[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3188":3,"related-3188":48},{"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,"search_phrases":25,"slug":28,"view_count":15,"doi":29,"paper":30,"created_at":47},3188,"Identification of granitic pegmatites based on GF-5 hyperspectral data and LSTM-Transformer model","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.acags.2026.100406","Lithium is a key strategic mineral to support new energy and advanced technology, mainly enriched in granitic pegmatite. The integration of hyperspectral remote sensing and deep learning frameworks has emerged as a leading approach for quantitative identification of pegmatites. However, deep learning algorithms are often limited by the scarcity of labeled samples and the challenge of extracting features from high-dimensional spectral data. In this study, a data augmentation-driven LSTM-Transformer framework is proposed for pegmatite mapping using GF-5 hyperspectral remote sensing data. In view of limited samples, this study constructed an enhanced training data set by integrating field-measured and infrared scanning spectra, simulating real spectral variability and linear mixing to improve data diversity. The proposed LSTM-Transformer architecture combines LSTM’s advantages in local sequence feature modeling and Transformer’s capacity to capture long-range global relationships, thereby enhancing the detection of weakly diagnostic spectral features. The framework was demonstrated in the Jingerquan lithium deposit, Xinjiang, China. The high-probability areas delineated by the proposed model successfully captured several pegmatite dikes documented in previous geological studies. The prediction results were further validated through field sampling. Geochemical analyses of specimens collected from the predicted high-probability target areas show an average Li 2 O grade of 7.86 wt.%, which meets industrial requirements and confirms the reliability of the proposed method. Overall, these results provide the potential of the proposed framework for pegmatite mapping and subsequent mineral exploration under the conditions of limited labeled samples.","锂是支撑新能源与先进技术的关键战略矿产，主要富集于花岗伟晶岩中。高光谱遥感与深度学习框架的融合已成为伟晶岩定量识别的前沿方法。然而，深度学习算法常受限于标注样本稀缺以及高维光谱数据特征提取的难题。本研究提出了一种数据增强驱动的LSTM-Transformer框架，用于基于GF-5高光谱遥感数据的伟晶岩填图。针对样本有限的问题，本研究通过整合野外实测光谱与红外扫描光谱构建了增强训练数据集，模拟真实光谱变异与线性混合以提升数据多样性。所提出的LSTM-Transformer架构结合了LSTM在局部序列特征建模方面的优势与Transformer捕捉长程全局关系的能力，从而增强了对弱诊断光谱特征的检测。该框架在中国新疆镜儿泉锂矿床进行了验证。所提模型圈定的高概率区域成功捕获了以往地质研究中记录的若干伟晶岩脉。预测结果进一步通过野外采样得到验证。从预测高概率目标区采集的标本地球化学分析显示，Li₂O平均品位为7.86 wt.%，满足工业要求，证实了所提方法的可靠性。总体而言，这些结果展示了所提框架在标注样本有限条件下用于伟晶岩填图及后续矿产勘查的潜力。",null,"Applied Computing and Geosciences","2026-09-19T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦高光谱遥感与深度学习用于伟晶岩型锂矿识别，属地质矿产勘探领域，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],"深度学习","遥感监测","高光谱遥感","锂矿勘探",[26,27],"GF-5 高光谱 伟晶岩","新疆 镜儿泉 锂矿","GF-5高光谱伟晶岩-3188","10.1016\u002Fj.acags.2026.100406",{"doi":29,"openalex_id":31,"authors":32,"venue":10,"cited_by_count":15,"oa_url":39,"card":40,"direction":44,"ingested_from":46},"W7213617576",[33,36],{"name":34,"orcid":35},"Zhong Li","https:\u002F\u002Forcid.org\u002F0000-0002-2312-3746",{"name":37,"orcid":38},"Ziye Wang","https:\u002F\u002Forcid.org\u002F0000-0001-6538-5798","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS259019742600090X\u002Fpdf",{"tldr":41,"method":42,"finding":43,"direction":44,"opportunity":45},"提出数据增强驱动的LSTM-Transformer框架，用GF-5高光谱数据识别花岗伟晶岩。","GF-5高光谱数据，融合实测与红外扫描光谱增强样本，构建LSTM-Transfo","在新疆金儿泉锂矿成功圈定伟晶岩脉，验证区Li2O平均品位7.86 wt.%。","农业遥感与作物表型","可将该高光谱-深度学习框架迁移至农田土壤属性或作物养分反演，解决标签稀缺问题。","openalex","2026-09-22T23:30:25.608490Z",{"total":49,"page":50,"page_size":49,"items":51},6,1,[52,83,131,164,188,238],{"id":53,"title":54,"url":55,"summary":56,"summary_zh":57,"content":9,"source_name":58,"source_url":55,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":59,"sources":61,"tags":63,"search_phrases":66,"slug":69,"view_count":15,"doi":70,"paper":71,"created_at":82},3015,"Forecasting Urbanization Dynamics On İstanbul’s European Side Using Deep Learning And Extrapolation Techniques","https:\u002F\u002Fdoi.org\u002F10.2478\u002Fjlecol-2026-0037","Abstract Rapid urbanization in metropolitan regions poses significant environmental, social, and infrastructural challenges, necessitating advanced analytical approaches to monitor and predict urban growth. This study investigates the spatio-temporal dynamics of urbanization on the European side of Istanbul from 2013 to 2024 using Landsat 8 imagery and a deep learning (DL)–based Land Cover Classification model integrated within ArcGIS Pro. The U-Net–based pre-trained model generated 15-class Land Use\u002FLand Cover (LULC) maps, which were validated against the Urban Atlas dataset, resulting in high classification accuracies for forest and water classes (PA: 0.84–0.94; UA: 0.87–0.87) and an overall binary urban\u002Fnon-urban accuracy of 87 %, confirming the robustness of the employed DL approach. Spatio-temporal analyses of LULC data were conducted using both Ordinary Least Squares (OLS) and nonlinear regression functions to examine urban growth trends and project future development for 2025, 2026, and 2027. The results indicate a strong linear increase in urbanized areas across most districts, with total developed area on the European side projected to reach approximately 807 km² by 2027, representing a nearly 50% increase compared to 2013. These findings highlight the significant pressure of urban expansion on natural and agricultural lands and emphasize the need for informed planning strategies. By integrating remote sensing, deep learning, and predictive modeling, this study provides actionable insights for sustainable urban development, offering a replicable framework for monitoring rapid urbanization and supporting policy decisions to mitigate environmental and socio-spatial impacts in rapidly growing metropolitan regions.","摘要 大都市区域的快速城市化带来了显著的环境、社会和基础设施挑战，亟需先进的分析方法来监测和预测城市增长。本研究利用Landsat 8影像和集成于ArcGIS Pro中的基于深度学习（DL）的土地覆盖分类模型，研究了2013年至2024年伊斯坦布尔欧洲一侧城市化的时空动态。基于U-Net的预训练模型生成了15类土地利用\u002F土地覆盖（LULC）地图，并依据Urban Atlas数据集进行了验证，森林和水体类别的分类精度较高（生产者精度PA：0.84–0.94；用户精度UA：0.87–0.87），城市\u002F非城市二分类总体精度达87%，证实了所采用深度学习方法稳健可靠。研究采用普通最小二乘法（OLS）和非线性回归函数对LULC数据进行时空分析，以考察城市增长趋势并预测2025年、2026年和2027年的未来发展。结果表明，大多数区域的城市化面积呈显著线性增长，预计到2027年欧洲一侧的总建成区面积将达到约807 km²，较2013年增长近50%。这些发现凸显了城市扩张对自然和农业用地的巨大压力，并强调了科学规划策略的必要性。通过整合遥感、深度学习和预测建模，本研究为可持续城市发展提供了可操作的见解，为监测快速城市化提供了一个可复制的框架，并支持旨在缓解快速增长的都市区域中环境和社会空间影响的政策决策。","Journal of Landscape Ecology",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":60},"研究伊斯坦布尔城市扩张与土地覆盖预测，属城市遥感与景观生态领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[62],{"name":58,"url":55},[21,22,64,65],"土地利用","城市扩张",[67,68],"伊斯坦布尔 城市扩张 遥感","U-Net 土地覆盖分类","伊斯坦布尔城市扩张遥感-3015","10.2478\u002Fjlecol-2026-0037",{"doi":70,"openalex_id":72,"authors":73,"venue":58,"cited_by_count":15,"oa_url":55,"card":77,"direction":44,"ingested_from":46},"W7213670913",[74],{"name":75,"orcid":76},"Gizem Dinç","https:\u002F\u002Forcid.org\u002F0000-0003-2406-604X",{"tldr":78,"method":79,"finding":80,"direction":44,"opportunity":81},"用Landsat 8影像和U-Net深度学习模型分析伊斯坦布尔欧洲侧2013-2024年城市化动态并","Landsat 8影像、ArcGIS Pro中U-Net预训练模型生成15类LU","城市面积呈强线性增长，2027年预计达807 km²，较2013年增长近50%，挤压自然与农业用地。","可借鉴该遥感+深度学习+外推框架，研究快速城市化对城郊农业用地与耕地保护的时空影响。","2026-09-20T23:30:21.335156Z",{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":9,"source_name":89,"source_url":86,"published_at":90,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":91,"score_detail":92,"sources":98,"tags":100,"search_phrases":104,"slug":107,"view_count":15,"doi":108,"paper":109,"created_at":130},2776,"Mapping coastal forest retreat using convolutional neural networks and different satellite imagery","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0357346","Coastal forests are increasingly threatened by saturated soil and elevated salinity levels resulting from sea level rise, saltwater intrusion, and storm surges. In response to rising salinization and flooding, healthy coastal forests that rely on freshwater (both wetland forests and low-elevation upland forests) are transitioning into landscapes dominated by dead or dying trees, known as ghost forests. Situated among salt-tolerant shrubs and grasses, ghost forests eventually become marshes or open water. Here, our main objective was to quantify the dynamics and pathways of these forest landscape conversions, as well as the factors contributing to the changes, which is vital for understanding the progression of coastal ecosystem degradation and forecasting future changes. We focused first on identifying the best method to track forest landscape change by exploring the role of multiple remote sensing indices (i.e., multispectral, bi-seasonal, topographical, and phenological metrics) in enhancing the performance of deep learning models (convolutional neural networks, CNNs) for land cover classification in the coastal plain of North Carolina using surface reflectance of Landsat 8 and Sentinel-2 images. Then, we used the best available data (Landsat 8) to understand long-term change and identify patterns of land cover change from 1985 to 2021. Our study reveals that incorporating phenology and topographical indices enhances the separability of the ghost forests class from all other vegetation classes. In our assessment, the higher-resolution Sentinel-2 data (F1 Score = 96.3) outperformed Landsat images (F1 score = 93.4) for the 2021 co-available year. However, Landsat remains an important tool used due to its long-term data record. Therefore, we used Landsat to determine that 21% of forests were lost between 1985 and 2021, and that the rate of loss is increasing. Between 2010 and 2021, 23,876 ha of forest were converted to marsh, ghost forest, and shrub, which is 1.5 times higher than the 16,968 ha lost between 1985 and 2010. These conversions from forest to ghost forest and marshes were driven primarily by proximity to the channel, salinity, and the increasing rate of relative sea level rise (RSLR), which are the key environmental drivers of observed changes. By quantifying these changes, we highlight regions most vulnerable to environmental stressors, providing a basis for targeted conservation strategies.","沿海森林正日益受到海平面上升、盐水入侵和风暴潮导致的土壤饱和与盐度升高的威胁。为应对不断加剧的盐渍化和洪水，依赖淡水的健康沿海森林（包括湿地森林和低海拔高地森林）正在转变为以死亡或濒死树木为主的景观，即“幽灵森林”（ghost forests）。幽灵森林与耐盐灌木和草本植物交错分布，最终演变为沼泽或开阔水域。本研究的主要目标是量化这些森林景观转换的动态过程和路径，以及促成这些变化的因素，这对于理解沿海生态系统退化的进程和预测未来变化至关重要。我们首先聚焦于确定追踪森林景观变化的最佳方法，通过探索多种遥感指数（即多光谱、双季节、地形和物候指标）在提升深度学习模型（卷积神经网络，CNNs）土地覆盖分类性能中的作用，研究区域为北卡罗来纳州沿海平原，使用Landsat 8和Sentinel-2影像的地表反射率数据。随后，我们使用最佳可用数据（Landsat 8）来理解长期变化并识别1985年至2021年的土地覆盖变化模式。研究表明，纳入物候和地形指数可增强幽灵森林类别与所有其他植被类别的可分性。在我们的评估中，对于2021年两星共存的年份，较高分辨率的Sentinel-2数据（F1分数 = 96.3）优于Landsat影像（F1分数 = 93.4）。然而，Landsat因其长期数据记录仍是一个重要的使用工具。因此，我们使用Landsat确定1985年至2021年间21%的森林已经消失，且消失速率正在加快。2010年至2021年间，23,876公顷森林转换为沼泽、幽灵森林和灌木，比1985年至2010年间损失的16,968公顷高出1.5倍。这些从森林到幽灵森林和沼泽的转换主要由距河道距离、盐度和相对海平面上升（RSLR）速率的增加所驱动，这些是观测到的变化的关键环境驱动因素。通过量化这些变化，我们突出了最易受环境胁迫影响的区域，为有针对性的保护策略提供了依据。","PLoS ONE","2026-09-15T00:00:00Z",82,{"impact":93,"substance":94,"depth":93,"authority":95,"freshness":96,"relevant":50,"comment":97},18,23,14,9,"发表于PLoS ONE的研究用CNN结合Landsat与Sentinel-2影像量化海岸森林退化为幽灵森林的过程，方法新颖、数据跨度36年，对沿海生态保护与遥感应用有实质参考价值。",[99],{"name":89,"url":86},[21,22,101,102,103],"海岸防护林","土地覆盖变化","生态退化",[105,106],"土地覆盖变化 海岸防护林 深度学习 生态退化","土地覆盖变化 海岸防护林","土地覆盖变化海岸防护林深度学习生态退化-2776","10.1371\u002Fjournal.pone.0357346",{"doi":108,"openalex_id":110,"authors":111,"venue":89,"cited_by_count":15,"oa_url":123,"card":124,"direction":129,"ingested_from":46},"W7213246924",[112,115,118,120],{"name":113,"orcid":114},"Tawakalitu Titilayo Tajudeen","https:\u002F\u002Forcid.org\u002F0000-0003-2405-134X",{"name":116,"orcid":117},"Marcelo Ardón","https:\u002F\u002Forcid.org\u002F0000-0001-7275-2672",{"name":119,"orcid":9},"Mirela Tulbure",{"name":121,"orcid":122},"Katherine L. Martin","https:\u002F\u002Forcid.org\u002F0000-0001-6020-9250","https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle\u002Ffile?id=10.1371\u002Fjournal.pone.0357346&type=printable",{"tldr":125,"method":126,"finding":127,"direction":44,"opportunity":128},"用CNN结合多源遥感指数监测1985-2021年北卡海岸森林退化与幽灵林扩张。","Landsat 8与Sentinel-2影像，多光谱、物候、地形指数，卷积神经网","物候与地形指数提升幽灵林识别；1985-2021年21%森林消失且速率加快，近河道、盐度和海平面上升","可迁移该CNN框架到其他海岸带，融合时序Sentinel数据与海平面上升情景预测幽灵林未来扩张。","智慧农业 \u002F 农业物联网","2026-09-17T23:30:15.391843Z",{"id":132,"title":133,"url":134,"summary":135,"summary_zh":9,"content":9,"source_name":136,"source_url":9,"published_at":137,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":138,"score_detail":139,"sources":143,"tags":145,"search_phrases":150,"slug":153,"view_count":15,"doi":9,"paper":154,"created_at":163},3248,"Crop recommendation in precision agriculture: a systematic literature review of methods, trends, and challenges（精准农业中的作物推荐：方法、趋势与挑战系统综述）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37471110","MDPI 发表精准农业作物推荐方法系统综述：从183项研究中严格筛选129篇发表于2020-2026年的文章，使用PRISMA协议进行综合分析。研究表明集成学习方法（特别是随机森林和XGBoost）在各种农业数据集的预测性能上具有强大能力；支持向量机、决策树、k近邻等传统ML方法仍被广泛使用；同时CNN和LSTM被用于遥感和时间相关农业分析。最常用的数据集来源是Kaggle，典型输入包括土壤养分（NPK）、土壤pH、天气条件和NDVI、EVI等卫星指数。研究主要研究空白：有限的实时部署、低多数据源集成、低跨区域验证、低模型可解释性。研究表明可扩展、可解释的AI系统对农业实际应用具有重要意义。","MDPI","2026-09-22T00:00:00Z",81,{"impact":93,"substance":140,"depth":93,"authority":141,"freshness":13,"relevant":50,"comment":142},22,13,"基于PRISMA的129篇文献系统综述，梳理作物推荐主流方法与四大研究空白，对农业AI落地有参考价值。",[144],{"name":136,"url":134},[146,147,148,149,22],"智慧农业","农业人工智能","机器学习","作物推荐",[151,152],"精准农业 作物推荐 系统综述","XGBoost 随机森林 作物推荐","精准农业作物推荐系统综述-3248",{"doi":9,"openalex_id":9,"authors":155,"venue":9,"cited_by_count":15,"oa_url":9,"card":156,"direction":160,"ingested_from":162},[],{"tldr":157,"method":158,"finding":159,"direction":160,"opportunity":161},"系统综述129篇2020-2026年文献，梳理精准农业作物推荐的方法、趋势与挑战。","PRISMA协议系统综述，分析183项研究筛选出的129篇文献。","集成学习（随机森林、XGBoost）表现最强，主要空白为实时部署、多源集成、跨区域验证与可解释性。","农业人工智能与决策模型","可探索可解释、可跨区域泛化的实时作物推荐系统，并融合多源遥感与物联网数据。","agent","2026-09-23T00:04:33.331160Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":9,"content":169,"source_name":170,"source_url":9,"published_at":11,"category":171,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":173,"sources":177,"tags":179,"search_phrases":183,"slug":186,"view_count":15,"doi":9,"paper":9,"created_at":187},3230,"农业科普进校园 科技种子润心田——黑龙江省农科院\"农业科普进校园\"活动走进萧红中学","https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1079072228_121106822","黑龙江省农科院\"农业科普进校园\"活动走进哈尔滨市萧红中学，七位专家把实验室里的科研成果\"翻译\"成生动有趣的科普课堂，围绕食品安全、现代种业、智慧农业、遥感监测、生物防治、健康养殖、未来食品等方向，用图文展示、实物观察、视频演示和互动问答，把硬核科研变成通俗易懂的知识。作为省农科院2026年全国科普月重点活动之一，本次活动紧扣\"科技改变生活 创新赢得未来\"主题，聚焦现代农业科普和青少年科学素养提升，是落实全国科普月\"科普讲堂话前沿\"\"科学文化进基层\"部署、推动农业科普资源进校园的具体实践。","## 农业科普进校园 科技种子润心田 ——省农科院“农业科普进校园”活动走进萧红中学\n\n2026-09-21 17:29 来源: [大东北生活资讯](https:\u002F\u002Fwww.sohu.com\u002F)\n\n发布于：北京市\n\n当“农药去哪了”“玉米的秘密”“未来餐桌”这些话题出现在中学课堂，农业就不再是课本里遥远的名词，而是一场可以看、可以问、可以触摸的科技之旅。\n\n近日，省农科院“农业科普进校园”活动走进哈尔滨市萧红中学。七位专家把实验室里的科研成果“翻译”成生动有趣的科普课堂，带着同学们零距离感受现代农业的魅力。\n\n活动现场，专家们围绕食品安全、现代种业、智慧农业、遥感监测、生物防治、健康养殖、未来食品等方向，用图文展示、实物观察、视频演示和互动问答，把硬核科研变成通俗易懂的知识。同学们在观察、提问、交流中，慢慢理解了“藏粮于技”的意义，也感受到科技给农业带来的改变。\n\n作为省农科院2026年全国科普月重点活动之一，本次活动紧扣“科技改变生活 创新赢得未来”主题，聚焦现代农业科普和青少年科学素养提升，是落实全国科普月“科普讲堂话前沿”“科学文化进基层”部署、推动农业科普资源进校园的具体实践。以青少年为纽带，科研机构与社会公众之间多了一座沟通的桥；通过“请进来”与“走出去”，省农科院在粮食安全、乡村振兴等领域的创新成果被更多年轻人看见。知农、爱农、兴农的种子，正在校园里悄悄发芽。\n\n接下来，省农科院还将继续深化与学校的合作，让更多青少年在沉浸式体验中走近农业、爱上科学，为龙江农业现代化和乡村振兴积蓄青春力量。\n\n![Image 1](https:\u002F\u002Fq0.itc.cn\u002Fq_70\u002Fimages03\u002F20260921\u002Fccb551427fec4af1a347d1e5b521017a.jpeg)\n\n![Image 2](https:\u002F\u002Fq6.itc.cn\u002Fq_70\u002Fimages03\u002F20260921\u002F9f478e5a65a44654acd668ed043bff16.jpeg)\n\n**黑 龙 江 省 农 业 科 学 院 党 宣**\n\n信 息 来 源：科技推广处张唯一\n\n审核：李佳峰\n\n编辑：王红蕾[返回搜狐，查看更多](https:\u002F\u002Fwww.sohu.com\u002F?strategyid=00001 \"点击进入搜狐首页\")","黑龙江省农业科学院","报道",39,{"impact":174,"substance":174,"depth":49,"authority":13,"freshness":175,"relevant":50,"comment":176},8,7,"省级农科院面向中学的科普活动通稿，属全国科普月系列活动，有科普教育价值但信息增量有限，适合作为科普类资讯收录。",[178],{"name":170,"url":167},[146,180,22,181,182],"农业科普","全国科普月","青少年科学素养",[184,185],"黑龙江省农科院 农业科普进校园","萧红中学 农业科普","黑龙江省农科院农业科普进校园-3230","2026-09-23T00:04:30.535981Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":9,"source_name":194,"source_url":191,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":200,"tags":202,"search_phrases":206,"slug":209,"view_count":15,"doi":210,"paper":211,"created_at":237},3189,"Influence of Drought Events on Vegetation and Land Use Dynamics Utilising Orbital Data: A Case Study in the Pajeú River Basin, Pernambuco","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i03.269030","Monitoring drought in the northeastern Semi-Arid region is a challenge compounded by high rainfall variability and a scarcity of in situ monitoring networks, which makes the use of geotechnologies for water resource management essential. This study aimed to analyze land use and occupation dynamics from 2002 to 2022 in the Pajeú River basin in Pernambuco, Brazil, and their relationship with drought events, using indices based on remote sensing. The Standardized Precipitation Index (SPI) was applied to characterize meteorological drought, while the Vegetation Health Index (VHI) and Normalized Vegetation Water Supply Index (NVSWI) were used to assess the ecosystem's response. The results indicated that the period from 2011 to 2016 was the most severe and prolonged drought, which was reinforced by low VHI and NVSWI values, indicating water stress on vegetation during the same period. The correlation analysis revealed that land use classes, such as vegetation and water, responded directly to drought, whereas degraded anthropogenic areas, including pasture and exposed soil, exhibited the opposite behavior. The joint analysis of the three indices and land use and occupation data provided a comprehensive understanding of the evolution of drought in the study area, highlighting the importance of a multiple approach to monitoring complex environments.","监测半干旱东北部地区的干旱是一项挑战，降雨变率大且原位监测网络稀缺使这一挑战更加复杂，因此利用地理技术进行水资源管理至关重要。本研究旨在利用基于遥感的指数，分析2002年至2022年巴西伯南布哥州帕热乌河流域的土地利用与覆盖动态及其与干旱事件的关系。采用标准化降水指数（SPI）表征气象干旱，同时使用植被健康指数（VHI）和归一化植被供水指数（NVSWI）评估生态系统的响应。结果表明，2011年至2016年是最严重且持续时间最长的干旱期，同期较低的VHI和NVSWI值进一步证实了这一点，表明植被在此期间受到水分胁迫。相关性分析显示，植被和水体等土地利用类别对干旱有直接响应，而牧场和裸露土壤等退化的人为区域则表现出相反的行为。三个指数与土地利用及覆盖数据的联合分析为理解研究区域干旱演变提供了全面的认识，凸显了多方法途径在监测复杂环境中的重要性。","Journal of Hyperspectral Remote Sensing",61,{"impact":174,"substance":93,"depth":197,"authority":198,"freshness":174,"relevant":50,"comment":199},15,12,"该研究利用遥感指数分析巴西半干旱流域干旱与植被动态，方法扎实但属区域性案例，对国内三农信息化参考价值有限。",[201],{"name":194,"url":191},[203,22,64,204,205],"水资源管理","干旱监测","植被指数",[207,208],"Pajeú River basin 干旱 遥感","SPI VHI NVSWI 植被","PajeúRiverbasin干旱遥感-3189","10.29150\u002Fjhrs.v16i03.269030",{"doi":210,"openalex_id":212,"authors":213,"venue":194,"cited_by_count":15,"oa_url":231,"card":232,"direction":44,"ingested_from":46},"W7213792298",[214,217,220,223,226,228],{"name":215,"orcid":216},"Juliana Farias Santos de Moraes","https:\u002F\u002Forcid.org\u002F0000-0002-3241-844X",{"name":218,"orcid":219},"Estephania Silva Jovino","https:\u002F\u002Forcid.org\u002F0000-0002-6694-3533",{"name":221,"orcid":222},"Alex Vinícius de Melo Vieira","https:\u002F\u002Forcid.org\u002F0009-0002-5204-3734",{"name":224,"orcid":225},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":227,"orcid":9},"Sylvana Sylvana Melo dos Santos",{"name":229,"orcid":230},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998","https:\u002F\u002Fperiodicos.ufpe.br\u002Frevistas\u002Fjhrs\u002Farticle\u002Fdownload\u002F269030\u002F52814",{"tldr":233,"method":234,"finding":235,"direction":44,"opportunity":236},"利用遥感指数分析巴西Pajeú河流域2002-2022年干旱对植被与土地利用的影响。","采用SPI、VHI和NVSWI指数，结合遥感数据与土地利用分类。","2011-2016年干旱最严重，植被和水体响应直接，而牧场和裸地呈相反趋势。","可融合多源遥感与机器学习，构建半干旱区干旱-植被-土地利用耦合预警模型。","2026-09-22T23:30:26.557816Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":9,"source_name":244,"source_url":245,"published_at":246,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":247,"score_detail":248,"sources":250,"tags":252,"search_phrases":256,"slug":259,"view_count":15,"doi":260,"paper":261,"created_at":274},3187,"Spatio-temporal analysis of Land Use and Land Cover (LULC) change using high resolution satellite data: A case study of Nagaon, Assam","https:\u002F\u002Fdoi.org\u002F10.31018\u002Fjans.v18i3.7774","Geographic information systems (GIS) coupled with satellite remote sensing (RS) have had a significant impact on the assessment and mapping of land surface dynamics, specifically the analysis of land-use land-cover (LULC) change. This study uses high-resolution multispectral LISS-IV (Linear Imaging Self-Scanning Sensor-IV) imagery with a 5.8-meter resolution to evaluate LULC trends in the Nagaon district of Assam between 2015 and 2024. Land use classes in a variety of categories, including vegetation, water bodies, agricultural land, built-up areas, scrubland, sandbars, tea plantations, and trees outside forests (TOF), were identified applying the maximum likelihood classification algorithm. Overall, The accuracy was 85.7% in 2015 and 90.3% in 2024, with respective Kappa Coefficients of 0.836 and 0.889, respectively. The results showed that between 2015 and 2024, the areas of tea plantations, natural vegetation, scrubland, and agricultural land fell by 0.26%, 6.14%, 0.26%, and 9.83%, respectively. Conversely, the waterbody, sandbar, built-up, and TOF have all increased by 0.29%, 0.91%, 0.82%, and 14.46%, respectively. This noticeable shift from conventional agricultural and natural vegetation landscapes toward tree-based systems and urban expansion on the study area, emphasize on matters concerning climate stress, declining soil fertility, economic benefits, policy support, and the need for resilient livelihoods.","地理信息系统（GIS）与卫星遥感（RS）相结合，对地表动态的评估与制图产生了显著影响，尤其是土地利用与土地覆盖（LULC）变化分析。本研究利用分辨率为5.8米的高分辨率多光谱LISS-IV（线性成像自扫描传感器-IV）影像，评估了阿萨姆邦纳冈县2015年至2024年间的LULC变化趋势。采用最大似然分类算法，识别了植被、水体、农地、建设用地、灌丛地、沙洲、茶园及林外树木（TOF）等多种土地利用类别。总体而言，2015年分类精度为85.7%，2024年为90.3%，Kappa系数分别为0.836和0.889。结果表明，2015年至2024年间，茶园、自然植被、灌丛地和农地面积分别减少了0.26%、6.14%、0.26%和9.83%。相反，水体、沙洲、建设用地和TOF分别增加了0.29%、0.91%、0.82%和14.46%。研究区从传统农业和自然植被景观向林基系统和城市扩张的显著转变，凸显了气候胁迫、土壤肥力下降、经济效益、政策支持以及韧性生计需求等相关问题。","Journal of Applied and Natural Science","https:\u002F\u002Fjournals.ansfoundation.org\u002Findex.php\u002Fjans\u002Farticle\u002Fview\u002F7774","2026-09-20T00:00:00Z",62,{"impact":174,"substance":93,"depth":197,"authority":141,"freshness":174,"relevant":50,"comment":249},"基于高分辨率遥感的区域土地利用变化实证研究，方法规范、数据翔实，对农业遥感监测有参考价值，但属地方性案例，公共影响有限。",[251],{"name":244,"url":245},[253,22,64,254,255],"农业遥感","印度农业","植被覆盖",[257,258],"Nagaon Assam LULC 遥感","LISS-IV 土地利用变化","NagaonAssamLULC遥感-3187","10.31018\u002Fjans.v18i3.7774",{"doi":260,"openalex_id":262,"authors":263,"venue":244,"cited_by_count":15,"oa_url":245,"card":269,"direction":44,"ingested_from":46},"W7213942371",[264,266],{"name":265,"orcid":9},"J. C. Das",{"name":267,"orcid":268},"Prodyut Bhattacharya","https:\u002F\u002Forcid.org\u002F0000-0002-4294-5585",{"tldr":270,"method":271,"finding":272,"direction":44,"opportunity":273},"利用高分辨率卫星影像分析印度阿萨姆邦纳冈地区2015-2024年土地利用\u002F覆盖变化。","LISS-IV 5.8米多光谱影像，最大似然分类，精度与Kappa系数评估。","农业用地和自然植被分别减少9.83%和6.14%，而林外树木和建设用地分别增加14.46%和0.82","可结合时序高分辨率影像与农户调查，探究林外树木扩张对农业韧性和碳汇的驱动机制。","2026-09-22T23:30:25.539323Z"]