[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3666":3,"related-3666":57},{"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":56},3666,"Influence of Water Borehole Development on Rangeland Ecosystem Dynamics in Amballo Location, Obbu Ward, Moyale Sub-County, Marsabit County, Kenya","https:\u002F\u002Fdoi.org\u002F10.71274\u002Fijpp.v14i6.725","Sustainable management of arid and semi-arid rangelands is essential for maintaining ecosystem health and supporting pastoral livelihoods. This study examined the influence of water infrastructure development on rangeland ecosystem dynamics in the Amballo location of Obbu Ward, Moyale Sub-County, Marsabit County, Kenya. A mixed-methods approach integrating vegetation surveys, remote sensing and household interviews was employed. Vegetation data were collected from transects established near the borehole and a control site, while Landsat imagery from 2000, 2007, and 2020 was used to assess long-term vegetation cover changes. Data were analyzed using descriptive statistics, two-sample t-tests, and the Shannon–Wiener diversity index. The findings showed significant reductions in woody plant density ( p = 0.006) and herbaceous biomass (p = 0.013) near the borehole compared with the control site. Plant diversity was also lower near the water source (H′ = 2.20) than in the control area (H′ = 2.86), indicating increased dominance of disturbance-tolerant species. Remote sensing analysis revealed a 16.97% decline in open grassland and a 17.60% increase in bare land between 2000 and 2020, suggesting progressive rangeland degradation. Household responses identified livestock concentration, overgrazing, trampling, tree harvesting, drought, and climate variability as key drivers of vegetation change. The study concludes that while water infrastructure improves water access and support pastoral livelihoods, they also contribute to localized degradation by altering livestock distribution and increasing grazing pressure around water points. Improved grazing management, strategic water point planning and restoration of degraded areas, are recommended to promote sustainable pastoralism.","干旱和半干旱牧场的可持续管理对于维持生态系统健康和支持牧民生计至关重要。本研究考察了水利基础设施发展对肯尼亚马萨比特郡莫亚莱分区奥布瓦区安巴洛地区牧场生态系统动态的影响。研究采用混合方法，整合了植被调查、遥感影像和农户访谈。植被数据采集自钻孔附近设置的样带和对照样地，同时利用2000年、2007年和2020年的Landsat影像评估长期植被覆盖变化。数据分析采用描述性统计、双样本t检验和Shannon-Wiener多样性指数。研究结果表明，与对照样地相比，钻孔附近的木本植物密度（p = 0.006）和草本生物量（p = 0.013）显著降低。水源附近的植物多样性也低于对照区域（H′ = 2.20 vs H′ = 2.86），表明耐干扰物种的优势度增加。遥感分析显示，2000年至2020年间，开阔草地减少了16.97%，裸地增加了17.60%，表明牧场退化逐步加剧。农户访谈结果指出，牲畜集中、过度放牧、践踏、树木砍伐、干旱和气候变率是植被变化的主要驱动因素。研究结论认为，水利基础设施虽然改善了水资源可及性并支持了牧民生计，但也通过改变牲畜分布和增加水源点周围的放牧压力，导致了局部退化。建议改进放牧管理、优化水源点规划并恢复退化区域，以促进可持续的畜牧业发展。",null,"International Journal of Professional Practice","2026-09-25T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,21,17,12,9,1,"以遥感与样方调查量化水井周边牧场退化，方法扎实、数据具体，但属肯尼亚区域案例，对国内三农信息化仅有方法借鉴价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感监测","草原生态","水资源开发","荒漠化治理","牧民生计",[33,34],"肯尼亚 Marsabit 牧场 水井","Landsat 植被覆盖 牧场退化","肯尼亚Marsabit牧场水井-3666",0,"10.71274\u002Fijpp.v14i6.725",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":47,"card":48,"direction":54,"ingested_from":55},"W7214388681",[41,43,45],{"name":42,"orcid":9},"Adan Jarso Adi",{"name":44,"orcid":9},"Mworia Mugambi",{"name":46,"orcid":9},"David Mushimiyimana","https:\u002F\u002Fijpp.kemu.ac.ke\u002Findex.php\u002Fijpp\u002Farticle\u002Fdownload\u002F725\u002F290",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"研究肯尼亚半干旱牧区水井建设对牧场生态的影响。","植被样带调查、Landsat遥感与农户访谈的混合方法。","水井周边木本密度、草本生物量和多样性显著下降，牧场退化。","农业遥感与作物表型","可结合多时相遥感与放牧模型，量化水点布局对牧场退化的阈值效应。","智慧农业 \u002F 农业物联网","openalex","2026-09-28T23:30:13.016262Z",{"total":58,"page":22,"page_size":58,"items":59},6,[60,114,156,193,238,286],{"id":61,"title":62,"url":63,"summary":64,"summary_zh":65,"content":9,"source_name":66,"source_url":63,"published_at":67,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":68,"score_detail":69,"sources":73,"tags":75,"search_phrases":79,"slug":82,"view_count":22,"doi":83,"paper":84,"created_at":113},2536,"Mapping Native Grass Cover with Random Forest Models: Sentinel-2 Versus Sentinel-2 Combined with Sentinel-1 SAR-Derived GLCM Texture Metrics","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183150","Temperate native grasslands in southeastern Australia have been extensively cleared for agriculture, and the remaining patches are under growing pressure from further land use change, climate variability, and invasive species. Mapping and monitoring their distribution and the cover of native and exotic grasses are critical for their conservation and management. Field-based methods are not always scalable or time-effective, and this study aimed to develop a scalable method to map and monitor the fractional cover-class maps of native C3 and native C4 grass cover as a component of remnant native grasslands on the western outskirts of Melbourne, Victoria, Australia. Field-based reference data for training and validation of random forest machine learning models were collected across multiple sites in 2021. Sentinel-2 optical spectral bands and vegetation indices were used as the primary input data, and Sentinel-1 Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture metrics were assessed for their capacity to improve the model. Results show that random forest models trained on Sentinel-2 data without GLCM texture information derived from Sentinel-1 SAR data provided a moderate overall accuracy (C3: 59.1%, C4: 78.1%). Class-specific metrics showed that reliability was highest for better represented lower-cover classes, particularly the 6–25% native C3 class and the 0–5% native C4 class, while higher-cover classes were less reliable because of the limited number of training and validation samples. Grass cover fractions were modelled well for sparse to moderate grass cover, but dense grass cover was not modelled accurately, probably due to limited high-cover samples in the training dataset. Model performance was not improved by the inclusion of Sentinel-1 SAR-derived GLCM texture metrics, indicating that C-band VH-polarised SAR is not sensitive to the fine-scale structural heterogeneity that characterises native grassland ecosystems. Sparse native C3 and C4 grasses could be mapped most reliably in the lower-cover classes as a component of grasslands with optical remote sensing, and the method developed here can now be applied to enable evidence-based management of grasslands, biodiversity conservation and the monitoring of grassland composition in the WGR and elsewhere. Higher-resolution structural datasets and more sophisticated machine learning approaches may be required to accurately predict native C3 and C4 grass cover fractions in denser grasslands.","澳大利亚东南部的温带原生草原已被大面积开垦用于农业，残余斑块正面临土地利用进一步变化、气候变率和入侵物种日益增大的压力。对其分布以及原生和外来草类覆盖度进行制图和监测，对于草原的保护和管理至关重要。基于实地调查的方法并非总是可扩展或省时的，本研究旨在开发一种可扩展的方法，以制图和监测作为残余原生草原组成部分的原生C3和原生C4草类覆盖度的分数覆盖等级图，研究区位于澳大利亚维多利亚州墨尔本西郊。用于训练和验证随机森林机器学习模型的实地参考数据于2021年在多个样点采集。研究以Sentinel-2光学光谱波段和植被指数作为主要输入数据，并评估了Sentinel-1合成孔径雷达（SAR）衍生的灰度共生矩阵（GLCM）纹理指标对提升模型性能的能力。结果表明，仅使用Sentinel-2数据（不含Sentinel-1 SAR衍生的GLCM纹理信息）训练的随机森林模型提供了中等的总体精度（C3：59.1%，C4：78.1%）。分类别指标显示，对于代表性较好的低覆盖度类别，可靠性最高，尤其是6–25%原生C3类别和0–5%原生C4类别，而较高覆盖度类别的可靠性较低，原因是训练和验证样本数量有限。草类覆盖度分数在稀疏至中等草类覆盖条件下建模效果良好，但茂密草类覆盖未能准确建模，可能是由于训练数据集中高覆盖度样本有限。纳入Sentinel-1 SAR衍生的GLCM纹理指标并未改善模型性能，表明C波段VH极化SAR对原生草原生态系统所特有的精细尺度结构异质性不敏感。作为草原的组成部分，稀疏的原生C3和C4草类在低覆盖度类别中利用光学遥感可最可靠地制图，本研究开发的方法现可应用于西维多利亚草原（WGR）及其他地区，以实现基于证据的草原管理、生物多样性保护和草原组成监测。准确预测原生C3和C4草类覆盖度分数可能需要更高分辨率的结构数据集和更先进的机器学习方法。","Remote Sensing","2026-09-13T00:00:00Z",71,{"impact":20,"substance":70,"depth":19,"authority":71,"freshness":17,"relevant":22,"comment":72},20,14,"方法新颖、结论明确（SAR纹理未提升精度），对草地遥感监测有参考价值，但属细分领域研究，影响范围有限。",[74],{"name":66,"url":63},[76,77,27,28,78],"智慧农业","机器学习","植被覆盖",[80,81],"智慧农业 机器学习 植被覆盖 草原生态","智慧农业 机器学习","智慧农业机器学习植被覆盖草原生态-2536","10.3390\u002Frs18183150",{"doi":83,"openalex_id":85,"authors":86,"venue":66,"cited_by_count":36,"oa_url":63,"card":108,"direction":52,"ingested_from":55},"W7212561645",[87,90,93,96,98,100,103,105],{"name":88,"orcid":89},"Sabah Sabaghy","https:\u002F\u002Forcid.org\u002F0000-0002-9453-8922",{"name":91,"orcid":92},"M. Abuzar","https:\u002F\u002Forcid.org\u002F0000-0002-6101-1307",{"name":94,"orcid":95},"Steve J. Sinclair","https:\u002F\u002Forcid.org\u002F0000-0002-4282-1021",{"name":97,"orcid":9},"Tony Dugdale",{"name":99,"orcid":9},"Vanessa Hutchins",{"name":101,"orcid":102},"Yogendra K. Karna","https:\u002F\u002Forcid.org\u002F0000-0002-2120-4710",{"name":104,"orcid":9},"Jonathan Wilson",{"name":106,"orcid":107},"Kathryn Sheffield","https:\u002F\u002Forcid.org\u002F0000-0003-2624-9739",{"tldr":109,"method":110,"finding":111,"direction":52,"opportunity":112},"用随机森林结合Sentinel-2与Sentinel-1纹理特征，绘制澳洲原生草地C3\u002FC4草覆盖度","2021年野外样点训练随机森林，Sentinel-2光谱与植被指数为主，Sent","仅用Sentinel-2精度中等（C3 59.1%、C4 78.1%），加入SAR纹理未提升，高覆盖","高覆盖度草地样本不足且C波段SAR不敏感，可探索高分辨率结构数据与深度模型提升密草覆盖反演。","2026-09-15T23:30:21.287053Z",{"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":125,"tags":127,"search_phrases":131,"slug":134,"view_count":36,"doi":135,"paper":136,"created_at":155},3676,"Assessing Land Use, Land Cover Changes, and Urbanization Impacts on Water Stress in West African Watersheds: A Systematic Methodological Review","https:\u002F\u002Fdoi.org\u002F10.3791\u002F72287","Water stress is one of the major environmental and socio-economic constraints in West Africa, where population growth, agricultural expansion, soil sealing, hydroclimatic variability, and rising pressure on surface water and groundwater rapidly transform watershed functioning. Evaluating the impacts of land use and land cover (LULC) changes and urbanization requires methodological approaches that link the spatial dynamics of landscapes to observed or simulated hydrological responses. This review provides a comparative and critical analysis of the main techniques used in the region: instrumented watersheds and in situ observations, controlled field and laboratory experiments, satellite remote sensing and multi-temporal analysis, groundwater-oriented methods, conceptual, semi-distributed, physically based, and integrated surface-subsurface hydrological modeling, statistical and machine-learning approaches, and hybrid methods coupling land-use and climate scenarios. The search and selection process followed a transparent, PRISMA-based protocol and yielded 51 included records, of which 35 are anchored in West Africa. Hydrological modeling driven by remote sensing dominates the corpus, accounting for 15 of the 35 West African records (43%), particularly with SWAT, ACRU, WaSiM, CEQUEAU, SHETRAN, HydroGeoSphere, and ParFlow-CLM; reported Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), and coefficient of determination generally range from about 0.6 to 0.9, which is satisfactory to very good against standard evaluation guidelines. Statistical and machine-learning studies account for 8 of 35 records, whereas urban- and groundwater-focused studies remain scarce. Moreover, 8 of 35 records originate from a single research network, so several regional conclusions rest on a narrow and partly non-independent evidence base. In situ networks remain essential for process understanding but are constrained by cost and low density; purely satellite-based approaches suffer from cloud cover and spectral confusion in subhumid zones. The most promising advances lie in multi-source, multi-scale frameworks combining targeted instrumentation, satellite time series, integrated hydrological modeling, territorial scenarios, systematic uncertainty analyses, and co-construction with water-management and planning actors.","水资源压力是西非主要的环境和社会经济制约因素之一。在该地区，人口增长、农业扩张、土壤封闭、水文气候变率以及地表水和地下水压力的不断上升，正迅速改变着流域功能。评估土地利用与土地覆盖（LULC）变化及城市化带来的影响，需要将景观的空间动态与观测或模拟的水文响应相联系的方法学途径。本综述对该地区所采用的主要技术进行了比较性和批判性分析：受控流域与实地观测、受控野外与实验室实验、卫星遥感与多时相分析、面向地下水的方法、概念性、半分布式、基于物理过程及地表-地下耦合的综合水文模型、统计与机器学习方法，以及耦合土地利用与气候情景的混合方法。检索与筛选过程遵循透明、基于PRISMA的协议，最终纳入51篇文献，其中35篇以西非为研究区。由遥感驱动的水文建模在文献主体中占主导地位，在35篇西非文献中占15篇（43%），尤以SWAT、ACRU、WaSiM、CEQUEAU、SHETRAN、HydroGeoSphere和ParFlow-CLM为主；所报告的纳什-萨特克利夫效率（NSE）、克林-古普塔效率（KGE）和决定系数通常约在0.6至0.9之间，对照标准评价指南属于满意至很好水平。统计与机器学习研究在35篇文献中占8篇，而聚焦城市和地下水的研究仍然稀少。此外，35篇文献中有8篇来自同一研究网络，因此若干区域性结论建立在狭窄且部分非独立的证据基础之上。实地观测网络对于过程理解仍然不可或缺，但受成本和低密度制约；纯卫星方法在半湿润区则受云覆盖和光谱混淆的影响。最有前景的进展在于多源、多尺度框架，其结合了针对性观测、卫星时间序列、综合水文建模、区域情景、系统不确定性分析，以及与水资源管理和规划主体的共同构建。","Journal of Visualized Experiments",66,{"impact":17,"substance":70,"depth":19,"authority":123,"freshness":17,"relevant":22,"comment":124},13,"系统综述方法学扎实、数据翔实，但聚焦西非流域且与国内农业信息化关联较弱，仅具方法参考价值。",[126],{"name":120,"url":117},[77,27,128,129,130],"土地利用","水文模型","西非流域",[132,133],"西非流域 土地利用 水文模型","SWAT 遥感 水资源压力","西非流域土地利用水文模型-3676","10.3791\u002F72287",{"doi":135,"openalex_id":137,"authors":138,"venue":120,"cited_by_count":36,"oa_url":9,"card":150,"direction":52,"ingested_from":55},"W7214394296",[139,141,144,146,148],{"name":140,"orcid":9},"Valère-Carin Jofack Sokeng",{"name":142,"orcid":143},"Nakouana Timité","https:\u002F\u002Forcid.org\u002F0000-0002-3894-1450",{"name":145,"orcid":9},"Toto Marc Zahui",{"name":147,"orcid":9},"Kouamé Koffi Fernand",{"name":149,"orcid":9},"Koné Tiémoman",{"tldr":151,"method":152,"finding":153,"direction":52,"opportunity":154},"系统综述西非流域土地利用变化与城市化对水资源压力的评估方法。","PRISMA系统综述，纳入51篇文献，比较遥感、水文模型与机器学习等方法。","遥感驱动水文模型占主导（43%），城市与地下水研究稀缺，证据基础偏窄。","可构建多源多尺度框架，融合实地观测、遥感时序与集成水文模型，并开展不确定性分析与利益相关者协同。","2026-09-28T23:30:31.949583Z",{"id":157,"title":158,"url":159,"summary":160,"summary_zh":161,"content":9,"source_name":162,"source_url":159,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":163,"score_detail":164,"sources":168,"tags":170,"search_phrases":175,"slug":178,"view_count":36,"doi":179,"paper":180,"created_at":192},3675,"Long-term (2000-2023) Monitoring and Hotspot Identification of Total Suspended Solids and Turbidity in Lake Tana, Ethiopia, Using High Temporal Resolution MODIS Satellite Imagery","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faeac9e","Abstract Monitoring water quality is challenging in data-scarce regions. This study examines the use of remote sensing to assess key water quality parameters in Lake Tana, Northwestern Ethiopia, to support the sustainable management of Lake water resources. The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery was applied for long-term spatiotemporal assessment of total suspended solids (TSS) and turbidity. The results show that annual mean TSS increased from 350 mg\u002Fl in 2000 to 640 mg\u002Fl in 2020, while turbidity increased from 120 to about 500 NTU before both parameters declined during 2020-2023. Seasonal analysis indicates that the highest TSS and turbidity occurred during the peak rainy month of July. The spatial variation revealed that elevated TSS and turbidity levels were concentrated primarily at the inlets of the major feeder rivers to Lake Tana. This reflects a substantial influx of sediment into the Lake during rainy seasons. During post-rainy months, TSS is redistributed across different sections of the Lake by wind. The findings of this work demonstrate the ability of remote sensing to provide long-term monitoring of water quality parameters, offering valuable insights for the sustainable management of Lake water resources.","摘要 在数据稀缺地区，水质监测面临挑战。本研究探讨了利用遥感技术评估埃塞俄比亚西北部塔纳湖（Lake Tana）关键水质参数，以支持湖泊水资源的可持续管理。研究采用中分辨率成像光谱仪（MODIS）卫星影像，对总悬浮固体（TSS）和浊度进行了长期时空评估。结果表明，年均TSS从2000年的350 mg\u002Fl增至2020年的640 mg\u002Fl，浊度从120 NTU增至约500 NTU，随后两者在2020—2023年间均有所下降。季节性分析表明，TSS和浊度最高值出现在降雨高峰的7月。空间变化显示，较高的TSS和浊度水平主要集中在塔纳湖主要补给河流的入湖口处，反映了雨季大量泥沙流入湖泊。在雨后月份，TSS受风力作用在湖泊不同区域重新分布。本研究结果证明了遥感技术在水质参数长期监测方面的能力，为湖泊水资源的可持续管理提供了有价值的见解。","Environmental Research Communications",74,{"impact":20,"substance":165,"depth":166,"authority":123,"freshness":21,"relevant":22,"comment":167},22,18,"利用MODIS长时序遥感监测塔纳湖水质，方法成熟、数据跨度长，对数据稀缺地区水质管理有参考价值，但属区域案例研究，公共影响有限。",[169],{"name":162,"url":159},[27,171,172,173,174],"水质监测","埃塞俄比亚","湖泊生态","MODIS",[176,177],"Lake Tana MODIS 水质监测","埃塞俄比亚 塔纳湖 悬浮物","LakeTanaMODIS水质监测-3675","10.1088\u002F2515-7620\u002Faeac9e",{"doi":179,"openalex_id":181,"authors":182,"venue":162,"cited_by_count":36,"oa_url":186,"card":187,"direction":52,"ingested_from":55},"W7214348540",[183],{"name":184,"orcid":185},"Maru Fentaw Getie","https:\u002F\u002Forcid.org\u002F0009-0003-9743-4365","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faeac9e\u002Fpdf",{"tldr":188,"method":189,"finding":190,"direction":52,"opportunity":191},"利用MODIS影像长时序监测埃塞俄比亚塔纳湖悬浮物与浊度并识别热点区域。","MODIS高时间分辨率遥感影像，2000-2023年TSS与浊度反演及时空分析。","TSS与浊度2000-2020年显著上升，2020-2023年回落，高值集中于主要入湖河流口。","可结合降水与土地利用数据，量化流域农业侵蚀对湖泊水质贡献并构建预警模型。","2026-09-28T23:30:30.946130Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":200,"score_detail":201,"sources":203,"tags":205,"search_phrases":210,"slug":213,"view_count":36,"doi":214,"paper":215,"created_at":237},3674,"Mapping and dynamic monitoring of desertification on the Qinghai-Tibetan Plateau using surface Albedo and Solar-Induced Chlorophyll Fluorescence","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0359348","Desertification poses a significant threat to humanity's long-term survival and sustainable development. In this study, four types of 2D desertification assessment models were created by integrating surface albedo with four vegetation-related indicators: NDVI, MSAVI, EVI, and solar-induced chlorophyll fluorescence (SIF). A comprehensive analysis of the spatiotemporal variations in desertification on the Qinghai-Tibetan Plateau (QTP) from 2001 to 2020 was then conducted. Among the four models, the SA-SIF model demonstrated the highest overall accuracy (0.8675; 95% CI: 0.8512-0.8838) and Kappa coefficient (0.8452; 95% CI: 0.8289-0.8615), significantly outperforming the other three models (p \u003C 0.01, McNemar's test). The SA-SIF model's mapping results revealed substantial regional variability in desertification on the QTP, with the degree of desertification decreasing from northwest to southeast. Over the past decade, the expansion of severe and extremely severe desertification on the QTP has shown signs of slowing, although future trends may become more complex due to the interplay of climatic and anthropogenic factors. The SA-SIF model can serve as a reference for future desertification control operations on the QTP.","荒漠化对人类长期生存与可持续发展构成重大威胁。本研究通过将地表反照率与四种植被相关指标——归一化植被指数（NDVI）、修正土壤调整植被指数（MSAVI）、增强型植被指数（EVI）和日光诱导叶绿素荧光（SIF）——相结合，构建了四类二维荒漠化评估模型，并据此对2001年至2020年青藏高原荒漠化的时空变化进行了综合分析。在四种模型中，SA-SIF模型的总体精度（0.8675；95%置信区间：0.8512–0.8838）和Kappa系数（0.8452；95%置信区间：0.8289–0.8615）最高，显著优于其他三种模型（p \u003C 0.01，McNemar检验）。SA-SIF模型的制图结果揭示了青藏高原荒漠化存在显著的区域差异，荒漠化程度自西北向东南递减。过去十年间，青藏高原重度及极重度荒漠化的扩张呈现放缓迹象，但受气候与人为因素交互作用的影响，未来趋势可能趋于复杂。SA-SIF模型可为青藏高原未来的荒漠化治理工作提供参考。","PLoS ONE",78,{"impact":166,"substance":165,"depth":166,"authority":71,"freshness":58,"relevant":22,"comment":202},"提出SA-SIF荒漠化评估模型并揭示青藏高原2001-2020年荒漠化时空演变，方法新颖、结论可靠，对高原生态治理有参考价值。",[204],{"name":199,"url":196},[206,27,207,208,209],"荒漠化","青藏高原","SIF","生态治理",[211,212],"青藏高原 荒漠化 遥感监测","SA-SIF 模型 叶绿素荧光","青藏高原荒漠化遥感监测-3674","10.1371\u002Fjournal.pone.0359348",{"doi":214,"openalex_id":216,"authors":217,"venue":199,"cited_by_count":36,"oa_url":231,"card":232,"direction":52,"ingested_from":55},"W7214339598",[218,221,224,226,228],{"name":219,"orcid":220},"Zhijian Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-4764-2943",{"name":222,"orcid":223},"Hui Lin","https:\u002F\u002Forcid.org\u002F0000-0003-1278-4351",{"name":225,"orcid":9},"Lei Wu",{"name":227,"orcid":9},"Linling Tang",{"name":229,"orcid":230},"Xin Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-8859-7240","https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle\u002Ffile?id=10.1371\u002Fjournal.pone.0359348&type=printable",{"tldr":233,"method":234,"finding":235,"direction":52,"opportunity":236},"构建地表反照率与SIF结合的荒漠化评估模型，监测青藏高原2001-2020年荒漠化动态。","融合地表反照率与NDVI、MSAVI、EVI、SIF构建四种二维模型，对比精度。","SA-SIF模型精度最高（总体精度0.8675），荒漠化程度自西北向东南递减，重度扩张减缓。","可探索SIF与多源遥感协同的荒漠化早期预警，并量化气候与人类活动贡献。","2026-09-28T23:30:30.884081Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":9,"source_name":244,"source_url":241,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":36,"score_detail":245,"sources":247,"tags":249,"search_phrases":253,"slug":256,"view_count":36,"doi":257,"paper":258,"created_at":285},3673,"A low-cost, low-tech methodology for population estimation in heterogeneous, data-scarce urban settings, applied in the mountainous area of Qwaqwa, South Africa","https:\u002F\u002Fdoi.org\u002F10.1080\u002F0035919x.2026.2729963","Accurate population data is essential for effective planning for service and resource allocation, urban development and climate change adaptation – particularly in mountainous regions, where complex topography poses challenges to both data collection and infrastructure development. This paper presents a low-cost, low-tech and replicable hybrid methodology to estimate the population size and spatial distribution and applies it in Qwaqwa, a semi-rural, semi-urban mountainous region in South Africa. Using high-resolution satellite imagery, was manually mapped 91,624 main residential buildings. The study area was stratified into seven urban structure types based on morphological attributes, with 917 randomly sampled sites surveyed, with a response rate of 52.7%. Survey data provided occupancy rates, which were used to calculate yard-level population estimates and extrapolate population density across strata. The study reveals high variability in residents per yard, with no strong relationship between population density and visible physical attributes of main buildings and yards. The assessment resulted in an average simulated population of approximately 360,000 inhabitants (range 320,000–400,000), with a mean of 3.94 inhabitants per main building, including uninhabited buildings. The distribution of the population in Qwaqwa reflects the spatial imprint of apartheid-era planning, topographic constraints and ongoing urbanisation pressures, which together shape where people live and the level of demand placed on local infrastructure and services such as water access, transport and public facilities. The findings highlight the limitations of traditional remote sensing methods in mountainous terrain, where settlement patterns are shaped by both complex topography and socio-economic dynamics.","准确的人口数据对于有效规划服务和资源分配、城市发展以及气候变化适应至关重要——尤其是在山区，复杂的地形对数据收集和基础设施建设均构成挑战。本文提出了一种低成本、低技术门槛且可复制的混合方法，用于估算人口规模与空间分布，并将其应用于南非半乡村、半城市的山区——奎奎（Qwaqwa）。利用高分辨率卫星影像，人工绘制了91,624栋主要住宅建筑。研究区根据形态特征被划分为七种城市结构类型，随机抽取917个样点进行调查，应答率为52.7%。调查数据提供了入住率，用于计算院落级人口估算值，并据此推算各分层的人口密度。研究揭示了各院落居民数量的高度变异性，人口密度与主要建筑及院落的可见物理属性之间不存在强相关关系。评估得出的模拟人口平均约为360,000人（范围320,000–400,000），每栋主要建筑平均3.94人（含无人居住建筑）。奎奎的人口分布反映了种族隔离时期规划的空间印记、地形限制以及持续的城市化压力，这些因素共同塑造了人们的居住位置以及对当地基础设施和服务的需求水平，如供水、交通和公共设施。研究结果凸显了传统遥感方法在山区地形中的局限性，在这些地区，聚落格局同时受到复杂地形和社会经济动态的影响。","Transactions of the Royal Society of South Africa",{"impact":36,"substance":36,"depth":36,"authority":36,"freshness":36,"relevant":36,"comment":246},"该论文聚焦南非山区人口估算方法，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[248],{"name":244,"url":241},[250,27,251,252],"城乡融合","南非","人口估算",[254,255],"Qwaqwa 人口估算","南非 山区 遥感","Qwaqwa人口估算-3673","10.1080\u002F0035919x.2026.2729963",{"doi":257,"openalex_id":259,"authors":260,"venue":244,"cited_by_count":36,"oa_url":278,"card":279,"direction":52,"ingested_from":55},"W7214276367",[261,264,266,268,270,272,274,276],{"name":262,"orcid":263},"Jess L. Delves","https:\u002F\u002Forcid.org\u002F0000-0002-2092-2905",{"name":265,"orcid":9},"S. Schneiderbauer",{"name":267,"orcid":9},"R. Schomacker",{"name":269,"orcid":9},"S. Terzi",{"name":271,"orcid":9},"Melissa Hansen",{"name":273,"orcid":9},"Pulane Pudumo",{"name":275,"orcid":9},"Zinhle Mbongo",{"name":277,"orcid":9},"Jonas Pieper","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F0035919X.2026.2729963?needAccess=true",{"tldr":280,"method":281,"finding":282,"direction":283,"opportunity":284},"提出低成本低技术混合方法，估算南非山区Qwaqwa的人口规模与空间分布。","高分辨率卫星影像人工制图、分层随机抽样调查与占用率外推。","人口约36万，密度与建筑可见物理属性无强关联，反映种族隔离规划与地形约束。","数字乡村与农业信息化","可探索将低成本人口估算与农业服务、资源分配结合，弥补山区数据稀缺下的规划空白。","2026-09-28T23:30:29.581603Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":292,"source_url":289,"published_at":293,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":36,"score_detail":294,"sources":296,"tags":298,"search_phrases":303,"slug":306,"view_count":36,"doi":307,"paper":308,"created_at":322},3671,"CLOUD-BASED MULTI-TEMPORAL SAR-OPTICAL FUSION USING RANDOM FOREST FOR GEOSPATIAL INTELLIGENCE MONITORING OF KIPP IKN NUSANTARA","https:\u002F\u002Fdoi.org\u002F10.21163\u002Fgt_2027.221.08","The development of Indonesia's new capital (IKN) Nusantara drives dramatic land-cover transformation across a strategic 17,599-hectare area under the persistent cloud cover of Kalimantan, where optical remote sensing alone is insufficient for continuous monitoring.This study proposes a cloud-based multi-temporal SAR-optical fusion approach using Random Forest classification for geospatial-intelligence (GEOINT) monitoring of the Core Government Area (KIPP) of IKN.Using Google Earth Engine, we processed 70 Sentinel-1 GRD and 52 Sentinel-2 images for 2022 and 61 and 81 images for 2024, building a 12-band fusion stack (SAR backscatter VV, VH, VV\u002FVH; optical B2, B3, B4, B8, B11, B12; and NDVI, NDBI, NDWI).Six experiments compared SAR-only, optical-only, and fusion configurations.The Random Forest fusion achieved 88.89% overall accuracy and 0.8609 kappa for 2022 and 85.12% and 0.8127 for 2024 (assessed against ESA WorldCover 2021 class definitions; see Section 2.7), outperforming SAR-only (63.20%) and optical-only (86.44%).Change detection revealed 899.64 ha of direct vegetation-to-built-up conversion, a 60% expansion of built-up area driven by IKN construction.This is the first cloud-based multi-temporal fusion framework applied to KIPP IKN monitoring, with implications for spatial planning, security surveillance, and environmental compliance.","印度尼西亚新首都（IKN）努山塔拉的开发，在加里曼丹持续云覆盖下推动了一个战略性的17,599公顷区域发生剧烈的土地覆盖转变，仅依靠光学遥感不足以实现持续监测。本研究提出了一种基于云的多时相SAR-光学融合方法，采用随机森林分类对IKN核心政府区（KIPP）进行地理空间情报（GEOINT）监测。利用Google Earth Engine，我们处理了2022年的70景Sentinel-1 GRD和52景Sentinel-2影像，以及2024年的61景和81景影像，构建了一个12波段融合数据集（SAR后向散射VV、VH、VV\u002FVH；光学B2、B3、B4、B8、B11、B12；以及NDVI、NDBI、NDWI）。六组实验比较了仅SAR、仅光学和融合配置。随机森林融合在2022年达到88.89%的总体精度和0.8609的kappa系数，2024年为85.12%和0.8127（依据ESA WorldCover 2021类别定义进行评估；见第2.7节），优于仅SAR（63.20%）和仅光学（86.44%）。变化检测揭示了899.64公顷的植被直接转为建成区，IKN建设推动建成区面积扩张60%。这是首个应用于KIPP IKN监测的基于云的多时相融合框架，对空间规划、安全监控和环境合规具有重要意义。","Geographia Technica","2026-09-26T00:00:00Z",{"impact":36,"substance":36,"depth":36,"authority":36,"freshness":36,"relevant":36,"comment":295},"该研究聚焦印尼新首都建设的遥感监测，属地理空间情报领域，与三农、农业信息化、智慧农业等主题无直接关联，故不进入每日精选。",[297],{"name":292,"url":289},[299,27,300,301,302],"随机森林","土地覆盖变化","SAR光学融合","空间规划",[304,305],"Sentinel-1 Sentinel-2 融合","土地覆盖变化 SAR光学融合 空间规划 遥感监测","Sentinel-1Sentinel-2融合-3671","10.21163\u002Fgt_2027.221.08",{"doi":307,"openalex_id":309,"authors":310,"venue":292,"cited_by_count":36,"oa_url":316,"card":317,"direction":52,"ingested_from":55},"W7214434955",[311,313],{"name":312,"orcid":9},"Fajar Sidik Suganda",{"name":314,"orcid":315},"Asep Adang Supriyadi","https:\u002F\u002Forcid.org\u002F0000-0003-1103-6669","https:\u002F\u002Ftechnicalgeography.org\u002Fpdf\u002F1_2027\u002F08_suganda.pdf",{"tldr":318,"method":319,"finding":320,"direction":52,"opportunity":321},"提出云端多时相SAR-光学融合与随机森林方法，监测印尼新首都核心区土地覆盖变化。","Google Earth Engine处理Sentinel-1\u002F2影像，构建12","融合分类精度达88.89%，检测到899.64公顷植被转建设用地，建成区扩张60%。","可迁移至多云农业区作物监测，探索融合特征优化与跨区域泛化能力。","2026-09-28T23:30:27.951741Z"]