,"direction":73,"ingested_from":75},"W7221528348",[132,134,136,139],{"name":133,"orcid":9},"Giuseppina Costanzo",{"name":135,"orcid":9},"Gianluigi Ferrara",{"name":137,"orcid":138},"Claudio Parente","https:\u002F\u002Forcid.org\u002F0000-0002-8328-9764",{"name":140,"orcid":9},"Vincenzo Verde","https:\u002F\u002Fisprs-archives.copernicus.org\u002Farticles\u002FXLVIII-M-13-2026\u002F1\u002F2026\u002Fisprs-archives-XLVIII-M-13-2026-1-2026.pdf",{"tldr":143,"method":144,"finding":145,"direction":73,"opportunity":146},"比较四种 Sentinel-2 光谱指数在意大利 Pollino 国家公园 2025 年林火损伤制图","Sentinel-2 火前火后影像差值 + 最大似然监督分类，用混淆矩阵评估四种","NBR 与 NBR+ 精度最高（OA 0.976、0.974），NBR-SWIR 因裸土误判仅 0.","可探索多指数融合与机器学习分类器，提升复杂地表下火烧迹地自动制图鲁棒性。","2026-10-13T23:30:23.838067Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":153,"content":9,"source_name":154,"source_url":151,"published_at":155,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":156,"score_detail":157,"sources":160,"tags":162,"primary_topic":9,"secondary_topics":165,"classification":166,"search_phrases":178,"slug":181,"view_count":52,"doi":182,"paper":183,"created_at":199},1638,"Forest fire burned area extraction and evaluation of post-fire ecological restoration based on remote sensing technology","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1882685","Forest fires are a major threat to the ecosystem stability in the face of a growing global climate change; however, long-term post-fire recovery quantitative assessment is methodologically limited in mountainous terrain. In this study, OTSU-optimized differenced Normalized Burn Ratio (dNBR) thresholds were integrated with multi-temporal Remote Sensing Ecological Index (RSEI) model to systematically investigate the extent of burned area, classification of burn severity and spatiotemporal patterns of post-fire ecological recovery for the representative wildfire event, the 3.30 Lushan fire (March 2020) in Xichang City, China. Results indicated that there were 28.26 km 2 of burned area, and the OTSU-derived dNBR classification thresholds of 0.18\u002F0.30\u002F0.42 effectively discriminated low, moderate and high-severity burn areas in the Lushan fire. The RSEI dropped from 0.69 (pre-fire) to 0.33 (post-fire) for an overall decrease in ecological quality of 52.2%. The recovery trajectory unfolded in three phases: rapid recovery in the initial phase with the mean RSEI reaching 0.47 in 2021; decelerated recovery in the second phase, where the RSEI slightly decreased to 0.45 in 2022; and the third phase of accelerated recovery to 0.56 in 2023, and then stabilized at 0.55 in 2024. The Good and Excellent grades recovered to just 37.2% (11.26 km 2 ) by 2024, suggesting that ecological restoration is not yet complete. There were significant differences in the post-fire ecological recovery rates in different burn severity areas. Specifically, increasing burn severity promotes the rate of ecological recovery. These findings show that ecological restoration in highly complex mountainous environments requires longer than the four-year observation period to achieve full restoration, especially in the high severity burn areas. This study confirms the applicability of the integrated dNBR–RSEI assessment framework, and offers a scientific basis and theoretical support for post-fire ecological restoration planning in mountainous Southwestern China.","森林火灾在全球气候变化日益加剧的背景下，对生态系统稳定性构成重大威胁；然而，在地形复杂的山区，火灾后长期恢复的定量评估在方法上仍存在局限。本研究将OTSU优化的差分归一化燃烧指数（dNBR）阈值与多时相遥感生态指数（RSEI）模型相结合，系统研究了代表性野火事件——中国西昌市“3·30”泸山火灾（2020年3月）的过火面积、燃烧严重程度分级及火灾后生态恢复的时空格局。结果表明：过火面积为28.26 km²，基于OTSU的dNBR分级阈值0.18\u002F0.30\u002F0.42能有效区分泸山火灾中的轻度、中度和重度燃烧区域。RSEI从火灾前的0.69降至火灾后的0.33，生态质量总体下降52.2%。恢复轨迹呈现三个阶段：初期快速恢复阶段，2021年平均RSEI达到0.47；第二阶段恢复减速，2022年RSEI略微下降至0.45；第三阶段恢复加速，2023年升至0.56，随后在2024年稳定于0.55。截至2024年，良和优等级面积仅恢复到37.2%（11.26 km²），表明生态恢复尚未完成。不同燃烧严重程度区域的火灾后生态恢复速率存在显著差异。具体而言，燃烧严重程度的增加促进了生态恢复速率。这些发现表明，在高度复杂的山地环境中，生态恢复需要超过四年的观测期才能实现完全恢复，尤其是在重度燃烧区域。本研究证实了dNBR–RSEI综合评估框架的适用性，并为中国西南山区火灾后生态恢复规划提供了科学依据和理论支持。","Frontiers in Environmental Science","2026-09-03T00:00:00Z",69,{"impact":90,"substance":158,"depth":92,"authority":90,"freshness":34,"relevant":43,"comment":159},20,"研究提出集成dNBR与RSEI的评估框架，为山地森林火灾后生态恢复提供科学依据，对农业生态遥感有参考价值。",[161],{"name":154,"url":151},[163,21,22,164],"农业遥感","生态恢复",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":167},{"candidates":168,"agricultural_evidence":14,"reason":47},[169,174],{"code":33,"score":170,"supported":35,"title":171,"abstract":172,"tags":173},16,[39],[38,39],[163,21],{"code":42,"score":34,"supported":35,"title":175,"abstract":176,"tags":177},[],[114,115],[22],[179,180],"农业遥感 森林火灾 生态恢复 遥感监测","农业遥感 森林火灾","农业遥感森林火灾生态恢复遥感监测-1638","10.3389\u002Ffenvs.2026.1882685",{"doi":182,"openalex_id":184,"authors":185,"venue":154,"cited_by_count":15,"oa_url":193,"card":194,"direction":73,"ingested_from":75},"W7207710349",[186,188,190],{"name":187,"orcid":9},"Chuan Luo",{"name":189,"orcid":9},"Xin Su",{"name":191,"orcid":192},"Haitao Li","https:\u002F\u002Forcid.org\u002F0000-0001-5322-5375","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fenvironmental-science\u002Farticles\u002F10.3389\u002Ffenvs.2026.1882685\u002Fpdf",{"tldr":195,"method":196,"finding":197,"direction":73,"opportunity":198},"结合dNBR与RSEI评估森林火灾过火面积及恢复动态，发现四年未完全恢复。","OTSU优化的dNBR阈值与多时相RSEI模型结合，分析火灾前后生态质量。","过火面积28.26km²，生态质量下降52.2%，恢复分三阶段，高烈度区恢复更快但四年未完全恢复。","可延伸研究不同植被类型和地形下的长期恢复阈值，以及气候因子对恢复速率的影响，建立预测模型。","2026-09-04T23:30:32.157199Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":205,"content":9,"source_name":206,"source_url":203,"published_at":207,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":208,"score_detail":209,"sources":212,"tags":214,"primary_topic":9,"secondary_topics":219,"classification":220,"search_phrases":249,"slug":252,"view_count":15,"doi":253,"paper":254,"created_at":278},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":92,"substance":91,"depth":210,"authority":90,"freshness":94,"relevant":43,"comment":211},17,"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。",[213],{"name":206,"url":203},[21,215,216,217,218],"土地利用","森林保护","亚马逊","CBERS-4",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":221},{"candidates":222,"agricultural_evidence":35,"reason":248},[223,230,234,238,242],{"code":224,"score":34,"supported":35,"title":225,"abstract":226,"tags":229},"H6",[],[227,228],"生物多样性","biodiversity",[215],{"code":33,"score":34,"supported":35,"title":231,"abstract":232,"tags":233},[],[38,39],[21],{"code":42,"score":78,"supported":35,"title":235,"abstract":236,"tags":237},[],[114,115],[],{"code":118,"score":119,"supported":35,"title":239,"abstract":240,"tags":241},[],[122],[],{"code":243,"score":119,"supported":35,"title":244,"abstract":245,"tags":247},"I7",[],[246],"研究方法",[],"多个产业证据相当，需要确认主归属",[250,251],"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":253,"openalex_id":255,"authors":256,"venue":206,"cited_by_count":15,"oa_url":203,"card":273,"direction":73,"ingested_from":75},"W7223141286",[257,260,262,265,268,271],{"name":258,"orcid":259},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":261,"orcid":9},"Gabriel Henrique Santos Silva",{"name":263,"orcid":264},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":266,"orcid":267},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":269,"orcid":270},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":272,"orcid":9},"Sylvana Melo dos Santos",{"tldr":274,"method":275,"finding":276,"direction":73,"opportunity":277},"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",{"id":280,"title":281,"url":282,"summary":283,"summary_zh":284,"content":9,"source_name":285,"source_url":282,"published_at":207,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":286,"score_detail":287,"sources":290,"tags":292,"primary_topic":297,"secondary_topics":301,"classification":302,"search_phrases":325,"slug":328,"view_count":15,"doi":329,"paper":330,"created_at":362},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA",79,{"impact":170,"substance":91,"depth":92,"authority":288,"freshness":94,"relevant":43,"comment":289},14,"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[291],{"name":285,"url":282},[21,293,294,295,296],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":118,"slug":122,"name":298,"path":299,"parent_code":300},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":303,"locked":14,"version":28,"method":29,"evidence":304},"classified",{"candidates":305,"agricultural_evidence":35},[306,311,315,321],{"code":118,"score":288,"supported":35,"title":307,"abstract":308,"tags":310},[122],[309,122],"土壤",[],{"code":33,"score":34,"supported":35,"title":312,"abstract":313,"tags":314},[],[38,39],[21],{"code":42,"score":78,"supported":35,"title":316,"abstract":317,"tags":320},[],[318,319],"气候适应","干旱",[],{"code":224,"score":43,"supported":14,"title":322,"abstract":323,"tags":324},[],[],[295],[326,327],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":329,"openalex_id":331,"authors":332,"venue":285,"cited_by_count":15,"oa_url":282,"card":357,"direction":73,"ingested_from":75},"W7223069171",[333,335,337,339,342,344,347,350,352,354],{"name":334,"orcid":9},"Yue Li",{"name":336,"orcid":9},"Mengfei Shi",{"name":338,"orcid":9},"Fei Zhang",{"name":340,"orcid":341},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":343,"orcid":9},"Gowhar Meraj",{"name":345,"orcid":346},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":348,"orcid":349},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":351,"orcid":9},"Yuanfang Chai",{"name":353,"orcid":9},"Hongtao Xu",{"name":355,"orcid":356},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":358,"method":359,"finding":360,"direction":73,"opportunity":361},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z",{"id":364,"title":365,"url":366,"summary":367,"summary_zh":368,"content":9,"source_name":369,"source_url":366,"published_at":370,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":286,"score_detail":371,"sources":373,"tags":375,"primary_topic":380,"secondary_topics":386,"classification":387,"search_phrases":404,"slug":407,"view_count":15,"doi":408,"paper":409,"created_at":420},5565,"A Unified Survival-Based Energy Framework Linking Plant Color, Stress, and Productivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23540961","Plant coloration, expressed through leaf greenness, floral pigmentation, and fruit color intensity, reflects the integrated outcome of pigment biosynthesis, optical absorption, metabolic regulation, and energy–mass exchange with the environment. While the biochemical pathways governing the synthesis and degradation of major pigment classes—including chlorophylls, carotenoids, and anthocyanins—are well established, existing models largely interpret color expression as a localized biochemical phenomenon. Such approaches do not explicitly account for whole-plant energy balance, transpiration-driven losses, or thermodynamic constraints, limiting their ability to explain widely observed patterns of color saturation, stress-induced fading, and species-specific stability across environmental gradients. Here we develop a unified framework for plant color generation and stability that treats pigmentation as a survival-limited energetic process embedded within the broader dynamics of absorption, transport, metabolism, and entropy production. Central to the framework is a dimensionless energy survival factor, Ψ=AE\u002FTE+ε where AE represents absorbed and retained energetic and elemental resources relevant to pigment formation, TE denotes transpiration-linked, metabolic, and environmental dissipation losses, and ε captures irreducible entropy-producing degradation inherent to non-equilibrium biological systems. By construction, 0\u003CΨ\u003C1, ensuring full compliance with the second law of thermodynamics. When combined with pigment-specific optical absorption coefficients and physiological limitation functions, the framework yields a physically interpretable description of both pigment concentration and color stability. Empirical ranges for absorbed photosynthetically active radiation (≈45–70%), excitation survival (≈85–95%), and respiratory and transpiration losses (≈30–60% of assimilated resources) naturally constrain Ψ, predicting observed limits on sustained pigment accumulation without invoking biochemical inefficiency or ad hoc correction factors. The analysis demonstrates that increased light availability or enhanced biosynthetic capacity alone does not intensify coloration unless absorbed resources survive transport and loss processes. Stress-induced chlorosis, transient anthocyanin accumulation, and saturation of greenness under high irradiance emerge as direct consequences of reduced energy survival rather than impaired pigment synthesis. By explicitly separating survival from synthesis, the framework reconciles molecular-scale pigment biochemistry with whole-plant and ecosystem-scale color dynamics. This survival-based formulation provides a unified physical basis for interpreting plant color variation across species and environments and offers a scalable foundation for crop stress diagnostics, remote-sensing interpretation, and comparative plant phenotyping. More broadly, it extends survival-limited energy principles to visible plant traits, advancing plant science beyond isolated biochemical descriptions toward a systems-level understanding of how energy governs phenotypic expression.","植物色泽通过叶片绿色度、花色苷着色和果实颜色强度体现，反映了色素生物合成、光学吸收、代谢调控以及与环境的能量-物质交换的综合结果。尽管主要色素类别——包括叶绿素、类胡萝卜素和花青素——的合成与降解生化途径已较为明确，但现有模型大多将颜色表达解释为局部生化现象。此类方法未明确考虑整株植物能量平衡、蒸腾驱动的损耗或热力学约束，限制了其解释广泛观察到的颜色饱和度、胁迫诱导褪色以及跨环境梯度的物种特异性稳定性等模式的能力。本文构建了一个统一的植物颜色生成与稳定性框架，将色素沉着视为一种受生存限制的能量过程，并将其嵌入吸收、运输、代谢和熵产生的更广泛动力学之中。该框架的核心是一个无量纲能量生存因子Ψ=AE\u002FTE+ε，其中AE代表与色素形成相关的被吸收并保留的能量和元素资源，TE表示与蒸腾相关的、代谢的和环境的耗散损失，ε则捕捉非平衡生物系统固有的不可约熵产生降解。根据构建方式，0\u003CΨ\u003C1，确保完全符合热力学第二定律。当与色素特异性光学吸收系数和生理限制函数相结合时，该框架可对色素浓度和颜色稳定性给出具有物理可解释性的描述。吸收性光合有效辐射（约45–70%）、激发能存活率（约85–95%）以及呼吸和蒸腾损失（约占同化资源的30–60%）的经验范围自然约束了Ψ，从而预测出所观察到的持续色素积累限度，而无需引入生化低效性或特设校正因子。分析表明，仅增加光照可用性或增强生物合成能力并不会加深颜色，除非被吸收的资源能够在运输和损失过程中存活下来。胁迫诱导的黄化、瞬时花青素积累以及高辐照下绿色度饱和，均表现为能量生存率降低而非色素合成受损的直接后果。通过明确分离生存","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",{"impact":92,"substance":91,"depth":92,"authority":90,"freshness":94,"relevant":43,"comment":372},"提出以能量生存因子统一解释植物颜色与胁迫的物理框架，方法新颖且对遥感解译和作物诊断有潜在价值，但属预印本理论模型，尚缺田间验证。",[374],{"name":369,"url":366},[376,21,377,378,379],"智慧农业","植物表型","作物胁迫诊断","能量平衡",{"code":381,"slug":382,"name":383,"path":384,"parent_code":385},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":303,"locked":14,"version":28,"method":29,"evidence":388},{"candidates":389,"agricultural_evidence":35},[390,395,400],{"code":381,"score":119,"supported":35,"title":391,"abstract":392,"tags":394},[],[393],"萝卜",[],{"code":396,"score":43,"supported":14,"title":397,"abstract":398,"tags":399},"I1",[],[],[376],{"code":33,"score":43,"supported":14,"title":401,"abstract":402,"tags":403},[],[],[21],[405,406],"植物颜色 能量生存因子","作物胁迫 遥感诊断","植物颜色能量生存因子-5565","10.5281\u002Fzenodo.23540961",{"doi":408,"openalex_id":410,"authors":411,"venue":369,"cited_by_count":15,"oa_url":366,"card":415,"direction":73,"ingested_from":75},"W7223098474",[412],{"name":413,"orcid":414},"Mokhdum Azam Mashrafi","https:\u002F\u002Forcid.org\u002F0009-0002-1801-1130",{"tldr":416,"method":417,"finding":418,"direction":73,"opportunity":419},"提出基于生存的能量框架，用能量存活因子解释植物颜色生成与稳定性。","构建无量纲能量存活因子Ψ=AE\u002F(TE+ε)，结合色素吸收系数与生理限制函数。","颜色深浅取决于吸收资源经运输损耗后的存活率，而非单纯合成能力。","可将Ψ因子与冠层光谱\u002F表型遥感结合，发展胁迫早期诊断与生产力预测模型。","2026-10-22T11:20:31.195819Z",{"id":422,"title":423,"url":424,"summary":425,"summary_zh":426,"content":9,"source_name":369,"source_url":424,"published_at":370,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":427,"score_detail":428,"sources":430,"tags":432,"primary_topic":436,"secondary_topics":441,"classification":442,"search_phrases":480,"slug":483,"view_count":15,"doi":484,"paper":485,"created_at":497},5563,"AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531525","Abstract: Crop diseases and pest infestations remain persistent threats to plantation productivity, with pests estimated to destroy up to 40% of global crop production and plant diseases and invasive insects imposing substantial economic losses worldwide (Food and Agriculture Organization of the United Nations [FAO], 2019). Climate change is intensifying these risks by altering the geographic range, seasonality, and population dynamics of pathogens and pests (Gullino et al., 2021; Skendžić et al., 2021). This paper examines how artificial intelligence (AI) — including convolutional neural networks (CNNs), vision transformers (ViTs), unmanned aerial vehicle (UAV)-based multispectral and hyperspectral sensing, and Internet of Things (IoT)-enabled monitoring — can strengthen the early detection of biotic stress in plantation crops and thereby support plantation risk management. Drawing on recent peer-reviewed and industry literature, the paper synthesises evidence on detection performance, deployment modalities, and pathways for integration into enterprise risk-management systems. It proposes a conceptual framework in which AI-enabled early-warning systems operate as a continuous, spatially explicit risk-sensing layer linking agronomic observations with operational, financial, and insurance decision-making. AI can improve the timeliness, spatial precision, and consistency of surveillance; however, its risk-management value depends on field validation, representative training data, reliable infrastructure, affordable deployment, and appropriate governance. AI should therefore currently be positioned as decision support that augments, rather than replaces, agronomic expertise and ground-truthing. Keywords: artificial intelligence; plant disease detection; pest infestation; precision agriculture; plantation risk management; machine learning; remote sensing. Title: AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management Author: Dr. Geetha Govindan K Gopalakrishnan International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 2, October 2026 - March 2027 Page No: 54-59 Research Publish Journals Website: www.researchpublish.com Published Date: 21-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-management","摘要：作物病害与虫害侵染始终是种植园生产力的持续威胁，据估计，害虫每年可摧毁全球高达40%的作物产量，植物病害与入侵昆虫在全球范围内造成重大经济损失（联合国粮食及农业组织[FAO]，2019）。气候变化通过改变病原体与害虫的地理分布范围、季节性规律及种群动态，正在加剧这些风险（Gullino等，2021；Skendžić等，2021）。本文探讨人工智能（AI）——包括卷积神经网络（CNN）、视觉变换器（ViT）、基于无人机（UAV）的多光谱与高光谱遥感，以及物联网（IoT）赋能的监测技术——如何强化种植园作物生物胁迫的早期检测，从而支撑种植园风险管理。本文基于近期同行评审文献与行业文献，综合梳理了检测性能、部署模式及其融入企业风险管理系统的路径等方面的证据。文章提出一个概念框架，其中AI赋能的预警系统作为持续运行、空间显性的风险感知层，将农艺观测与运营、财务及保险决策相衔接。AI能够提升监测的及时性、空间精度与一致性；然而，其风险管理价值取决于田间验证、具有代表性的训练数据、可靠的基础设施、可负担的部署成本以及适当的治理。因此，当前应将AI定位为决策支持工具，用以增强而非替代农艺专业知识与实地核验。关键词：人工智能；植物病害检测；虫害侵染；精准农业；种植园风险管理；机器学习；遥感。题目：AI驱动的作物病害与虫害侵染早期检测：种植园风险管理的新范式。作者：Geetha Govindan K Gopalakrishnan博士。《国际管理与商业创新期刊》ISSN 2348-7585（在线）第14卷第2期，2026年10月—2027年3月，第54-59页。研究出版期刊网站：www.researchpublish.com。发布日期：2026年10月21日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-ma",56,{"impact":90,"substance":170,"depth":93,"authority":78,"freshness":94,"relevant":43,"comment":429},"综述性论文，系统梳理AI与遥感在作物病虫害早期预警中的应用并提出风险管理框架，有参考价值但方法创新与数据规模有限，信源为普通出版平台。",[431],{"name":369,"url":424},[376,433,21,434,435],"农业人工智能","风险管理","病虫害监测",{"code":437,"slug":438,"name":439,"path":440,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":443,"locked":14,"version":28,"method":29,"evidence":444},"partial",{"candidates":445,"agricultural_evidence":35,"reason":479},[446,455,461,469,475],{"code":447,"score":13,"supported":35,"title":448,"abstract":449,"tags":454},"I2",[],[450,451,452,453],"机器学习","machine learning","artificial intelligence","decision support",[433],{"code":33,"score":13,"supported":35,"title":456,"abstract":457,"tags":460},[],[38,39,458,459],"multispectral","hyperspectral",[21],{"code":462,"score":94,"supported":35,"title":463,"abstract":464,"tags":468},"I5",[],[465,466,467],"物联网","internet of things","IoT",[],{"code":396,"score":34,"supported":35,"title":470,"abstract":471,"tags":474},[],[472,473],"精准农业","precision agriculture",[376],{"code":42,"score":78,"supported":35,"title":476,"abstract":477,"tags":478},[],[114,115],[],"一级领域明确，二级方向需要细分",[481,482],"AI 作物病害 早期检测","无人机 多光谱 病虫害监测","AI作物病害早期检测-5563","10.5281\u002Fzenodo.23531525",{"doi":484,"openalex_id":486,"authors":487,"venue":369,"cited_by_count":15,"oa_url":424,"card":490,"direction":496,"ingested_from":75},"W7223085400",[488],{"name":489,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":491,"method":492,"finding":493,"direction":494,"opportunity":495},"综述AI（CNN、ViT、无人机遥感、物联网）用于作物病虫害早期检测并融入种植园风险管理。","文献综述，整合CNN、ViT、无人机多\u002F高光谱与IoT监测的检测性能与部署方式。","AI可提升监测及时性与空间精度，但价值取决于田间验证、代表性数据与治理，应作决策支持而非替代专家。","农业人工智能与决策模型","可将AI预警层与保险、金融风控系统对接，研究跨域数据融合与可解释风险定价的空白。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.428080Z"]