[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2174":3,"related-2174":92},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":69,"slug":72,"view_count":22,"doi":73,"paper":74,"created_at":91},2174,"Optical Remote Sensing and Machine Learning Enable Scalable Carbon Mapping in Himalayan Forests Despite Spectral Saturation Challenges","https:\u002F\u002Fdoi.org\u002F10.32942\u002Fx29d5f","Accurate aboveground biomass (AGB) estimation is critical for climate change mitigation and carbon accounting in mountain regions, yet spatially explicit mapping remains limited by reliance on conventional field methods and underexplored machine learning approaches in the Himalaya. Bhutan, a carbon-negative nation maintaining 70% forest cover, faces urgent demand to develop robust monitoring, reporting, and verification (MRV) systems for carbon market participation under the Paris Agreement Article 6. This study developed a random forest (RF) model that integrates Sentinel-2A multispectral imagery, nine vegetation indices, topographic variables, and field-measured AGB from 99 plots across 244.6 hectares in the Kyentshen Community Forest. Using recursive feature elimination, 10 key predictors were identified from 27 candidate variables: Band 11, SLAVI, Band 12, WDRVI, NDVI, elevation, NDWI, GNDVI, band ratio (r3), and Band 9. The optimized RF model achieved R² = 0.34 and RMSE = 67.98 ± 19.79 t\u002Fha on training data (Ntree = 500, Mtry = 3). However, test-set performance declined substantially (R² = 0.42, RMSE = 86.14 t\u002Fha), indicating overfitting and reflecting limitations of spectral saturation in high-biomass stands (>300 t\u002Fha). A Wilcoxon signed-rank test revealed systematic overestimation (predicted median: 118.36 t\u002Fha vs. observed: 105.15 t\u002Fha; p = 0.03). Despite moderate accuracy, this study demonstrates that freely available Sentinel-2 data combined with machine learning offers a cost-effective, scalable alternative to traditional field mapping for rapid AGB assessment across Bhutan's temperate forests. Vegetation indices, particularly SLAVI and WDRVI, proved superior to raw spectral bands, offering improved sensitivity to dense canopies. The spatial-transect methodology and 10-predictor framework provide a replicable foundation for advancing Bhutan's forest carbon accounting systems. We recommend integrating synthetic aperture radar (SAR) or LiDAR to overcome optical saturation, enabling higher accuracy for robust MRV compliance and carbon credit monetization. This work directly supports the maintenance of Bhutan's carbon-neutral status and establishes the technical baseline for scaling AGB monitoring across mountain regions where engineered infrastructure capacity is limited, but climate action urgency is paramount.","准确估算地上生物量（AGB）对于山区减缓气候变化和碳核算至关重要，但空间显式制图仍受限于对传统野外方法的依赖以及喜马拉雅地区机器学习方法研究不足。不丹是一个维持70%森林覆盖率的负碳国家，迫切需要建立稳健的监测、报告与核查（MRV）体系，以参与《巴黎协定》第六条下的碳市场。本研究开发了一个随机森林（RF）模型，整合了Sentinel-2A多光谱影像、9个植被指数、地形变量以及Kyentshen社区森林244.6公顷范围内99个样地的实测AGB数据。通过递归特征消除法，从27个候选变量中筛选出10个关键预测因子：Band 11、SLAVI、Band 12、WDRVI、NDVI、海拔、NDWI、GNDVI、波段比值（r3）和Band 9。优化后的RF模型在训练数据上达到R² = 0.34，RMSE = 67.98 ± 19.79 t\u002Fha（Ntree = 500，Mtry = 3）。然而，测试集性能显著下降（R² = 0.42，RMSE = 86.14 t\u002Fha），表明存在过拟合，并反映了高生物量林分（>300 t\u002Fha）中光谱饱和的局限性。Wilcoxon符号秩检验揭示了系统性高估（预测中位数：118.36 t\u002Fha vs. 观测值：105.15 t\u002Fha；p = 0.03）。尽管精度中等，本研究表明，免费可获取的Sentinel-2数据结合机器学习为不丹温带森林的快速AGB评估提供了一种成本效益高、可扩展的传统野外制图替代方案。植被指数，特别是SLAVI和WDRVI，被证明优于原始光谱波段，对密集冠层具有更高的敏感性。空间样带方法和10预测因子框架为推进不丹森林碳核算体系提供了可复制的基础。我们建议整合合成孔径雷达（SAR）或激光雷达（LiDAR）以克服光学饱和，从而实现更高精度，满足稳健的MRV合规和碳信用货币化需求。本研究直接支持不丹维持碳中和地位，并为在工程基础设施能力有限但气候行动紧迫性极高的山区推广AGB监测建立了技术基线。",null,"OpenAlex","2026-09-09T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"研究用Sentinel-2与随机森林实现山地森林生物量制图，方法可复现但精度中等，对农业遥感与碳汇监测有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业人工智能","遥感监测","碳核算","森林碳汇","生物量估算",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":68},[39,48,55,63],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":47},"I2",15,true,[44],"machine learning",[46,44],"机器学习",[27],{"code":49,"score":20,"supported":42,"title":50,"abstract":52,"tags":54},"I3",[51],"remote sensing",[53],"multispectral",[31,28],{"code":56,"score":57,"supported":42,"title":58,"abstract":59,"tags":62},"H7",6,[],[60,61],"气候变化","climate change",[],{"code":64,"score":22,"supported":14,"title":65,"abstract":66,"tags":67},"H4",[],[],[30],"农业相关性需要复核",[70,71],"农业人工智能 生物量估算 森林碳汇 遥感监测","农业人工智能 生物量估算","农业人工智能生物量估算森林碳汇遥感监测-2174","10.32942\u002Fx29d5f",{"doi":73,"openalex_id":75,"authors":76,"venue":9,"cited_by_count":82,"oa_url":83,"card":84,"direction":88,"ingested_from":90},"W7212083800",[77,79],{"name":78,"orcid":9},"Namgay Wangchuk",{"name":80,"orcid":81},"Yonten Dorji","https:\u002F\u002Forcid.org\u002F0000-0002-6508-9582",0,"https:\u002F\u002Fecoevorxiv.org\u002Frepository\u002Fobject\u002F14866\u002Fdownload\u002F25843\u002F",{"tldr":85,"method":86,"finding":87,"direction":88,"opportunity":89},"用Sentinel-2与随机森林估算不丹喜马拉雅森林生物量，实现可扩展碳制图。","Sentinel-2多光谱、9个植被指数、地形因子与99个样地实测AGB，随机森","模型精度中等且高生物量区存在光谱饱和，SLAVI与WDRVI优于原始波段。","农业遥感与作物表型","融合SAR或LiDAR克服光学饱和，提升山地森林碳储量估算与MRV精度。","openalex","2026-09-11T23:30:32.934604Z",{"total":57,"page":22,"page_size":57,"items":93},[94,174,254,329,398,474],{"id":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":9,"source_name":100,"source_url":97,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":102,"score_detail":103,"sources":107,"tags":109,"primary_topic":113,"secondary_topics":118,"classification":119,"search_phrases":156,"slug":159,"view_count":82,"doi":160,"paper":161,"created_at":173},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","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",56,{"impact":17,"substance":104,"depth":20,"authority":57,"freshness":105,"relevant":22,"comment":106},16,9,"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。",[108],{"name":100,"url":97},[110,27,28,111,112],"智慧农业","风险管理","病虫害监测",{"code":114,"slug":115,"name":116,"path":117,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":120,"locked":14,"version":35,"method":36,"evidence":121},"partial",{"candidates":122,"agricultural_evidence":42,"reason":155},[123,129,135,143,151],{"code":40,"score":13,"supported":42,"title":124,"abstract":125,"tags":128},[],[46,44,126,127],"artificial intelligence","decision support",[27],{"code":49,"score":13,"supported":42,"title":130,"abstract":131,"tags":134},[],[132,51,53,133],"遥感","hyperspectral",[28],{"code":136,"score":105,"supported":42,"title":137,"abstract":138,"tags":142},"I5",[],[139,140,141],"物联网","internet of things","IoT",[],{"code":144,"score":145,"supported":42,"title":146,"abstract":147,"tags":150},"I1",7,[],[148,149],"精准农业","precision agriculture",[110],{"code":56,"score":57,"supported":42,"title":152,"abstract":153,"tags":154},[],[60,61],[],"一级领域明确，二级方向需要细分",[157,158],"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":160,"openalex_id":162,"authors":163,"venue":100,"cited_by_count":82,"oa_url":97,"card":166,"direction":172,"ingested_from":90},"W7223085400",[164],{"name":165,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":167,"method":168,"finding":169,"direction":170,"opportunity":171},"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",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":187,"tags":189,"primary_topic":192,"secondary_topics":198,"classification":203,"search_phrases":225,"slug":228,"view_count":82,"doi":229,"paper":230,"created_at":253},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","Smart Agricultural Technology","2026-10-17T00:00:00Z",77,{"impact":104,"substance":184,"depth":185,"authority":20,"freshness":21,"relevant":22,"comment":186},22,18,"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[188],{"name":180,"url":177},[110,190,28,191,31],"无人机","冬小麦",{"code":193,"slug":194,"name":195,"path":196,"parent_code":197},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[199],{"code":49,"slug":200,"name":201,"path":202,"parent_code":114},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":204,"locked":14,"version":35,"method":36,"evidence":205},"classified",{"candidates":206,"agricultural_evidence":42},[207,213,217,221],{"code":193,"score":208,"supported":42,"title":209,"abstract":211,"tags":212},26,[210,194],"winter wheat",[195,210,194],[191],{"code":49,"score":104,"supported":42,"title":214,"abstract":215,"tags":216},[51],[132,51],[31,28],{"code":40,"score":57,"supported":42,"title":218,"abstract":219,"tags":220},[],[46,44],[],{"code":144,"score":22,"supported":14,"title":222,"abstract":223,"tags":224},[],[],[110],[226,227],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":229,"openalex_id":231,"authors":232,"venue":180,"cited_by_count":82,"oa_url":177,"card":248,"direction":88,"ingested_from":90},"W7222806066",[233,235,237,239,242,244,246],{"name":234,"orcid":9},"Guangtao Wang",{"name":236,"orcid":9},"Sumei Yao",{"name":238,"orcid":9},"Yanzheng Xie",{"name":240,"orcid":241},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":243,"orcid":9},"Xiangdong Chen",{"name":245,"orcid":9},"Yuhao Zhang",{"name":247,"orcid":9},"Lina Jiang",{"tldr":249,"method":250,"finding":251,"direction":88,"opportunity":252},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":10,"source_url":257,"published_at":260,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":261,"score_detail":262,"sources":265,"tags":267,"primary_topic":9,"secondary_topics":271,"classification":272,"search_phrases":294,"slug":297,"view_count":22,"doi":298,"paper":299,"created_at":328},5179,"Monitoring Carbon Hotspots in Patchy Landscapes from Sparse Lidar and Dense Satellite Time Series","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx56j7f","Monitoring aboveground biomass (AGB) stocks as an indicator of carbon storage is essential for climate mitigation, adaptation, and land management. Across much of Earth’s land surface, carbon stocks are concentrated in fine-scale hotspots, including isolated trees in pastures, shrub patches in tundra, and riparian vegetation in drylands. These carbon hotspots are vulnerable to change but are difficult to monitor with remote sensing at broad scales due to three interconnected challenges: (1) spatial heterogeneity, (2) temporal change, and (3) modeling uncertainty. To address these challenges, we developed a workflow that integrates moderate-resolution satellite sensors, aerial lidar collections, and forestry-derived allometric models to produce annual maps of AGB in riparian corridors in the semi-arid western United States. To resolve spatial heterogeneity, we modeled canopy height and vegetation class at 10-m resolution, achieving canopy height mean absolute error of 1.56 m and overall vegetation classification accuracy of 84%. To measure uncertainty, we quantified prediction intervals using conformal prediction for canopy height and classification, ensemble disagreement for epistemic uncertainty, and Bayesian posterior distributions for allometric biomass models. The resulting annual maps provide pixel-level AGB estimates with statistically verified uncertainty bounds. These products reveal gradual changes in riparian biomass and can guide management decisions, from prioritizing restoration efforts to identifying early warning signs of ecosystem degradation. This framework could be adapted for monitoring AGB in other patchy landscapes, incorporated into different or changing land cover categories for future work, and updated as more and better data sources become available.","监测地上生物量（AGB）储量作为碳储量的指标，对于气候减缓、适应和土地管理至关重要。在地球陆地表面的大部分区域，碳储量集中在细尺度热点区域，包括牧场中的孤立树木、苔原中的灌木斑块以及旱地中的河岸植被。这些碳热点易受变化影响，但由于三个相互关联的挑战，难以在广阔尺度上通过遥感进行监测：（1）空间异质性，（2）时间变化，（3）建模不确定性。为应对这些挑战，我们开发了一套工作流程，整合中等分辨率卫星传感器、航空激光雷达采集和林业衍生的异速生长模型，以生成美国西部半干旱地区河岸走廊AGB的年度地图。为解决空间异质性问题，我们在10米分辨率下对冠层高度和植被类别进行建模，实现了冠层高度平均绝对误差1.56米和植被分类总体精度84%。为度量不确定性，我们使用保形预测量化冠层高度和分类的预测区间，使用集成不一致性度量认知不确定性，并使用贝叶斯后验分布构建异速生长生物量模型。由此生成的年度地图提供了具有统计验证不确定性边界的像素级AGB估算。这些产品揭示了河岸生物量的渐进变化，并可指导管理决策，从优先安排恢复工作到识别生态系统退化的早期预警信号。该框架可适用于监测其他斑块状景观中的AGB，可纳入未来工作中不同或变化的土地覆盖类别，并可随着更多更好的数据源的出现而更新。","2026-10-15T00:00:00Z",73,{"impact":17,"substance":263,"depth":185,"authority":20,"freshness":105,"relevant":22,"comment":264},21,"方法新颖、不确定性量化扎实，但属区域尺度遥感论文，对农业信息化有间接参考价值，未达每日精选门槛。",[266],{"name":10,"url":257},[28,268,31,269,270],"生态监测","碳储量","激光雷达",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":273},{"candidates":274,"agricultural_evidence":14,"reason":68},[275,279,285,289],{"code":49,"score":21,"supported":42,"title":276,"abstract":277,"tags":278},[],[132,51],[31,28],{"code":56,"score":280,"supported":42,"title":281,"abstract":282,"tags":284},3,[],[283],"干旱",[],{"code":64,"score":22,"supported":14,"title":286,"abstract":287,"tags":288},[],[],[269],{"code":290,"score":22,"supported":14,"title":291,"abstract":292,"tags":293},"H6",[],[],[268],[295,296],"稀疏激光雷达 卫星时序 生物量","河岸带 碳热点 遥感监测","稀疏激光雷达卫星时序生物量-5179","10.31223\u002Fx56j7f",{"doi":298,"openalex_id":300,"authors":301,"venue":9,"cited_by_count":82,"oa_url":322,"card":323,"direction":88,"ingested_from":90},"W7222218373",[302,305,307,309,311,313,315,318,320],{"name":303,"orcid":304},"Erik Nati‐Johnson","https:\u002F\u002Forcid.org\u002F0009-0009-3923-4115",{"name":306,"orcid":9},"Jodi Brandt",{"name":308,"orcid":9},"Nicholas Kolarik",{"name":310,"orcid":9},"Rose Graves",{"name":312,"orcid":9},"Anand Roopsind",{"name":314,"orcid":9},"Marie-Anne de Graff",{"name":316,"orcid":317},"Juan Camilo Rojas Lucero","https:\u002F\u002Forcid.org\u002F0009-0004-4934-3108",{"name":319,"orcid":9},"Colin Martin",{"name":321,"orcid":9},"Trevor Caughlin","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F15555\u002Fdownload\u002F26955\u002F",{"tldr":324,"method":325,"finding":326,"direction":88,"opportunity":327},"融合稀疏激光雷达与密集卫星时序，绘制半干旱河岸带地上生物量年度图。","中分辨率卫星、航空激光雷达与异速生长模型，结合共形预测与贝叶斯不确定性量化。","10米分辨率冠层高度误差1.56米、分类精度84%，可检测河岸生物量渐变。","可将该框架迁移至农田防护林、零星树木等破碎景观碳热点监测，并融合更高分辨率数据。","2026-10-17T23:30:31.577923Z",{"id":330,"title":331,"url":332,"summary":333,"summary_zh":334,"content":9,"source_name":335,"source_url":332,"published_at":336,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":337,"score_detail":338,"sources":340,"tags":342,"primary_topic":344,"secondary_topics":348,"classification":349,"search_phrases":382,"slug":385,"view_count":82,"doi":386,"paper":387,"created_at":397},5159,"Integration of IoT And Satellite Data for Real-Time Agricultural Crop Stress Monitoring Using Spatio-Temporal Transformers","https:\u002F\u002Fdoi.org\u002F10.31127\u002Ftuje.1871227","This research work presents an integrated framework for real-time crop stress monitoring that combines ground-based Internet of Things (IoT) sensors and multi-temporal satellite remote sensing data. Due to growing environmental uncertainty and limited resources, the early identification of crop stress is crucial for maximizing agricultural productivity. IoT sensors yield localized, real-time measurements of key variables like soil moisture, temperature, and humidity, whereas satellite images yield wide-area vegetation and thermal indicators (e.g., NDVI, EVI, LST). The solution utilizes a Spatio-Temporal Transformer (ST-Transformer) model to integrate these heterogeneous data streams to make early crop disease and irrigation stress detection. By integrating spatial satellite imagery with temporal IoT sequences, STT leverages attention mechanisms to offer a more holistic analysis, which translates to an impressive accuracy of 88% and a solid F1-Score of 0.855. This dual capacity to concurrently handle spatial and temporal dimensions allows STT to effectively generalize across diverse crop stress categories, providing insights that are both timely and actionable","本研究工作提出了一种集成框架，用于结合地面物联网（IoT）传感器与多时相卫星遥感数据实现作物胁迫的实时监测。由于环境不确定性日益加剧且资源有限，作物胁迫的早期识别对于最大化农业生产力至关重要。物联网传感器可提供土壤湿度、温度和湿度等关键变量的局部实时测量，而卫星图像则可提供广域植被与热指标（如NDVI、EVI、LST）。该方案利用时空Transformer（ST-Transformer）模型整合这些异构数据流，以实现作物病害与灌溉胁迫的早期检测。通过将空间卫星影像与时间物联网序列相结合，STT借助注意力机制提供更全面的分析，从而实现了88%的显著准确率和0.855的稳健F1分数。这种同时处理空间与时间维度的双重能力使STT能够有效泛化于多种作物胁迫类别，提供既及时又可操作的洞察。","Turkish Journal of Engineering","2026-10-16T00:00:00Z",75,{"impact":104,"substance":263,"depth":19,"authority":17,"freshness":105,"relevant":22,"comment":339},"该研究将物联网与多时相卫星数据融合，用时空Transformer实现作物胁迫早期识别，方法新颖、指标明确，属智慧农业细分领域有实质增量的技术进展，值得入选。",[341],{"name":335,"url":332},[110,27,139,343,28],"作物胁迫",{"code":136,"slug":345,"name":346,"path":347,"parent_code":114},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[],{"status":204,"locked":14,"version":35,"method":36,"evidence":350},{"candidates":351,"agricultural_evidence":42},[352,356,360,367,374,378],{"code":136,"score":185,"supported":42,"title":353,"abstract":354,"tags":355},[141],[139,140,141],[139],{"code":49,"score":145,"supported":42,"title":357,"abstract":358,"tags":359},[],[132,51],[28],{"code":361,"score":57,"supported":42,"title":362,"abstract":363,"tags":366},"H2",[],[364,365],"土壤","soil",[],{"code":368,"score":57,"supported":42,"title":369,"abstract":370,"tags":373},"H3",[],[371,372],"灌溉","irrigation",[],{"code":144,"score":22,"supported":14,"title":375,"abstract":376,"tags":377},[],[],[110],{"code":40,"score":22,"supported":14,"title":379,"abstract":380,"tags":381},[],[],[27],[383,384],"ST-Transformer 作物胁迫","IoT 卫星 遥感 作物监测","ST-Transformer作物胁迫-5159","10.31127\u002Ftuje.1871227",{"doi":386,"openalex_id":388,"authors":389,"venue":335,"cited_by_count":82,"oa_url":332,"card":392,"direction":172,"ingested_from":90},"W7222730658",[390],{"name":391,"orcid":9},"Sunitha Thankappan",{"tldr":393,"method":394,"finding":395,"direction":172,"opportunity":396},"融合IoT传感器与多时相卫星数据，用时空Transformer实现作物胁迫实时监测。","IoT土壤温湿度数据+卫星NDVI\u002FEVI\u002FLST，构建ST-Transform","模型准确率达88%、F1为0.855，可早期识别病害与灌溉胁迫。","可探索多源异构数据在真实田块尺度上的轻量化部署与跨区域迁移泛化。","2026-10-17T23:30:13.663599Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":403,"content":9,"source_name":100,"source_url":401,"published_at":260,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":404,"sources":406,"tags":408,"primary_topic":410,"secondary_topics":414,"classification":420,"search_phrases":457,"slug":460,"view_count":22,"doi":461,"paper":462,"created_at":473},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。",{"impact":185,"substance":104,"depth":41,"authority":17,"freshness":105,"relevant":22,"comment":405},"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[407],{"name":100,"url":401},[110,27,409,60,28],"可持续发展",{"code":40,"slug":411,"name":412,"path":413,"parent_code":114},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[415],{"code":56,"slug":416,"name":417,"path":418,"parent_code":419},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":204,"locked":14,"version":35,"method":36,"evidence":421},{"candidates":422,"agricultural_evidence":42},[423,429,433,437,441,447,453],{"code":40,"score":185,"supported":42,"title":424,"abstract":425,"tags":428},[126],[426,46,44,427,126],"深度学习","deep learning",[27],{"code":56,"score":41,"supported":42,"title":430,"abstract":431,"tags":432},[61],[60,61],[60],{"code":144,"score":145,"supported":42,"title":434,"abstract":435,"tags":436},[],[148,149],[110],{"code":49,"score":145,"supported":42,"title":438,"abstract":439,"tags":440},[],[132,51],[28],{"code":64,"score":57,"supported":42,"title":442,"abstract":443,"tags":446},[],[444,445],"碳排放","carbon emission",[],{"code":290,"score":57,"supported":42,"title":448,"abstract":449,"tags":452},[],[450,451],"生物多样性","biodiversity",[],{"code":136,"score":57,"supported":42,"title":454,"abstract":455,"tags":456},[],[139,140],[],[458,459],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":461,"openalex_id":463,"authors":464,"venue":100,"cited_by_count":82,"oa_url":401,"card":467,"direction":172,"ingested_from":90},"W7222092855",[465],{"name":466,"orcid":9},"Deepmala Suryavanshi",{"tldr":468,"method":469,"finding":470,"direction":471,"opportunity":472},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","2026-10-16T23:30:15.059867Z",{"id":475,"title":476,"url":477,"summary":478,"summary_zh":479,"content":9,"source_name":480,"source_url":477,"published_at":481,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":482,"score_detail":483,"sources":486,"tags":488,"primary_topic":492,"secondary_topics":493,"classification":495,"search_phrases":508,"slug":511,"view_count":82,"doi":512,"paper":513,"created_at":533},4897,"Wild boar collision data and satellite computer vision refine habitat suitability mapping across France","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10344-026-02160-1","Abstract Wild boar (Sus scrofa) populations have expanded rapidly across Europe, leading to escalating human-wildlife conflicts (HWCs), notably wildlife-vehicle collisions (WVCs), increased agricultural damage and disease transmission. In continental France these issues are compounded by the species’ ecological adaptability exhibiting increasing overlaps with urbanization and transportation networks. In this study, we leverage time-series environmental data, computer vision and Species Distribution Modeling (SDM) to predict wild boar habitat suitability and investigate its spatiotemporal drivers. We use presence-only data from WVC reports on the national railway and road networks, along with publicly available GBIF observation collections, as complementary occurrence streams in our SDMs, while addressing inherent sampling biases of these datasets with tailored corrections. A key innovation of this study is the integration of national scale, annually updated high-resolution land cover predictors explicitly focused on wild boar resource preference. By fine-tuning a multitemporal Vision Transformer foundational AI model on multispectral satellite remote sensing imagery we capture subtle seasonal phenological differences. Our results highlight clear spatial, seasonal and annual variations in wild boar habitat suitability. The multitemporal SDM pipeline yields meaningful predictions when extrapolating to novel environmental scenarios such as climate extremes. Adding rail and road WVC records to GBIF-based SDMs retained high performance metrics while altering predicted spatial patterns and feature importance distribution. The methodological and ecological insights gained through this study provide additional knowledge for French transportation planning, agriculture and wildlife management. Identifying regions with high seasonal habitat suitability can inform targeted interventions. More broadly, our results demonstrate that advanced, data-driven methods are becoming indispensable for proactively and sustainably addressing HWCs in an increasingly anthropogenic world.","摘要 野猪（Sus scrofa）种群在欧洲迅速扩张，导致人兽冲突（HWCs）不断升级，尤其是野生动物车辆碰撞（WVCs）、农业损害加剧和疾病传播。在法国大陆，该物种的生态适应性使其与城市化和交通网络的重叠日益增加，进一步加剧了这些问题。本研究利用时间序列环境数据、计算机视觉和物种分布模型（SDM）预测野猪栖息地适宜性，并探究其时空驱动因素。我们使用国家铁路和公路网络中WVC报告的存在-only数据，以及公开可用的GBIF观测集合，作为SDM中的互补出现数据流，同时通过针对性校正处理这些数据集固有的采样偏差。本研究的一个关键创新是整合了国家尺度、年度更新的高分辨率土地覆盖预测因子，明确聚焦于野猪的资源偏好。通过在多光谱卫星遥感影像上微调多时相视觉Transformer基础AI模型，我们捕捉到了细微的季节物候差异。我们的结果突出了野猪栖息地适宜性在空间、季节和年际上的明显变化。多时相SDM流程在推断至气候极端等新型环境情景时产生了有意义的预测。将铁路和公路WVC记录添加到基于GBIF的SDM中，在保持高性能指标的同时改变了预测的空间格局和特征重要性分布。本研究获得的方法学和生态学见解为法国交通规划、农业和野生动物管理提供了额外知识。识别具有高季节性栖息地适宜性的区域可为针对性干预提供依据。更广泛地说，我们的结果表明，在日益受到人类影响的世界中，先进的数据驱动方法正成为前瞻性和可持续地应对人兽冲突不可或缺的手段。","European Journal of Wildlife Research","2026-10-13T00:00:00Z",81,{"impact":185,"substance":184,"depth":185,"authority":484,"freshness":105,"relevant":22,"comment":485},14,"该研究将卫星遥感与计算机视觉用于野猪栖息地建模，方法新颖、数据规模大，对交通规划与农业野生动物管理有参考价值，但属法国区域研究，国内产业影响有限。",[487],{"name":480,"url":477},[27,28,489,490,491],"栖息地评估","野生动物管理","人兽冲突",{"code":40,"slug":411,"name":412,"path":413,"parent_code":114},[494],{"code":49,"slug":200,"name":201,"path":202,"parent_code":114},{"status":204,"locked":14,"version":35,"method":36,"evidence":496},{"candidates":497,"agricultural_evidence":42},[498,504],{"code":40,"score":41,"supported":42,"title":499,"abstract":501,"tags":503},[500],"computer vision",[502,500],"计算机视觉",[27],{"code":49,"score":13,"supported":42,"title":505,"abstract":506,"tags":507},[],[132,51,53],[28],[509,510],"法国 野猪 栖息地 遥感","野猪 车辆碰撞 卫星影像","法国野猪栖息地遥感-4897","10.1007\u002Fs10344-026-02160-1",{"doi":512,"openalex_id":514,"authors":515,"venue":480,"cited_by_count":82,"oa_url":477,"card":528,"direction":88,"ingested_from":90},"W7221702437",[516,519,521,524,526],{"name":517,"orcid":518},"Adrian Meyer","https:\u002F\u002Forcid.org\u002F0000-0002-8652-1477",{"name":520,"orcid":9},"Théo Reibel",{"name":522,"orcid":523},"Кevin Мorelle","https:\u002F\u002Forcid.org\u002F0000-0001-9041-6541",{"name":525,"orcid":9},"Mathias Kneubühler",{"name":527,"orcid":9},"Denis Jordan",{"tldr":529,"method":530,"finding":531,"direction":88,"opportunity":532},"融合野猪碰撞数据与卫星视觉模型，预测法国野猪栖息地适宜性及其时空驱动因素。","时间序列环境数据、Vision Transformer卫星影像、物种分布模型与G","栖息地适宜性呈明显时空变化，加入碰撞记录改变预测格局与特征重要性。","可将碰撞数据与遥感表型结合，拓展至其他野生动物-农业冲突的时空预警与决策。","2026-10-14T23:30:27.244673Z"]