[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3543":3,"related-3543":59},{"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":58},3543,"AHP-Based Flood Risk, Vulnerability, and Public Health Assessment in Chattogram, Bangladesh: Mitigation and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.70028\u002Fdcea.v3i2.127","Chattogram City Corporation (CCC), the principal port city and second largest urban economy in Bangladesh, is increasingly vulnerable to monsoon flooding, tidal backwater from the Karnaphuli River and Bay of Bengal, unplanned urbanization, and deterioration of natural drainage canals (khals). This study develops an integrated Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) framework to assess flood risk across the 41 wards of CCC and examine its association with household level vulnerability and public health outcomes. Seven biophysical and anthropogenic criteria: elevation, proximity to rivers\u002Fkhals, drainage congestion, rainfall intensity, land use\u002Fimperviousness, slope, and population density were standardized, weighted using Saaty’s 1–9 scale, and integrated through weighted linear overlay. The resulting pairwise comparison matrix produced a principal eigenvalue (λmax) of 7.130 and a consistency ratio (CR) of 0.016, indicating acceptable weighting consistency. Approximately 43.6% of CCC was clas-sified as having High or Very High flood risk, concentrated mainly in low lying wards of the central and southwestern areas. Questionnaire data from 360 households indicated significantly higher prevalence of waterborne diarrheal disease, skin infections, and psychological distress in Very High risk wards than in Low risk wards. The July 2026 record rainfall event, which af-fected approximately 450,000 people in the greater Chattogram area, further demonstrated the practical relevance of the risk map, with reported waterlogging hotspots broadly corresponding to High and Very High risk zones. The findings support integrated structural, non structural, and building scale interventions, alongside policy measures and future applications of remote sensing, machine learning, and participatory governance to strengthen urban flood resilience.","吉大港市（Chattogram City Corporation, CCC）是孟加拉国的主要港口城市和第二大城市经济体，日益受到季风洪水、卡纳普里河与孟加拉湾潮水顶托、无序城市化以及自然排水渠（khals）退化等多重威胁。本研究构建了地理信息系统（GIS）与层次分析法（AHP）相结合的综合框架，用以评估吉大港市41个选区的洪水风险，并考察其与家庭层面脆弱性及公共卫生结果之间的关联。研究选取七个生物物理与人为因素指标——高程、距河流\u002F排水渠距离、排水拥堵程度、降雨强度、土地利用\u002F不透水面积、坡度及人口密度，对其进行标准化处理，采用Saaty 1–9标度确定权重，并通过加权线性叠加进行综合。所得成对比较矩阵的主特征值（λmax）为7.130，一致性比率（CR）为0.016，表明权重一致性处于可接受范围。吉大港市约43.6%的区域被划为高或极高洪水风险区，主要集中在中心及西南部地势低洼的选区。来自360户家庭的问卷数据显示，极高风险选区的水源性腹泻、皮肤感染及心理困扰患病率显著高于低风险选区。2026年7月的创纪录降雨事件影响了吉大港大都会区约45万人，进一步印证了该风险地图的实际应用价值——所报告的内涝热点区域与高及极高风险区大体吻合。研究结果支持结构性、非结构性及建筑尺度干预措施的综合实施，同时建议配套政策手段，并在未来应用遥感、机器学习及参与式治理以增强城市洪水韧性。",null,"Disaster in Civil Engineering and Architecture","2026-09-24T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦孟加拉国城市洪水风险与公共卫生评估，属城市防灾领域，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],"遥感监测","GIS评估","公共卫生","城市防洪",[26,27],"Chattogram 洪水风险 AHP","孟加拉 城市内涝 风险评估","Chattogram洪水风险AHP-3543","10.70028\u002Fdcea.v3i2.127",{"doi":29,"openalex_id":31,"authors":32,"venue":10,"cited_by_count":15,"oa_url":49,"card":50,"direction":56,"ingested_from":57},"W7214192445",[33,36,38,40,42,44,46],{"name":34,"orcid":35},"Mohammad Abdul Aziz","https:\u002F\u002Forcid.org\u002F0000-0001-6197-4798",{"name":37,"orcid":9},"S M Alauddin Abdullah",{"name":39,"orcid":9},"Sheikh Abu Bakar Siddique",{"name":41,"orcid":9},"Rajesh Majumder",{"name":43,"orcid":9},"Sirajul Islam Talukdar",{"name":45,"orcid":9},"Mohammad Asif Uddin Asif",{"name":47,"orcid":48},"Sudip Kumar Pal","https:\u002F\u002Forcid.org\u002F0000-0002-1511-6984","https:\u002F\u002Fjournal.popularscientist.org\u002Findex.php\u002Fdcea\u002Farticle\u002Fdownload\u002F127\u002F91",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"用GIS与AHP评估孟加拉国吉大港41个选区的洪水风险，并关联家庭脆弱性与公共卫生结果。","GIS空间分析、AHP层次分析法（7项指标加权叠加）、360户问卷调查。","约43.6%区域为高或极高洪水风险，极高风险区水源性腹泻、皮肤感染和心理困扰显著更高。","农业绿色发展与碳","可引入遥感与机器学习动态更新风险图，并探究洪水风险与农业面源污染、碳汇损失的耦合机制。","智慧农业 \u002F 农业物联网","openalex","2026-09-26T23:30:11.469605Z",{"total":60,"page":61,"page_size":60,"items":62},6,1,[63,93,136,173,211,249],{"id":64,"title":65,"url":66,"summary":67,"summary_zh":9,"content":68,"source_name":69,"source_url":9,"published_at":70,"category":71,"cover_url":9,"hotness":13,"is_selected":14,"score":72,"score_detail":73,"sources":80,"tags":82,"search_phrases":88,"slug":91,"view_count":15,"doi":9,"paper":9,"created_at":92},3586,"黑龙江省农科院科技包联现场观摩会举办——'松粳22'优质食味水稻新品种示范田实测亩产557.5公斤","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588143.html","9月22日，黑龙江省农科院科技包联哈尔滨市现场观摩会暨乔府大院第十一届开镰节开幕式上，试验田新品种展示区块18个水稻品种分区规整排布，其中9个是黑龙江省农科院自主选育品种。黑龙江省农科院生物技术研究所（五常水稻研究所）副所长、研究员闫平介绍，新品种'松粳22'150亩示范田按照14.5%标准含水率折算，平均亩产达到557.5公斤，在优质食味水稻中属于上乘水平，抗倒伏能力显著优于传统优质稻。秸秆基质育苗示范点位展示秸秆基质育苗技术，今年在哈尔滨18个点位示范。","![Image 1](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Fshare_logo.jpg)\n\n[![Image 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[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)\n\n[![Image 3](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Ffdj.png)](https:\u002F\u002Fsearch.stdaily.com:8888\u002Ffounder\u002FNewSearchServlet.do?siteID=1)[登录](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html?url=https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588143.html)[-](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html)\n\n 所在位置： [中国科技网首页](https:\u002F\u002Fwww.stdaily.com\u002Findex.html)>[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)> 正文 \n\n# 乡村行 看振兴丨科技赋能丰产 定制深耕良田——黑龙江省农科院科技包联现场观摩会举办\n\n 2026-09-25 12:39:56 来源: 科技日报 点击数：\n\n![Image 4](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimg\u002F20240301dzv.png)0\n\n[](javascript:; \"新浪微博\")[](javascript:; \"微信\")\n\n**科技日报记者 朱虹**\n\n金色稻海漫过黑龙江五常的黑土地，稻浪深处，开镰号令响起。随着乔府大院董事长乔文志一声“开镰”，嘉宾们手持镰刀踏入金黄稻田，割下了今秋第一束稻穗。\n\n9月22日，黑龙江省农科院科技包联哈尔滨市现场观摩会暨乔府大院第十一届开镰节开幕式上，传统农耕与现代科技撞个满怀，一场丰收开镰，也是一次看得见摸得着的农业科技大考。\n\n在试验田新品种展示区块，18个水稻品种分区规整排布，其中9个是黑龙江省农科院自主选育品种。该院生物技术研究所（五常水稻研究所）副所长、研究员闫平站在田垄之间，伸手托起沉甸甸的“松粳22”稻穗，招呼围观嘉宾观察茎秆与籽粒状态。现场发布前，实收测产数据显示，150亩示范田按照14.5%标准含水率折算，松粳22平均亩产达到557.5公斤，在优质食味水稻中属于上乘水平。\n\n“传统稻花香大米口感出众，可茎秆偏软，遇上风雨天气极易大面积倒伏。”闫平指着成片挺立的稻株讲解道，“今天展示的新品种，抗倒伏能力显著优于传统优质稻。”\n\n一句“抗倒伏”，点破了好米难产的旧账。传统优质稻往往“娇贵”——品质好却秆软易倒、产量不稳，种高端米像押宝。新品种把“好吃”和“抗倒”拧在一起：抗倒伏，意味着风雨天不趴窝、机械收割进得去、规模种植稳得住，高端米供给才算有了“压舱石”。从“靠天吃饭”到“凭种稳产”，良种攻关为高端稻米产业蹚出一条新路。\n\n米食品鉴区，新米出锅，香气四溢，米粒莹润油亮。定制客户捧着一碗米饭感慨：“一碗好米的根基就在种子，科研选育的新品种，让老百姓既吃得饱，更吃得好。”\n\n观摩队伍在试验田里走走停停。秸秆基质育苗示范点位旁，黑龙江省农科院耕作栽培研究所作物栽培与装备研究室主任董文军被团团围住，他弯腰捧起一把棕褐色基质，讲起秸秆基质育苗的门道。\n\n“原料就是经多年沤熟的秸秆有机肥，配上三成秸秆打包回收时附带的表层土，育苗完全不用再挖黑土。”董文军介绍，杀菌剂、控旺剂都已预配好，农户拿到手直接铺苗床，取土、晒土、混拌这些累活全省了。“今年在哈尔滨18个点位示范，秧苗长势不比传统育苗土差，产量持平就可以得到推广，前期试验发现不同的配方基质比当地基质和黑土能增产5%到15%。”董文军说。秸秆从田间收走、发酵成基质，秧苗育成再回归水田。这条闭环，把曾经的“田间负担”变成“黑土养料”，围观的种植大户连连点头，当场追问推广条件。\n\n智慧农业贯穿作物生长每一环。黑龙江省农科院农业遥感与信息研究所副所长张有智介绍：“无人机图像结合人工智能模型算法，实现作物的长势情况和杂草分布反演识别，自动生成施肥处方图和施药处方图，直接推送到无人机飞手账号进行作业。作业精度能到厘米级。”今年试验，这套技术实现减肥6%、减药17%左右。\n\n黑龙江省农科院耕作栽培研究所所长李柱刚告诉科技日报记者，2023年黑龙江省农科院建立科技包联服务机制以来，哈尔滨已建成粮食单产提升示范田、科技服务田28个，示范推广面积25800亩。\n\n“为龙头企业提供高端定制服务，是科技包联的重头戏。”黑龙江省农科院副院长焦少杰站在田埂上说，“从品种定向选育、栽培技术集成，到植保方案、加工品鉴，全程‘一对一’定制，让高端米从‘卖原粮’升级为‘卖标准、卖品质’。”他介绍，黑龙江省农科院已深化科技包联13个市（地）服务机制，推动“百项技术、千个基地、万名人才”增粮示范落地。\n\n责任编辑：王倩\n\n相关稿件：\n\n![Image 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观摩会","黑龙江省农科院松粳22水稻-3586","2026-09-27T00:05:16.125922Z",{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":70,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":107,"tags":109,"search_phrases":114,"slug":117,"view_count":15,"doi":118,"paper":119,"created_at":135},3568,"Interpretable multi-index framework for extracting dry-season built-up areas: benchmarking machine learning with Sentinel-2","https:\u002F\u002Fdoi.org\u002F10.4995\u002Fraet.2027.25885","Spectral similarity between built-up surfaces and exposed dry soil significantly constrains built-up area extraction during dry seasons. Although machine-learning techniques can address this challenge by modeling complex spectral relationships, they generally require sufficiently large and representative labeled training datasets. This study therefore investigates a less data-demanding, rule-based multi-index approach for dry-season built-up mapping, aiming to reduce reliance on large labeled training datasets while maintaining effective classification performance. Sentinel-2A imagery acquired in April 2024 was analyzed, and temporal validation was conducted for 2020, 2022, and 2024. Among the tested combinations, the Built-up Area Extraction Index (BAEI), Dry BareSoil Index (DBSI), and Normalized Difference Vegetation Index (NDVI) achieved the highest performance, yielding 95 % overall accuracy and a Kappa coefficient of 0.89. This represents a substantial improvement over singleindex BAEI (80 % overall accuracy; Kappa 0.55), increasing the built-up user’s accuracy from 61 % to 92 %. Multitemporal validation confirmed that the optimized BAEI–DBSI–NDVI decision rules remained stable across the 2020–2024 dry-season images without recalibration, achieving 93–96 % overall accuracy and a built-up F1-score of 84–93 %. Spatial analysis demonstrated robust performance across peripheral and agricultural zones, with moderate variability in dense urban cores due to spectral heterogeneity. To evaluate the operational robustness of the proposed framework, it was benchmarked against machine-learning classifiers, specifically Support Vector Machine and Random Forest (RF). Under the specific conditions of this study, the proposed rule-based method (95 % accuracy) marginally outperformed both SVM (93 %) and RF (92 %), while offering greater transparency and reducing dependency on large, labeled training datasets. Furthermore, feature importance analysis confirmed the critical role of these selected indices in resolving spectral confusion. These findings suggest that the proposed framework offers a transparent and computationally efficient approach for dry-season urban monitoring in tropical coastal environments similar to Visakhapatnam.","建成表面与裸露干土之间的光谱相似性显著制约了旱季建成区提取。尽管机器学习技术可通过建模复杂光谱关系来应对这一挑战，但其通常需要足够大且具有代表性的标记训练数据集。因此，本研究探讨了一种对数据需求较低、基于规则的多指数方法用于旱季建成区制图，旨在减少对大规模标记训练数据集的依赖，同时保持有效的分类性能。研究分析了2024年4月获取的Sentinel-2A影像，并对2020年、2022年和2024年进行了时间验证。在测试的组合中，建成区提取指数（BAEI）、干裸土指数（DBSI）和归一化差异植被指数（NDVI）表现最佳，总体精度达95%，Kappa系数为0.89。相较于单一指数BAEI（总体精度80%；Kappa 0.55），这一结果有显著提升，建成区用户精度从61%提高至92%。多时相验证证实，优化后的BAEI–DBSI–NDVI决策规则在2020—2024年旱季影像上无需重新校准即可保持稳定，总体精度达93%—96%，建成区F1分数为84%—93%。空间分析表明，该方法在外围和农业区域表现稳健，而在密集城市核心区由于光谱异质性存在中等程度变异。为评估所提框架的业务化稳健性，将其与机器学习分类器进行了基准比较，具体为支持向量机（SVM）和随机森林（RF）。在本研究的特定条件下，所提出的基于规则的方法（95%精度）略优于SVM（93%）和RF（92%），同时具有更高的透明性并减少了对大规模标记训练数据集的依赖。此外，特征重要性分析证实了所选指数在解决光谱混淆方面的关键作用。这些发现表明，所提框架为类似维沙卡帕特南的热带沿海环境旱季城市监测提供了一种透明且计算高效的方法。","Revista de teledetección: Revista de la Asociación Española de Teledetección",72,{"impact":102,"substance":103,"depth":104,"authority":77,"freshness":105,"relevant":61,"comment":106},12,21,17,9,"该研究提出可解释的多指数规则框架，在旱季建成区提取上以更少标注数据达到95%精度并优于SVM\u002FRF，方法新颖、验证充分，对农业遥感与乡村土地利用监测有参考价值。",[108],{"name":99,"url":96},[110,111,112,21,113],"Sentinel-2","机器学习","NDVI","建成区提取",[115,116],"Sentinel-2 建成区提取","BAEI DBSI NDVI 旱季","Sentinel-2建成区提取-3568","10.4995\u002Fraet.2027.25885",{"doi":118,"openalex_id":120,"authors":121,"venue":99,"cited_by_count":15,"oa_url":96,"card":128,"direction":134,"ingested_from":57},"W7214385607",[122,125],{"name":123,"orcid":124},"Sarah Sejari","https:\u002F\u002Forcid.org\u002F0009-0002-7300-9084",{"name":126,"orcid":127},"Vazeer Mahammood","https:\u002F\u002Forcid.org\u002F0000-0003-1667-5232",{"tldr":129,"method":130,"finding":131,"direction":132,"opportunity":133},"提出可解释多指数规则框架，用Sentinel-2提取旱季建成区，性能优于机器学习。","Sentinel-2A影像，BAEI、DBSI、NDVI组合规则，对比SVM与随","BAEI-DBSI-NDVI规则达95%精度、Kappa 0.89，优于单指数及SVM\u002FRF且无需重","农业遥感与作物表型","可探索该规则框架向其他气候带与传感器迁移，并结合少量样本的半监督学习提升城市核心区精度。","农业人工智能与决策模型","2026-09-26T23:30:53.200163Z",{"id":137,"title":138,"url":139,"summary":140,"summary_zh":141,"content":9,"source_name":142,"source_url":139,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":143,"score_detail":144,"sources":146,"tags":148,"search_phrases":152,"slug":155,"view_count":15,"doi":156,"paper":157,"created_at":172},3558,"Meteorolojik Kuraklığın Vejetasyon Sağlığı Üzerindeki Mekânsal ve Zamansal Etkilerinin SPI ve NDVI ile Analizi: Gediz Havzası Örneği","https:\u002F\u002Fdoi.org\u002F10.48123\u002Frsgis.1858019","Kuraklık, özellikle yarı kurak iklim koşullarına sahip havzalarda su kaynakları, tarımsal üretim ve ekosistem sürdürülebilirliği üzerinde belirleyici etkiler oluşturan başlıca doğal afetlerden biridir. Bu bağlamda, kuraklığın bitki örtüsü üzerindeki mekânsal ve zamansal etkilerinin bütüncül yaklaşımlarla değerlendirilmesi büyük önem taşımaktadır. Bu çalışmada, meteorolojik kuraklığın vejetasyon sağlığı üzerindeki mekânsal ve zamansal etkileri, Standartlaştırılmış Yağış İndeksi (SPI) ve Normalize Fark Bitki Örtüsü İndeksi (NDVI) kullanılarak Gediz Havzası örneğinde analiz edilmiştir. SPI analizleri 1970–2023 yılları arasındaki uzun dönem yağış verilerine dayanırken, NDVI verileri 2000–2023 dönemine ait MODIS uydu görüntülerinden elde edilmiştir. İki veri seti arasındaki ilişkiler, ortak dönem olan 2000–2023 yılları için incelenmiştir. Elde edilen bulgular, meteorolojik kuraklık ile vejetasyon sağlığı arasındaki ilişkinin mevsimsel olarak değişkenlik gösterdiğini ortaya koymaktadır. En güçlü ilişki yaz mevsiminde gözlenmiş (r ≈ 0.70) ve bu durum vejetasyonun yağış eksikliklerine en duyarlı olduğu dönemin yaz ayları olduğunu göstermiştir. Buna karşılık, kış (r ≈ 0.05) ve ilkbahar (r ≈ 0.02) mevsimlerinde ilişki oldukça zayıf bulunmuştur. Belirlenen kurak yıllarda (2004, 2008 ve 2022) NDVI değerlerinde belirgin düşüşler gözlenmiştir. Sonuç olarak, vejetasyonun kuraklığa verdiği tepkinin yıl boyunca homojen olmadığı, mevsimsel iklim koşulları ve diğer çevresel faktörlere bağlı olarak değiştiği belirlenmiştir.","干旱是主要自然灾害之一，尤其在具有半干旱气候条件的流域中，对水资源、农业生产和生态系统可持续性产生决定性影响。在此背景下，以整体性方法评估干旱对植被的时空影响具有重要意义。本研究以盖迪兹流域为例，利用标准化降水指数（SPI）和归一化植被指数（NDVI）分析了气象干旱对植被健康的时空影响。SPI分析基于1970—2023年的长期降水数据，而NDVI数据则来自2000—2023年期间的MODIS卫星影像。两个数据集之间的关系在共同时段2000—2023年进行了分析。研究结果表明，气象干旱与植被健康之间的关系呈现季节性变化。最显著的关系出现在夏季（r ≈ 0.70），这表明夏季是植被对降水亏缺最为敏感的时期。相比之下，冬季（r ≈ 0.05）和春季（r ≈ 0.02）的关系则非常微弱。在确定的干旱年份（2004年、2008年和2022年），NDVI值出现了明显下降。综上，植被对干旱的响应在全年并非均匀一致，而是随季节性气候条件及其他环境因素而变化。","Turkish Journal of Remote Sensing and GIS",71,{"impact":102,"substance":103,"depth":104,"authority":77,"freshness":78,"relevant":61,"comment":145},"基于SPI与NDVI长时序数据揭示气象干旱对植被健康影响的季节性差异，方法规范、结论可靠，对农业干旱遥感监测有参考价值，但属区域案例研究，公共影响有限。",[147],{"name":142,"url":139},[112,21,149,150,151],"干旱监测","植被指数","SPI",[153,154],"Gediz Havzası SPI NDVI","气象干旱 植被健康 遥感","GedizHavzasıSPINDVI-3558","10.48123\u002Frsgis.1858019",{"doi":156,"openalex_id":158,"authors":159,"venue":142,"cited_by_count":15,"oa_url":166,"card":167,"direction":132,"ingested_from":57},"W7214166880",[160,163],{"name":161,"orcid":162},"Kemal Yurddaş","https:\u002F\u002Forcid.org\u002F0000-0003-4691-4038",{"name":164,"orcid":165},"Murat Karabulut","https:\u002F\u002Forcid.org\u002F0000-0002-1456-6908","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F5578303",{"tldr":168,"method":169,"finding":170,"direction":132,"opportunity":171},"用SPI和NDVI分析土耳其盖迪兹流域气象干旱对植被健康的时空影响。","基于1970-2023年降水SPI与2000-2023年MODIS NDVI，做","干旱与植被关系随季节变化，夏季最强(r≈0.70)，冬春极弱；干旱年NDVI明显下降。","可引入滞后效应与多尺度SPI，结合土壤水分和灌溉数据，提升干旱对植被影响的预测能力。","2026-09-26T23:30:31.357310Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":9,"source_name":179,"source_url":176,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":181,"sources":184,"tags":186,"search_phrases":191,"slug":194,"view_count":15,"doi":195,"paper":196,"created_at":210},3557,"Recent Changes in Lake Chad’s Surface Water Extent: A Remote Sensing Assessment Using Sentinel-2 Land Cover Data (2017-2025)","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx5b22z","Lake Chad is an endorheic freshwater lake located at the junction of four countries; Nigeria, Niger, Chad, and Cameroon in West-Central Africa. Lake Chad, once one of Africa’s largest freshwater bodies, has experienced significant hydrological fluctuations over the past decades. While long-term historical analyses highlight a severe 90% surface area contraction since the 1960s, short-term operational variations over the last decade require precise quantification to inform active transboundary water management strategies. To address this critical gap, this study aims to develop a replicable remote sensing framework using high-resolution satellite data to quantify Lake Chad’s open water and flooded vegetation dynamics from 2017 to 2025, providing evidence-based insights for sustainable water resource management. The methodology encompasses a comprehensive remote sensing analysis of the contemporary surface water extent of Lake Chad using the high-resolution (10-meter) Esri Sentinel-2 Land Use\u002FLand Cover (LULC) time-series dataset. Through a systematic methodology involving image clipping, reclassification, and area calculation, the study tracked changes in open water, flooded vegetation (wetlands), and total lake system extent. Findings reveal a substantial recovery of the lake system, with total area expanding from 5,808.12 km² in 2017 to 11,588.14 km² in 2025, representing an absolute net growth of 5,780.02 km² (99.5% increase). While the lake has not fully recovered to its historic 1960s extent of approximately 25,000 km², it currently exhibits its most water-abundant state since the turn of the 21st century. These findings have significant implications for water resource management, regional food security, and the livelihoods of approximately 50 million people dependent on the Lake Chad basin.","乍得湖是位于非洲中西部尼日利亚、尼日尔、乍得和喀麦隆四国交界处的内陆淡水湖。乍得湖曾是非洲最大的淡水水体之一，过去数十年来经历了显著的水文波动。尽管长期历史分析显示，自20世纪60年代以来其表面积严重萎缩了90%，但近十年来的短期运行变化仍需精确定量，以指导当前活跃的跨境水资源管理策略。为填补这一关键空白，本研究旨在开发一个可复制的遥感框架，利用高分辨率卫星数据量化2017年至2025年乍得湖开阔水域和淹没植被的动态变化，为可持续水资源管理提供循证依据。方法上，本研究采用高分辨率（10米）Esri Sentinel-2土地利用\u002F土地覆盖（LULC）时间序列数据集，对乍得湖当代地表水范围进行了全面的遥感分析。通过图像裁剪、重分类和面积计算等系统方法，研究追踪了开阔水域、淹没植被（湿地）及湖泊系统总范围的变化。研究结果显示，湖泊系统大幅恢复，总面积从2017年的5,808.12平方公里扩展至2025年的11,588.14平方公里，绝对净增长5,780.02平方公里（增幅99.5%）。尽管该湖尚未完全恢复到20世纪60年代约25,000平方公里的历史范围，但目前呈现出21世纪以来水量最丰沛的状态。这些发现对水资源管理、区域粮食安全以及依赖乍得湖流域的约5,000万人的生计具有重大意义。","OpenAlex",79,{"impact":74,"substance":182,"depth":74,"authority":102,"freshness":105,"relevant":61,"comment":183},22,"基于Sentinel-2十年时序数据量化乍得湖水面恢复99.5%，方法可复制、数据翔实，对跨境水资源与区域粮食安全有参考价值，但属非洲区域研究，国内三农关联度有限。",[185],{"name":179,"url":176},[187,188,21,189,190],"粮食安全","水资源管理","湖泊湿地","跨境流域",[192,193],"乍得湖 Sentinel-2 遥感","乍得湖 水域面积 变化","乍得湖Sentinel-2遥感-3557","10.31223\u002Fx5b22z",{"doi":195,"openalex_id":197,"authors":198,"venue":9,"cited_by_count":15,"oa_url":204,"card":205,"direction":132,"ingested_from":57},"W7214164320",[199,202],{"name":200,"orcid":201},"Umar Yusuf","https:\u002F\u002Forcid.org\u002F0009-0008-3703-1069",{"name":203,"orcid":9},"Umar Usman","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F15133\u002Fdownload\u002F26255\u002F",{"tldr":206,"method":207,"finding":208,"direction":132,"opportunity":209},"基于Sentinel-2土地覆盖数据量化2017-2025年乍得湖地表水面积变化。","使用10米分辨率Esri Sentinel-2 LULC时序数据，经裁剪、重分类","乍得湖总面积从5808增至11588平方公里，净增99.5%，为21世纪以来最丰水状态。","可延伸研究乍得湖扩张对流域农业灌溉、湿地生态及跨境水资源管理的影响。","2026-09-26T23:30:31.292812Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":216,"content":9,"source_name":217,"source_url":214,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":218,"score_detail":219,"sources":222,"tags":224,"search_phrases":229,"slug":232,"view_count":15,"doi":233,"paper":234,"created_at":248},3555,"Ecosystem Resilience and Land-Use Governance: Remote Sensing Insights for Environmental Management in a Tropical Dry Forest","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00267-026-02629-4","Seasonally Dry Tropical Forests (SDTFs), such as the Brazilian Caatinga, are highly vulnerable to climate change and chronic anthropogenic disturbances. Despite extensive land-use conversion, understanding how vegetation productivity and resilience vary across different land governance regimes remains a critical gap. Here, we integrated two decades (2004-2024) of remote sensing data to evaluate Net Primary Productivity (NPP) and Water Use Efficiency (WUE). We applied interannual climate anomaly analyses (Z-scores) and adapted a trend-based classification framework to assess ecosystem trajectories. These biophysical metrics were cross-referenced with the spatial boundaries of Conservation Units, Indigenous Lands, Quilombola Territories, and private properties, including a distance-decay edge effect analysis. While long-term temporal trends for NPP and WUE remained relatively stable over the two decades, standardized anomaly analyses revealed a strong, statistically significant inverse coupling between productivity and water-use efficiency (r = - 0.741, p \u003C 0.001), primarily mediated by surface moisture retention (LSWI) rather than immediate precipitation alone. Spatial analyses demonstrated that Conservation Units act as the main ecological refugia, maintaining peak restoration within a 0-2 km internal buffer. Notably, Indigenous Lands and Quilombola Territories exhibited remarkable edge-to-core resilience, maintaining homogeneous conservation levels that effectively buffer external degradation. To safeguard the Caatinga's structural integrity against desertification, public policies must move beyond reactive drought subsidies towards proactive landscape management that legally empowers traditional communities and incentivizes conservation on private lands.","季节性干旱热带森林（Seasonally Dry Tropical Forests, SDTFs），如巴西卡廷加（Caatinga），极易受到气候变化和长期人为干扰的影响。尽管土地利用转换广泛发生，但关于植被生产力与恢复力在不同土地治理制度下如何变化的认识仍存在关键空白。本研究整合了二十年（2004—2024年）的遥感数据，评估净初级生产力（Net Primary Productivity, NPP）和水分利用效率（Water Use Efficiency, WUE）。我们采用年际气候异常分析（Z分数），并改编了基于趋势的分类框架以评估生态系统轨迹。这些生物物理指标与保护单元、原住民土地、基隆博拉领地及私有财产的空间边界进行了交叉比对，包括距离衰减边缘效应分析。尽管NPP和WUE的长期时间趋势在二十年间保持相对稳定，但标准化异常分析揭示出生产力与水分利用效率之间存在强烈的、统计显著的反向耦合关系（r = -0.741，p \u003C 0.001），这一关系主要由地表水分保持（LSWI）而非仅由即时降水所介导。空间分析表明，保护单元是主要的生态避难所，在0—2 km的内部缓冲区内维持着最高的恢复水平。值得注意的是，原住民土地和基隆博拉领地表现出显著边缘到核心的恢复力，维持着均质的保护水平，有效缓冲了外部退化。为保障卡廷加的结构完整性以抵御荒漠化，公共政策必须超越被动的干旱补贴，转向积极的景观管理，在法律上赋权传统社区并激励私有土地上的保护行为。","Environmental Management",80,{"impact":74,"substance":182,"depth":74,"authority":220,"freshness":78,"relevant":61,"comment":221},14,"基于20年遥感数据揭示保护地与原住民领地生态缓冲作用，方法扎实、结论对土地治理有参考价值，但属区域案例研究，公共影响有限。",[223],{"name":217,"url":214},[21,225,226,227,228],"生态系统韧性","土地利用治理","热带干旱林","退化土地修复",[230,231],"巴西 Caatinga 遥感 植被生产力","保护地 原住民领地 生态韧性","巴西Caatinga遥感植被生产力-3555","10.1007\u002Fs00267-026-02629-4",{"doi":233,"openalex_id":235,"authors":236,"venue":217,"cited_by_count":15,"oa_url":242,"card":243,"direction":132,"ingested_from":57},"W7214117687",[237,240],{"name":238,"orcid":239},"Lucas  Nascimento da Silva","https:\u002F\u002Forcid.org\u002F0009-0004-5435-0986",{"name":241,"orcid":9},"Bartolomeu Israel de Souza","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs00267-026-02629-4.pdf",{"tldr":244,"method":245,"finding":246,"direction":132,"opportunity":247},"整合20年遥感数据评估巴西卡廷加NPP与WUE，分析不同土地治理制度下的生态韧性。","2004-2024年遥感NPP\u002FWUE、气候异常Z评分、趋势分类与边缘衰减分析。","保护单元是主要生态避难所，原住民与Quilombola领地边缘到核心韧性突出。","可探究传统社区土地权属如何通过制度设计提升干旱森林韧性，并量化边缘效应的政策阈值。","2026-09-26T23:30:30.120235Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":254,"content":9,"source_name":255,"source_url":252,"published_at":70,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":256,"score_detail":257,"sources":259,"tags":261,"search_phrases":265,"slug":268,"view_count":15,"doi":269,"paper":270,"created_at":290},3553,"Advances and gaps in mitigation and management practices across critical landscapes and scales","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jenvman.2026.131013","Environmental systems are increasingly stressed by the interacting effects of climate change, land-use change, agricultural intensification, urbanization, and emerging contaminants. This paper synthesizes contributions to the Journal of Environmental Management special collection, “Advances and gaps in mitigation and management practices across critical landscapes and scales,” which examines current progress and remaining challenges in environmental mitigation and best management practices. The reviewed studies span diverse landscapes, scales, and management contexts, with emphasis on water quality, water quantity, pollutant transport, ecological resilience, and sustainable agricultural systems. Four major themes emerge: the combined pressures of climate and human activity on environmental systems; innovations in modeling, monitoring, remote sensing, and statistical forecasting; evaluation of management interventions and mitigation strategies; and cross-cutting insights related to spatial variability, extreme events, long-term dynamics, and implementation gaps. Collectively, the studies demonstrate that mitigation effectiveness is highly context-dependent and influenced by hydrologic connectivity, land use, climate variability, management history, and social factors. Advances in process-based models, machine learning, geospatial technologies, and monitoring, reporting, and verification frameworks are improving the ability to predict environmental responses and evaluate intervention outcomes. However, persistent gaps remain in long-term monitoring, maintenance of management practices, adaptation to extreme events, and translation of scientific findings into policy and practice. This synthesis highlights the need for integrated, adaptive, and stakeholder-informed approaches to strengthen environmental resilience across critical landscapes.","环境系统日益受到气候变化、土地利用变化、农业集约化、城市化以及新兴污染物相互作用的影响。本文综合了《环境管理杂志》特刊“关键景观与尺度下减缓与管理实践的进展与差距”的贡献，该特刊审视了环境减缓与最佳管理实践的当前进展及剩余挑战。所综述的研究涵盖多样化的景观、尺度和管理情境，重点关注水质、水量、污染物迁移、生态韧性和可持续农业系统。由此浮现出四大主题：气候与人类活动对环境系统的复合压力；建模、监测、遥感与统计预测方面的创新；管理干预与减缓策略的评估；以及关于空间变异性、极端事件、长期动态与实施差距的交叉性见解。总体而言，这些研究表明，减缓效果高度依赖于具体情境，并受水文连通性、土地利用、气候变异性、管理历史和社会因素的影响。基于过程的模型、机器学习、地理空间技术以及监测、报告与核查框架的进步，正在提升预测环境响应和评估干预结果的能力。然而，在长期监测、管理实践的维持、极端事件的适应以及科学发现向政策与实践的转化方面，仍存在持续性的差距。本综述强调，需要采取综合性、适应性且利益相关者知情的方法，以增强关键景观的环境韧性。","Journal of Environmental Management",78,{"impact":74,"substance":75,"depth":74,"authority":220,"freshness":78,"relevant":61,"comment":258},"核心期刊综述，系统梳理环境缓解与管理实践进展与缺口，对农业面源污染与水质管理有参考价值，但非农业信息化直接落地成果。",[260],{"name":255,"url":252},[111,262,21,263,264],"农业面源污染","农业可持续发展","水质管理",[266,267],"Journal of Environmental Management 缓解措施 管理实践","农业可持续发展 农业面源污染 机器学习 水质管理","JournalofEnvironmentalManagement缓解措施管理实践-3553","10.1016\u002Fj.jenvman.2026.131013",{"doi":269,"openalex_id":271,"authors":272,"venue":255,"cited_by_count":15,"oa_url":252,"card":285,"direction":132,"ingested_from":57},"W7214310594",[273,276,279,282],{"name":274,"orcid":275},"Fouad H. Jaber","https:\u002F\u002Forcid.org\u002F0000-0001-8643-8668",{"name":277,"orcid":278},"Latif Kalin","https:\u002F\u002Forcid.org\u002F0000-0001-9562-8834",{"name":280,"orcid":281},"Soni  Mulmi Pradhanang","https:\u002F\u002Forcid.org\u002F0000-0002-1142-9457",{"name":283,"orcid":284},"Aleksey Y. Sheshukov","https:\u002F\u002Forcid.org\u002F0000-0002-4842-908X",{"tldr":286,"method":287,"finding":288,"direction":54,"opportunity":289},"综述环境减缓与管理实践进展，指出成效高度依赖情境且存在实施缺口。","综合多景观尺度研究，涵盖过程模型、机器学习、遥感与MRV框架。","减缓效果受水文连通、土地利用、气候与历史管理影响，长期监测与政策转化仍不足。","可研究农业景观中管理实践的长期维持机制与极端事件下的适应性策略。","2026-09-26T23:30:29.702279Z"]