[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3369":3,"related-3369":75},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":74},3369,"Understanding Land Use\u002FLand Cover Dynamics and Land Surface Temperature Variations Through Machine Learning and Geospatial Approach: A Case Study of Kullu District, Himachal Pradesh, India","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fatmos17100916","Machine learning (ML)-based classification provides an effective approach for extracting land use\u002Fland cover (LULC) information from multi-temporal satellite observations. This study evaluates Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Forest (RF) for mapping LULC changes in Kullu district, Himachal Pradesh, India, using Landsat data from 1991 to 2022. SVM achieved the highest classification accuracy and was therefore used for subsequent LULC mapping. Six LULC classes were identified and their temporal changes were examined in relation to Landsat-derived land surface temperature (LST). The built-up area increased from 4.37% in 1991 to 20.40% in 2022, whereas vegetation declined from 34.41% to 16.23% and snow cover from 15.20% to 3.02%. The area-weighted mean LST was 4.10 °C in 1991, 10.03 °C in 2001, 8.43 °C in 2011, and 4.18 °C in 2022, representing a marginal net increase of 0.08 °C over the study period. Class-wise analysis showed that barren and agricultural lands generally recorded higher mean LST, while vegetation and waterbodies exhibited comparatively lower values. The relatively high snow-cover LST in 2022 may reflect reduced and fragmented seasonal snow cover and snowmelt conditions during image acquisition. Correlation analysis revealed an inverse relationship between LST and vegetation and snow-cover indicators, with R2 values reaching 0.66 for NDVI and generally exceeding 0.62 for NDSI. Overall, the findings demonstrate substantial LULC transformation and pronounced spatial differences in surface temperature, emphasizing the role of vegetation and snow cover in regulating thermal conditions and providing useful information for sustainable land management and spatial planning in the ecologically sensitive Himalayan environment.","基于机器学习的分类为从多时相卫星观测中提取土地利用\u002F土地覆盖（LULC）信息提供了一种有效途径。本研究利用1991年至2022年的Landsat数据，评估了最大似然分类（MLC）、支持向量机（SVM）和随机森林（RF）在印度喜马偕尔邦库鲁地区LULC变化制图中的表现。SVM取得了最高的分类精度，因此被用于后续的LULC制图。研究识别出六种LULC类别，并结合Landsat反演的地表温度（LST）分析了其时间变化。建设用地面积从1991年的4.37%增至2022年的20.40%，而植被从34.41%降至16.23%，积雪覆盖从15.20%降至3.02%。面积加权平均LST在1991年为4.10 °C，2001年为10.03 °C，2011年为8.43 °C，2022年为4.18 °C，研究期间净增幅度仅为0.08 °C。分类别分析表明，裸地和农用地通常具有较高的平均LST，而植被和水体的LST相对较低。2022年积雪覆盖区LST相对较高，可能反映了影像获取时期季节性积雪覆盖减少、破碎化及融雪条件的影响。相关分析揭示了LST与植被和积雪覆盖指标之间的反比关系，NDVI的R²达到0.66，NDSI的R²普遍超过0.62。总体而言，研究结果揭示了显著的LULC转变和地表温度的明显空间差异，强调了植被和积雪覆盖在调节热力条件中的作用，并为生态敏感的喜马拉雅地区的可持续土地管理和空间规划提供了有用信息。",null,"Atmosphere","2026-09-23T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,17,13,9,1,"基于Landsat与机器学习揭示喜马拉雅地区土地利用与地表温度长期演变，数据翔实但属区域案例研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"机器学习","遥感","土地利用","地表温度","喜马拉雅",[33,34],"Kullu 土地利用 地表温度","Landsat 机器学习 土地覆盖","Kullu土地利用地表温度-3369",0,"10.3390\u002Fatmos17100916",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":66,"direction":72,"ingested_from":73},"W7214105064",[41,44,46,49,52,55,58,60,63],{"name":42,"orcid":43},"Sanjeev Jha","https:\u002F\u002Forcid.org\u002F0000-0002-6851-7722",{"name":45,"orcid":9},"Nirbhav",{"name":47,"orcid":48},"Atul Saini","https:\u002F\u002Forcid.org\u002F0000-0001-9840-2486",{"name":50,"orcid":51},"Toushif Jaman","https:\u002F\u002Forcid.org\u002F0000-0001-6225-5648",{"name":53,"orcid":54},"Harish Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-4738-1248",{"name":56,"orcid":57},"Pradeep Kumar Jha","https:\u002F\u002Forcid.org\u002F0000-0002-5938-1747",{"name":59,"orcid":9},"Fahdah Falah Ben Hasher",{"name":61,"orcid":62},"Abinash SİLWAL","https:\u002F\u002Forcid.org\u002F0000-0002-2285-4643",{"name":64,"orcid":65},"Mohamed Zhran","https:\u002F\u002Forcid.org\u002F0000-0002-1112-387X",{"tldr":67,"method":68,"finding":69,"direction":70,"opportunity":71},"用机器学习与遥感分析印度库鲁区1991-2022年土地利用变化及其对地表温度的影响。","Landsat时序数据，对比MLC、SVM、随机森林分类，结合NDVI\u002FNDSI","建设用地从4.37%增至20.40%，植被与积雪大幅减少，LST净增0.08°C，植被和积雪与LST","农业遥感与作物表型","可引入深度学习与多源遥感，在喜马拉雅生态敏感区开展LULC-LST耦合模拟与情景预测。","农业人工智能与决策模型","openalex","2026-09-24T23:30:34.225644Z",{"total":76,"page":22,"page_size":76,"items":77},6,[78,114,151,195,243,287],{"id":79,"title":80,"url":81,"summary":82,"summary_zh":83,"content":9,"source_name":84,"source_url":81,"published_at":85,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":86,"score_detail":87,"sources":91,"tags":93,"search_phrases":96,"slug":99,"view_count":36,"doi":100,"paper":101,"created_at":113},3196,"Geospatial and machine learning techniques for spatiotemporal analyses of urban dynamics in the Coimbatore city, South India","https:\u002F\u002Fdoi.org\u002F10.3846\u002Fjeelm.2026.28264","Rapid urbanization has drastically changed the land use and environmental conditions in Indian cities and need to be monitored continuously for sustainable urban planning. The study used Landsat satellite images of the years 2001, 2013 and 2023 to examine the spatiotemporal urban dynamics of the Coimbatore city of South India. The impacts of urban sprawl on the environment were evaluated from Land Use\u002F Land Cover (LULC), Land Surface Temperature (LST) and spectral indices (NDVI, NDWI and NDBI). The built-up land increased by 1.21% (2001–2013) and 1.67% (2013–2023) and agricultural land decreased by 1.62% (2013–2023). The LULC classification had an Overall Accuracy of 95.76% with a Kappa coefficient of 0.95. The Kappa coefficient of 0.96 indicates that the ANN-CA model has high predictive reliability in predicting the LULC scenario in 2031. The results show that the continued expansion of cities leads to an increase in land surface temperature and a decrease in vegetation cover. This has major implications for sustainable urban development and contributes to SDG 11 and SDG 13.","快速城市化极大地改变了印度城市的土地利用和环境状况，需要持续监测以支持可持续城市规划。本研究利用2001年、2013年和2023年的Landsat卫星影像，考察了印度南部哥印拜陀市的时空城市动态。研究从土地利用\u002F土地覆盖（LULC）、地表温度（LST）和光谱指数（NDVI、NDWI和NDBI）方面评估了城市蔓延对环境的影响。建设用地在2001—2013年间增加了1.21%，在2013—2023年间增加了1.67%；农业用地在2013—2023年间减少了1.62%。LULC分类的总体精度为95.76%，Kappa系数为0.95。Kappa系数0.96表明，ANN-CA模型在预测2031年LULC情景方面具有较高的预测可靠性。结果表明，城市持续扩张导致地表温度升高和植被覆盖减少。这对可持续城市发展具有重要影响，并有助于实现可持续发展目标11和可持续发展目标13。","Journal of Environmental Engineering and Landscape Management","2026-09-21T00:00:00Z",62,{"impact":17,"substance":88,"depth":89,"authority":20,"freshness":17,"relevant":22,"comment":90},18,15,"基于Landsat多时相遥感与ANN-CA模型的印度城市扩张研究，方法规范、数据翔实，对农业用地变化与遥感监测有参考价值，但属境外区域案例，公共影响有限。",[92],{"name":84,"url":81},[94,28,29,30,95],"可持续发展","城市扩张",[97,98],"Coimbatore 城市扩张 遥感","LULC LST NDVI 印度城市","Coimbatore城市扩张遥感-3196","10.3846\u002Fjeelm.2026.28264",{"doi":100,"openalex_id":102,"authors":103,"venue":84,"cited_by_count":36,"oa_url":81,"card":108,"direction":72,"ingested_from":73},"W7213862636",[104,106],{"name":105,"orcid":9},"Nagamani Singamuthu",{"name":107,"orcid":9},"Elangovan Krishnan",{"tldr":109,"method":110,"finding":111,"direction":70,"opportunity":112},"基于Landsat影像与机器学习分析印度哥印拜陀市2001-2023年城市扩张及其环境效应。","Landsat影像、LULC分类、NDVI\u002FNDWI\u002FNDBI指数、ANN-CA","建设用地增加、农地减少，城市扩张导致地表温度上升、植被覆盖下降。","可结合多源遥感与深度学习提升城市扩张预测精度，并探究其对周边农业用地的长期影响。","2026-09-22T23:30:43.496849Z",{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":9,"source_name":120,"source_url":117,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":124,"tags":126,"search_phrases":130,"slug":133,"view_count":36,"doi":134,"paper":135,"created_at":150},3362,"Satellite-Based Delineation of the Urban–Rural Interface Using Landscape Heterogeneity Metrics: Evidence from Three Argentine Cities","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101777","Urban–rural interfaces are transitional territories characterized by the coexistence of urban, agricultural, and natural land covers. Despite their relevance for territorial planning and environmental management, their spatial delineation remains methodologically challenging due to their dynamic and heterogeneous nature. This study proposes a reproducible remote sensing-based approach for delineating urban–rural interfaces and evaluating their spatial coherence through landscape heterogeneity metrics. The method was applied to three intermediate Argentine cities representing contrasting environmental contexts: Córdoba, Rosario, and Mendoza. Interface areas were identified by combining nighttime light intensity, the Normalized Built-up Area Index (NBAI), and the Normalized Difference Vegetation Index (NDVI) within Google Earth Engine. To evaluate the coherence of the resulting delineations, five alternative interface configurations were generated using different nighttime light thresholds and assessed through landscape metrics describing diversity, interspersion, fragmentation, and spatial structure. The workflow produced spatially coherent candidate interface areas in all three cities, producing spatial patterns consistent with local urban expansion processes. The highest heterogeneity values occurred at different interface extents among cities, with intermediate configurations generally showing the strongest responses. Among the evaluated metrics, Shannon Diversity Index (SHDI) and Interspersion and Juxtaposition Index (IJI) provided the most consistent results. The findings suggest that spatial heterogeneity constitutes a useful criterion for assessing the coherence of interface delineations, while highlighting that maximum heterogeneity does not necessarily correspond to the most operationally suitable boundary for territorial management.","城乡界面是以城市、农业和自然土地覆盖并存为特征的过渡性地域。尽管其对于国土规划与环境管理具有重要意义，但由于其动态性和异质性，空间划定在方法上仍具挑战性。本研究提出了一种可重复的基于遥感的方法，用于划定城乡界面，并通过景观异质性指标评估其空间一致性。该方法应用于三座代表不同环境背景的阿根廷中等城市：科尔多瓦、罗萨里奥和门多萨。界面区域通过在Google Earth Engine中结合夜间灯光强度、归一化建成区指数（NBAI）和归一化植被指数（NDVI）进行识别。为评估所得划定结果的一致性，利用不同的夜间灯光阈值生成了五种替代界面配置，并通过描述多样性、交错性、破碎化和空间结构的景观指标进行评估。该工作流程在三座城市中均生成了空间一致的候选界面区域，其空间格局与当地城市扩张过程相符。最高异质性值在各城市出现的界面范围不同，中间配置通常表现出最强的响应。在评估的指标中，香农多样性指数（SHDI）和交错与毗邻指数（IJI）提供了最为一致的结果。研究结果表明，空间异质性可作为评估界面划定一致性的有用标准，同时强调最大异质性并不一定对应于国土管理中最具操作适宜性的边界。","Land",66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":22,"comment":123},"方法可复现、指标组合有新意，但研究对象为阿根廷城市，对国内三农与农业信息化的直接参考价值有限。",[125],{"name":120,"url":117},[127,28,29,128,129],"城乡融合","景观格局","阿根廷",[131,132],"阿根廷 城乡结合部 遥感","Google Earth Engine 夜间灯光 NDVI","阿根廷城乡结合部遥感-3362","10.3390\u002Fland15101777",{"doi":134,"openalex_id":136,"authors":137,"venue":120,"cited_by_count":36,"oa_url":117,"card":145,"direction":70,"ingested_from":73},"W7214122245",[138,141,143],{"name":139,"orcid":140},"Yuliana Céliz","https:\u002F\u002Forcid.org\u002F0000-0003-3810-8490",{"name":142,"orcid":9},"Beatriz Giobellina",{"name":144,"orcid":9},"Ramiro Sarandón",{"tldr":146,"method":147,"finding":148,"direction":70,"opportunity":149},"提出基于遥感与景观异质性指标划定城乡结合部的方法，并在三座阿根廷城市验证。","Google Earth Engine结合夜间灯光、NBAI和NDVI，用景观指","SHDI和IJI最一致，但最大异质性边界未必最适合管理。","可探索将城乡界面异质性指标与农业多功能性、生态系统服务结合，优化国土空间规划。","2026-09-24T23:30:20.991175Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":164,"tags":166,"search_phrases":170,"slug":173,"view_count":36,"doi":174,"paper":175,"created_at":194},3328,"Yield ranking of spring wheat breeding lines absent from model training within two contrasting seasons in northern Kazakhstan","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102588","Reliable assessment of yield models in breeding trials requires evaluation on lines that have not contributed observations to model development. We examined this problem in an advanced spring wheat yield nursery in northern Kazakhstan during the 2024 and 2025 seasons. Of 300 plots, 176 from 22 entries formed the model development set, while 124 plots representing 31 breeding lines were completely excluded from training. Eleven variables were retained from 82 candidates using the training data alone; the candidates were derived from UAV multispectral imagery, ERA5-Land reanalysis, LiDAR-derived static plot microtopography, phenology and maturity group. LightGBM yielded a pooled R² of 0.587 for the excluded lines; resampling breeding lines within season gave a mean R² of 0.564 with a 95 % confidence interval from 0.252 to 0.776. The pooled value partly reflected the strong contrast between seasons rather than discrimination among lines within a season. Random division of plots produced an R² of 0.890, indicating that apparent predictive accuracy depended strongly on validation design. Agreement between observed and predicted line rankings was moderate, with Spearman correlations of 0.612 in 2024 and 0.650 in 2025, although uncertainty was wide because only 14 and 17 independent lines were available. Six modelling approaches produced overlapping uncertainty intervals, with no clear evidence of superiority, and the complete combination of data sources was not separable from simpler configurations retaining the multispectral block. A separate analysis in which screening, selection and fitting used 2024 data alone and evaluation used 2025 data showed a severe loss of performance, with R² = −8.81 and a Spearman correlation of −0.082 that was indistinguishable from zero. Poor transfer persisted among entries grown in both seasons, so genotype novelty alone was insufficient to explain the failure. Breeding lines absent from training could therefore be ranked with moderate consistency when both observed seasonal regimes were represented during model development, whereas prediction in an unobserved season remained unreliable. The study covers one nursery complex and two seasons, so transfer across locations and broader conditions remains to be established.","在育种试验中，要对产量模型进行可靠评估，必须在未参与模型开发的品系上进行评价。我们在2024年和2025年生长季于哈萨克斯坦北部的一个高级春小麦产量圃中考察了这一问题。在300个小区中，来自22个品系的176个小区构成模型开发集，而代表31个育种品系的124个小区则完全排除在训练之外。仅使用训练数据，从82个候选变量中保留了11个变量；这些候选变量来源于无人机多光谱影像、ERA5-Land再分析数据、LiDAR衍生的静态小区微地形、物候和成熟期组。LightGBM对被排除品系给出的合并R²为0.587；在生长季内对育种品系进行重采样得到的平均R²为0.564，95%置信区间为0.252至0.776。合并值部分反映了生长季之间的强烈差异，而非生长季内品系之间的区分能力。对小区进行随机划分得到的R²为0.890，表明表观预测精度在很大程度上取决于验证设计。观测品系排名与预测品系排名之间的一致性为中等，2024年和2025年的Spearman相关系数分别为0.612和0.650，但由于仅有14个和17个独立品系可用，不确定性范围较宽。六种建模方法产生了相互重叠的不确定性区间，没有明确证据表明哪一种更优，并且完整的数据源组合与保留多光谱模块的较简单配置无法区分。另一项分析中，筛选、选择和拟合仅使用2024年数据，而评估使用2025年数据，结果显示性能严重下降，R² = −8.81，Spearman相关系数为−0.082，与零无法区分。在两个生长季均种植的品系之间，较差的迁移性依然存在，因此仅用品系新颖性不足以解释这种失败。因此，当模型开发过程中涵盖了所观测到的两种生长季情形时，未参与训练的育种品系可以以中等一致性进行排名，而在未观测生长季中的预测仍然不可靠。本研究仅涵盖一个圃系复合体和两个生长季，因此跨地点和更广泛条件下的迁移性仍有待确立。","Smart Agricultural Technology","2026-09-22T00:00:00Z",74,{"impact":161,"substance":162,"depth":88,"authority":20,"freshness":21,"relevant":22,"comment":163},12,22,"基于无人机多光谱与气象数据的春小麦育种品系产量预测研究，验证设计严谨、结论审慎，对智慧育种与遥感估产有参考价值，但属单点试验、地域性强，未达重大突破层级。",[165],{"name":157,"url":154},[167,168,27,169,28],"智慧农业","产量预测","小麦育种",[171,172],"哈萨克斯坦 春小麦 产量预测","UAV 多光谱 育种试验","哈萨克斯坦春小麦产量预测-3328","10.1016\u002Fj.atech.2026.102588",{"doi":174,"openalex_id":176,"authors":177,"venue":157,"cited_by_count":36,"oa_url":154,"card":189,"direction":70,"ingested_from":73},"W7214044328",[178,181,184,187],{"name":179,"orcid":180},"Dastan Yelubayev","https:\u002F\u002Forcid.org\u002F0000-0001-5358-7982",{"name":182,"orcid":183},"TIMUR SAVIN","https:\u002F\u002Forcid.org\u002F0000-0002-3550-647X",{"name":185,"orcid":186},"Ismail Tokbergenov","https:\u002F\u002Forcid.org\u002F0000-0002-0656-9914",{"name":188,"orcid":9},"Bakhtiyar Zhanzakov",{"tldr":190,"method":191,"finding":192,"direction":70,"opportunity":193},"评估春小麦育种品系产量模型在未参与训练品系上的跨季预测能力。","无人机多光谱、ERA5-Land、LiDAR与物候数据，LightGBM等六种模","两季均参与训练时品系排名中等一致，但预测未观测季节完全失效。","需研究跨地点、跨年份可迁移的表型预测模型与验证设计，避免随机划分高估精度。","2026-09-24T23:30:03.201775Z",{"id":196,"title":197,"url":198,"summary":199,"summary_zh":200,"content":9,"source_name":201,"source_url":198,"published_at":202,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":203,"score_detail":204,"sources":207,"tags":209,"search_phrases":213,"slug":216,"view_count":36,"doi":217,"paper":218,"created_at":242},3076,"Research on the Inversion Method of Total Phosphorus Concentration in Water Bodies Based on PSO-Adam-BP Neural Network Model","https:\u002F\u002Fdoi.org\u002F10.15244\u002Fpjoes\u002F220960","This study investigated Baiyangdian Lake in the Xiong’an New Area to invert total phosphorus (TP) concentrations using Sentinel-2 multispectral imagery and in situ measurements, aiming to support water-quality assessment and eutrophication management. Based on the multispectral remote sensing data of Sentinel-2 and measured total phosphorus data of water bodies, the study used specific feature combinations such as NDTI, B5, B11-B12 difference, and sensitive combination bands of measured data as model inputs to construct a PSO-Adam-BP neural network machine learning model for total phosphorus concentration inversion. Compared with the traditional BP and PSO-BP baseline models, the proposed framework significantly improved the accuracy of the model, with an R2 value reaching 0.9351. Moreover, it successfully reduced the average relative error by up to 53.5%, from 4.80% to 2.24%, and decreased the maximum relative error by up to 31.7%, from 11.21% to 7.66%, demonstrating the model’s highly robust and precise inversion performance. This study provides a new method for water quality detection in Baiyangdian Lake and is of great significance to the protection and development of the water environment in Xiong’an New Area.","本研究以雄安新区白洋淀为研究对象，利用Sentinel-2多光谱影像和实地测量数据反演总磷（TP）浓度，旨在为水质评价和富营养化管理提供支持。基于Sentinel-2多光谱遥感数据和白洋淀水体总磷实测数据，研究采用NDTI、B5、B11-B12差值等特征组合及实测数据的敏感组合波段作为模型输入，构建了PSO-Adam-BP神经网络机器学习模型用于总磷浓度反演。与传统BP和PSO-BP基准模型相比，所提出的框架显著提高了模型精度，R²值达到0.9351。此外，该模型成功将平均相对误差最多降低53.5%，从4.80%降至2.24%，并将最大相对误差最多降低31.7%，从11.21%降至7.66%，展现出高度稳健且精确的反演性能。本研究为白洋淀水质检测提供了新方法，对雄安新区水环境保护与开发具有重要意义。","Polish Journal of Environmental Studies","2026-09-18T00:00:00Z",72,{"impact":89,"substance":205,"depth":19,"authority":20,"freshness":76,"relevant":22,"comment":206},21,"基于Sentinel-2与PSO-Adam-BP模型实现白洋淀总磷浓度高精度反演，方法新颖、数据可靠，对雄安水环境智慧监测有实用价值。",[208],{"name":201,"url":198},[210,27,28,211,212],"农业人工智能","水质监测","白洋淀",[214,215],"白洋淀 总磷 遥感反演","Sentinel-2 水质 反演","白洋淀总磷遥感反演-3076","10.15244\u002Fpjoes\u002F220960",{"doi":217,"openalex_id":219,"authors":220,"venue":201,"cited_by_count":36,"oa_url":236,"card":237,"direction":70,"ingested_from":73},"W7213537374",[221,223,225,228,231,233],{"name":222,"orcid":9},"Guanxing Wang",{"name":224,"orcid":9},"Cui Jia",{"name":226,"orcid":227},"Linghan Gao","https:\u002F\u002Forcid.org\u002F0000-0002-8537-8787",{"name":229,"orcid":230},"Shan An","https:\u002F\u002Forcid.org\u002F0000-0001-7796-6952",{"name":232,"orcid":9},"Jia Xi",{"name":234,"orcid":235},"Yaxue Liu","https:\u002F\u002Forcid.org\u002F0009-0006-9025-8030","https:\u002F\u002Fwww.pjoes.com\u002Fpdf-220960-145347?filename=Research-on-the-Inversion.pdf",{"tldr":238,"method":239,"finding":240,"direction":70,"opportunity":241},"用PSO-Adam-BP神经网络结合Sentinel-2影像反演白洋淀总磷浓度。","Sentinel-2多光谱与实测数据，NDTI等特征组合，PSO-Adam-BP","R²达0.9351，平均相对误差由4.80%降至2.24%，精度显著优于BP与PSO-BP。","可迁移至其他内陆水体与多参数水质反演，探索时序泛化与跨区域迁移能力。","2026-09-21T23:30:25.856035Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":85,"category":12,"cover_url":9,"hotness":250,"is_selected":14,"score":251,"score_detail":252,"sources":255,"tags":259,"search_phrases":263,"slug":266,"view_count":36,"doi":267,"paper":268,"created_at":286},3068,"MAPPING LAND USE OF BENUE STATE UNIVERSITY, MAKURDI MAIN CAMPUS, BENUE STATE, NIGERIA USING GEOSPATIAL TECHNIQUES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22865603","This study examined the spatial distribution and extent of land use types within Benue State University (BSU), Makurdi Main Campus, using Geographic Information System (GIS) and remote sensing techniques. Satellite imagery obtained from the National Space Research and Development Agency (NASRDA) was processed and analyzed in ArcGIS 10.8. Land use classification was achieved through on-screen digitization supported by intensive field survey and ground-truthing. The results reveal a heterogeneous land use structure with a total mapped area of 166.69 hectares. Agricultural land use dominates with 20.35%, followed by residential (15.24%) and forest cover (12.29%). Environmentally sensitive land uses, including forestry, green areas, and marshlands, collectively account for 29.18% of the total mapped area, indicating significant ecological value. Institutional land uses (administrative, educational, and religious) account for 15.50%, while commercial and mixed uses constitute 9.29%. The findings highlight a semi-urban institutional landscape undergoing gradual transformation, with increasing development pressures. The study demonstrates the effectiveness of GIS in land use planning and recommends sustainable land management strategies to balance development with environmental conservation.","本研究利用地理信息系统（GIS）和遥感技术，考察了马库尔迪贝努埃州立大学（BSU）主校区内土地利用类型的空间分布与范围。研究对来自国家空间研究与发展局（NASRDA）的卫星影像在ArcGIS 10.8中进行了处理与分析。土地利用分类通过屏幕数字化完成，并辅以密集的实地调查与地面验证。结果显示，该区域土地利用结构具有异质性，总制图面积为166.69公顷。农业用地占主导地位，为20.35%，其次为住宅用地（15.24%）和林地覆盖（12.29%）。环境敏感型土地利用类型，包括林业用地、绿地和沼泽地，合计占总制图面积的29.18%，表明其具有显著的生态价值。机构用地（行政、教育和宗教）占15.50%，而商业和混合用途占9.29%。研究结果凸显出一个正处于逐步转型中的半城市机构景观，其面临日益增大的开发压力。本研究证明了GIS在土地利用规划中的有效性，并建议采取可持续土地管理策略，以平衡开发与环境保护。","Zenodo (CERN European Organization for Nuclear Research)",25,56,{"impact":76,"substance":253,"depth":20,"authority":161,"freshness":21,"relevant":22,"comment":254},16,"基于GIS与遥感的校园土地利用分类研究，方法规范、数据具体，但属尼日利亚个案，对国内三农与农业信息化参考价值有限。",[256,257],{"name":249,"url":246},{"name":249,"url":258},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22865602",[28,29,260,261,262],"GIS","土地分类","校园规划",[264,265],"土地分类 土地利用 校园规划 遥感","土地分类 土地利用","土地分类土地利用校园规划遥感-3068","10.5281\u002Fzenodo.22865603",{"doi":267,"openalex_id":269,"authors":270,"venue":249,"cited_by_count":36,"oa_url":246,"card":281,"direction":70,"ingested_from":73},"W7213752705",[271,273,275,277,279],{"name":272,"orcid":9},"Alex S. Ortese",{"name":274,"orcid":9},"Innocent E. Bello",{"name":276,"orcid":9},"I. K. SAMAILA",{"name":278,"orcid":9},"M. ALKALI",{"name":280,"orcid":9},"A. Mahmud",{"tldr":282,"method":283,"finding":284,"direction":70,"opportunity":285},"用GIS和遥感技术绘制尼日利亚贝努埃州立大学主校区土地利用分布图。","NASRDA卫星影像，ArcGIS 10.8屏幕数字化，结合实地调查与地面验证。","农业用地占20.35%居首，环境敏感用地共占29.18%，校园呈半城市化转型压力。","可延伸至校园及城郊农业用地动态监测与生态敏感区保护规划研究。","2026-09-21T23:30:23.914355Z",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":292,"content":9,"source_name":293,"source_url":290,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":296,"sources":298,"tags":300,"search_phrases":303,"slug":306,"view_count":36,"doi":307,"paper":308,"created_at":326},3060,"Geospatial Intelligence for Peri-Urban Land-Use Conflicts: Evaluating Agricultural Suitability against Rapid Urbanisation using the Analytical Hierarchy Process and Cloud Computing","https:\u002F\u002Fdoi.org\u002F10.59543\u002F6mpcwr41","This paper presents a geospatial multi-criteria evaluation of agricultural potential in the suburban region of Bapatla using eight physical and land-use characteristics: elevation, slope, road accessibility, proximity to water bodies, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Land Use\u002FLand Cover (LULC), and soil texture, processed using Google Earth Engine. The Analytic Hierarchy Process (AHP) was used to determine the relative weights of each criterion. NDVI received the highest weight (26.33%), followed by LST, slope, and proximity to water bodies (14.96% each), while elevation received the lowest weight (4.6%) due to the region's flat terrain. Weighted overlay analysis classified the 142.25 km² study area into Suitable (108.90 km²; 76.55%), Not Suitable (32.60 km²; 22.92%), and Highly Suitable (0.75 km²; 0.53%) categories. Suitable areas are mainly distributed across the southern and peripheral agricultural zones, whereas unsuitable areas are concentrated within Bapatla Urban and its surroundings. The limited extent of highly suitable land highlights the scarcity of optimal agricultural sites. The results reveal land-use conflicts driven primarily by urbanisation rather than environmental constraints. The AHP-weighted suitability map provides an evidence-based tool for agricultural land conservation, water-resource management, and sustainable urban expansion.","本文基于八项自然与土地利用特征，对巴帕特拉（Bapatla）郊区农业潜力进行了地理空间多准则评价，这些特征包括：海拔、坡度、道路可达性、距水体远近、地表温度（LST）、归一化植被指数（NDVI）、土地利用\u002F土地覆盖（LULC）以及土壤质地，并利用Google Earth Engine进行处理。采用层次分析法（AHP）确定各准则的相对权重。NDVI权重最高（26.33%），其次为LST、坡度和距水体远近（均为14.96%），而海拔因该地区地形平坦权重最低（4.6%）。加权叠加分析将142.25 km²的研究区划分为适宜（108.90 km²；76.55%）、不适宜（32.60 km²；22.92%）和高适宜（0.75 km²；0.53%）三类。适宜区主要分布于南部及外围农业区，而不适宜区集中于巴帕特拉城区及其周边。高适宜土地面积有限，凸显了优质农业用地的稀缺性。结果表明，土地利用冲突主要由城市化驱动，而非环境限制。基于AHP的适宜性地图为农业用地保护、水资源管理和可持续城市扩张提供了循证工具。","Journal of Urban Intelligence and Smart Systems","2026-09-19T00:00:00Z",70,{"impact":161,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":22,"comment":297},"该论文利用遥感与AHP方法评估城郊农业用地冲突，方法新颖、数据详实，对农业土地保护有参考价值，但属细分领域研究，影响范围有限。",[299],{"name":293,"url":290},[167,301,302,28,29],"农业遥感","农业信息化",[304,305],"Bapatla 农业用地 城市化","Google Earth Engine 农业适宜性","Bapatla农业用地城市化-3060","10.59543\u002F6mpcwr41",{"doi":307,"openalex_id":309,"authors":310,"venue":293,"cited_by_count":36,"oa_url":319,"card":320,"direction":325,"ingested_from":73},"W7213644047",[311,313,315,317],{"name":312,"orcid":9},"Sreerama Naik Naik S R",{"name":314,"orcid":9},"T K Prasad",{"name":316,"orcid":9},"Feba Jose Jasmine",{"name":318,"orcid":9},"Jayapal G","https:\u002F\u002Fjuiss.org\u002Findex.php\u002Fjuiss\u002Farticle\u002Fdownload\u002F366\u002F231",{"tldr":321,"method":322,"finding":323,"direction":70,"opportunity":324},"用AHP与云平台评估印度Bapatla城郊农业适宜性，揭示城市化引发的土地利用冲突。","Google Earth Engine处理8个因子，AHP加权叠加分析142.2","76.55%区域适宜农业，但高度适宜仅0.53%，冲突主因是城市化而非环境限制。","可引入时序遥感与动态城市扩张模拟，构建城郊农业保护与城市增长协同优化模型。","智慧农业 \u002F 农业物联网","2026-09-21T23:30:09.448414Z"]