International journal of applied earth observation and geoinformation : ITC journal最新文献_第6页

Multi-Platform geodetic synergy of InSAR, UAV, optical, and HD-ERT constrains kinematic evolution of the Jungong landslide (Yellow River Basin) InSAR、无人机、光学和HD-ERT多平台协同测量对黄河准公滑坡运动演化的约束

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-02-01 Epub Date: 2026-01-05 DOI: 10.1016/j.jag.2025.105082

Xiaoyu Liu , Wu Zhu , Yuxin Zhou , Jiewei Zhan , Zhanxi Wei , Jing Wu , Haixing Shang , Chao Du

Following the September 20, 2019 instability event, the Jungong landslide—a large-scale red-bed feature in the upper Yellow River Basin—has exhibited persistent creep, necessitating systematic kinematic analysis to constrain deformation drivers. In this context, we conducted a multidisciplinary approach integrating interferometric synthetic aperture radar (InSAR), unmanned aerial vehicle (UAV) surveys, optical satellite remote sensing, and high-density electrical resistivity tomography (HD-ERT) to investigate its kinematic evolution. Firstly, interferometric processing of SAR imagery from ALOS/PALSAR-1, ALOS/PALSAR-2 and Sentinel-1 systems (March 2007-August 2024) revealed continuous creeping with maximum deformation velocity reaching −129 mm/yr in descending Sentinel-1. Based on morphological and deformation characteristics, the slope was divided into four secondary zones. Through digital image correlation (DIC) of optical images, horizontal displacements exceeding 20 m induced by instability were detected at the front edge of Zone I. The three-dimensional (3D) deformation field was then inverted by combining multi-orbit InSAR observations and a topography-constrained model, revealing significant spatial heterogeneity of displacement characteristics. The maximum velocities in the eastward, northward, and vertical directions were −107, 53, and −71 mm/yr, respectively. Additionally, the internal structure along two profiles was detected using HD-ERT. Finally, a method combining Singular Spectrum Analysis (SSA) and wavelet transform was proposed to quantitatively analyze the temporal relationship between periodic displacements and rainfall. Different zones exhibited varying degrees of correlation with rainfall, with a time lag of approximately 45 days in Zone I. This multidisciplinary approach enhances our understanding of the kinematic behavior of the Jungong landslide, providing critical reference for future hazard assessment.

在2019年9月20日的不稳定事件之后，黄河上游的大型红层特征军公滑坡表现出持续的蠕变，需要系统的运动学分析来约束变形驱动因素。在此背景下，我们采用了多学科方法，结合干涉合成孔径雷达（InSAR）、无人机（UAV）测量、光学卫星遥感和高密度电阻率层析成像（HD-ERT）来研究其运动学演变。首先，对ALOS/PALSAR-1、ALOS/PALSAR-2和Sentinel-1系统（2007年3月- 2024年8月）的SAR图像进行干涉处理，发现Sentinel-1在下降过程中连续爬行，最大变形速度达到- 129 mm/yr。根据边坡的形态和变形特征，将其划分为4个次生带。通过光学图像的数字图像相关（DIC），在i区前缘检测到由失稳引起的超过20 m的水平位移，并结合多轨道InSAR观测和地形约束模型反演三维变形场，发现位移特征具有明显的空间异质性。东、北、垂直方向最大流速分别为- 107、53和- 71 mm/yr。此外，利用HD-ERT检测了沿两条剖面的内部结构。最后，提出了一种结合奇异谱分析（SSA）和小波变换的方法来定量分析周期性位移与降水的时间关系。不同区域与降雨的相关程度不同，i区滞后时间约为45 天。该多学科方法增强了我们对君公滑坡运动学行为的理解，为未来的灾害评估提供了重要参考。

{"title":"Multi-Platform geodetic synergy of InSAR, UAV, optical, and HD-ERT constrains kinematic evolution of the Jungong landslide (Yellow River Basin)","authors":"Xiaoyu Liu , Wu Zhu , Yuxin Zhou , Jiewei Zhan , Zhanxi Wei , Jing Wu , Haixing Shang , Chao Du","doi":"10.1016/j.jag.2025.105082","DOIUrl":"10.1016/j.jag.2025.105082","url":null,"abstract":"<div><div>Following the September 20, 2019 instability event, the Jungong landslide—a large-scale red-bed feature in the upper Yellow River Basin—has exhibited persistent creep, necessitating systematic kinematic analysis to constrain deformation drivers. In this context, we conducted a multidisciplinary approach integrating interferometric synthetic aperture radar (InSAR), unmanned aerial vehicle (UAV) surveys, optical satellite remote sensing, and high-density electrical resistivity tomography (HD-ERT) to investigate its kinematic evolution. Firstly, interferometric processing of SAR imagery from ALOS/PALSAR-1, ALOS/PALSAR-2 and Sentinel-1 systems (March 2007-August 2024) revealed continuous creeping with maximum deformation velocity reaching −129 mm/yr in descending Sentinel-1. Based on morphological and deformation characteristics, the slope was divided into four secondary zones. Through digital image correlation (DIC) of optical images, horizontal displacements exceeding 20 m induced by instability were detected at the front edge of Zone I. The three-dimensional (3D) deformation field was then inverted by combining multi-orbit InSAR observations and a topography-constrained model, revealing significant spatial heterogeneity of displacement characteristics. The maximum velocities in the eastward, northward, and vertical directions were −107, 53, and −71 mm/yr, respectively. Additionally, the internal structure along two profiles was detected using HD-ERT. Finally, a method combining Singular Spectrum Analysis (SSA) and wavelet transform was proposed to quantitatively analyze the temporal relationship between periodic displacements and rainfall. Different zones exhibited varying degrees of correlation with rainfall, with a time lag of approximately 45 days in Zone I. This multidisciplinary approach enhances our understanding of the kinematic behavior of the Jungong landslide, providing critical reference for future hazard assessment.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105082"},"PeriodicalIF":8.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145919767","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

Standardized compound drought and heatwave index: A new compound drought and heatwave events monitoring index considering evapotranspiration effects 标准化干旱与热浪复合指数：一种考虑蒸散效应的新型干旱与热浪复合监测指标

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1016/j.jag.2025.105023

Shengpeng Cao , Lei Li , Chunyang He , Tao Qi

Compound drought and heatwave events (CDHEs), as critical extreme climate phenomena, have attracted substantial scientific attention because of their profound impact on the sustainability of socioecological systems. Nevertheless, current identification methods rely only on the combined influence of precipitation and temperature changes and fail to consider the contribution of evapotranspiration in drought‒heatwave. This resulted in a low estimation of the spatial extent and severity of such compound events. Here, we developed a new standardized compound drought and heatwave index (SCDHI) using the Gaussian copula probability distribution modeling, combining the standardized precipitation evapotranspiration index (SPEI) with the standardized temperature index (STI). We found that compared with the existing indicator, the SCDHI improved significantly in monitoring accuracy and monitoring capability for CDHEs, particularly in assessing vegetation responses. By explicitly considering the impact of evapotranspiration on the intensification of droughts, the SCDHI effectively corrected the underestimated deviations that are commonly present in traditional methods in vegetation areas. The proposed index demonstrates strong potential for multiscale monitoring of CDHEs, enhancing assessments of their impact on the environment and society.

复合干旱和热浪事件（CDHEs）作为一种重要的极端气候现象，因其对社会生态系统的可持续性产生深远影响而引起了科学界的广泛关注。然而，目前的识别方法仅依赖于降水和温度变化的综合影响，而没有考虑蒸散发对干旱-热浪的贡献。这导致对这些复合事件的空间范围和严重程度的估计较低。将标准化降水蒸散指数（SPEI）与标准化温度指数（STI）相结合，采用高斯耦合概率分布模型建立了标准化干旱与热浪复合指数（SCDHI）。我们发现，与现有指标相比，SCDHI在监测CDHEs的精度和监测能力上都有显著提高，尤其是在评估植被响应方面。通过明确考虑蒸散发对干旱加剧的影响，SCDHI有效地纠正了传统方法在植被区普遍存在的低估偏差。建议的指数显示，在多尺度监测温室气体排放工程方面具有强大的潜力，可加强评估其对环境和社会的影响。

{"title":"Standardized compound drought and heatwave index: A new compound drought and heatwave events monitoring index considering evapotranspiration effects","authors":"Shengpeng Cao , Lei Li , Chunyang He , Tao Qi","doi":"10.1016/j.jag.2025.105023","DOIUrl":"10.1016/j.jag.2025.105023","url":null,"abstract":"<div><div>Compound drought and heatwave events (CDHEs), as critical extreme climate phenomena, have attracted substantial scientific attention because of their profound impact on the sustainability of socioecological systems. Nevertheless, current identification methods rely only on the combined influence of precipitation and temperature changes and fail to consider the contribution of evapotranspiration in drought‒heatwave. This resulted in a low estimation of the spatial extent and severity of such compound events. Here, we developed a new standardized compound drought and heatwave index (SCDHI) using the Gaussian copula probability distribution modeling, combining the standardized precipitation evapotranspiration index (SPEI) with the standardized temperature index (STI). We found that compared with the existing indicator, the SCDHI improved significantly in monitoring accuracy and monitoring capability for CDHEs, particularly in assessing vegetation responses. By explicitly considering the impact of evapotranspiration on the intensification of droughts, the SCDHI effectively corrected the underestimated deviations that are commonly present in traditional methods in vegetation areas. The proposed index demonstrates strong potential for multiscale monitoring of CDHEs, enhancing assessments of their impact on the environment and society.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105023"},"PeriodicalIF":8.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738412","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

Desertification expansion significantly suppresses photosynthetic peak capacity of arid ecosystems at the global scale 在全球尺度上，沙漠化扩张显著抑制了干旱生态系统光合峰值容量

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-02-01 Epub Date: 2025-12-18 DOI: 10.1016/j.jag.2025.105042

Kaiyang Qiu , Qingbin Zhang , Yingzhong Xie , Mingjie Shi , Chengyun Wang , Tong Dong , Jun Ma , Panxing He

Arid ecosystems occupy about two-fifths of the global land surface, and fluctuations in their productivity play a pivotal role in global carbon sequestration and ecosystem service provision. However, the global-scale effect of desertification expansion on the annual maximum photosynthetic peak has not yet been systematically quantified. In this study, 30-m high-resolution desert cover data (GLCLUC) and multi-source remote-sensing photosynthetic indicators were integrated, using a space-for-time substitution framework to establish a global desertification scenario classification system. We quantitatively evaluated the influence of diverse desert expansion and contraction scenarios on the ecosystem photosynthetic peak (GPP_max). Results indicate that the average GPP_max in high-intensity expansion regions (HIEs) is 8.23 g C m⁻² 8d⁻¹, whereas medium- to low-intensity expansion regions (MIEs) show a value of 8.95 g C m⁻² 8d⁻¹. By contrast, medium- to low-intensity contraction regions (MIRs) and high-intensity contraction regions (HIRs) demonstrate markedly higher GPP_max values of 10.64 g C m⁻² 8d⁻¹ and 17.64 g C m⁻² 8d⁻¹, respectively. Regarding the photosynthetic peak difference (ΔGPP_max), expansion scenarios (HIEs, MIEs) significantly decrease ecosystem photosynthetic potential, with average ΔGPP_max reductions of 1.19–3.95 g C m⁻² 8d⁻¹ relative to contraction scenarios (HIRs, MIRs). The most pronounced losses occur in South America, North America, and Eurasia, with South America exhibiting reductions exceeding 6 g C m⁻² 8d⁻¹. Additionally, ecosystems with initially higher photosynthetic potential experience greater GPP_max declines under intense desert expansion. This study provides the first global-scale evidence revealing how different desertification pathways modify ecosystem photosynthetic peaks and their regional disparities, offering critical scientific support for ecological restoration, carbon sequestration strategies, and land management across arid landscapes.

干旱生态系统约占全球陆地面积的五分之二，其生产力的波动在全球固碳和提供生态系统服务方面发挥着关键作用。然而，在全球尺度上，沙漠化扩张对年最大光合峰值的影响尚未得到系统的量化。本研究将30 m高分辨率沙漠覆盖数据（GLCLUC）与多源遥感光合指标相结合，采用时空替代框架建立全球沙漠化情景分类体系。定量评价了不同荒漠扩张收缩情景对生态系统光合峰值（GPPmax）的影响。结果表明，高强度膨胀区（HIEs）的平均GPPmax为8.23 g C m−2 8d−1，中低强度膨胀区（MIEs）的平均GPPmax为8.95 g C m−2 8d−1。相比之下，中低强度收缩区（MIRs）和高强度收缩区（HIRs）的GPPmax值分别为10.64 g C m−2 8d−1和17.64 g C m−2 8d−1。在光合峰值差（ΔGPPmax）方面，扩张情景（HIEs, MIEs）显著降低了生态系统光合潜力，相对于收缩情景（HIRs, MIRs），平均ΔGPPmax降低1.19-3.95 g C m−2 8d−1。最显著的损失发生在南美洲、北美洲和欧亚大陆，南美洲的减少量超过6 g C m−28d−1。此外，具有较高光合潜力的生态系统在剧烈的沙漠扩张下会经历更大的GPPmax下降。该研究首次提供了全球尺度的证据，揭示了不同沙漠化途径如何改变生态系统光合峰值及其区域差异，为干旱景观的生态恢复、碳固存策略和土地管理提供了重要的科学支持。

{"title":"Desertification expansion significantly suppresses photosynthetic peak capacity of arid ecosystems at the global scale","authors":"Kaiyang Qiu , Qingbin Zhang , Yingzhong Xie , Mingjie Shi , Chengyun Wang , Tong Dong , Jun Ma , Panxing He","doi":"10.1016/j.jag.2025.105042","DOIUrl":"10.1016/j.jag.2025.105042","url":null,"abstract":"<div><div>Arid ecosystems occupy about two-fifths of the global land surface, and fluctuations in their productivity play a pivotal role in global carbon sequestration and ecosystem service provision. However, the global-scale effect of desertification expansion on the annual maximum photosynthetic peak has not yet been systematically quantified. In this study, 30-m high-resolution desert cover data (GLCLUC) and multi-source remote-sensing photosynthetic indicators were integrated, using a space-for-time substitution framework to establish a global desertification scenario classification system. We quantitatively evaluated the influence of diverse desert expansion and contraction scenarios on the ecosystem photosynthetic peak (GPP<sub>max</sub>). Results indicate that the average GPP<sub>max</sub> in high-intensity expansion regions (HIEs) is 8.23 g C m<sup>−2</sup> 8d<sup>−1</sup>, whereas medium- to low-intensity expansion regions (MIEs) show a value of 8.95 g C m<sup>−2</sup> 8d<sup>−1</sup>. By contrast, medium- to low-intensity contraction regions (MIRs) and high-intensity contraction regions (HIRs) demonstrate markedly higher GPP<sub>max</sub> values of 10.64 g C m<sup>−2</sup> 8d<sup>−1</sup> and 17.64 g C m<sup>−2</sup> 8d<sup>−1</sup>, respectively. Regarding the photosynthetic peak difference (ΔGPP<sub>max</sub>), expansion scenarios (HIEs, MIEs) significantly decrease ecosystem photosynthetic potential, with average ΔGPP<sub>max</sub> reductions of 1.19–3.95 g C m<sup>−2</sup> 8d<sup>−1</sup> relative to contraction scenarios (HIRs, MIRs). The most pronounced losses occur in South America, North America, and Eurasia, with South America exhibiting reductions exceeding 6 g C m<sup>−2</sup> 8d<sup>−1</sup>. Additionally, ecosystems with initially higher photosynthetic potential experience greater GPP<sub>max</sub> declines under intense desert expansion. This study provides the first global-scale evidence revealing how different desertification pathways modify ecosystem photosynthetic peaks and their regional disparities, offering critical scientific support for ecological restoration, carbon sequestration strategies, and land management across arid landscapes.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105042"},"PeriodicalIF":8.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791193","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

Clip-based road-marking detection with LLM-guided driving prompts 基于剪辑的道路标记检测与llm引导的驾驶提示

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-02-01 Epub Date: 2025-12-08 DOI: 10.1016/j.jag.2025.105012

Shaofan Sheng , Nicolette Formosa , Yuxiang Feng , Mohammed Quddus

The advancement of artificial intelligence (AI) has significantly improved the perception and decision-making abilities of autonomous vehicles (AVs), yet real-time and accurate road marking detection remains difficult under faded markings, nighttime scenes and adverse road–weather conditions. This paper presents RoadGPT, a vision–language pipeline that couples a CLIP-based detector (RoadCLIP) with a planner-facing, LLM-generated advisory layer. The model is trained and evaluated on 3,696 road marking images covering 14 UK Highway Code classes, comprising 2,576 real images (69.7 %) from Google Street View and 1,120 text-to-image synthetic images (30.3 %) that broaden rare appearances and degraded conditions. We use 2,968 images for training (2,072 real, 896 virtual) and 728 images for testing (504 real, 224 virtual), keeping the same 70:30 real–virtual ratio in both splits. On this test set, RoadCLIP attains 98.5 % precision, 97.9 % recall and 98.2 % F1 for non-lane markings, while lane-marking subclasses reach up to 89.8 % F1. The advisory layer transforms recognised markings into structured driving prompts and is assessed via semantic similarity using Sentence-BERT (all-mpnet-base-v2) cosine scores against Highway Code-based references, achieving 89.3 % similarity, alongside an external LLM-as-judge rating of 4.68/5 for accuracy, completeness, and concise effectiveness. The full camera-to-advisory path runs in real time at 135 FPS (batch size 1, 224 × 224) on an RTX 4070 under a unified timing protocol. A remaining limitation is that visually similar lane-marking classes and extreme low-light scenes still reduce discriminability compared with symbol-like, non-lane markings.

人工智能（AI）的进步大大提高了自动驾驶汽车（av）的感知和决策能力，但在褪色的标记、夜间场景和恶劣的道路天气条件下，实时准确的道路标记检测仍然很困难。本文介绍了RoadGPT，一种视觉语言管道，将基于clip的检测器（RoadCLIP）与面向规划器的llm生成的咨询层耦合在一起。该模型在涵盖14个英国公路法规类别的3,696张道路标记图像上进行了训练和评估，其中包括来自谷歌街景的2,576张真实图像（69.7%）和1,120张文本到图像的合成图像（30.3%），这些图像扩展了罕见的外观和退化的条件。我们使用2,968张图像用于训练（2,072张真实图像，896张虚拟图像）和728张图像用于测试（504张真实图像，224张虚拟图像），在两个分割中保持相同的70:30实-虚比例。在这个测试集中，RoadCLIP在非车道标记上达到了98.5%的准确率，97.9%的召回率和98.2%的F1，而车道标记子类达到了89.8%的F1。咨询层将识别的标记转换为结构化的驾驶提示，并使用Sentence-BERT （all-mpnet-base-v2）余弦分数对基于公路法规的参考进行语义相似性评估，达到89.3%的相似性，以及外部llm作为法官的准确性，完整性和简洁有效性评级为4.68/5。完整的摄像机到咨询路径在统一定时协议下的RTX 4070上以135 FPS（批处理大小1,224 × 224）实时运行。剩下的一个限制是，视觉上相似的车道标记类别和极端低光场景仍然会降低与符号式非车道标记相比的可识别性。

{"title":"Clip-based road-marking detection with LLM-guided driving prompts","authors":"Shaofan Sheng , Nicolette Formosa , Yuxiang Feng , Mohammed Quddus","doi":"10.1016/j.jag.2025.105012","DOIUrl":"10.1016/j.jag.2025.105012","url":null,"abstract":"<div><div>The advancement of artificial intelligence (AI) has significantly improved the perception and decision-making abilities of autonomous vehicles (AVs), yet real-time and accurate road marking detection remains difficult under faded markings, nighttime scenes and adverse road–weather conditions. This paper presents RoadGPT, a vision–language pipeline that couples a CLIP-based detector (RoadCLIP) with a planner-facing, LLM-generated advisory layer. The model is trained and evaluated on 3,696 road marking images covering 14 UK Highway Code classes, comprising 2,576 real images (69.7 %) from Google Street View and 1,120 text-to-image synthetic images (30.3 %) that broaden rare appearances and degraded conditions. We use 2,968 images for training (2,072 real, 896 virtual) and 728 images for testing (504 real, 224 virtual), keeping the same 70:30 real–virtual ratio in both splits. On this test set, RoadCLIP attains 98.5 % precision, 97.9 % recall and 98.2 % F1 for non-lane markings, while lane-marking subclasses reach up to 89.8 % F1. The advisory layer transforms recognised markings into structured driving prompts and is assessed via semantic similarity using Sentence-BERT (all-mpnet-base-v2) cosine scores against Highway Code-based references, achieving 89.3 % similarity, alongside an external LLM-as-judge rating of 4.68/5 for accuracy, completeness, and concise effectiveness. The full camera-to-advisory path runs in real time at 135 FPS (batch size 1, 224 × 224) on an RTX 4070 under a unified timing protocol. A remaining limitation is that visually similar lane-marking classes and extreme low-light scenes still reduce discriminability compared with symbol-like, non-lane markings.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105012"},"PeriodicalIF":8.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738409","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 three-decade dynamics and drivers of thermokarst lakes on the Tibetan Plateau

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1016/j.jag.2025.105022

Guoqing Yang , Haijun Qiu , Ninglian Wang , Dongdong Yang , Ya Liu , Kailiang Zhao

Recent climate warming has accelerated permafrost thaw and dynamics of thermokarst lakes (TLs) on the Tibetan Plateau (TP). Yet, owing to the lack of long-term monitoring of TLs, our understanding of lake evolution processes and their driving factors remains uncertain. Here, using the global surface water product and time-series Landsat imagery, we identified 58,538 TLs (0.01–3 km²) and determined the primary occurrence year of lake changes from 1990 to 2022. Our results indicated that TLs on the TP are primarily located in the central inland region, over 82 % of lakes experienced area expansion, and only 15 % in the northwest show decrease in area. Annual number of lake expansion peaked in 2016, whereas lake shrinkage was most common in 2019. The calculated lake area errors, field investigations, and validation of lake disturbance time demonstrated high accuracy and consistency. We applied the optimal machine learning regression model to distinguish the different drivers for lake expansion and shrinkage. The topographic and climatic factors are primary drivers for lake expansion, while differences in evaporation trend and soil temperature trend might contribute to lake shrinkage. This study highlights the vulnerability of permafrost on the TP to climate change, which can contribute to carbon sequestration estimation and infrastructure maintenance.

然而，由于缺乏长期监测，我们对湖泊演变过程及其驱动因素的认识仍然不确定。利用全球地表水产品和时间序列Landsat图像，我们确定了58,538个tl (0.01-3 km2)，并确定了1990 - 2022年湖泊变化的主要发生年份。结果表明：TP上的湖泊主要分布在中部内陆地区，超过82%的湖泊面积扩大，只有15%的湖泊面积减少。湖泊扩张的年度数量在2016年达到顶峰，而湖泊萎缩在2019年最为常见。计算的湖泊面积误差、野外调查和湖泊扰动时间验证结果表明，湖泊扰动时间具有较高的准确性和一致性。我们应用最优机器学习回归模型来区分湖泊扩张和收缩的不同驱动因素。地形和气候因素是湖泊扩张的主要驱动因素，而蒸发趋势和土壤温度趋势的差异可能是湖泊收缩的主要驱动因素。该研究强调了青藏高原冻土对气候变化的脆弱性，这有助于碳固存估算和基础设施维护。

{"title":"Unraveling three-decade dynamics and drivers of thermokarst lakes on the Tibetan Plateau","authors":"Guoqing Yang , Haijun Qiu , Ninglian Wang , Dongdong Yang , Ya Liu , Kailiang Zhao","doi":"10.1016/j.jag.2025.105022","DOIUrl":"10.1016/j.jag.2025.105022","url":null,"abstract":"<div><div>Recent climate warming has accelerated permafrost thaw and dynamics of thermokarst lakes (TLs) on the Tibetan Plateau (TP). Yet, owing to the lack of long-term monitoring of TLs, our understanding of lake evolution processes and their driving factors remains uncertain. Here, using the global surface water product and time-series Landsat imagery, we identified 58,538 TLs (0.01–3 km<sup>2</sup>) and determined the primary occurrence year of lake changes from 1990 to 2022. Our results indicated that TLs on the TP are primarily located in the central inland region, over 82 % of lakes experienced area expansion, and only 15 % in the northwest show decrease in area. Annual number of lake expansion peaked in 2016, whereas lake shrinkage was most common in 2019. The calculated lake area errors, field investigations, and validation of lake disturbance time demonstrated high accuracy and consistency. We applied the optimal machine learning regression model to distinguish the different drivers for lake expansion and shrinkage. The topographic and climatic factors are primary drivers for lake expansion, while differences in evaporation trend and soil temperature trend might contribute to lake shrinkage. This study highlights the vulnerability of permafrost on the TP to climate change, which can contribute to carbon sequestration estimation and infrastructure maintenance.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105022"},"PeriodicalIF":8.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738410","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

A novel two-step method for ratoon rice mapping using Sentinel-1/2 time series 基于Sentinel-1/2时间序列的两步水稻制图方法

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-02-01 Epub Date: 2026-01-08 DOI: 10.1016/j.jag.2025.105083

Yue Wang , Yuechen Li , Xiaolin Zhu , Jin Chen , Ruyin Cao , Xiong Yao , Wujun Zhang

Ratoon rice plays a vital role in boosting land productivity and contributing to stable food supplies under the context of global climate change. This system offers these advantages by demonstrating the capacity to produce an additional grain yield of 5–6 t ha⁻¹ from the ratoon season, while simultaneously reducing the growth duration by 44–48 days compared to conventional double-season rice cultivation. However, its precise spatial distribution remains unclear under varying climatic and surface conditions, and remote sensing-based monitoring of ratoon rice has received limited attention. Existing methods often struggle to accurately distinguish ratoon rice from other morphologically similar types, such as double-season rice, and are further hampered by frequent cloud cover and rainfall, compromising optical sensing effectiveness. This study proposes a two-step ratoon rice (TSRR) mapping method using multi-source remote sensing data at the parcel scale. The TSRR method integrates Sentinel-1A synthetic aperture radar (SAR) and Sentinel-2 optical imagery, utilizing the distinct growth characteristics of main-season and ratoon rice. It employs object-based segmentation and two novel indices—the SAR-based paddy rice index (SPRI) and the SAR-based ratoon rice index (SRRI)—without relying on detailed phenological information. Results indicate that the TSRR method effectively distinguishes ratoon rice from other paddy rice types, achieving an average overall accuracy (OA) of 0.87, with particularly high performance in separating ratoon rice from double-season rice. The TSRR method demonstrates strong robustness and transferability across different regions, which can provide a reliable solution for large-scale paddy rice mapping, especially in cloud-prone areas with limited optical data availability, and offers valuable support for crop monitoring, yield estimation, and national agricultural inventory initiatives.

在全球气候变化的背景下，大米在提高土地生产力和稳定粮食供应方面发挥着至关重要的作用。该系统具有这些优势，因为它显示了从再生季节开始额外生产5-6吨/公顷粮食的能力，同时与传统的双季稻种植相比，生长期缩短了44-48天。然而，在不同的气候和地表条件下，其精确的空间分布尚不清楚，基于遥感的水稻监测受到的关注有限。现有的方法往往难以准确区分水稻与其他形态相似的水稻，如双季稻，并且由于频繁的云层和降雨，进一步阻碍了光学传感的有效性。本文提出了一种基于多源遥感数据的两步成片水稻（TSRR）制图方法。TSRR方法结合了Sentinel-1A合成孔径雷达（SAR）和Sentinel-2光学图像，利用了主季稻和次季稻不同的生长特征。该方法采用了基于目标的分割和两个新的指数——基于sar的水稻指数（SPRI）和基于sar的生长期水稻指数（SRRI），而不依赖于详细的物候信息。结果表明，TSRR方法能有效区分籼稻和其他水稻类型，平均总体准确率（OA）为0.87，其中在区分籼稻和双季稻方面表现尤为优异。TSRR方法具有较强的鲁棒性和跨区域可转移性，可为大规模水稻制图提供可靠的解决方案，特别是在光学数据可用性有限的多云地区，并为作物监测、产量估计和国家农业清查举措提供有价值的支持。

{"title":"A novel two-step method for ratoon rice mapping using Sentinel-1/2 time series","authors":"Yue Wang , Yuechen Li , Xiaolin Zhu , Jin Chen , Ruyin Cao , Xiong Yao , Wujun Zhang","doi":"10.1016/j.jag.2025.105083","DOIUrl":"10.1016/j.jag.2025.105083","url":null,"abstract":"<div><div>Ratoon rice plays a vital role in boosting land productivity and contributing to stable food supplies under the context of global climate change. This system offers these advantages by demonstrating the capacity to produce an additional grain yield of 5–6 t ha<sup>−1</sup> from the ratoon season, while simultaneously reducing the growth duration by 44–48 days compared to conventional double-season rice cultivation. However, its precise spatial distribution remains unclear under varying climatic and surface conditions, and remote sensing-based monitoring of ratoon rice has received limited attention. Existing methods often struggle to accurately distinguish ratoon rice from other morphologically similar types, such as double-season rice, and are further hampered by frequent cloud cover and rainfall, compromising optical sensing effectiveness. This study proposes a two-step ratoon rice (TSRR) mapping method using multi-source remote sensing data at the parcel scale. The TSRR method integrates Sentinel-1A synthetic aperture radar (SAR) and Sentinel-2 optical imagery, utilizing the distinct growth characteristics of main-season and ratoon rice. It employs object-based segmentation and two novel indices—the SAR-based paddy rice index (SPRI) and the SAR-based ratoon rice index (SRRI)—without relying on detailed phenological information. Results indicate that the TSRR method effectively distinguishes ratoon rice from other paddy rice types, achieving an average overall accuracy (OA) of 0.87, with particularly high performance in separating ratoon rice from double-season rice. The TSRR method demonstrates strong robustness and transferability across different regions, which can provide a reliable solution for large-scale paddy rice mapping, especially in cloud-prone areas with limited optical data availability, and offers valuable support for crop monitoring, yield estimation, and national agricultural inventory initiatives.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105083"},"PeriodicalIF":8.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926334","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

Modular and adaptive implementation of Semantic Segmentation Models for Satellite Images and Open Source tools suitable for complex geographical contexts 卫星图像语义分割模型的模块化和自适应实现以及适用于复杂地理环境的开源工具

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-02-01 Epub Date: 2026-01-08 DOI: 10.1016/j.jag.2025.105069

Adrien Le Guillou , Simona Niculescu

Semantic segmentation, the process of assigning a semantic label to each pixel in an image, is a critical computer vision task for extracting detailed information from remote sensing data. However, its application to complex geographical contexts, such as coastal wetlands, is often constrained by the need for highly specialized implementations, class imbalance, and limited accessibility for non-specialists. This paper introduces a novel, modular, and adaptive open-source framework for semantic segmentation tailored to satellite imagery. Designed for maximum flexibility, the framework supports both binary and multi-class segmentation tasks and incorporates specific training strategies to handle severe class imbalances inherent in ecological detection, such as salt marsh mapping. The implementation provides a fully configurable pipeline that bridges the gap between Geographic Information Systems (GIS) and Deep Learning (DL). It integrates QGIS for intuitive spatial preprocessing and grid generation with a Python-based training and prediction workflow, thereby democratizing access to advanced segmentation techniques. The framework is architecture-agnostic, allowing the seamless deployment and benchmarking of various state-of-the-art encoder–decoder models, which are effective at combining multi-scale contextual information with high spatial resolution. A key contribution is the integration of a multifaceted training methodology that includes hybrid loss functions with dynamic class weighting and spectral-consistent data augmentation to ensure robust model generalization from limited and imbalanced datasets. We demonstrate the framework’s efficacy and scalability through two distinct case studies: a multi-class land cover classification on the Crozon Peninsula using Pléiades and a binary salt marsh detection in the Mont-Saint-Michel Bay Sentinel-2 imagery. The results show that accurate segmentation can be achieved with modest computational resources, promoting more sustainable and ethical AI applications in environmental monitoring. This work provides a critical tool for researchers and practitioners aiming to apply advanced DL segmentation to domain specific remote sensing challenges beyond conventional benchmarks.

语义分割是为图像中的每个像素分配语义标签的过程，是从遥感数据中提取详细信息的关键计算机视觉任务。然而，它在复杂地理环境中的应用，如沿海湿地，往往受到高度专业化实施的需要、类别不平衡和非专业人员的有限可及性的限制。本文介绍了一种新颖的、模块化的、自适应的开源框架，用于卫星图像的语义分割。该框架具有最大的灵活性，支持二元和多类分割任务，并结合特定的训练策略来处理生态检测中固有的严重类不平衡，例如盐沼测绘。该实现提供了一个完全可配置的管道，弥合了地理信息系统（GIS）和深度学习（DL）之间的差距。它将QGIS与基于python的训练和预测工作流程集成在一起，用于直观的空间预处理和网格生成，从而使高级分割技术的访问民主化。该框架与架构无关，允许各种最先进的编码器-解码器模型的无缝部署和基准测试，这些模型可以有效地将多尺度上下文信息与高空间分辨率相结合。一个关键的贡献是集成了多方面的训练方法，包括混合损失函数与动态类加权和频谱一致的数据增强，以确保从有限和不平衡的数据集稳健的模型泛化。我们通过两个不同的案例研究证明了该框架的有效性和可扩展性：在Crozon半岛使用placimiades进行多类土地覆盖分类，以及在mont saint - michel湾Sentinel-2图像中进行二元盐沼检测。结果表明，在适度的计算资源下，可以实现准确的分割，促进人工智能在环境监测中的应用更具可持续性和伦理性。这项工作为研究人员和实践者提供了一个重要的工具，旨在将先进的深度学习分割应用于超越传统基准的特定领域的遥感挑战。

{"title":"Modular and adaptive implementation of Semantic Segmentation Models for Satellite Images and Open Source tools suitable for complex geographical contexts","authors":"Adrien Le Guillou , Simona Niculescu","doi":"10.1016/j.jag.2025.105069","DOIUrl":"10.1016/j.jag.2025.105069","url":null,"abstract":"<div><div>Semantic segmentation, the process of assigning a semantic label to each pixel in an image, is a critical computer vision task for extracting detailed information from remote sensing data. However, its application to complex geographical contexts, such as coastal wetlands, is often constrained by the need for highly specialized implementations, class imbalance, and limited accessibility for non-specialists. This paper introduces a novel, modular, and adaptive open-source framework for semantic segmentation tailored to satellite imagery. Designed for maximum flexibility, the framework supports both binary and multi-class segmentation tasks and incorporates specific training strategies to handle severe class imbalances inherent in ecological detection, such as salt marsh mapping. The implementation provides a fully configurable pipeline that bridges the gap between Geographic Information Systems (GIS) and Deep Learning (DL). It integrates QGIS for intuitive spatial preprocessing and grid generation with a Python-based training and prediction workflow, thereby democratizing access to advanced segmentation techniques. The framework is architecture-agnostic, allowing the seamless deployment and benchmarking of various state-of-the-art encoder–decoder models, which are effective at combining multi-scale contextual information with high spatial resolution. A key contribution is the integration of a multifaceted training methodology that includes hybrid loss functions with dynamic class weighting and spectral-consistent data augmentation to ensure robust model generalization from limited and imbalanced datasets. We demonstrate the framework’s efficacy and scalability through two distinct case studies: a multi-class land cover classification on the Crozon Peninsula using Pléiades and a binary salt marsh detection in the Mont-Saint-Michel Bay Sentinel-2 imagery. The results show that accurate segmentation can be achieved with modest computational resources, promoting more sustainable and ethical AI applications in environmental monitoring. This work provides a critical tool for researchers and practitioners aiming to apply advanced DL segmentation to domain specific remote sensing challenges beyond conventional benchmarks.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105069"},"PeriodicalIF":8.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926335","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

A novel deep learning framework for High-Throughput peanut seedling identification across diverse germplasm and complex field environments 基于深度学习的花生苗木高通量鉴定框架

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.jag.2025.105061

Jiangtao Zhao , Zhenhai Li , Bo Bai , Xue Kong , Jishun Yang , Guowei Li , Tadese Anberbir , Xiaobin Xu

Accurate peanut seedling recognition is essential for quantifying emergence rates, a key phenotyping task in breeding programs for this important global oilseed. This task is challenged by field heterogeneity from morphological variation (genotype, growth stage, planting density) and imaging variability (flight altitude, solar angle). To address this, object-based image analysis (OBIA) and deep learning approaches were evaluated using UAV remote sensing imagery collected under systematically varied field conditions. An enhanced framework, P-YOLOv11s, was developed for UAV-based peanut seedling detection, incorporating a P2 layer for fine-scale features, an asymptotic feature pyramid network for multi-scale fusion, and an iEMA attention mechanism for occlusion robustness. The experimental design encompassed significant agronomic diversity (1025 genotypes, nitrogen regimes, planting years, densities, and eco-zones), developmental stages (three- to six-leaf), and flight configurations (15, 25, 40 m altitudes; four diurnal intervals). P-YOLOv11s demonstrated strong robustness, achieving a mean Average Precision (AP) of 93.5 %, with 62 % fewer false detections than OBIA and a 4.8 % higher AP than other YOLO variants. Flight altitude was the most influential factor, with 15 m yielding the best results. Peak accuracy (99.4 %) occurred at the four- to five-leaf stage, while solar angle had a minimal effect (<1.7 % variation). The framework achieved subplot-level precision (μ = 0.42 plants), addressing the challenge of accurate field-based phenotyping under real-world constraints.

准确的花生幼苗识别对于量化出苗率至关重要，这是这种重要的全球油籽育种计划的关键表型任务。这一任务受到来自形态变异（基因型、生长期、种植密度）和成像变异（飞行高度、太阳角度）的野外异质性的挑战。为了解决这一问题，基于目标的图像分析（OBIA）和深度学习方法使用在系统不同的野外条件下收集的无人机遥感图像进行了评估。开发了一种增强的基于无人机的花生苗检测框架P-YOLOv11s，该框架结合了用于精细尺度特征的P2层、用于多尺度融合的渐近特征金字塔网络和用于遮挡鲁棒性的iEMA注意机制。试验设计包括显著的农艺多样性（1025个基因型、氮肥制度、种植年份、密度和生态区）、发育阶段（三叶至六叶）和飞行配置（15、25、40米海拔；4个昼夜间隔）。p - yolov11表现出很强的鲁棒性，平均平均精度（AP）达到93.5%，比OBIA低62%，比其他YOLO变体高4.8%。飞行高度是影响最大的因素，飞行高度为15 m时效果最好。最高精度（99.4%）出现在四到五叶期，而太阳角度的影响最小（<； 1.7%的变化）。该框架达到了亚图级精度（μ = 0.42株），解决了在现实世界约束下准确的基于田间表型的挑战。

{"title":"A novel deep learning framework for High-Throughput peanut seedling identification across diverse germplasm and complex field environments","authors":"Jiangtao Zhao , Zhenhai Li , Bo Bai , Xue Kong , Jishun Yang , Guowei Li , Tadese Anberbir , Xiaobin Xu","doi":"10.1016/j.jag.2025.105061","DOIUrl":"10.1016/j.jag.2025.105061","url":null,"abstract":"<div><div>Accurate peanut seedling recognition is essential for quantifying emergence rates, a key phenotyping task in breeding programs for this important global oilseed. This task is challenged by field heterogeneity from morphological variation (genotype, growth stage, planting density) and imaging variability (flight altitude, solar angle). To address this, object-based image analysis (OBIA) and deep learning approaches were evaluated using UAV remote sensing imagery collected under systematically varied field conditions. An enhanced framework, P-YOLOv11s, was developed for UAV-based peanut seedling detection, incorporating a P2 layer for fine-scale features, an asymptotic feature pyramid network for multi-scale fusion, and an iEMA attention mechanism for occlusion robustness. The experimental design encompassed significant agronomic diversity (1025 genotypes, nitrogen regimes, planting years, densities, and eco-zones), developmental stages (three- to six-leaf), and flight configurations (15, 25, 40 m altitudes; four diurnal intervals). P-YOLOv11s demonstrated strong robustness, achieving a mean Average Precision (AP) of 93.5 %, with 62 % fewer false detections than OBIA and a 4.8 % higher AP than other YOLO variants. Flight altitude was the most influential factor, with 15 m yielding the best results. Peak accuracy (99.4 %) occurred at the four- to five-leaf stage, while solar angle had a minimal effect (<1.7 % variation). The framework achieved subplot-level precision (μ = 0.42 plants), addressing the challenge of accurate field-based phenotyping under real-world constraints.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105061"},"PeriodicalIF":8.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925607","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

Detecting gaps between urban expansion and lighting infrastructure growth using daytime and nighttime satellite imagery 利用日间和夜间卫星图像检测城市扩张和照明基础设施增长之间的差距

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.jag.2026.105087

Tzu-Hsin Karen Chen , Wei Chen , Eleanor C. Stokes , Yuyu Zhou

Characterizing the evolution of urban settlements is vital for informed urban planning that mitigates associated risks. Urban development has traditionally been examined in two dimensions using Earth observation: land cover change, monitored through daytime optical remote sensing, and lighting infrastructural change, observed using nighttime remote sensing. However, these two types of change have often been analyzed in isolation, limiting a comprehensive understanding of their combined impacts on urbanization. This study bridges this gap by simultaneously analyzing monthly Black Marble nighttime light (NTL) data and World Settlement Footprint data to compare lighting and urban land cover change in the Mediterranean region. Our findings reveal that 80% of urbanization-associated pixels display either urban land expansion or lighting growth, but not both. Confusion matrix highlights regional variations: commission errors are particularly high in West Asia (74%), indicating increases in nightlights driven by densification or road improvements without corresponding land conversion. Conversely, omission errors are higher in Western Europe (52%) and North Africa (47%), where urban land expansion occurs without observable lighting infrastructure growth, reflecting phenomena such as informal settlement growth, industrial infill, and energy-saving practices. This study enhances our understanding of the urbanization process through satellite observations, emphasizing the need for a more comprehensive monitoring approach that captures the diverse dimensions of urban growth.

描述城市住区演变的特征对于减轻相关风险的明智城市规划至关重要。城市发展传统上通过地球观测在两个维度上进行考察：通过白天光学遥感监测的土地覆盖变化，以及使用夜间遥感观察的照明基础设施变化。然而，这两种类型的变化往往被单独分析，限制了对它们对城市化的综合影响的全面了解。本研究通过同时分析每月Black Marble夜间灯光（NTL）数据和World Settlement Footprint数据来比较地中海地区的照明和城市土地覆盖变化，从而弥补了这一差距。我们的研究结果表明，80%的城市化相关像素要么显示城市土地扩张，要么显示照明增长，但并非两者兼而有之。混淆矩阵突出了区域差异：西亚的佣金错误率特别高（74%），表明夜间照明的增加是由高密度化或道路改善驱动的，而没有相应的土地转换。相反，西欧（52%）和北非（47%）的遗漏错误更高，在这些地区，城市土地扩张发生在没有可观察到的照明基础设施增长的情况下，反映了诸如非正式定居点增长、工业填充和节能实践等现象。本研究通过卫星观测加强了我们对城市化进程的理解，强调需要一种更全面的监测方法，以捕捉城市增长的各个方面。

{"title":"Detecting gaps between urban expansion and lighting infrastructure growth using daytime and nighttime satellite imagery","authors":"Tzu-Hsin Karen Chen , Wei Chen , Eleanor C. Stokes , Yuyu Zhou","doi":"10.1016/j.jag.2026.105087","DOIUrl":"10.1016/j.jag.2026.105087","url":null,"abstract":"<div><div>Characterizing the evolution of urban settlements is vital for informed urban planning that mitigates associated risks. Urban development has traditionally been examined in two dimensions using Earth observation: land cover change, monitored through daytime optical remote sensing, and lighting infrastructural change, observed using nighttime remote sensing. However, these two types of change have often been analyzed in isolation, limiting a comprehensive understanding of their combined impacts on urbanization. This study bridges this gap by simultaneously analyzing monthly Black Marble nighttime light (NTL) data and World Settlement Footprint data to compare lighting and urban land cover change in the Mediterranean region. Our findings reveal that 80% of urbanization-associated pixels display either urban land expansion or lighting growth, but not both. Confusion matrix highlights regional variations: commission errors are particularly high in West Asia (74%), indicating increases in nightlights driven by densification or road improvements without corresponding land conversion. Conversely, omission errors are higher in Western Europe (52%) and North Africa (47%), where urban land expansion occurs without observable lighting infrastructure growth, reflecting phenomena such as informal settlement growth, industrial infill, and energy-saving practices. This study enhances our understanding of the urbanization process through satellite observations, emphasizing the need for a more comprehensive monitoring approach that captures the diverse dimensions of urban growth.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105087"},"PeriodicalIF":8.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925922","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

MB-UDF: A self-supervised model for continuous representation of seafloor topography using multibeam echo sounder data MB-UDF：使用多波束回声测深数据连续表示海底地形的自监督模型

IF 8.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.jag.2025.105056

Luotao Zhang , Chunqing Ran , Xiaobo Zhang , Hao Yu , Shuo Han , Yilan Chen

Conventional methodologies for processing Multibeam Echo Sounder (MBES) data have primarily relied on the generation of Digital Elevation Models (DEMs) from bathymetric point clouds. Nevertheless, these approaches demonstrate inherent limitations in their capacity to faithfully represent the continuous and intricate morphology of the seafloor, a critical requirement for comprehensive geophysical analyses. This study introduces MB-UDF, a novel self-supervised learning framework that trains neural networks to represent Unsigned Distance Functions (UDF) for high-fidelity reconstruction of continuous 3D seafloor surfaces from MBES data. The primary contributions of MB-UDF encompass a specialized point cloud sampling mechanism and an efficient self-supervised learning strategy, both meticulously designed to address the inherent characteristics of MBES data. Our PyTorch implementation is open-sourced and available at https://github.com/Parallelopiped/MB-UDF. To rigorously evaluate the performance of MB-UDF, we established a comprehensive MBES dataset incorporating diverse bathymetric terrains. Experimental results demonstrate that our method significantly outperforms existing 3D reconstruction techniques in terms of normal consistency and surface continuity, exhibiting enhanced robustness and superior precision compared to conventional DEM approaches. The proposed MB-UDF framework provides innovative methodological tools for advancing research in marine geology, seafloor mapping, and related domains.

处理多波束回声测深仪（MBES）数据的传统方法主要依赖于从测深点云生成数字高程模型（dem）。然而，这些方法在忠实地表示海底连续和复杂形态的能力方面显示出固有的局限性，这是综合地球物理分析的关键要求。本研究引入了MB-UDF，这是一种新颖的自监督学习框架，用于训练神经网络来表示Unsigned Distance Functions (UDF)，以便从MBES数据中高保真地重建连续3D海底表面。MB-UDF的主要贡献包括一个专门的点云采样机制和一个有效的自监督学习策略，两者都是精心设计的，以解决MBES数据的固有特征。我们的PyTorch实现是开源的，可以在https://github.com/Parallelopiped/MB-UDF上获得。为了严格评估MB-UDF的性能，我们建立了一个包含不同水深地形的综合MBES数据集。实验结果表明，我们的方法在法向一致性和表面连续性方面明显优于现有的3D重建技术，与传统的DEM方法相比，具有增强的鲁棒性和更高的精度。提议的MB-UDF框架为推进海洋地质、海底测绘和相关领域的研究提供了创新的方法工具。

{"title":"MB-UDF: A self-supervised model for continuous representation of seafloor topography using multibeam echo sounder data","authors":"Luotao Zhang , Chunqing Ran , Xiaobo Zhang , Hao Yu , Shuo Han , Yilan Chen","doi":"10.1016/j.jag.2025.105056","DOIUrl":"10.1016/j.jag.2025.105056","url":null,"abstract":"<div><div>Conventional methodologies for processing Multibeam Echo Sounder (MBES) data have primarily relied on the generation of Digital Elevation Models (DEMs) from bathymetric point clouds. Nevertheless, these approaches demonstrate inherent limitations in their capacity to faithfully represent the continuous and intricate morphology of the seafloor, a critical requirement for comprehensive geophysical analyses. This study introduces MB-UDF, a novel self-supervised learning framework that trains neural networks to represent Unsigned Distance Functions (UDF) for high-fidelity reconstruction of continuous 3D seafloor surfaces from MBES data. The primary contributions of MB-UDF encompass a specialized point cloud sampling mechanism and an efficient self-supervised learning strategy, both meticulously designed to address the inherent characteristics of MBES data. Our PyTorch implementation is open-sourced and available at <span><span>https://github.com/Parallelopiped/MB-UDF</span><svg><path></path></svg></span>. To rigorously evaluate the performance of MB-UDF, we established a comprehensive MBES dataset incorporating diverse bathymetric terrains. Experimental results demonstrate that our method significantly outperforms existing 3D reconstruction techniques in terms of normal consistency and surface continuity, exhibiting enhanced robustness and superior precision compared to conventional DEM approaches. The proposed MB-UDF framework provides innovative methodological tools for advancing research in marine geology, seafloor mapping, and related domains.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"146 ","pages":"Article 105056"},"PeriodicalIF":8.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926087","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