[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3361":3,"related-3361":58},{"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":57},3361,"Spatiotemporal Patterns and Associations of Ecological Quality and Carbon Storage Across Three Major Agricultural Provinces in Central China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101781","Ecological quality, vegetation productivity, and carbon storage represent different ecosystem properties and may respond differently to land-use change. This study assessed their spatiotemporal patterns and spatial relationships across Hubei, Henan, and Anhui, three major agricultural provinces in central China. Ecological quality was assessed using the Remote Sensing Ecological Index (RSEI), vegetation productivity was estimated as net primary productivity (NPP) using the Carnegie–Ames–Stanford Approach (CASA) model, and carbon storage was estimated using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Spearman correlation, GeoDetector, and a descriptive coordination index were used to compare their spatial relationships. Ecological quality declined from 2000 to 2010 and recovered only modestly thereafter, with persistently lower values in the eastern and northern plains than in the western and southern mountains. From 1990 to 2020, built-up land expanded by 112.03%, while model-estimated carbon storage declined by 34.59 × 106 t. RSEI and NPP were predominantly positively correlated, but correlations varied substantially among provinces, ecological-quality classes, and land-use types. RSEI had the greatest explanatory power for spatial variation in carbon storage among the composite factors (q = 0.420–0.467), and NDVI was the strongest RSEI component. GeoDetector identified bivariate and nonlinear enhancement, indicating that paired factors explained more spatial variation than individual factors. Moderate and primary coordination together accounted for more than 99% of valid pixels, while severe imbalance declined from 0.88% to 0.44%. These findings show that the three indicators are spatially associated but not interchangeable. Together, the results support protecting forested mountain areas, limiting built-up expansion and cropland loss in plains and urban fringes, and using multiple indicators to identify areas requiring local assessment.","生态质量、植被生产力与碳储量表征不同的生态系统属性，可能对土地利用变化作出不同响应。本研究评估了华中三个农业大省——湖北、河南和安徽——上述指标的时空格局及其空间关系。生态质量采用遥感生态指数（RSEI）评估，植被生产力以Carnegie–Ames–Stanford Approach（CASA）模型估算的净初级生产力（NPP）表征，碳储量则采用生态系统服务与权衡综合评估（InVEST）模型估算。运用Spearman相关分析、地理探测器及描述性协调指数比较其空间关系。2000—2010年生态质量下降，此后仅略有恢复，东部和北部平原的值持续低于西部和南部山区。1990—2020年，建设用地扩张112.03%，而模型估算的碳储量下降34.59 × 10⁶ t。RSEI与NPP总体呈正相关，但相关性在各省份、生态质量等级和土地利用类型间差异显著。在复合因子中，RSEI对碳储量空间变异的解释力最强（q = 0.420–0.467），NDVI是RSEI中最强的组分。地理探测器识别出双因子和非线性增强，表明成对因子解释的空间变异多于单因子。中度协调和初级协调合计占有效像元的99%以上，而严重失调从0.88%降至0.44%。研究结果表明，三个指标在空间上相互关联但不可互换。综合而言，研究结果支持保护山地森林区域、限制平原和城市边缘的建设用地扩张与耕地流失，并利用多指标识别需开展局部评估的区域。",null,"Land","2026-09-23T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,8,1,"基于RSEI、CASA与InVEST模型对中部三大农业省生态质量与碳储量的长时序空间关联分析，方法规范、数据扎实，对农业生态保护与国土空间管控有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业生态","遥感监测","土地利用变化","生态质量","碳储量",[33,34],"湖北 河南 安徽 农业生态","RSEI 碳储量 遥感","湖北河南安徽农业生态-3361",0,"10.3390\u002Fland15101781",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":49,"direction":55,"ingested_from":56},"W7214114544",[41,43,46],{"name":42,"orcid":9},"Xiaohan Liu",{"name":44,"orcid":45},"Lei Wang","https:\u002F\u002Forcid.org\u002F0000-0002-7163-3644",{"name":47,"orcid":48},"Hui Min Zhao","https:\u002F\u002Forcid.org\u002F0000-0001-7587-1116",{"tldr":50,"method":51,"finding":52,"direction":53,"opportunity":54},"评估中部三大农业省生态质量、植被生产力与碳储量的时空格局及空间关联。","用RSEI、CASA模型、InVEST模型结合Spearman相关、GeoDet","生态质量先降后微升，建设用地增112%，碳储减3459万吨；三指标空间关联但不可互换。","农业绿色发展与碳","可探究不同农业省土地利用变化下生态质量与碳储量的非线性驱动机制及分区管控策略。","农业遥感与作物表型","openalex","2026-09-24T23:30:20.927597Z",{"total":59,"page":22,"page_size":59,"items":60},6,[61,104,141,188,236,269],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":66,"content":9,"source_name":67,"source_url":64,"published_at":68,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":69,"score_detail":70,"sources":73,"tags":75,"search_phrases":79,"slug":82,"view_count":36,"doi":83,"paper":84,"created_at":103},3182,"Linking socioeconomic drivers to land use and land cover change for sustainable landscape governance a systematic review","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09445-9","Land use and land cover (LULC) change is one of the most environmental issues that impact the ecosystems, biodiversity and sustainable resource management in the world. The pressures have increased on land systems especially in the developing countries because of the rapid socioeconomic development, increase in population and demand of food and natural resources. Despite the large number of literature examining the drivers and effects of LULC change, the contributions of socio-economic drivers and local governance projects to sustainable landscape management is still disjointed in the current literature. This paper seeks to summarize existing literature regarding the socio-economic factors determining LULC change and assess the role of local projects and governance systems that can contribute to the attainment of sustainable landscape results. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework was followed in the implementation of a systematic review approach. In the search, peer-reviewed literature on sustainable land management, socioeconomic causes, and land use change was identified using predefined combinations of keywords, which included land use change, socioeconomic reasons, and sustainable land management in major academic databases, including Scopus. Having implemented the inclusion and exclusion criteria, 43 research articles were selected to be analyzed in detail. The information in the sampled studies was analyzed to determine significant socio-economic forces, governance processes, methodology, and sustainability results of LULC change. The findings indicate that agricultural development, population increase, market demand, and urbanization have been one of the most common reported socio-economic forces affecting land transformation. It is also found that local governance structure, community involvement and decentralized resource management initiatives are significant to enhance sustainable landscape management and reduce environmental degradation. In addition, the technology has increased the ability to control and study the land use dynamics through remote sensing and spatial modeling. All in all, this review shows the relevance of considering socio-economic aspects alongside governance models in order to tackle the issues of LULC change. The study provides important policy insights for sustainable land management and identifies future research priorities for developing integrated approaches to landscape governance and environmental sustainability.","土地利用与土地覆盖（LULC）变化是影响全球生态系统、生物多样性和可持续资源管理的最重要环境问题之一。由于社会经济快速发展、人口增长以及对粮食和自然资源需求的增加，土地系统面临的压力不断加大，尤其是在发展中国家。尽管已有大量文献考察了LULC变化的驱动因素和影响，但当前文献中关于社会经济驱动因素和地方治理项目对可持续景观管理贡献的研究仍较为分散。本文旨在总结现有文献中关于决定LULC变化的社会经济因素，并评估地方项目和治理体系在促进实现可持续景观成果方面的作用。本研究采用系统综述方法，并遵循PRISMA（系统综述和荟萃分析首选报告条目）框架。在检索过程中，通过预定义的关键词组合，在Scopus等主要学术数据库中识别了关于可持续土地管理、社会经济成因和土地利用变化的同行评审文献，关键词包括土地利用变化、社会经济原因和可持续土地管理。在实施纳入和排除标准后，共选取43篇研究论文进行详细分析。对样本研究中的信息进行分析，以确定影响LULC变化的重要社会经济力量、治理过程、方法学和可持续性结果。研究结果表明，农业发展、人口增长、市场需求和城市化是影响土地转型的最常见社会经济力量。研究还发现，地方治理结构、社区参与和分散化资源管理举措对于加强可持续景观管理和减少环境退化具有重要意义。此外，技术通过遥感和空间建模提高了控制和研究土地利用动态的能力。总而言之，本综述表明，在应对LULC变化问题时，将社会经济因素与治理模式一并考虑具有重要相关性。该研究为可持续土地管理提供了重要的政策启示，并指出了未来研究优先方向，以开发综合方法来推进景观","Discover Applied Sciences","2026-09-20T00:00:00Z",62,{"impact":21,"substance":19,"depth":17,"authority":71,"freshness":21,"relevant":22,"comment":72},12,"系统综述梳理社会经济驱动与地方治理对土地利用变化的影响，方法规范但结论偏综述性，对农业信息化与乡村治理有参考价值。",[74],{"name":67,"url":64},[76,77,28,29,78],"乡村振兴","可持续农业","景观治理",[80,81],"LULC 社会经济驱动 系统综述","土地利用变化 景观治理 PRISMA","LULC社会经济驱动系统综述-3182","10.1007\u002Fs42452-026-09445-9",{"doi":83,"openalex_id":85,"authors":86,"venue":67,"cited_by_count":36,"oa_url":64,"card":97,"direction":55,"ingested_from":56},"W7213761184",[87,89,92,94],{"name":88,"orcid":9},"Manoj Yadav",{"name":90,"orcid":91},"Vipasha Sharma","https:\u002F\u002Forcid.org\u002F0000-0002-7450-8443",{"name":93,"orcid":9},"Somendra Kumar",{"name":95,"orcid":96},"Arti Choudhary","https:\u002F\u002Forcid.org\u002F0000-0002-1330-4658",{"tldr":98,"method":99,"finding":100,"direction":101,"opportunity":102},"系统综述43篇文献，梳理社会经济驱动因素与地方治理对土地利用变化的影响。","采用PRISMA框架，在Scopus等数据库检索并筛选43篇文献进行系统分析。","农业开发、人口增长、市场需求和城市化是主要驱动力；地方治理与社区参与对可持续景观管理至关重要。","数字乡村与农业信息化","可结合遥感与空间建模，量化社会经济与治理因素对土地利用变化的交互作用，支撑乡村景观治理决策。","2026-09-22T23:30:25.164574Z",{"id":105,"title":106,"url":107,"summary":108,"summary_zh":9,"content":9,"source_name":109,"source_url":107,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":116,"tags":118,"search_phrases":122,"slug":125,"view_count":36,"doi":126,"paper":127,"created_at":140},3180,"Satellite-Based Assessment of Vegetation and Hydrological Dynamics at the Garâa of Sejnane Ramsar Wetland (Tunisia): A Multi-Temporal NDVI and NDWI Analysis","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10690691\u002Fv1","Satellite-Based Assessment of Vegetation and Hydrological Dynamics at the Garâa of Sejnane Ramsar Wetland (Tunisia): A Multi-Temporal NDVI and NDWI Analysis。Research Square","Research Square","2026-09-21T00:00:00Z",56,{"impact":21,"substance":19,"depth":113,"authority":59,"freshness":114,"relevant":22,"comment":115},15,9,"基于多时相NDVI\u002FNDWI的湿地植被与水文动态遥感评估，方法规范但属区域性案例研究，公共影响有限。",[117],{"name":109,"url":107},[119,27,28,120,121],"NDVI","湿地保护","NDWI",[123,124],"Sejnane 湿地 NDVI NDWI","突尼斯 拉姆萨尔湿地 遥感","Sejnane湿地NDVINDWI-3180","10.21203\u002Frs.3.rs-10690691\u002Fv1",{"doi":126,"openalex_id":128,"authors":129,"venue":109,"cited_by_count":36,"oa_url":107,"card":9,"direction":55,"ingested_from":56},"W7213955587",[130,132,134,137],{"name":131,"orcid":9},"Imen Khemiri",{"name":133,"orcid":9},"Chahida Chemingui",{"name":135,"orcid":136},"Alaeddine Rouissi","https:\u002F\u002Forcid.org\u002F0009-0000-9319-5182",{"name":138,"orcid":139},"Sahar Abidi","https:\u002F\u002Forcid.org\u002F0000-0003-3355-6876","2026-09-22T23:30:23.965045Z",{"id":142,"title":143,"url":144,"summary":145,"summary_zh":146,"content":9,"source_name":147,"source_url":144,"published_at":148,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":149,"score_detail":150,"sources":155,"tags":157,"search_phrases":160,"slug":163,"view_count":36,"doi":164,"paper":165,"created_at":187},2785,"Remote Sensing-Based Assessment of Interannual Change of the Ecological Sustainability of Agricultural Landscapes in Northern Benin","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189542","Assessing ecological sustainability in agricultural landscapes requires approaches that integrate land-cover change, its ecological effects, and their spatial determinants. This study analysed changes between 2023 and 2024 across six agricultural landscapes in northern Benin using the Landscape Ecological Sustainability Index (LESI), calculated for 1 km2 landscape cells from Human Disturbance Coefficients (HDCs) assigned to satellite-derived land-cover classes. Interannual changes were assessed using ΔLESI, the Wilcoxon signed-rank test, Global Moran’s I, and the Local Indicators of Spatial Association (LISA). The contribution of land-cover transitions to HDC change was quantified and a Monte Carlo sensitivity analysis based on classification accuracy was additionally used to assess the robustness of the observed interannual changes to classification uncertainty, and complementary univariate and bivariate regression models examined the relationships between LESI variations and 11 biophysical and geographical variables, including their pairwise interactions. The interannual comparison between 2023 and 2024 showed a decrease in ecological sustainability in four sites, a slight increase in one, and relative stability in another. Significant spatial autocorrelation was detected in all sites (Moran’s I = 0.44–0.65; p \u003C 0.001). Some spatially limited transitions exerted important effects on HDC change. Sensitivity analysis confirmed that the direction of ΔHDC was robust to classification uncertainty in five of the six landscapes. The low explanatory power of the univariate models (R2 ≤ 0.081) indicates that no single variable independently explains the observed changes. The bivariate interaction analyses further showed that some associations were context-dependent, although their explanatory power remained limited, with the best-performing model accounting for only 10.9% of the variation in ΔLESI. This integrated framework provides a reproducible approach for assessing, mapping, and prioritising interannual change of ecological sustainability from remote sensing data to support evidence-based agricultural landscape planning and sustainable land management.","评估农业景观的生态可持续性，需要整合土地覆盖变化、其生态效应及其空间决定因素的方法。本研究利用景观生态可持续性指数（Landscape Ecological Sustainability Index, LESI），分析了贝宁北部六个农业景观在2023年至2024年间的变化。该指数基于分配给卫星衍生土地覆盖类别的人类干扰系数（Human Disturbance Coefficients, HDCs），以1 km²景观单元为尺度进行计算。年际变化通过ΔLESI、Wilcoxon符号秩检验、全局Moran's I和局部空间关联指标（Local Indicators of Spatial Association, LISA）进行评估。研究量化了土地覆盖转变对HDC变化的贡献，并额外采用基于分类精度的蒙特卡洛敏感性分析，以评估观测到的年际变化对分类不确定性的稳健性；同时，通过互补的单变量和双变量回归模型，检验了LESI变化与11个生物物理和地理变量之间的关系，包括其两两交互作用。2023年至2024年的年际比较显示，四个样点的生态可持续性下降，一个样点略有上升，另一个样点则相对稳定。所有样点均检测到显著的空间自相关（Moran's I = 0.44–0.65；p \u003C 0.001）。一些空间范围有限的转变对HDC变化产生了重要影响。敏感性分析证实，在六个景观中的五个，ΔHDC的方向对分类不确定性具有稳健性。单变量模型的解释力较低（R² ≤ 0.081），表明没有单一变量能独立解释观测到的变化。双变量交互分析进一步表明，一些关联具有情境依赖性，尽管其解释力仍然有限，表现最佳的模型仅解释了ΔLESI变异的10.9%。这一整合框架提供了一种可重复的方法，用于从遥感数据评估、制图和优先排序生态可持续性的年际变化，以支持基于证据的农业景观规划和可持续土地管理。","Sustainability","2026-09-17T00:00:00Z",67,{"impact":21,"substance":151,"depth":152,"authority":153,"freshness":114,"relevant":22,"comment":154},20,17,13,"方法体系完整、结论稳健的遥感评估研究，但聚焦西非贝宁地方尺度，对国内三农实践的直接参考价值有限。",[156],{"name":147,"url":144},[77,27,28,158,159],"土地利用","贝宁",[161,162],"可持续农业 农业生态 土地利用 遥感监测","可持续农业 农业生态","可持续农业农业生态土地利用遥感监测-2785","10.3390\u002Fsu18189542",{"doi":164,"openalex_id":166,"authors":167,"venue":147,"cited_by_count":36,"oa_url":144,"card":182,"direction":55,"ingested_from":56},"W7213443483",[168,171,174,176,179],{"name":169,"orcid":170},"Mikhaïl Jean De Dieu Dotou Padonou","https:\u002F\u002Forcid.org\u002F0009-0006-8295-4984",{"name":172,"orcid":173},"Antoine Denis","https:\u002F\u002Forcid.org\u002F0000-0002-3245-7131",{"name":175,"orcid":9},"Yvon-Carmen Hountondji",{"name":177,"orcid":178},"Bernard Tychon","https:\u002F\u002Forcid.org\u002F0000-0002-9367-7306",{"name":180,"orcid":181},"Gérard Nounagnon Gouwakinnou","https:\u002F\u002Forcid.org\u002F0000-0002-3595-9831",{"tldr":183,"method":184,"finding":185,"direction":55,"opportunity":186},"基于遥感与景观生态可持续性指数评估贝宁北部农业景观2023-2024年际生态可持续性变化。","用LISI指数、ΔLESI、空间自相关、蒙特卡洛敏感性分析及回归模型分析土地覆盖","四个景观生态可持续性下降，空间自相关显著，但单变量与双变量模型解释力均很低。","可引入时序遥感与机器学习，探究多尺度驱动因子交互对农业景观可持续性年际变化的非线性影响。","2026-09-17T23:30:34.344705Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":9,"source_name":194,"source_url":191,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":196,"sources":198,"tags":200,"search_phrases":204,"slug":207,"view_count":36,"doi":208,"paper":209,"created_at":235},2675,"Long-term ecological quality dynamics and spatial associations in arid and semi-arid Northwest China using an adaptive PRSEI","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71853-z","Long-term ecological monitoring in arid and semi-arid regions requires indices that capture region-specific stressors while remaining comparable through time. We developed an adaptive particular remote-sensing ecological index (PRSEI) for Gansu Province, China, using growing-season kNDVI, wetness (WET), land surface temperature (LST), sandification index (SI), and a PM10-based particulate indicator (TI) from 2000 to 2024. A unified principal component analysis (PCA) with common scaling defined the primary time series, while annual PCA and fixed weights were used for sensitivity testing. Unified PC1 explained 79.12% of total variance, and mean PRSEI increased from 0.343 to 0.419 (slope = 0.00432 yr⁻¹; R² = 0.778). Unified PCA and fixed weights agreed on 99.90% of pixel-level trend directions, whereas annual and unified PCA agreed on only 39.73%, indicating strong methodological sensitivity. Adaptive PRSEI was highly correlated with original PRSEI ( r = 0.991) and traditional RSEI ( r = 0.975), showing the strongest consistency with mapped restoration transitions (81.47%) but weaker consistency with degradation transitions (34.22%). Precipitation, soil type, and land use\u002Fland cover were the most stable spatial explanatory factors. Overall, ecological quality improved broadly but heterogeneously, supporting adaptive PRSEI as a complementary multi-stressor index for drylands rather than a universal replacement for RSEI.","干旱与半干旱地区的长期生态监测需要既能反映区域特有胁迫因子、又能在时间上保持可比性的指数。本研究针对中国甘肃省构建了一种自适应特定遥感生态指数（PRSEI），所用数据为2000—2024年生长季的kNDVI、湿度（WET）、地表温度（LST）、沙化指数（SI）以及基于PM10的颗粒物指标（TI）。采用统一主成分分析（PCA）与共同标准化方案确定主时间序列，同时以逐年PCA和固定权重进行敏感性检验。统一PC1解释了总方差的79.12%，PRSEI均值从0.343上升至0.419（斜率=0.00432 yr⁻¹；R²=0.778）。统一PCA与固定权重在99.90%的像元水平趋势方向上一致，而逐年PCA与统一PCA仅在一致性为39.73%，表明方法敏感性较强。自适应PRSEI与原始PRSEI高度相关（r=0.991），与传统RSEI也高度相关（r=0.975），其与制图恢复转变的一致性最高（81.47%），但与退化转变的一致性较弱（34.22%）。降水、土壤类型和土地利用\u002F土地覆盖是最稳定的空间解释因子。总体而言，生态质量广泛改善但存在空间异质性，支持将自适应PRSEI作为干旱区多胁迫因子的补充性指数，而非RSEI的通用替代方案。","Scientific Reports","2026-09-15T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":197},"提出自适应遥感生态指数PRSEI并揭示西北旱区生态质量长期改善趋势，方法新颖、数据扎实，对旱区农业生态监测有参考价值。",[199],{"name":194,"url":191},[201,202,28,30,203],"农业信息化","甘肃","干旱半干旱区",[205,206],"干旱半干旱区 农业信息化 生态质量 遥感监测","干旱半干旱区 农业信息化","干旱半干旱区农业信息化生态质量遥感监测-2675","10.1038\u002Fs41598-026-71853-z",{"doi":208,"openalex_id":210,"authors":211,"venue":194,"cited_by_count":36,"oa_url":229,"card":230,"direction":55,"ingested_from":56},"W7213360699",[212,214,216,218,220,222,225,227],{"name":213,"orcid":9},"Jiangmin Wu",{"name":215,"orcid":9},"Bin Lian",{"name":217,"orcid":9},"Qirui Zhang",{"name":219,"orcid":9},"Zhen Yan",{"name":221,"orcid":9},"Wei Zhao",{"name":223,"orcid":224},"Jiachen Yang","https:\u002F\u002Forcid.org\u002F0000-0003-2558-552X",{"name":226,"orcid":9},"Zaixing Chen",{"name":228,"orcid":9},"Yuxiang Lan","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71853-z_reference.pdf",{"tldr":231,"method":232,"finding":233,"direction":55,"opportunity":234},"构建自适应PRSEI指数，评估2000-2024年甘肃生态质量动态与空间关联。","基于生长季kNDVI、湿度、地表温度、沙化指数和PM10指标，用统一PCA构建P","生态质量整体改善但异质，PRSEI与RSEI高度一致，方法敏感性显著。","可探索多胁迫指数在干旱区不同尺度的适用性，并耦合人类活动与政策因素。","2026-09-16T23:30:30.948770Z",{"id":237,"title":238,"url":239,"summary":240,"summary_zh":241,"content":9,"source_name":242,"source_url":239,"published_at":243,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":244,"score_detail":245,"sources":247,"tags":249,"search_phrases":252,"slug":255,"view_count":36,"doi":256,"paper":257,"created_at":268},2532,"Remote Sensing-Based Spatiotemporal Assessment of Ecological Quality using RSEI from 2016 to 2025: Gala Lake Basin, Türkiye","https:\u002F\u002Fdoi.org\u002F10.35229\u002Fjaes.1965400","Wetland-agricultural basins are among the most sensitive landscapes to ecological change because vegetation dynamics, surface moisture, bare soil exposure, and thermal stress interact within the same hydrological system. This study assessed the spatiotemporal ecological quality of the Gala Lake Basin, Türkiye, from 2016 to 2025 using the Remote Sensing Ecological Index (RSEI). The basin was selected because it contains Gala Lake, associated wetlands, agricultural lands, and seasonally variable water surfaces, making it a representative and environmentally important wetland-agricultural landscape in northwestern Türkiye. Landsat 8 OLI\u002FTIRS and Landsat 9 OLI-2\u002FTIRS-2 images were processed in Google Earth Engine to derive NDVI, WET, NDBSI, and LST, representing greenness, wetness, dryness, and heat, respectively. MNDWI was used to mask open-water areas before RSEI construction. The four indicators were normalized and integrated using PCA, and annual RSEI maps were classified into five ecological quality levels. The results revealed that ecological quality in the basin showed strong interannual variability rather than a uniform trend. The mean RSEI reached its lowest level in 2022 and its highest in 2025. Class-based analysis showed a clear reduction in low-ecological-quality areas and an expansion of moderate-ecological-quality areas by the end of the study period. PCA results confirmed that PC1 captured the dominant ecological gradient in all years, supporting its use for annual RSEI construction. This study demonstrates that RSEI provides a robust and reproducible framework for monitoring ecological quality in wetland-agricultural basins and offers baseline information for long-term environmental management of the Gala Lake Basin.","湿地-农业流域是对生态变化最为敏感的景观之一，因为植被动态、地表湿度、裸土暴露和热胁迫在同一水文系统内相互作用。本研究利用遥感生态指数（RSEI）评估了土耳其加拉湖流域2016年至2025年的时空生态质量。选择该流域是因为其包含加拉湖、相关湿地、农业用地和季节性变化的水面，使其成为土耳其西北部具有代表性和环境重要性的湿地-农业景观。在Google Earth Engine中处理Landsat 8 OLI\u002FTIRS和Landsat 9 OLI-2\u002FTIRS-2影像，以提取NDVI、WET、NDBSI和LST，分别代表绿度、湿度、干度和热度。在构建RSEI之前，使用MNDWI掩膜开阔水域。四个指标经归一化后通过PCA进行整合，并将年度RSEI图分为五个生态质量等级。结果表明，流域生态质量表现出强烈的年际变化，而非一致趋势。RSEI均值在2022年降至最低水平，在2025年达到最高水平。基于等级的分析显示，到研究期末，低生态质量区域明显减少，中等生态质量区域扩大。PCA结果证实，PC1在所有年份均捕捉到了主导生态梯度，支持其用于年度RSEI构建。本研究表明，RSEI为监测湿地-农业流域生态质量提供了一个稳健且可重复的框架，并为加拉湖流域的长期环境管理提供了基线信息。","Journal of Anatolian Environmental and Animal Sciences","2026-09-14T00:00:00Z",63,{"impact":21,"substance":19,"depth":17,"authority":71,"freshness":114,"relevant":22,"comment":246},"基于Landsat与Google Earth Engine的湿地-农业流域生态质量遥感评估，方法规范、数据扎实，但属区域案例研究，对国内三农实践的直接参考价值有限。",[248],{"name":242,"url":239},[201,28,250,30,251],"湿地农业","RSEI",[253,254],"农业信息化 湿地农业 生态质量 遥感监测","农业信息化 湿地农业","农业信息化湿地农业生态质量遥感监测-2532","10.35229\u002Fjaes.1965400",{"doi":256,"openalex_id":258,"authors":259,"venue":242,"cited_by_count":36,"oa_url":239,"card":263,"direction":55,"ingested_from":56},"W7213086036",[260],{"name":261,"orcid":262},"Enes Özgenç","https:\u002F\u002Forcid.org\u002F0000-0003-0878-6418",{"tldr":264,"method":265,"finding":266,"direction":55,"opportunity":267},"基于RSEI评估土耳其Gala湖流域2016-2025年生态质量时空变化。","Landsat影像在GEE中提取NDVI、WET、NDBSI、LST，PCA构建","生态质量年际波动明显，2022年最低、2025年最高，低质量区减少、中质量区扩张。","可结合作物物候与灌溉数据，解析湿地-农业复合区生态质量波动的驱动机制。","2026-09-15T23:30:20.761810Z",{"id":270,"title":271,"url":272,"summary":273,"summary_zh":274,"content":9,"source_name":275,"source_url":272,"published_at":276,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":36,"score_detail":277,"sources":279,"tags":281,"search_phrases":284,"slug":287,"view_count":36,"doi":288,"paper":289,"created_at":307},2065,"Multitemporal Assessment of Anthropogenic Pressure Through Land Use Change and the Anthropic Transformation Index (ATI) in the Piratininga Lagoon System Watershed, Brazil","https:\u002F\u002Fdoi.org\u002F10.14393\u002Fsn-v38-2026-81975","This study used remote sensing and geographic information systems to perform a multitemporal analysis of land-use changes and assess anthropogenic pressure using the Anthropic Transformation Index (ATI) in the Piratininga lagoon system watershed in the municipality of Niterói, Rio de Janeiro, Brazil. We used a multitemporal approach with aerial photographs from 1976 (FAB-DRM) and high-resolution satellite images from 2003, 2013, and 2022 obtained from Google Earth Pro. The data were processed through georeferencing, manual vectorization, and classification into nine (9) land use classes based on the MapBiomas Collection 8 classification legend. The results show a continuous expansion of urban areas throughout the period analyzed, particularly in environmentally sensitive areas near the margins of the lagoon system, leading to the suppression of riparian vegetation and increased pressure on the aquatic system. Although forest cover has increased in recent years, this change is mainly associated with the regeneration of secondary vegetation rather than effective ecological restoration. The ITA value of 5.292 classifies the watershed as degraded, reflecting high anthropogenic alteration. These findings highlight the need to combine spatial indicators with environmental management strategies to support urban planning and ecosystem conservation. The proposed approach provides a robust framework for monitoring anthropogenic impacts on coastal lagoon systems and supports decision-making for sustainable watershed management.","本研究利用遥感与地理信息系统，对巴西里约热内卢州尼泰罗伊市皮拉蒂宁加泻湖系统流域的土地利用变化进行了多时相分析，并采用人类改造指数（Anthropic Transformation Index, ATI）评估了人为压力。研究采用多时相方法，使用了1976年的航空照片（FAB-DRM）以及2003年、2013年和2022年从Google Earth Pro获取的高分辨率卫星影像。数据经地理配准、人工矢量化，并依据MapBiomas第8版分类图例划分为九（9）类土地利用类型。结果表明，在整个分析时期内，城市区域持续扩张，尤其是在泻湖系统边缘附近的环境敏感区域，导致河岸植被遭到破坏，水生系统承受的压力加剧。尽管近年来森林覆盖有所增加，但这一变化主要与次生植被的再生有关，而非有效的生态恢复。ATI值为5.292，将该流域归类为退化状态，反映出较高的人为改造程度。这些发现凸显了将空间指标与环境管理策略相结合以支持城市规划和生态系统保护的必要性。所提出的方法为监测沿海泻湖系统的人为影响提供了稳健框架，并为可持续流域管理决策提供了支持。","Sociedade & natureza","2026-09-08T00:00:00Z",{"impact":36,"substance":36,"depth":36,"authority":36,"freshness":36,"relevant":36,"comment":278},"研究聚焦巴西沿海潟湖流域土地利用与人类活动压力评估，属城市生态与遥感领域，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。",[280],{"name":275,"url":272},[28,29,282,283],"流域生态","人居环境压力",[285,286],"人居环境压力 土地利用变化 流域生态 遥感监测","人居环境压力 土地利用变化","人居环境压力土地利用变化流域生态遥感监测-2065","10.14393\u002Fsn-v38-2026-81975",{"doi":288,"openalex_id":290,"authors":291,"venue":275,"cited_by_count":36,"oa_url":301,"card":302,"direction":55,"ingested_from":56},"W7211972914",[292,295,298],{"name":293,"orcid":294},"Minércia Job Macamo","https:\u002F\u002Forcid.org\u002F0009-0002-8955-0705",{"name":296,"orcid":297},"Braian de Oliveira Salvador","https:\u002F\u002Forcid.org\u002F0009-0006-0514-3694",{"name":299,"orcid":300},"Fábio Ferreira Dias","https:\u002F\u002Forcid.org\u002F0000-0003-2078-7405","https:\u002F\u002Fseer.ufu.br\u002Findex.php\u002Fsociedadenatureza\u002Farticle\u002Fdownload\u002F81975\u002F44391",{"tldr":303,"method":304,"finding":305,"direction":55,"opportunity":306},"基于多时相遥感评估巴西Piratininga泻湖流域土地利用变化与人为压力。","1976年航拍与2003-2022年高分卫星影像，地理配准、矢量化并分9类计算A","城市持续扩张侵占敏感区，森林增加多为次生恢复，ATI=5.292属退化，人为扰动高。","可将ATI与流域生态服务、水质监测耦合，构建沿海流域人为压力预警与规划决策模型。","2026-09-10T23:30:30.399094Z"]