Remote Sensing Applications-Society and Environment最新文献_第2页

Quantifying human-induced impacts on forest phenology using multi-source remote sensing data: A case study in the Gudao Oilfield, China 利用多源遥感数据量化人类活动对森林物候的影响——以孤岛油田为例

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 DOI: 10.1016/j.rsase.2026.101879

Han Yang , Hong Wang , Nobuaki Tanaka

Vegetation phenology reflects the seasonal dynamics of ecosystems. It responds to global climate change and is significantly influenced by local human activities. However, the spatial dimension of human-induced phenological impacts remains unclear. This study employed PlanetScope (PS, 3 m), Sentinel-2 (S2, 10 m), and Harmonized Landsat Sentinel-2 (HLS, 30 m) imagery to quantify the impacts of oil extraction and road-related activities on shelterbelt phenology in the Gudao Oilfield. Phenological changes within distance-based buffers around the disturbance sources were modelled using an exponential decay function to derive cumulative impact curves. Based on the Pareto principle, the distance at which the cumulative impact reached 80 % and the corresponding phenological change were used to characterize these human-induced impacts. Results showed that human activities advanced the start (SOS) and delayed the end (EOS) of the growing season compared with reference areas (>300 m from the road and >200 m from all oil wells). Estimated influence distances from PS and S2 imagery were 37.57–51.00 m for road-related activities and 38.93–43.43 m for oil extraction, comparable to the observed spatial extent of forest structural changes, with corresponding phenological changes of 2.40–3.91 and 4.50–6.65 days, respectively. Scale effects introduced uncertainty in quantifying human-induced impacts. At the coarser 30 m resolution (HLS imagery), influence distances were overestimated (>83.07 m) and phenological changes were underestimated (<1.94 days). This study provides a methodological framework for quantifying human-induced impacts on vegetation phenology and offers new insights into scale effects in ecological monitoring.

植被物候反映了生态系统的季节动态。它对全球气候变化作出反应，并受到当地人类活动的重大影响。然而，人为物候影响的空间维度尚不清楚。利用PlanetScope （PS, 3 m）、Sentinel-2 （S2, 10 m）和Harmonized Landsat Sentinel-2 （HLS, 30 m）影像，量化了采油和道路相关活动对孤岛油田林带物候的影响。在干扰源周围基于距离的缓冲区内的物候变化使用指数衰减函数建模，以得出累积影响曲线。基于Pareto原理，利用累积影响达到80%的距离和相应的物候变化特征来表征这些人为影响。结果表明：与参考区（距公路300 m，距所有油井200 m）相比，人类活动使生长季的开始（SOS）提前，结束（EOS）推迟；PS和S2影像对道路活动和采油活动的影响距离分别为37.57 ~ 51.00 m和38.93 ~ 43.43 m，与观测到的森林结构变化空间范围相当，对应的物候变化分别为2.40 ~ 3.91天和4.50 ~ 6.65天。尺度效应在量化人为影响时引入了不确定性。在较粗的30米分辨率（HLS图像）下，影响距离被高估（83.07米），物候变化被低估（1.94天）。该研究为量化人类活动对植被物候的影响提供了一个方法框架，并为生态监测中的尺度效应提供了新的见解。

{"title":"Quantifying human-induced impacts on forest phenology using multi-source remote sensing data: A case study in the Gudao Oilfield, China","authors":"Han Yang , Hong Wang , Nobuaki Tanaka","doi":"10.1016/j.rsase.2026.101879","DOIUrl":"10.1016/j.rsase.2026.101879","url":null,"abstract":"<div><div>Vegetation phenology reflects the seasonal dynamics of ecosystems. It responds to global climate change and is significantly influenced by local human activities. However, the spatial dimension of human-induced phenological impacts remains unclear. This study employed PlanetScope (PS, 3 m), Sentinel-2 (S2, 10 m), and Harmonized Landsat Sentinel-2 (HLS, 30 m) imagery to quantify the impacts of oil extraction and road-related activities on shelterbelt phenology in the Gudao Oilfield. Phenological changes within distance-based buffers around the disturbance sources were modelled using an exponential decay function to derive cumulative impact curves. Based on the Pareto principle, the distance at which the cumulative impact reached 80 % and the corresponding phenological change were used to characterize these human-induced impacts. Results showed that human activities advanced the start (SOS) and delayed the end (EOS) of the growing season compared with reference areas (>300 m from the road and >200 m from all oil wells). Estimated influence distances from PS and S2 imagery were 37.57–51.00 m for road-related activities and 38.93–43.43 m for oil extraction, comparable to the observed spatial extent of forest structural changes, with corresponding phenological changes of 2.40–3.91 and 4.50–6.65 days, respectively. Scale effects introduced uncertainty in quantifying human-induced impacts. At the coarser 30 m resolution (HLS imagery), influence distances were overestimated (>83.07 m) and phenological changes were underestimated (<1.94 days). This study provides a methodological framework for quantifying human-induced impacts on vegetation phenology and offers new insights into scale effects in ecological monitoring.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101879"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mapping of hydrothermal alteration zones linked to the volcanogenic-hosted massive sulphide deposits in the Betul Belt, Madhya Pradesh, India, using PRISMA hyperspectral imagery 利用PRISMA高光谱图像绘制印度中央邦Betul带与火山成因块状硫化物矿床相关的热液蚀变带

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 DOI: 10.1016/j.rsase.2025.101842

Saraah Imran , Ajanta Goswami , Sriram Jallu , Riyaaz Uddien Shaik , Pradeepkumar T

The Betul Belt, a Proterozoic supracrustal unit within the Central Indian Tectonic Zone, hosts volcanogenic-hosted massive sulphide (VHMS) mineralisation marked by hydrothermal alteration overprinted by regional metamorphism and structural complexity. Mapping such alteration in metamorphosed terranes is challenging due to spectral mixing, mineral overprinting, and lithological heterogeneity. In this study, we demonstrate the feasibility of PRISMA hyperspectral imagery for mineral alteration zone mapping using an integrated methodology that involves machine learning classification, image-based mineral indices, laboratory-based spectroscopic studies, and mineralogical and geochemical validation. The workflow combines Principal Component Analysis (PCA), Relative Band Depth (RBD) indices, and Support Vector Machine (SVM) classification. The results delineate distinct Mg-OH and Al-OH alteration zones, consistent with VHMS alteration models, achieving classification accuracies of 0.98 (Mg) and 0.97 (Al). Validation through field spectroscopy, petrographic thin section studies, SEM analyses, Spectral Feature Fitting (SFF), and bulk-rock geochemistry confirms the presence of tremolite-, muscovite-, and garnet-bearing assemblages associated with these zones. This integrated approach demonstrates the potential of PRISMA data for resolving hydrothermal alteration zonation in structurally complex metamorphic terranes, offering a scalable and cost-effective workflow for mineral exploration in similar geological settings.

Betul带是印度中部构造带的一个元古代表壳单元，以热液蚀变为标志的火山带块状硫化物（VHMS）矿化，其上覆有区域变质作用和构造复杂性。由于光谱混合、矿物叠印和岩性非均质性，在变质地体中绘制这种蚀变是具有挑战性的。在这项研究中，我们证明了PRISMA高光谱图像用于矿物蚀变带制图的可行性，该方法包括机器学习分类、基于图像的矿物指数、基于实验室的光谱研究以及矿物学和地球化学验证。该工作流结合了主成分分析（PCA）、相对频带深度（RBD）指数和支持向量机（SVM）分类。结果与VHMS蚀变模型一致，划分出明显的Mg- oh和Al- oh蚀变带，分类精度分别为0.98 （Mg）和0.97 （Al）。通过现场光谱、岩石薄片研究、扫描电镜分析、光谱特征拟合（SFF）和大块岩石地球化学验证，证实了与这些带相关的透闪石、白云母和石榴石组合的存在。这种综合方法证明了PRISMA数据在解决结构复杂的变质地体中热液蚀变带的潜力，为类似地质环境下的矿产勘探提供了一种可扩展且具有成本效益的工作流程。

{"title":"Mapping of hydrothermal alteration zones linked to the volcanogenic-hosted massive sulphide deposits in the Betul Belt, Madhya Pradesh, India, using PRISMA hyperspectral imagery","authors":"Saraah Imran , Ajanta Goswami , Sriram Jallu , Riyaaz Uddien Shaik , Pradeepkumar T","doi":"10.1016/j.rsase.2025.101842","DOIUrl":"10.1016/j.rsase.2025.101842","url":null,"abstract":"<div><div>The Betul Belt, a Proterozoic supracrustal unit within the Central Indian Tectonic Zone, hosts volcanogenic-hosted massive sulphide (VHMS) mineralisation marked by hydrothermal alteration overprinted by regional metamorphism and structural complexity. Mapping such alteration in metamorphosed terranes is challenging due to spectral mixing, mineral overprinting, and lithological heterogeneity. In this study, we demonstrate the feasibility of PRISMA hyperspectral imagery for mineral alteration zone mapping using an integrated methodology that involves machine learning classification, image-based mineral indices, laboratory-based spectroscopic studies, and mineralogical and geochemical validation. The workflow combines Principal Component Analysis (PCA), Relative Band Depth (RBD) indices, and Support Vector Machine (SVM) classification. The results delineate distinct Mg-OH and Al-OH alteration zones, consistent with VHMS alteration models, achieving classification accuracies of 0.98 (Mg) and 0.97 (Al). Validation through field spectroscopy, petrographic thin section studies, SEM analyses, Spectral Feature Fitting (SFF), and bulk-rock geochemistry confirms the presence of tremolite-, muscovite-, and garnet-bearing assemblages associated with these zones. This integrated approach demonstrates the potential of PRISMA data for resolving hydrothermal alteration zonation in structurally complex metamorphic terranes, offering a scalable and cost-effective workflow for mineral exploration in similar geological settings.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101842"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Influence of atmospheric boundary-layer dynamics on air quality of the middle- and high-density urban areas of Colombia 大气边界层动力学对哥伦比亚中部和高密度城区空气质量的影响

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 DOI: 10.1016/j.rsase.2026.101874

Luis M. Hernández Beleño , Gregori de Arruda Moreira , Eliana Vergara-Vásquez , Yiniva Camargo Caicedo , David J. O'Connor , Andrés M. Vélez-Pereira

The interplay between emissions and atmospheric boundary-layer dynamics shapes urban air quality (AQ) in Colombia's complex topography. This study assesses the influence of the atmospheric boundary layer on AQ across contrasting physiographic regions. The ERA5 reanalysis dataset was used to obtain hourly ABLH and VC estimates for the period 2020–2024, while COSMIC-2 profiles were used to derive Temperature Elevation Profile (TEP) variables, including inversion-base height and thermal gradients. Urban AQ data from 78 monitoring stations were obtained from SISAIRE, focusing on PM₁₀, PM_2.5, and O₃. The analysis combines exceedance rates (98th-percentile thresholds), diurnal and seasonal cycles, nonparametric correlations, and Gaussian linear models stratified by stable/unstable ABL conditions and dry/wet seasons. Our results show frequent exceedances in Antioquia and Bogotá, where PM_2.5 daily exceedance medians reach 1.11 % and 0.87 %, respectively. Norte de Santander exhibits the highest PM_2.5 median exceedance rate (7.18 %), while departments such as Cesar and Magdalena show low-to-moderate levels. O₃ responses are strongly modulated by thermal structure, with direct associations between ABLH, inversion strength, and O₃ peaks, particularly in high-elevation terrains. Physiography and circulation patterns explain regional contrasts, with stagnation-prone basins showing stronger pollution accumulation. We conclude that ventilation conditions strongly influence particulate pollution, whereas peak O₃ is governed primarily by precursor emissions and temperature-driven photochemistry. These findings highlight the need for meteorology-aware AQ management strategies, especially in densely populated Andean basins.

排放和大气边界层动力学之间的相互作用决定了哥伦比亚复杂地形下的城市空气质量。本研究评估了不同地理区域大气边界层对空气质量的影响。ERA5再分析数据集用于获得2020-2024年每小时ABLH和VC估计，而COSMIC-2剖面用于获得温度高程剖面（TEP）变量，包括反演基高和热梯度。来自SISAIRE的78个监测站的城市空气质量数据，重点关注PM10、PM2.5和O3。该分析结合了超过率（第98百分位阈值）、日和季节周期、非参数相关性以及由稳定/不稳定ABL条件和干/湿季节分层的高斯线性模型。我们的研究结果显示，安蒂奥基亚和波哥大的PM2.5日超标中位数分别达到1.11%和0.87%。北桑坦德的PM2.5中位数超标率最高（7.18%），而凯撒和马格达莱纳等省的PM2.5中位数超标率为中低水平。O3响应受到热结构的强烈调节，在ABLH、逆温强度和O3峰值之间存在直接关联，特别是在高海拔地区。地形和环流模式解释了区域差异，容易停滞的盆地表现出更强的污染积累。我们得出结论，通风条件强烈影响颗粒污染，而O3峰值主要由前体排放和温度驱动的光化学控制。这些发现突出表明需要有气象意识的空气质量管理策略，特别是在人口稠密的安第斯盆地。

{"title":"Influence of atmospheric boundary-layer dynamics on air quality of the middle- and high-density urban areas of Colombia","authors":"Luis M. Hernández Beleño , Gregori de Arruda Moreira , Eliana Vergara-Vásquez , Yiniva Camargo Caicedo , David J. O'Connor , Andrés M. Vélez-Pereira","doi":"10.1016/j.rsase.2026.101874","DOIUrl":"10.1016/j.rsase.2026.101874","url":null,"abstract":"<div><div>The interplay between emissions and atmospheric boundary-layer dynamics shapes urban air quality (AQ) in Colombia's complex topography. This study assesses the influence of the atmospheric boundary layer on AQ across contrasting physiographic regions. The ERA5 reanalysis dataset was used to obtain hourly ABLH and VC estimates for the period 2020–2024, while COSMIC-2 profiles were used to derive Temperature Elevation Profile (TEP) variables, including inversion-base height and thermal gradients. Urban AQ data from 78 monitoring stations were obtained from SISAIRE, focusing on PM<sub>10</sub>, PM<sub>2.5</sub>, and O<sub>3</sub>. The analysis combines exceedance rates (98th-percentile thresholds), diurnal and seasonal cycles, nonparametric correlations, and Gaussian linear models stratified by stable/unstable ABL conditions and dry/wet seasons. Our results show frequent exceedances in Antioquia and Bogotá, where PM<sub>2.5</sub> daily exceedance medians reach 1.11 % and 0.87 %, respectively. Norte de Santander exhibits the highest PM<sub>2.5</sub> median exceedance rate (7.18 %), while departments such as Cesar and Magdalena show low-to-moderate levels. O<sub>3</sub> responses are strongly modulated by thermal structure, with direct associations between ABLH, inversion strength, and O<sub>3</sub> peaks, particularly in high-elevation terrains. Physiography and circulation patterns explain regional contrasts, with stagnation-prone basins showing stronger pollution accumulation. We conclude that ventilation conditions strongly influence particulate pollution, whereas peak O<sub>3</sub> is governed primarily by precursor emissions and temperature-driven photochemistry. These findings highlight the need for meteorology-aware AQ management strategies, especially in densely populated Andean basins.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101874"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unraveling mangrove degradation in Jardines de la Reina National Park, Cuba: Integration of Landsat-8, machine learning and environmental factors 解开古巴怡和雷纳国家公园红树林退化：Landsat-8、机器学习和环境因素的整合

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 DOI: 10.1016/j.rsase.2025.101861

Alexey Valero-Jorge , Roberto González De-Zayas , Angel Luis Becerra González , Felipe Matos-Pupo , Dian Nuraini Melati , Eduardo González-Ferreiro

Mangrove ecosystems are vital for coastal resilience, biodiversity, and climate regulation. This study assessed the spatial and temporal dynamics of mangrove cover in Jardines de la Reina National Park (JRNP), Cuba, between 2014 and 2024, using Landsat-8 imagery and five machine learning classifiers. Random Forest (RF) achieved the highest accuracy (97.60 %), with rigorous uncertainty propagation via Monte Carlo simulation, setting a new benchmark for mangrove mapping in the data-poor insular Caribbean. This method was selected for generating annual maps. Results revealed a loss of over 1500 ha of mangrove forest—an 18.65 % reduction—primarily in the western sector, especially Bretón and Alcatraz keys. NDVI trend analysis confirmed significant degradation in these areas, while central keys remained more stable. Environmental factor analysis identified mean sea level (MSL) as the dominant driver of mangrove loss, followed by annual precipitation. Limited freshwater and sediment input, exacerbated by damming and droughts, likely impaired mangrove resilience. Patchy dieback patterns were observed, with localized mortality within otherwise healthy stands. Herbivory by hutia (Capromys pilorides) may contribute to stress, but recent data are lacking. Although JRNP is a protected area, the dominance of external environmental drivers—particularly sea-level rise and reduced precipitation—poses challenges that may exceed current local conservation management capabilities. The study highlights the need for integrated field and remote sensing approaches to monitor ecosystem health. Future research should focus on sediment accretion, primary productivity, herbivory impacts, and hydrological connectivity. This framework offers a model for holistic mangrove in marine protected areas across the Caribbean and supports adaptive management strategies to address the challenges posed by climate change.

红树林生态系统对沿海恢复力、生物多样性和气候调节至关重要。本研究利用Landsat-8卫星图像和五种机器学习分类器，评估了2014年至2024年间古巴雷纳花园国家公园（JRNP）红树林覆盖的时空动态。随机森林（RF）通过蒙特卡罗模拟获得了最高的精度（97.60%），具有严格的不确定性传播，为数据贫乏的加勒比岛屿红树林制图设定了新的基准。选择这种方法生成年度地图。结果显示，损失了超过1500公顷的红树林，减少了18.65%，主要是在西部地区，特别是Bretón和阿尔卡特拉斯群岛。NDVI趋势分析证实了这些区域的显著退化，而中央键保持较稳定。环境因子分析表明，平均海平面是红树林损失的主要驱动因素，其次是年降水量。有限的淡水和沉积物输入，再加上筑坝和干旱，可能损害了红树林的恢复能力。观察到斑驳的枯死模式，在其他健康的林分中有局部死亡。竹属植物（Capromys pilorides）的食草性可能导致压力，但缺乏最近的数据。虽然JRNP是一个保护区，但外部环境驱动因素（特别是海平面上升和降水减少）的主导地位带来的挑战可能超出当前当地的保护管理能力。该研究强调需要采用综合的野外和遥感方法来监测生态系统健康。未来的研究应集中在泥沙增积、初级生产力、草食影响和水文连通性等方面。该框架为整个加勒比海洋保护区的整体红树林提供了一个模式，并支持适应性管理战略，以应对气候变化带来的挑战。

{"title":"Unraveling mangrove degradation in Jardines de la Reina National Park, Cuba: Integration of Landsat-8, machine learning and environmental factors","authors":"Alexey Valero-Jorge , Roberto González De-Zayas , Angel Luis Becerra González , Felipe Matos-Pupo , Dian Nuraini Melati , Eduardo González-Ferreiro","doi":"10.1016/j.rsase.2025.101861","DOIUrl":"10.1016/j.rsase.2025.101861","url":null,"abstract":"<div><div>Mangrove ecosystems are vital for coastal resilience, biodiversity, and climate regulation. This study assessed the spatial and temporal dynamics of mangrove cover in Jardines de la Reina National Park (JRNP), Cuba, between 2014 and 2024, using Landsat-8 imagery and five machine learning classifiers. Random Forest (RF) achieved the highest accuracy (97.60 %), with rigorous uncertainty propagation via Monte Carlo simulation, setting a new benchmark for mangrove mapping in the data-poor insular Caribbean. This method was selected for generating annual maps. Results revealed a loss of over 1500 ha of mangrove forest—an 18.65 % reduction—primarily in the western sector, especially Bretón and Alcatraz keys. NDVI trend analysis confirmed significant degradation in these areas, while central keys remained more stable. Environmental factor analysis identified mean sea level (MSL) as the dominant driver of mangrove loss, followed by annual precipitation. Limited freshwater and sediment input, exacerbated by damming and droughts, likely impaired mangrove resilience. Patchy dieback patterns were observed, with localized mortality within otherwise healthy stands. Herbivory by hutia (<em>Capromys pilorides</em>) may contribute to stress, but recent data are lacking. Although JRNP is a protected area, the dominance of external environmental drivers—particularly sea-level rise and reduced precipitation—poses challenges that may exceed current local conservation management capabilities. The study highlights the need for integrated field and remote sensing approaches to monitor ecosystem health. Future research should focus on sediment accretion, primary productivity, herbivory impacts, and hydrological connectivity. This framework offers a model for holistic mangrove in marine protected areas across the Caribbean and supports adaptive management strategies to address the challenges posed by climate change.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101861"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Driving mechanisms of nitrogen and phosphorus dynamics in the Daitou River basin: A multi-level perspective from Sentinel-2 imagery 大头河流域氮磷动态的驱动机制：基于Sentinel-2遥感影像的多层次视角

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 DOI: 10.1016/j.rsase.2025.101848

Yuanmao Zheng , Chenyan Wei , Lingluo Chen , Haiyan Fu , Haoxi Lin

It was found by the Central Environmental Protection Inspection Team of China that the Daitou River Basin in Tong' an District, Xiamen City, had prominent issues concerning its black and odorous water bodies. To explore the relationships between natural factors, socioeconomic development factors, and land use pattern factors in the Daitou River Basin and the concentrations of nitrogen (TN) and phosphorus (TP) in the water bodies, this study integrated monitoring data on TN and TP concentrations with statistical data to construct a model of the impact mechanisms of TN and TP concentrations. The study revealed the factors influencing TN and TP concentrations and their impact mechanisms and proposed corresponding treatment strategies and recommendations. Based on the geographical detector model, TN and TP concentrations were found to be significantly influenced by precipitation, runoff, population size, GDP, and the primary industry value. Among these drivers, the most pronounced effect on TN concentration was exerted by precipitation, with an explanatory power of 0.9376, whereas TP concentration was most strongly affected by GDP, reaching an explanatory power of 0.9777. According to the correlation-based analytical model, precipitation, population size, and the primary industry value were considered as the dominant factors governing the spatiotemporal distinction of TN and TP concentrations. Specifically, the explanatory power for TN concentration was observed to range from 0.618 to 0.878 for precipitation, 0.765 to 0.873 for the primary industry value, and 0.642 to 0.785 for population size; for TP concentration, these values were 0.692–0.876, 0.642–0.874, and 0.551–0.869, respectively. The research results of this study can provide more scientific and solid theoretical basis for formulating water ecological treatment measures.

近日，中央环境保护督察组发现，厦门市通安区大头河流域水体黑臭问题突出。为探索岱头河流域自然因素、社会经济发展因素和土地利用模式因素与水体中氮、磷浓度的关系，本研究将TN、TP浓度监测数据与统计数据相结合，构建了TN、TP浓度影响机制模型。本研究揭示了影响总氮和总磷浓度的因素及其影响机制，并提出了相应的处理策略和建议。基于地理探测器模型，发现全氮和总磷浓度受降水、径流、人口规模、GDP和第一产业价值的显著影响。其中，降水对全氮浓度的影响最为显著，解释能力为0.9376，而全磷浓度受GDP的影响最为强烈，解释能力为0.9777。根据相关性分析模型，降水量、人口规模和第一产业价值是控制全氮和总磷浓度时空差异的主导因素。其中，降水量对TN浓度的解释能力为0.618 ~ 0.878，第一产业值对TN浓度的解释能力为0.765 ~ 0.873，人口规模对TN浓度的解释能力为0.642 ~ 0.785；TP浓度分别为0.692 ~ 0.876、0.642 ~ 0.874和0.551 ~ 0.869。本研究成果可为制定水生态治理措施提供更为科学、坚实的理论依据。

{"title":"Driving mechanisms of nitrogen and phosphorus dynamics in the Daitou River basin: A multi-level perspective from Sentinel-2 imagery","authors":"Yuanmao Zheng , Chenyan Wei , Lingluo Chen , Haiyan Fu , Haoxi Lin","doi":"10.1016/j.rsase.2025.101848","DOIUrl":"10.1016/j.rsase.2025.101848","url":null,"abstract":"<div><div>It was found by the Central Environmental Protection Inspection Team of China that the Daitou River Basin in Tong' an District, Xiamen City, had prominent issues concerning its black and odorous water bodies. To explore the relationships between natural factors, socioeconomic development factors, and land use pattern factors in the Daitou River Basin and the concentrations of nitrogen (TN) and phosphorus (TP) in the water bodies, this study integrated monitoring data on TN and TP concentrations with statistical data to construct a model of the impact mechanisms of TN and TP concentrations. The study revealed the factors influencing TN and TP concentrations and their impact mechanisms and proposed corresponding treatment strategies and recommendations. Based on the geographical detector model, TN and TP concentrations were found to be significantly influenced by precipitation, runoff, population size, GDP, and the primary industry value. Among these drivers, the most pronounced effect on TN concentration was exerted by precipitation, with an explanatory power of 0.9376, whereas TP concentration was most strongly affected by GDP, reaching an explanatory power of 0.9777. According to the correlation-based analytical model, precipitation, population size, and the primary industry value were considered as the dominant factors governing the spatiotemporal distinction of TN and TP concentrations. Specifically, the explanatory power for TN concentration was observed to range from 0.618 to 0.878 for precipitation, 0.765 to 0.873 for the primary industry value, and 0.642 to 0.785 for population size; for TP concentration, these values were 0.692–0.876, 0.642–0.874, and 0.551–0.869, respectively. The research results of this study can provide more scientific and solid theoretical basis for formulating water ecological treatment measures.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101848"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative analysis of PS-InSAR and DS-InSAR for deformation monitoring of transportation infrastructure in Greater Bay Area, China PS-InSAR与DS-InSAR在大湾区交通基础设施变形监测中的对比分析

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 DOI: 10.1016/j.rsase.2026.101872

Songbo Wu , Bochen Zhang , Yan Li , Siting Xiong , Xiaoli Ding

Transportation networks are vital for our economy, such as the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). However, their large scale makes them easily susceptible to the ground deformation. Synthetic Aperture Radar Interferometry (InSAR), as an efficient geodetic technique, has been widely used for regional surface deformation monitoring. However, choosing the best InSAR method for effective monitoring of diverse transportation infrastructure remains a key challenge. This study aims to address this issue in GBA region by conducting a systematic multi-sensor comparison of two widely used InSAR methods, i.e., Permanent Scatterer (PS-InSAR) and Distributed Scatterer (DS-InSAR). Based on three SAR data from Sentinel-1A, COSMO-SkyMed, and PALSAR-2, we conducted the ground deformation monitoring and statistical analysis for various infrastructure types (including high-speed railways, bridges, coastal highways, and airports) within the GBA. The spatial distribution, density, and coverage of the measurements, were evaluated. The experimental results were validated against GNSS benchmark data, confirming the reliability of the measurements. We quantitatively demonstrate that in urban areas of GBA, the suitability of a given technique depends primarily on the surface characteristics of the target and its surrounding environment. DS-InSAR performs better in low coherence region e.g., the construction zones and low-reflectivity pavements, achieving denser point than PS-InSAR. But it requires significantly more computation. In contrast, PS-InSAR effectively detects deformation hotspots and provides high-accuracy for stable linear structures such as cross-sea bridges. We further quantified the influence of key monitoring parameters, including observation period, sensor wavelength, and ground vegetation characteristics and compared their roles in monitoring the transportation infrastructure network. The study results provide a comprehensive evaluation of the monitoring effectiveness and efficiency of those two methods, which supports the selection of an optimal InSAR approach for future applications in the GBA.

交通网络对香港经济至关重要，例如粤港澳大湾区。然而，它们的规模大，容易受到地面变形的影响。合成孔径雷达干涉测量技术作为一种有效的大地测量技术，在区域地表变形监测中得到了广泛的应用。然而，选择最佳的InSAR方法来有效监测各种交通基础设施仍然是一个关键的挑战。本研究旨在通过对两种广泛使用的InSAR方法，即永久散射体（PS-InSAR）和分布式散射体（DS-InSAR）进行系统的多传感器比较，解决大湾区地区的这一问题。基于Sentinel-1A、COSMO-SkyMed和PALSAR-2卫星SAR数据，对大湾区内各类基础设施类型（包括高速铁路、桥梁、沿海公路和机场）进行了地面变形监测和统计分析。评估了测量的空间分布、密度和覆盖范围。实验结果与GNSS基准数据进行了验证，验证了测量结果的可靠性。我们定量地证明，在大湾区的城市地区，给定技术的适用性主要取决于目标及其周围环境的表面特征。DS-InSAR在低相干区域（如施工区域和低反射率路面）表现更好，比PS-InSAR获得更密集的点。但它需要更多的计算。相比之下，PS-InSAR可以有效地检测变形热点，并为跨海桥梁等稳定的线性结构提供高精度。我们进一步量化了关键监测参数的影响，包括观测周期、传感器波长和地面植被特征，并比较了它们在监测交通基础设施网络中的作用。研究结果对这两种方法的监测效果和效率进行了综合评价，为未来在大湾区的应用选择最佳的InSAR方法提供了支持。

{"title":"Comparative analysis of PS-InSAR and DS-InSAR for deformation monitoring of transportation infrastructure in Greater Bay Area, China","authors":"Songbo Wu , Bochen Zhang , Yan Li , Siting Xiong , Xiaoli Ding","doi":"10.1016/j.rsase.2026.101872","DOIUrl":"10.1016/j.rsase.2026.101872","url":null,"abstract":"<div><div>Transportation networks are vital for our economy, such as the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). However, their large scale makes them easily susceptible to the ground deformation. Synthetic Aperture Radar Interferometry (InSAR), as an efficient geodetic technique, has been widely used for regional surface deformation monitoring. However, choosing the best InSAR method for effective monitoring of diverse transportation infrastructure remains a key challenge. This study aims to address this issue in GBA region by conducting a systematic multi-sensor comparison of two widely used InSAR methods, i.e., Permanent Scatterer (PS-InSAR) and Distributed Scatterer (DS-InSAR). Based on three SAR data from Sentinel-1A, COSMO-SkyMed, and PALSAR-2, we conducted the ground deformation monitoring and statistical analysis for various infrastructure types (including high-speed railways, bridges, coastal highways, and airports) within the GBA. The spatial distribution, density, and coverage of the measurements, were evaluated. The experimental results were validated against GNSS benchmark data, confirming the reliability of the measurements. We quantitatively demonstrate that in urban areas of GBA, the suitability of a given technique depends primarily on the surface characteristics of the target and its surrounding environment. DS-InSAR performs better in low coherence region e.g., the construction zones and low-reflectivity pavements, achieving denser point than PS-InSAR. But it requires significantly more computation. In contrast, PS-InSAR effectively detects deformation hotspots and provides high-accuracy for stable linear structures such as cross-sea bridges. We further quantified the influence of key monitoring parameters, including observation period, sensor wavelength, and ground vegetation characteristics and compared their roles in monitoring the transportation infrastructure network. The study results provide a comprehensive evaluation of the monitoring effectiveness and efficiency of those two methods, which supports the selection of an optimal InSAR approach for future applications in the GBA.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101872"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian geostatistical models for predicting above-ground biomass from remote sensing data 利用遥感数据预测地上生物量的贝叶斯地质统计模型

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 DOI: 10.1016/j.rsase.2025.101855

Ricardo Coelho , Isabel Natário , Silvia Fraile

Monitoring and quantifying the amount of carbon stored in forests is crucial for shaping global policies, understanding climate change, and supporting emerging carbon credit markets. One key indicator that effectively describes carbon quantity is the Above-Ground Biomass (AGB), that has been traditionally estimated using allometric equations and data collected in forest inventory data. Recently, advances in technology have enabled the integration of remote sensing data in this process. In this study, Bayesian geostatistical models (using INLA and SPDE approaches) were applied to estimate AGB in two areas of the Atlantic Forest in northern Spain, Sierra de la Culebra and Montaña Palentina. Field data from the Cuarto Inventario Forestal Nacional (IFN4) of Spain were combined with satellite data from GEOSAT-2. Measurements such as texture, vegetation indices, and spectral variables were extracted from satellite reflectance bands and used as covariates in the models. The models incorporated a spatial random field to capture spatial autocorrelation, which was found to be significant in both areas. In Sierra de la Culebra, NDVI (Normalized Difference Vegetation Index), homogeneity, and Forest Type were statistically significant covariates in modelling AGB. In contrast, in Montaña Palentina, the Red and Blue reflectance bands were the main contributors to AGB estimation. Predictive results in Sierra de la Culebra were satisfactory (RMSE = 47.06 (t/ha); MSE = 2214.68 (t²/ha); MAE = 36.84 (t/ha);

R^{2}

= 0.52; rRMSE = 39%), while in Montaña Palentina, predictive performance was even better (RMSE = 24.41 (t

^{0.75}

/ha); MSE = 505.71 (t

^{1.5}

/ha); MAE = 20.02 (t

^{0.75}

/ha);

R^{2}

= 0.5; rRMSE = 36%).

监测和量化森林碳储量对于制定全球政策、了解气候变化和支持新兴碳信用市场至关重要。有效描述碳量的一个关键指标是地上生物量（AGB），传统上使用异速生长方程和森林清查数据中收集的数据来估算。最近，技术的进步使遥感数据能够纳入这一进程。在这项研究中，贝叶斯地质统计模型（使用INLA和SPDE方法）应用于西班牙北部大西洋森林的两个地区，Sierra de la Culebra和Montaña Palentina的AGB估计。来自西班牙国家森林项目（IFN4）的实地数据与来自GEOSAT-2的卫星数据相结合。从卫星反射波段提取纹理、植被指数和光谱变量等测量数据，并将其用作模型中的协变量。模型纳入了一个空间随机场来捕捉空间自相关性，这在两个区域都是显著的。在Sierra de la cullebra， NDVI（归一化植被指数）、均匀性和森林类型是模拟AGB的统计显著协变量。相比之下，在Montaña Palentina中，红色和蓝色反射带是AGB估计的主要贡献者。Sierra de la cullebra的预测结果令人满意（RMSE = 47.06 (t/ha)）；MSE = 2214.68 (t2/ha)；MAE = 36.84 (t/ha)；R2 = 0.52；rRMSE = 39%)，而在Montaña Palentina中，预测性能更好(RMSE = 24.41 (t0.75/ha)；MSE = 505.71 (t1.5/ha)；MAE = 20.02 (t0.75/ha)；R2 = 0.5；rRMSE = 36%)。

{"title":"Bayesian geostatistical models for predicting above-ground biomass from remote sensing data","authors":"Ricardo Coelho , Isabel Natário , Silvia Fraile","doi":"10.1016/j.rsase.2025.101855","DOIUrl":"10.1016/j.rsase.2025.101855","url":null,"abstract":"<div><div>Monitoring and quantifying the amount of carbon stored in forests is crucial for shaping global policies, understanding climate change, and supporting emerging carbon credit markets. One key indicator that effectively describes carbon quantity is the Above-Ground Biomass (AGB), that has been traditionally estimated using allometric equations and data collected in forest inventory data. Recently, advances in technology have enabled the integration of remote sensing data in this process. In this study, Bayesian geostatistical models (using INLA and SPDE approaches) were applied to estimate AGB in two areas of the Atlantic Forest in northern Spain, <em>Sierra de la Culebra</em> and <em>Montaña Palentina</em>. Field data from the <em>Cuarto Inventario Forestal Nacional</em> (IFN4) of Spain were combined with satellite data from GEOSAT-2. Measurements such as texture, vegetation indices, and spectral variables were extracted from satellite reflectance bands and used as covariates in the models. The models incorporated a spatial random field to capture spatial autocorrelation, which was found to be significant in both areas. In <em>Sierra de la Culebra</em>, NDVI (Normalized Difference Vegetation Index), homogeneity, and Forest Type were statistically significant covariates in modelling AGB. In contrast, in <em>Montaña Palentina</em>, the Red and Blue reflectance bands were the main contributors to AGB estimation. Predictive results in <em>Sierra de la Culebra</em> were satisfactory (RMSE = 47.06 (t/ha); MSE = 2214.68 (t<sup>2</sup>/ha); MAE = 36.84 (t/ha); <span><math><msup><mrow><mtext>R</mtext></mrow><mrow><mn>2</mn></mrow></msup></math></span> = 0.52; rRMSE = 39%), while in <em>Montaña Palentina</em>, predictive performance was even better (RMSE = 24.41 (t<span><math><msup><mrow></mrow><mrow><mn>0</mn><mo>.</mo><mn>75</mn></mrow></msup></math></span>/ha); MSE = 505.71 (t<span><math><msup><mrow></mrow><mrow><mn>1</mn><mo>.</mo><mn>5</mn></mrow></msup></math></span>/ha); MAE = 20.02 (t<span><math><msup><mrow></mrow><mrow><mn>0</mn><mo>.</mo><mn>75</mn></mrow></msup></math></span>/ha); <span><math><msup><mrow><mtext>R</mtext></mrow><mrow><mn>2</mn></mrow></msup></math></span> = 0.5; rRMSE = 36%).</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101855"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatiotemporal analysis and future projections of CO and O3 concentrations in Gauteng province using Sentinel-5P and CMIP6 models 基于Sentinel-5P和CMIP6模式的豪登省CO和O3浓度时空分析及未来预测

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 DOI: 10.1016/j.rsase.2026.101873

Aluwani Innocent Muneri , Thabang Maphanga , Benett Siyabonga Madonsela

This study is among the first to integrate high-resolution Sentinel-5P TROPOMI observations, ground-based SAAQIS measurements, and CMIP6 climate model outputs to generate long-term air quality projections for Gauteng Province. Gauteng, South Africa's economic hub, continues to experience severe air quality pressures driven by rapid urbanization, industrial emissions, and traffic density. However, long-term, multi-source projections for key pollutants such as carbon monoxide (CO) and ozone (O₃) remain limited for the region. The objectives of this research were to: (1) characterize the spatiotemporal distribution of CO and O₃ across Gauteng during the 2019–2020 baseline period using Sentinel-5P data (2) evaluate and validate CMIP6 climate model outputs against both satellite observations and SAAQIS ground-based measurements and (3) develop deterministic decadal projections for CO and O₃ for the year 2030 under the SSP2-4.5 scenario. Model validation showed that CMIP6 outputs systematically underestimated pollutant levels, with consistently negative mean bias errors for CO (≈−101 ppb) and O₃ (≈−18 ppb). Despite these biases, spatial correlations with observed concentrations were moderate to strong (r > 0.6). Under the SSP2-4.5 pathway, projections indicate a substantial decrease and increase in pollutants by 2030, with CO showing an increase (+4 to +7) on the other ha nd O₃ showing the most pronounced decline (−7 to −95), likely driven by reductions in precursor emissions and associated chemical feedback. Spatial patterns suggest that existing pollution hotspots will shift eastward, away from the Johannesburg–Pretoria urban core. Overall, the findings demonstrate the value of an integrated satellite-ground-modelling approach for regional air quality assessment. While the magnitude of projected changes should be interpreted with caution due to model resolution and inherent uncertainties, the study provides critical evidence to support future policy development and long-term air quality management in Gauteng Province.

这项研究是第一个整合高分辨率Sentinel-5P TROPOMI观测、地面SAAQIS测量和CMIP6气候模型输出，为豪登省生成长期空气质量预测的研究之一。豪登省是南非的经济中心，在快速城市化、工业排放和交通密度的推动下，它继续面临着严重的空气质量压力。然而，对该地区主要污染物如一氧化碳（CO）和臭氧（O3）的长期多源预测仍然有限。本研究的目标是：(1)利用Sentinel-5P数据表征2019-2020年基线期间豪登省CO和O3的时空分布特征；(2)根据卫星观测和SAAQIS地面测量评估和验证CMIP6气候模式输出；(3)在SSP2-4.5情景下对2030年CO和O3的确定性年代际预测。模型验证表明，CMIP6输出系统地低估了污染物水平，CO（≈−101 ppb）和O3（≈−18 ppb）的平均偏差始终为负。尽管存在这些偏差，但与观测到的浓度的空间相关性从中等到强（r > 0.6）。在SSP2-4.5路径下，预估表明，到2030年，污染物将大幅减少和增加，另一方面，CO呈现增加（+4至+7），而O3呈现最明显的下降（- 7至- 95），这可能是由前体排放减少和相关化学反馈所驱动的。空间格局表明，现有的污染热点将向东转移，远离约翰内斯堡-比勒陀利亚的城市核心。总的来说，这些发现证明了卫星-地面综合模拟方法对区域空气质量评估的价值。虽然由于模型分辨率和固有的不确定性，预测变化的幅度应谨慎解释，但该研究为支持豪登省未来的政策制定和长期空气质量管理提供了关键证据。

{"title":"Spatiotemporal analysis and future projections of CO and O3 concentrations in Gauteng province using Sentinel-5P and CMIP6 models","authors":"Aluwani Innocent Muneri , Thabang Maphanga , Benett Siyabonga Madonsela","doi":"10.1016/j.rsase.2026.101873","DOIUrl":"10.1016/j.rsase.2026.101873","url":null,"abstract":"<div><div>This study is among the first to integrate high-resolution Sentinel-5P TROPOMI observations, ground-based SAAQIS measurements, and CMIP6 climate model outputs to generate long-term air quality projections for Gauteng Province. Gauteng, South Africa's economic hub, continues to experience severe air quality pressures driven by rapid urbanization, industrial emissions, and traffic density. However, long-term, multi-source projections for key pollutants such as carbon monoxide (CO) and ozone (O<sub>3</sub>) remain limited for the region. The objectives of this research were to: (1) characterize the spatiotemporal distribution of CO and O<sub>3</sub> across Gauteng during the 2019–2020 baseline period using Sentinel-5P data (2) evaluate and validate CMIP6 climate model outputs against both satellite observations and SAAQIS ground-based measurements and (3) develop deterministic decadal projections for CO and O<sub>3</sub> for the year 2030 under the SSP2-4.5 scenario. Model validation showed that CMIP6 outputs systematically underestimated pollutant levels, with consistently negative mean bias errors for CO (≈−101 ppb) and O<sub>3</sub> (≈−18 ppb). Despite these biases, spatial correlations with observed concentrations were moderate to strong (r > 0.6). Under the SSP2-4.5 pathway, projections indicate a substantial decrease and increase in pollutants by 2030, with CO showing an increase (+4 to +7) on the other ha nd O<sub>3</sub> showing the most pronounced decline (−7 to −95), likely driven by reductions in precursor emissions and associated chemical feedback. Spatial patterns suggest that existing pollution hotspots will shift eastward, away from the Johannesburg–Pretoria urban core. Overall, the findings demonstrate the value of an integrated satellite-ground-modelling approach for regional air quality assessment. While the magnitude of projected changes should be interpreted with caution due to model resolution and inherent uncertainties, the study provides critical evidence to support future policy development and long-term air quality management in Gauteng Province.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101873"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Urban local climate zone classification through deep learning using spatio-temporal thermal imagery 基于时空热像的深度学习城市局地气候带分类

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 DOI: 10.1016/j.rsase.2026.101889

Michaja van Capel , Azarakhsh Rafiee , Roderik Lindenbergh

Rapid urbanization challenges urban micro-climates, strains resources and affects public health. Understanding micro-climate dynamics is key to effective mitigation and sustainable development. Local Climate Zone (LCZ) classification supports climate-resilient planning but is complicated by the diversity and complexity of diverse urban landscapes and the coexistence of varying land uses and materials within small areas. While LCZ classification typically uses multispectral imagery, LiDAR, and land-use data, these sources often miss temporal thermal dynamic patterns. Thermal satellite imagery improves LCZ classification by distinguishing zones with similar structures but differing thermal behavior. This research proposes using deep learning-based multitemporal semantic segmentation to classify urban LCZs based solely on temporal thermal patterns from ECOSTRESS satellite imagery. The methodology is applied in a in a case study around the near coastal cities of Rotterdam and The Hague in The Netherlands and demonstrates how spatial and temporal factors (both diurnal and seasonal) influence the performance of the semantic segmentation model on different LCZ classes. The study shows that a U-Net architecture applied on spatio-temporal thermal imagery effectively classifies urban LCZs, achieving a test accuracy of 0.75. Temporal factors significantly impact model performance, with higher accuracies observed for daytime (0.8) and Spring/Summer imagery (0.78), as these conditions provide clearer thermal separability for distinguishing LCZs. The model achieved its highest test accuracy (0.83) when trained and tested on thermal images with the highest LST values. This suggests that focusing on high-value LST images with sufficient variability enhances classification performance compared to a generalized approach using the full dataset.

快速城市化对城市微气候构成挑战，使资源紧张，并影响公共卫生。了解微气候动力学是有效减缓和可持续发展的关键。局部气候带（LCZ）分类支持气候适应性规划，但由于城市景观的多样性和复杂性，以及小区域内不同土地利用和材料的共存，使分类变得复杂。虽然LCZ分类通常使用多光谱图像、激光雷达和土地利用数据，但这些来源通常会错过时间热动态模式。热成像卫星图像通过区分结构相似但热行为不同的区域来改进LCZ分类。本研究提出基于深度学习的多时相语义分割，仅基于ECOSTRESS卫星图像的时间热模式对城市lcz进行分类。该方法在荷兰鹿特丹和海牙附近沿海城市的案例研究中得到了应用，并展示了空间和时间因素（昼夜和季节）如何影响语义分割模型在不同LCZ类别上的性能。研究表明，将U-Net架构应用于时空热像图，可以有效地对城市lcz进行分类，测试精度达到0.75。时间因素显著影响模型性能，白天（0.8）和春夏影像（0.78）的观测精度更高，因为这些条件为区分lccs提供了更清晰的热可分性。在LST值最高的热图像上进行训练和测试时，该模型达到了最高的测试精度（0.83）。这表明，与使用完整数据集的广义方法相比，专注于具有足够可变性的高值LST图像可以提高分类性能。

{"title":"Urban local climate zone classification through deep learning using spatio-temporal thermal imagery","authors":"Michaja van Capel , Azarakhsh Rafiee , Roderik Lindenbergh","doi":"10.1016/j.rsase.2026.101889","DOIUrl":"10.1016/j.rsase.2026.101889","url":null,"abstract":"<div><div>Rapid urbanization challenges urban micro-climates, strains resources and affects public health. Understanding micro-climate dynamics is key to effective mitigation and sustainable development. Local Climate Zone (LCZ) classification supports climate-resilient planning but is complicated by the diversity and complexity of diverse urban landscapes and the coexistence of varying land uses and materials within small areas. While LCZ classification typically uses multispectral imagery, LiDAR, and land-use data, these sources often miss temporal thermal dynamic patterns. Thermal satellite imagery improves LCZ classification by distinguishing zones with similar structures but differing thermal behavior. This research proposes using deep learning-based multitemporal semantic segmentation to classify urban LCZs based solely on temporal thermal patterns from ECOSTRESS satellite imagery. The methodology is applied in a in a case study around the near coastal cities of Rotterdam and The Hague in The Netherlands and demonstrates how spatial and temporal factors (both diurnal and seasonal) influence the performance of the semantic segmentation model on different LCZ classes. The study shows that a U-Net architecture applied on spatio-temporal thermal imagery effectively classifies urban LCZs, achieving a test accuracy of 0.75. Temporal factors significantly impact model performance, with higher accuracies observed for daytime (0.8) and Spring/Summer imagery (0.78), as these conditions provide clearer thermal separability for distinguishing LCZs. The model achieved its highest test accuracy (0.83) when trained and tested on thermal images with the highest LST values. This suggests that focusing on high-value LST images with sufficient variability enhances classification performance compared to a generalized approach using the full dataset.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101889"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and validation of a UAV-based ultra-wideband localization system for indoor search and rescue 基于无人机的超宽带室内搜救定位系统的开发与验证

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 DOI: 10.1016/j.rsase.2026.101884

Alessandro Cardoni, Gian Paolo Cimellaro

Search and rescue operations in post-disaster environments are time-critical and often expose first responders to significant risks due to the lack of reliable information on the location and condition of victims inside damaged buildings. Conventional indoor localization technologies are typically limited by the need for pre-installed infrastructure, short detection ranges, or low accuracy. This study introduces a real-time UAV-based indoor localization system designed to assist first responders by rapidly identifying and tracking occupants and rescuers inside buildings without relying on existing communication networks. The proposed system employs ultra-wideband locators mounted on unmanned aerial vehicles (UAVs) to detect Bluetooth Low Energy signals from wearable devices transmitting position and vital parameters. A dedicated software was developed to integrate UAV telemetry with localization data, visualizing real-time positions and corresponding accuracy levels. The system was validated in a challenging hangar environment characterized by steel and reinforced concrete elements that cause strong signal reflections. A sensitivity analysis was conducted by varying the UAVs elevation, their distance from the building, and the distance of the wearable devices from the external walls. Results show that optimal localization accuracy (error <1 m) was achieved when UAVs hovered at 6–10 m elevation and 10 m from the structure, with a 100 % probability of achieving the highest accuracy level. It was also observed that accuracy decreased linearly with the distance of tags from the external wall, dropping from 100 % to 25 % at 10 m indoors. Overall, the proposed UAV-based localization system achieves real-time tracking with sub-meter accuracy without any reliance on pre-installed communication networks, demonstrating strong potential to improve situational awareness and reduce rescue time in search and rescue operations.

灾后环境中的搜索和救援行动时间紧迫，由于缺乏关于受损建筑物内受害者位置和状况的可靠信息，往往使第一响应者面临重大风险。传统的室内定位技术通常受到预先安装基础设施、检测距离短或精度低的限制。本研究介绍了一种基于无人机的实时室内定位系统，旨在帮助急救人员在不依赖现有通信网络的情况下快速识别和跟踪建筑物内的居住者和救援人员。该系统采用安装在无人机（uav）上的超宽带定位器来检测来自可穿戴设备的蓝牙低能量信号，传输位置和重要参数。一种专用软件被开发用于集成无人机遥测与定位数据，可视化实时位置和相应的精度水平。该系统在具有挑战性的机库环境中进行了验证，其特点是钢和钢筋混凝土构件会产生强烈的信号反射。通过改变无人机的高度、与建筑物的距离以及可穿戴设备与外墙的距离进行敏感性分析。结果表明，当无人机悬停在6-10 m高度，距离结构10 m时，定位精度达到最佳（误差1 m），达到最高精度水平的概率为100%。还观察到，随着标签与外墙的距离，准确性呈线性下降，在室内10米处从100%下降到25%。总体而言，提出的基于无人机的定位系统实现了亚米精度的实时跟踪，而不依赖于预装的通信网络，在搜索和救援行动中显示出提高态势感知和减少救援时间的强大潜力。

{"title":"Development and validation of a UAV-based ultra-wideband localization system for indoor search and rescue","authors":"Alessandro Cardoni, Gian Paolo Cimellaro","doi":"10.1016/j.rsase.2026.101884","DOIUrl":"10.1016/j.rsase.2026.101884","url":null,"abstract":"<div><div>Search and rescue operations in post-disaster environments are time-critical and often expose first responders to significant risks due to the lack of reliable information on the location and condition of victims inside damaged buildings. Conventional indoor localization technologies are typically limited by the need for pre-installed infrastructure, short detection ranges, or low accuracy. This study introduces a real-time UAV-based indoor localization system designed to assist first responders by rapidly identifying and tracking occupants and rescuers inside buildings without relying on existing communication networks. The proposed system employs ultra-wideband locators mounted on unmanned aerial vehicles (UAVs) to detect Bluetooth Low Energy signals from wearable devices transmitting position and vital parameters. A dedicated software was developed to integrate UAV telemetry with localization data, visualizing real-time positions and corresponding accuracy levels. The system was validated in a challenging hangar environment characterized by steel and reinforced concrete elements that cause strong signal reflections. A sensitivity analysis was conducted by varying the UAVs elevation, their distance from the building, and the distance of the wearable devices from the external walls. Results show that optimal localization accuracy (error <1 m) was achieved when UAVs hovered at 6–10 m elevation and 10 m from the structure, with a 100 % probability of achieving the highest accuracy level. It was also observed that accuracy decreased linearly with the distance of tags from the external wall, dropping from 100 % to 25 % at 10 m indoors. Overall, the proposed UAV-based localization system achieves real-time tracking with sub-meter accuracy without any reliance on pre-installed communication networks, demonstrating strong potential to improve situational awareness and reduce rescue time in search and rescue operations.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101884"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0