[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4088":3,"related-4088":85},{"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":49,"slug":52,"view_count":15,"doi":53,"paper":54,"created_at":84},4088,"Empirically informed AHP-FR modeling for multi-hazard susceptibility mapping in a mountainous transport corridors","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-66468-3","Abstract Mountainous transport corridors are increasingly exposed to interacting natural hazards that intensify infrastructure, socio-economic, and land-use risks. However, most susceptibility assessments for high-mountain corridors still focus on single hazards or rely heavily on expert-based weighting, which can introduce subjectivity into spatial decision-making. This study develops an empirically informed Analytical Hierarchy Process–Frequency Ratio (AHP-FR) framework for integrated landslide, debris-flow, and flood susceptibility mapping along the Barakhan–Gilgit–Juglot section of the northern China–Pakistan Economic Corridor (CPEC). The main methodological contribution is the use of frequency-ratio-derived empirical evidence that was integrated with AHP-derived factor weights to calculate final subclass weights and thereby reducing reliance on purely subjective expert judgment. Field surveys, remote sensing datasets, historical hazard inventories, and 12 conditioning factors were integrated in a GIS environment to generate single-hazard susceptibility maps and an eight-class multi-hazard susceptibility map. Independent ROC-AUC validation showed acceptable to very good predictive performance, with AUC values of 0.803 for landslides, 0.762 for debris flows, and 0.872 for floods. The results indicate that 63% of the corridor is exposed to one or more hazards, including 59% exposed to two or more overlapping hazards; 41% are co-susceptible to landslides and debris flows, while 37% falls within the no\u002Fvery low multi-hazard susceptibility class. The spatial outputs identify major hazard-prone sections, including Attabad, Chalt, and Passu, providing practical guidance for road-maintenance prioritization, slope-stabilization planning, drainage improvement, and emergency preparedness. The added value of this study lies in combining empirical FR-based weighting with AHP and translating separate hazard maps into explicit multi-hazard combination classes, offering a reproducible decision-support framework for resilient planning in mountainous transport corridors.","摘要 山区交通走廊日益暴露于相互作用的自然灾害之下，这些灾害加剧了基础设施、社会经济和土地利用风险。然而，大多数高山走廊的易发性评估仍集中于单一灾害，或严重依赖基于专家经验的权重赋值，这可能在空间决策中引入主观性。本研究开发了一种基于经验数据的层次分析法-频率比（AHP-FR）框架，用于中巴经济走廊（CPEC）北段Barakhan–Gilgit–Juglot沿线滑坡、泥石流和洪水灾害的综合易发性制图。该方法的主要贡献在于利用频率比衍生的经验证据，将其与AHP衍生的因子权重相结合，以计算最终子类权重，从而减少对纯主观专家判断的依赖。研究在GIS环境中整合了野外调查、遥感数据集、历史灾害编目和12个致灾因子，生成了单灾种易发性图和八类多灾种易发性图。独立的ROC-AUC验证显示预测性能处于可接受到很好水平，滑坡AUC值为0.803，泥石流为0.762，洪水为0.872。结果表明，该走廊63%的区域暴露于一种或多种灾害，其中59%暴露于两种或以上重叠灾害；41%同时易受滑坡和泥石流影响，而37%属于无\u002F极低多灾种易发性类别。空间输出结果识别出主要灾害易发路段，包括Attabad、Chalt和Passu，为道路维护优先级排序、边坡稳定规划、排水改善和应急准备提供了实践指导。本研究的增值在于将基于经验FR的权重与AHP相结合，并将各单灾种图转化为明确的多灾种组合类别，为山区交通走廊的韧性规划提供了可重复的决策支持框架。",null,"Scientific Reports","2026-10-01T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦山区交通走廊多灾种易发性制图，属地质灾害与遥感领域，与三农、农业信息化、智慧农业主题无关，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"遥感监测","地质灾害","中巴经济走廊","多灾种评估",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":48},[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":35,"title":44,"abstract":45,"tags":47},"I4",3,[],[46],"GIS",[],"农业相关性需要复核",[50,51],"中巴经济走廊 地质灾害","AHP-FR 滑坡 泥石流","中巴经济走廊地质灾害-4088","10.1038\u002Fs41598-026-66468-3",{"doi":53,"openalex_id":55,"authors":56,"venue":10,"cited_by_count":15,"oa_url":76,"card":77,"direction":81,"ingested_from":83},"W7215052453",[57,60,63,65,67,69,71,74],{"name":58,"orcid":59},"Muhammad Awais Hussain","https:\u002F\u002Forcid.org\u002F0000-0001-6223-8020",{"name":61,"orcid":62},"Xinyu Xie","https:\u002F\u002Forcid.org\u002F0000-0002-0750-0599",{"name":64,"orcid":9},"Chenxu Su",{"name":66,"orcid":9},"Fatima Mehvish",{"name":68,"orcid":9},"Muhammad Muneer",{"name":70,"orcid":9},"Muhammad Akbar",{"name":72,"orcid":73},"Ahmed Othman Alanazi","https:\u002F\u002Forcid.org\u002F0009-0002-6500-8903",{"name":75,"orcid":9},"Masamba Pinto¹","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-66468-3_reference.pdf",{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"提出AHP-FR经验加权框架，绘制中巴经济走廊山区多灾种易发性图。","频率比与AHP结合，GIS整合野外调查、遥感与12个致灾因子。","63%走廊暴露于至少一种灾害，59%面临两种以上重叠灾害。","农业遥感与作物表型","可将该多灾种易发性框架迁移至农业基础设施与农田受灾风险评估。","openalex","2026-10-03T23:30:33.771580Z",{"total":86,"page":87,"page_size":86,"items":88},6,1,[89,155,212,298,383,441],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":95,"source_url":92,"published_at":96,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":97,"sources":99,"tags":101,"primary_topic":9,"secondary_topics":103,"classification":104,"search_phrases":124,"slug":127,"view_count":15,"doi":128,"paper":129,"created_at":154},4908,"Morphological characteristics and spatial distribution of shallow landslides in the Qinling-Daba Mountains, China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffeart.2026.1858890","Shallow landslides dominate the hazard inventory of the Qinba Mountains, China, yet their regional-scale characteristics and controlling factors remain poorly understood. Based on 4,315 historical shallow landslides (1998–2022), this study integrates field measurements, GIS-based spatial analysis, and statistical methods to investigate landslide morphology, spatial distribution, and conditioning factors. Methodologically, we used variable-specific correlation analyses (Spearman, Cramér’s V , Kruskal–Wallis H tests) and multi-buffer processing for linear features (faults, rivers, roads). Results reveal: (1) Power law scaling in area-volume ( V = 2.27 A 1.034 ) and size-frequency distribution (exponent 2.27), confirming self-organized criticality of the landslide system. (2) Regional differentiation: the Hanjiang River basin shows a gradient from high-density, small failures upstream to lower-density, larger failures downstream; the Jialingjiang and the Hanjiang tributary basins are dominated by larger landslides due to competent lithologies and higher relief. (3) Key controlling factors: epimetamorphic rocks, elevation \u003C1,000 m, slope \u003C30°, relief \u003C200 m, annual precipitation 800–1,100 mm, NDVI 0.5–0.7, population density >10 persons\u002Fkm 2 , and distance to roads (\u003C500 m), rivers (\u003C1,800 m) and faults (\u003C6,000 m). In general, macro-topography, regional tectonics, and climate collaboratively define the potential locations of landslides, whereas micro-topography, lithology, and vegetation cover govern their scale and type; Human activities change the dominant geometry of landslides to a certain extent. (4) The spatial pattern of landslide in continuous variables is analyzed at a 36 km distance threshold, with mean annual precipitation exhibiting the strongest positive spatial autocorrelation (Moran’s I = 0.93), and LISA maps show High-High clusters of precipitation and coefficient of elevation variation in Ankang, while elevation shows an opposite pattern. These findings provide a scientific basis for hazard mitigation and land-use planning in the Qinba Mountains and offer a methodological framework for landslide susceptibility studies in other geologically similar regions.","浅层滑坡是秦巴山区灾害清单中的主要类型，但其区域尺度特征及控制因素仍缺乏系统认识。基于1998—2022年间4 315处历史浅层滑坡数据，本研究综合野外实测、基于GIS的空间分析与统计方法，探讨了滑坡形态、空间分布及孕灾因素。方法上，采用变量特异性相关分析（Spearman、Cramér's V、Kruskal–Wallis H检验）以及针对线性要素（断层、河流、道路）的多缓冲带处理。结果表明：（1）面积—体积关系（V = 2.27 A^1.034）与规模—频率分布（指数2.27）均呈幂律标度，证实了滑坡系统的自组织临界性。（2）区域分异显著：汉江流域表现为从上游高密度小规模滑坡向下游低密度大规模滑坡的梯度变化；嘉陵江及汉江支流流域因岩性坚硬、地形起伏较大，以较大规模滑坡为主。（3）关键控制因素包括：浅变质岩、海拔\u003C1 000 m、坡度\u003C30°、地形起伏\u003C200 m、年降水量800~1 100 mm、NDVI 0.5~0.7、人口密度>10人\u002Fkm²，以及距道路\u003C500 m、距河流\u003C1 800 m、距断层\u003C6 000 m。总体而言，宏观地形、区域构造与气候共同决定了滑坡的潜在发生位置，而微观地形、岩性与植被覆盖控制其规模与类型；人类活动在一定程度上改变了滑坡的优势几何形态。（4）在36 km距离阈值下分析连续变量的滑坡空间格局，年均降水量表现出最强的正空间自相关（Moran's I = 0.93），LISA图显示安康地区降水量与高程变异系数呈高—高聚集，而高程则呈相反格局。上述发现为秦巴山区灾害减缓与土地利用规划提供了科学依据，并为其他地质条件类似区域的滑坡易发性研究提供了方法框架。","Frontiers in Earth Science","2026-10-13T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":98},"该论文研究秦巴山区浅层滑坡形态与空间分布，属地质灾害与自然地理领域，与三农、农业信息化、智慧农业主题无直接关联，不建议进入每日精选。",[100],{"name":95,"url":92},[21,102,22],"滑坡灾害",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":105},{"candidates":106,"agricultural_evidence":14,"reason":48},[107,114,120],{"code":42,"score":108,"supported":35,"title":109,"abstract":110,"tags":113},9,[],[111,46,112],"空间分析","spatial analysis",[],{"code":115,"score":43,"supported":35,"title":116,"abstract":117,"tags":119},"I7",[],[118],"methodological framework",[],{"code":33,"score":87,"supported":14,"title":121,"abstract":122,"tags":123},[],[],[21],[125,126],"秦岭大巴山 浅层滑坡","秦巴山区 滑坡分布","秦岭大巴山浅层滑坡-4908","10.3389\u002Ffeart.2026.1858890",{"doi":128,"openalex_id":130,"authors":131,"venue":95,"cited_by_count":15,"oa_url":147,"card":148,"direction":153,"ingested_from":83},"W7221663082",[132,134,136,138,140,143,145],{"name":133,"orcid":9},"Dingchao Qi",{"name":135,"orcid":9},"Wen Fan",{"name":137,"orcid":9},"Deng Longsheng",{"name":139,"orcid":9},"Yufei Song",{"name":141,"orcid":142},"Jun Li","https:\u002F\u002Forcid.org\u002F0000-0001-9198-0945",{"name":144,"orcid":9},"Yanbo Cao",{"name":146,"orcid":9},"Yiwei Ji","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fearth-science\u002Farticles\u002F10.3389\u002Ffeart.2026.1858890\u002Fpdf",{"tldr":149,"method":150,"finding":151,"direction":81,"opportunity":152},"基于4315个浅层滑坡数据，分析秦巴山区滑坡形态、空间分布及控制因素。","GIS空间分析、Spearman\u002FCramér's V\u002FKruskal-Wall","滑坡面积-体积呈幂律关系，宏观地形构造气候定位置，微观岩性植被定规模。","可结合遥感与机器学习构建滑坡易发性动态模型，并探究农业活动对滑坡的驱动机制。","农业人工智能与决策模型","2026-10-14T23:30:47.340224Z",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":160,"content":9,"source_name":161,"source_url":158,"published_at":162,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":163,"sources":165,"tags":167,"primary_topic":9,"secondary_topics":170,"classification":171,"search_phrases":188,"slug":191,"view_count":192,"doi":193,"paper":194,"created_at":211},3798,"Debris Flow Susceptibility Mapping Using Explainable Deep Learning in Eastern Hindukush Pakistan","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fesp.70419","ABSTRACT Debris flows are among the most destructive geomorphological hazards in mountainous regions, causing damage to human settlements, infrastructure and ecosystems. The Eastern Hindu Kush region of Pakistan is particularly vulnerable due to steep topography, intense monsoonal precipitation, active tectonics and increasing anthropogenic disturbance. This study develops an integrated debris flow susceptibility assessment framework by combining convolutional neural networks (CNN) with SHapley Additive exPlanations (SHAP). A total of 13 conditioning factors representing topographic, hydrological, geological, environmental and anthropogenic characteristics were prepared using GIS and remote sensing datasets at a spatial resolution of 30 × 30 m. The CNN model demonstrated strong predictive performance, achieving an area under the curve (AUC) of 0.94, overall accuracy (ACC) of 0.86, precision of 0.84, recall of 0.88, an F1‐score of 0.86 and a Kappa coefficient of 0.71. The resulting susceptibility map identified approximately 26.3% of the study area as high to very high susceptibility zones, predominantly concentrated along steep river valleys and highly dissected mountainous terrain. The susceptibility map was further validated by debris flow density analysis, which showed hazard density increasing from 0 events\u002Fkm 2 in very low zones to 0.048 events\u002Fkm 2 in very high zones, with most historical debris flow events concentrated within the very high susceptibility class. SHAP analysis identified slope, NDVI, rainfall, distance to roads and distance to streams as the most influential conditioning factors controlling debris flow occurrence. The proposed CNN–SHAP framework provides a robust and interpretable approach for regional‐scale debris flow susceptibility assessment, with applicability to similar mountainous environments. These findings provide valuable information for hazard mitigation, infrastructure planning and land‐use management in data‐scarce mountain regions.","泥石流是山区最具破坏性的地貌灾害之一，会对人类聚居地、基础设施和生态系统造成损害。巴基斯坦东兴都库什地区由于地形陡峭、季风降水强烈、构造活动活跃以及人为干扰日益加剧，尤为脆弱。本研究通过将卷积神经网络（CNN）与SHapley加法解释（SHAP）相结合，构建了一个综合性的泥石流易发性评估框架。利用GIS和遥感数据集，以30×30 m的空间分辨率制备了代表地形、水文、地质、环境和人为特征的13个 conditioning factors（孕灾因子）。CNN模型表现出较强的预测性能，曲线下面积（AUC）达到0.94，总体精度（ACC）为0.86，精确率为0.84，召回率为0.88，F1分数为0.86，Kappa系数为0.71。所得易发性图识别出约26.3%的研究区域为高至极高易发性区，主要集中于陡峭的河谷和高度切割的山地地形。易发性图进一步通过泥石流密度分析得到验证，结果显示灾害密度从极低区的0 events\u002Fkm²增加到极高区的0.048 events\u002Fkm²，且大多数历史泥石流事件集中在极高易发性类别内。SHAP分析识别出坡度、NDVI、降雨量、距道路距离和距河流距离是控制泥石流发生的最具影响力的孕灾因子。所提出的CNN–SHAP框架为区域尺度泥石流易发性评估提供了一种稳健且可解释的方法，适用于类似的山区环境。这些发现为数据稀缺山区的灾害减缓、基础设施规划和土地利用管理提供了有价值的信息。","Earth Surface Processes and Landforms","2026-09-27T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":164},"泥石流易发性遥感与深度学习研究，属地质灾害领域，与三农、农业信息化无直接关联，不建议进入每日精选。",[166],{"name":161,"url":158},[168,169,21,22],"深度学习","SHAP",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":172},{"candidates":173,"agricultural_evidence":14,"reason":48},[174,180,184],{"code":175,"score":13,"supported":35,"title":176,"abstract":178,"tags":179},"I2",[177],"deep learning",[],[169,168],{"code":33,"score":34,"supported":35,"title":181,"abstract":182,"tags":183},[],[38,39],[21],{"code":42,"score":43,"supported":35,"title":185,"abstract":186,"tags":187},[],[46],[],[189,190],"东兴都库什 泥石流 易发性","CNN SHAP 泥石流","东兴都库什泥石流易发性-3798",4,"10.1002\u002Fesp.70419",{"doi":193,"openalex_id":195,"authors":196,"venue":161,"cited_by_count":15,"oa_url":205,"card":206,"direction":81,"ingested_from":83},"W7214538761",[197,200,202],{"name":198,"orcid":199},"Kashif Ullah","https:\u002F\u002Forcid.org\u002F0000-0003-2880-0977",{"name":201,"orcid":9},"Xi Chen",{"name":203,"orcid":204},"Tie Liu","https:\u002F\u002Forcid.org\u002F0000-0002-6879-4818","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fesp.70419",{"tldr":207,"method":208,"finding":209,"direction":81,"opportunity":210},"用CNN结合SHAP绘制巴基斯坦东兴都库什泥石流易发性图并解释关键因子。","CNN模型+SHAP，基于GIS与遥感13个30米条件因子。","高易发区占26.3%，坡度、NDVI、降雨、道路与河流距离最关键。","可将CNN-SHAP框架迁移至山区农业灾害风险与耕地安全评估，弥补数据稀缺区研究空白。","2026-09-29T23:30:30.418175Z",{"id":213,"title":214,"url":215,"summary":216,"summary_zh":217,"content":9,"source_name":218,"source_url":215,"published_at":219,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":220,"score_detail":221,"sources":227,"tags":229,"primary_topic":9,"secondary_topics":234,"classification":235,"search_phrases":268,"slug":271,"view_count":15,"doi":272,"paper":273,"created_at":297},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":222,"substance":223,"depth":224,"authority":225,"freshness":108,"relevant":87,"comment":226},18,22,17,12,"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。",[228],{"name":218,"url":215},[21,230,231,232,233],"土地利用","森林保护","亚马逊","CBERS-4",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":236},{"candidates":237,"agricultural_evidence":35,"reason":267},[238,245,249,256,262],{"code":239,"score":34,"supported":35,"title":240,"abstract":241,"tags":244},"H6",[],[242,243],"生物多样性","biodiversity",[230],{"code":33,"score":34,"supported":35,"title":246,"abstract":247,"tags":248},[],[38,39],[21],{"code":250,"score":86,"supported":35,"title":251,"abstract":252,"tags":255},"H7",[],[253,254],"气候变化","climate change",[],{"code":257,"score":43,"supported":35,"title":258,"abstract":259,"tags":261},"H2",[],[260],"soil",[],{"code":115,"score":43,"supported":35,"title":263,"abstract":264,"tags":266},[],[265],"研究方法",[],"多个产业证据相当，需要确认主归属",[269,270],"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":272,"openalex_id":274,"authors":275,"venue":218,"cited_by_count":15,"oa_url":215,"card":292,"direction":81,"ingested_from":83},"W7223141286",[276,279,281,284,287,290],{"name":277,"orcid":278},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":280,"orcid":9},"Gabriel Henrique Santos Silva",{"name":282,"orcid":283},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":285,"orcid":286},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":288,"orcid":289},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":291,"orcid":9},"Sylvana Melo dos Santos",{"tldr":293,"method":294,"finding":295,"direction":81,"opportunity":296},"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":299,"title":300,"url":301,"summary":302,"summary_zh":303,"content":9,"source_name":304,"source_url":301,"published_at":219,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":305,"score_detail":306,"sources":310,"tags":312,"primary_topic":317,"secondary_topics":321,"classification":322,"search_phrases":345,"slug":348,"view_count":15,"doi":349,"paper":350,"created_at":382},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":307,"substance":223,"depth":222,"authority":308,"freshness":108,"relevant":87,"comment":309},16,14,"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。",[311],{"name":304,"url":301},[21,313,314,315,316],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":257,"slug":260,"name":318,"path":319,"parent_code":320},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":323,"locked":14,"version":28,"method":29,"evidence":324},"classified",{"candidates":325,"agricultural_evidence":35},[326,331,335,341],{"code":257,"score":308,"supported":35,"title":327,"abstract":328,"tags":330},[260],[329,260],"土壤",[],{"code":33,"score":34,"supported":35,"title":332,"abstract":333,"tags":334},[],[38,39],[21],{"code":250,"score":86,"supported":35,"title":336,"abstract":337,"tags":340},[],[338,339],"气候适应","干旱",[],{"code":239,"score":87,"supported":14,"title":342,"abstract":343,"tags":344},[],[],[315],[346,347],"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":349,"openalex_id":351,"authors":352,"venue":304,"cited_by_count":15,"oa_url":301,"card":377,"direction":81,"ingested_from":83},"W7223069171",[353,355,357,359,362,364,367,370,372,374],{"name":354,"orcid":9},"Yue Li",{"name":356,"orcid":9},"Mengfei Shi",{"name":358,"orcid":9},"Fei Zhang",{"name":360,"orcid":361},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":363,"orcid":9},"Gowhar Meraj",{"name":365,"orcid":366},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":368,"orcid":369},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":371,"orcid":9},"Yuanfang Chai",{"name":373,"orcid":9},"Hongtao Xu",{"name":375,"orcid":376},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":378,"method":379,"finding":380,"direction":81,"opportunity":381},"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":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":9,"source_name":389,"source_url":386,"published_at":390,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":305,"score_detail":391,"sources":393,"tags":395,"primary_topic":400,"secondary_topics":406,"classification":407,"search_phrases":424,"slug":427,"view_count":15,"doi":428,"paper":429,"created_at":440},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":222,"substance":223,"depth":222,"authority":225,"freshness":108,"relevant":87,"comment":392},"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。",[394],{"name":389,"url":386},[396,21,397,398,399],"智慧农业","植物表型","作物胁迫诊断","能量平衡",{"code":401,"slug":402,"name":403,"path":404,"parent_code":405},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":323,"locked":14,"version":28,"method":29,"evidence":408},{"candidates":409,"agricultural_evidence":35},[410,415,420],{"code":401,"score":43,"supported":35,"title":411,"abstract":412,"tags":414},[],[413],"萝卜",[],{"code":416,"score":87,"supported":14,"title":417,"abstract":418,"tags":419},"I1",[],[],[396],{"code":33,"score":87,"supported":14,"title":421,"abstract":422,"tags":423},[],[],[21],[425,426],"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":428,"openalex_id":430,"authors":431,"venue":389,"cited_by_count":15,"oa_url":386,"card":435,"direction":81,"ingested_from":83},"W7223098474",[432],{"name":433,"orcid":434},"Mokhdum Azam Mashrafi","https:\u002F\u002Forcid.org\u002F0009-0002-1801-1130",{"tldr":436,"method":437,"finding":438,"direction":81,"opportunity":439},"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":442,"title":443,"url":444,"summary":445,"summary_zh":446,"content":9,"source_name":389,"source_url":444,"published_at":390,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":447,"score_detail":448,"sources":451,"tags":453,"primary_topic":457,"secondary_topics":462,"classification":463,"search_phrases":500,"slug":503,"view_count":15,"doi":504,"paper":505,"created_at":516},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":225,"substance":307,"depth":449,"authority":86,"freshness":108,"relevant":87,"comment":450},13,"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。",[452],{"name":389,"url":444},[396,454,21,455,456],"农业人工智能","风险管理","病虫害监测",{"code":458,"slug":459,"name":460,"path":461,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":464,"locked":14,"version":28,"method":29,"evidence":465},"partial",{"candidates":466,"agricultural_evidence":35,"reason":499},[467,475,481,489,495],{"code":175,"score":13,"supported":35,"title":468,"abstract":469,"tags":474},[],[470,471,472,473],"机器学习","machine learning","artificial intelligence","decision support",[454],{"code":33,"score":13,"supported":35,"title":476,"abstract":477,"tags":480},[],[38,39,478,479],"multispectral","hyperspectral",[21],{"code":482,"score":108,"supported":35,"title":483,"abstract":484,"tags":488},"I5",[],[485,486,487],"物联网","internet of things","IoT",[],{"code":416,"score":34,"supported":35,"title":490,"abstract":491,"tags":494},[],[492,493],"精准农业","precision agriculture",[396],{"code":250,"score":86,"supported":35,"title":496,"abstract":497,"tags":498},[],[253,254],[],"一级领域明确，二级方向需要细分",[501,502],"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":504,"openalex_id":506,"authors":507,"venue":389,"cited_by_count":15,"oa_url":444,"card":510,"direction":515,"ingested_from":83},"W7223085400",[508],{"name":509,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":511,"method":512,"finding":513,"direction":153,"opportunity":514},"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。","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"]