International journal of applied earth observation and geoinformation : ITC journal最新文献

{"title":"PlaceField2BVec: A bionic geospatial location encoding method for hierarchical temporal memory model","authors":"Zugang Chen ,&nbsp;Shaohua Wang ,&nbsp;Kai Wu ,&nbsp;Guoqing Li ,&nbsp;Jing Li ,&nbsp;Jian Wang","doi":"10.1016/j.jag.2025.104402","DOIUrl":"10.1016/j.jag.2025.104402","url":null,"abstract":"<div><div>Encoding geospatial location is a fundamental problem for geospatial artificial intelligence (GeoAI) research. In recent years, some methods (such as Place2Vec, Space2Vec, and Sphere2Vec) were proposed to encode geospatial point as a high-dimensional vector. However, all these geospatial location encoders were designed to generate a real number vector. So, when applied to some of the brain-inspired neural networks, such as Hierarchical Temporal Memory (HTM), which required the input of a binary vector, the existing methods failed. To solve the problem, based on the research from neuroscience about place cell, we proposed a new geospatial location encoding method called PlaceField2BVec. The method used the place field model to encode a location. The place field was represented by the summation of four Gaussian functions, allowing it to be stretched or divided into multiple fields as the geospatial space expanded. Then we created an HTM and devised an experiment that simulated rats moving on tables of varying sizes. The moving trajectories were encoded by PlaceField2BVec and input to the HTM. After training, we found that the artificial neurons of HTM formed a place field similar to those of hippocampal neurons in the rat brain and the distribution patterns of the place field from the two kinds of neurons were consistent. At last, our method was compared with existing Space2BVec and Buffer2BVec in terms of location prediction accuracy and to demonstrate the robustness of the binary vector encoding methods, two brain-inspired artificial neural networks— HTM and BinaryLSTM were used. The result showed that, for HTM, in smaller geospatial space the PlaceField2BVec and Buffer2BVec had about the same accuracy on average but the highest accuracy of PlaceField2BVec is 100 %; when the geospatial space extended, our method had the highest accuracy and the average accuracy of PlaceField2BVec, Space2BVec, and Buffer2BVec is 83.9 %, 25.2 % and 69.7 % after 20 times’ training. For BinaryLSTM, PlaceField2BVec always had the highest accuracy in location prediction although the accuracy decreased as the space extended. Our research can be utilized for machine self-localization, navigation, and location-related GeoAI applications, and it also contributes to the theory of cognitive maps.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104402"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143271178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Change detection in heterogeneous images based on multiple pseudo-homogeneous image pairs","authors":"Huifu Zhuang ,&nbsp;Jianlin Guo ,&nbsp;Ming Hao ,&nbsp;Sen Du ,&nbsp;Kefei Zhang ,&nbsp;Xuesong Wang","doi":"10.1016/j.jag.2024.104321","DOIUrl":"10.1016/j.jag.2024.104321","url":null,"abstract":"<div><div>Due to the significant disparities in feature spaces of multi-source images, change detection (CD) of heterogeneous remote sensing images (HRSIs) remains a highly challenging problem. Currently, CD methods based on domain transfer networks (DTNs) have garnered significant attention. However, the computer scientists underutilize knowledge in the field of CD during DTNs design, and the existing CD methods do not fully utilize the heterogeneous complementary features contained in HRSIs. Therefore, this study proposes a novel CD method based on multiple pseudo-homogeneous image pairs. First, a cycle-consistent generative adversarial network with knowledge constraints (named as KCGAN) was designed for obtaining good pseudo-homogeneous images. In detail, both the domain knowledge that there are land cover changes in multi-temporal images and that the objects in an image can be described from different scales were well integrated into the design of KCGAN. Then, a multi-modal difference Siamese fusion network (named as MDSiamF) was proposed to extract change information from the multiple pseudo-homogeneous image pairs generated with KCGAN. Experiments conducted on three datasets showed that: 1) compared to existing domain transfer methods, the unchanged areas in the pseudo-homogeneous images obtained by KCGAN exhibit better feature consistency (with a peak signal-to-noise ratio higher than 20.85 and a PHash value higher than 0.9); 2) compared to state-of-the-art methods for CD of HRSIs, the proposed method shows stable and good CD performance (with an overall accuracy higher than 0.98 and a F1 Score higher than 0.78).</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104321"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing phenological differences of invasive shrubs in a forest matrix using high resolution VENµS time series","authors":"Liang Liang ,&nbsp;Jian Yang ,&nbsp;William C. Wittenbraker ,&nbsp;Ellen V. Crocker ,&nbsp;Monika A. Tomaszewska ,&nbsp;Geoffrey M. Henebry","doi":"10.1016/j.jag.2024.104333","DOIUrl":"10.1016/j.jag.2024.104333","url":null,"abstract":"<div><div>Many invasive shrubs in the eastern deciduous forests of the United States use the temporal niche before and after the native tree canopy leaf-on period (leafing out prior to most native species and retaining leaves after most natives senesce) to establish in the light-limited environment of the understory. To support an increased understanding of invasive shrub species’ ecology and distribution patterns and inform better management plans, this key phenological difference needs to be characterized in detail. Here we leveraged the high-resolution observations from the French-Israel VENµS mission to examine the phenological characteristics of a widespread invasive shrub species—Amur honeysuckle (AH; <em>Lonicera maackii</em> (Rupr.) Herder)—compared to native deciduous trees in Robinson Forest, Kentucky. VENµS offered daily superspectral (12 narrow bands) observations at 4 m resolution in a limited number of global sites, providing us with crucial data for the analysis. We identified three forest communities with respect to AH presence through field surveys (<em>i.e.,</em> uninvaded forest stands, forest stands with AH understory, and AH shrub thickets) and compared their VENµS-derived spectral signatures and time series of vegetation indices. In 2023, AH shrub thickets greened up one month earlier than uninvaded forest stands (mid-March vs. mid-April). AH leaf growth advanced into full green before the canopy tree greenup started in early April, marking an optimal window for isolating areas with AH understory from the uninvaded forest using remote sensing. Based on the phenological differences identified, we predicted the distribution of AH in the study area using a two-date differencing model and a spectral mixture analysis. Our detailed findings using VENµS data offer insights into the temporal dynamics of invasive shrubs and native trees in a typical eastern deciduous forest. While our prediction of the AH distribution was confounded by the presence of native early greening and/or evergreen understory plants at a few locations, it was still moderately accurate (overall accuracy ∼ 70 %) and its abundance estimates agreed with observations in forest stands with minimal native understory growth. Moving forward, high-resolution remote sensing observations combined with a phenology-based approach will likely support more precise monitoring and management of invasive understory plants in native forest ecosystems.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104333"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scale effects in mangrove mapping from ultra-high-resolution remote sensing imagery","authors":"Hanwen Zhang ,&nbsp;Shan Wei ,&nbsp;Xindan Liang ,&nbsp;Yiping Chen ,&nbsp;Hongsheng Zhang","doi":"10.1016/j.jag.2024.104310","DOIUrl":"10.1016/j.jag.2024.104310","url":null,"abstract":"<div><div>Mangroves, critical for ecological sustainability, are challenging to map accurately due to their fragmented nature and difficult accessibility. Existing datasets, often constrained to 10 m or above resolutions, could misrepresent fragmented mangrove regions and suffer from sampling biases, limiting their regional applicability. Furthermore, scale conversion’s spatial and statistical implications on mangrove mapping accuracy and area estimation remain largely unexplored. This study proposes a novel framework that leverages UHR (0.2 m) aerial photos and the DeepLabV3+ model for fine-scale mapping and systematically simulates and quantifies scale-induced effects. The resultant 20 cm-resolution mangrove map of Hong Kong achieved an overall accuracy (OA) of 92.1 %, with up to 53 % improvement compared to various existing datasets. It delineates complex boundaries in diverse coastal settings while preserving the structural integrity of fragmented patches. The total mangrove area in Hong Kong is estimated at ∼720 ha, with Deep Bay comprising 77.5 %. The scale effects analysis revealed pronounced sensitivity in fragmented habitats, where each 1 m increase in resolution could result in an average area underestimation of 5000 m² and up to 25 % OA degradation when transitioning from 0.2 m to 30 m. Moreover, integrating patch geometry and scale responses indicated that 6 m is the optimal scale for monitoring. Beyond this, OA could sharply decline to below 82 % at the commonly used 10 m resolution and drop as low as 66 % at 30 m. These findings highlight the critical importance of fine-scale mapping using UHR images for effective mangrove conservation and management.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104310"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Eliminating geometric distortion with dual-orbit Sentinel-1 SAR fusion for accurate glacial lake extraction in Southeast Tibet Plateau","authors":"Renzhe Wu ,&nbsp;Guoxiang Liu ,&nbsp;Xin Bao ,&nbsp;Jichao Lv ,&nbsp;Age Shama ,&nbsp;Bo Zhang ,&nbsp;Wenfei Mao ,&nbsp;Jie Chen ,&nbsp;Zhihan Yang ,&nbsp;Rui Zhang","doi":"10.1016/j.jag.2024.104329","DOIUrl":"10.1016/j.jag.2024.104329","url":null,"abstract":"<div><div>Glacial lakes (GLs), which serve as natural reservoirs, are also prospective sources of risk, and their risk levels are continuously increasing as a result of global climate warming. Nevertheless, GLs are situated in mountainous and valley regions, which are distinguished by their complex terrain and unpredictable weather conditions. This leads to restricted availability of optical imagery as a consequence of the frequent cloud cover. Synthetic Aperture Radar (SAR), however, encounters issues with geometric distortion. This paper introduces an unsupervised method based on geometric distortion detection (without orbit state information) and historical positioning using dual-orbit SAR imagery to research GL extraction effectively. This method detects low-quality pixels from dual-orbit SAR imagery through geometric distortion. It extracts GLs using a majority voting integration of unsupervised classification algorithms constrained by historical GL center points. The Southeastern Tibetan Plateau (SETP) was chosen as a representative region for the study, and experiments were conducted from July to August 2018 using dual-orbit Sentinel-1 imagery. A total of 600 refined samples were used for comparative verification. The results demonstrate that this method is capable of reliably identifying the active and passive geometric distortions in SAR imagery. The fusion of dual-orbit SAR based on geometric distortion can effectively enhance the classification performance of remote sensing imagery and achieve the acquisition of GL water storage area during the flood season. The geometric distortion rate was reduced from 29.9% to 7.9% after fusion correction, and the accuracy, recall rate, precision, Intersection over Union (IoU), and F1-Score were 0.989, 0.900, 0.908, 0.825, and 0.904, respectively. This serves as a reference for research that investigates the mechanisms of glacier-GL-climate change.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104329"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate estimation of grain number per panicle in winter wheat by synergistic use of UAV imagery and meteorological data","authors":"Yapeng Wu,&nbsp;Weiguo Yu,&nbsp;Yangyang Gu,&nbsp;Qi Zhang,&nbsp;Yuan Xiong,&nbsp;Hengbiao Zheng,&nbsp;Chongya Jiang,&nbsp;Xia Yao,&nbsp;Yan Zhu,&nbsp;Weixing Cao,&nbsp;Tao Cheng","doi":"10.1016/j.jag.2024.104320","DOIUrl":"10.1016/j.jag.2024.104320","url":null,"abstract":"<div><div>Rapid, accurate, and nondestructive estimation of grain number per panicle (GNPP) in winter wheat is crucial to accelerate smart breeding, improve precision crop management, and ensure food security. As two (panicle number per unit ground area and GNPP) of three commonly used yield components, GNPP was much less quantified with remotely sensed data than the former through visual counting. The limited research suffered from either low accuracies with ground canopy spectra or low efficiency with proximal panicle imaging systems. No studies have been reported on estimating GNPP with unmanned aerial vehicle (UAV) imagery, underscoring its strong advantages in high-resolution and efficient monitoring. To address these issues, this study proposed a practical approach for estimating GNPP in winter wheat by integrating UAV imagery and meteorological data with <em>meta</em>-learning ensemble regression. The potential contributions of different variables were examined for understanding the improvement in the spectral estimation of GNPP, including spectral indices (SIs), the optimal canopy height (CH) metric, and absorbed photosynthetic active radiation (APAR).</div><div>The results demonstrated that CH_P99 (CH at the 99th percentile in the region of interest) derived from red-green-blue (RGB) imagery exhibited the strongest correlation with measured plant height among all RGB- or multispectral (MS)-derived CH metrics. The incorporation of remotely sensed APAR and RGB-derived CH_P99 improved the accuracy of GNPP estimation over using merely color indices or SIs. Among all feature combinations, Comb. #6 (SIs + APAR_MS + CH_P99) yielded the highest overall accuracies in estimating GNPP for individual and multiple stages. Compared with the best anthesis models for Combs. #5–7 (<em>R_val</em>² = 0.52–0.64, RMSE = 2.85–2.47, RRMSE = 6.01–5.21 %), the multi-stage (heading + anthesis) models exhibited higher accuracies in independent validation (<em>R_val</em>² = 0.60–0.65, RMSE = 2.60–2.42, RRMSE = 5.48–5.10 %). The findings suggest this study has opened a new avenue for estimating GNPP with UAV remote sensing. The proposed method for the synergistic use of UAV imagery and meteorological data has great potential in the prediction of GNPP and grain yield over various regions with satellite imagery and climate datasets.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104320"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-variance estimation of live fuel moisture content using VIIRS data through radiative transfer model","authors":"Shuai Yang,&nbsp;Rui Chen,&nbsp;Binbin He,&nbsp;Yiru Zhang","doi":"10.1016/j.jag.2024.104311","DOIUrl":"10.1016/j.jag.2024.104311","url":null,"abstract":"<div><div>The Canopy Live Fuel Moisture Content (LFMC) is a pivotal factor in wildfire risk assessment within the fire triangle model, representing the ratio of canopy moisture content to its dry weight. Against the backdrop of degraded Moderate Resolution Imaging Spectroradiometer (MODIS) performance and the underutilization of Visible Infrared Imaging Radiometer Suite (VIIRS) in LFMC inversion, this study harnessed the coupled radiative transfer models (RTMs) to probe the spectral sensitivity of the VIIRS to LFMC and pinpoint the optimal band combination for LFMC inversion. To tackle the challenge of ill-posed inversion, we leveraged the correlation coefficient matrix to mitigate erroneous combinations of free parameters in the construction of the lookup table. Results showcase that VIIRS-based LFMC inversion yields marginally superior accuracy (R<span><math><mrow><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo></mrow></math></span> 0.57, R<span><math><mrow><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo></mrow></math></span> 0.32) for both grassland and forest types, with VIIRS-based inversion demonstrating a lower relative root mean square error (rRMSE <span><math><mo>=</mo></math></span> 5.84%), compared to results from the MODIS. By scrutinizing LFMC trends alongside precipitation (PP) data in four forest fires spanning from 2019 to 2022 in southwest China, varied degrees of LFMC decrease preceding fire outbreaks. Those results substantiated the validity of the proposed method for wildfire warning. Consequently, our study asserts the reliability of VIIRS in LFMC inversion, positioning it as a viable substitute and extension of MODIS. VIIRS offers continuous and effective product support for wildfire warning assessment, enhancing our ability to monitor and mitigate wildfire risks.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104311"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating RS data with fuzzy decision systems for innovative crop water needs assessment","authors":"Faezeh Sadat Hashemi ,&nbsp;Mohammad Javad Valadan Zoej ,&nbsp;Fahimeh Youssefi ,&nbsp;Huxiong Li ,&nbsp;Sanaz Shafian ,&nbsp;Mahdi Farnaghi ,&nbsp;Saied Pirasteh","doi":"10.1016/j.jag.2024.104338","DOIUrl":"10.1016/j.jag.2024.104338","url":null,"abstract":"<div><div>Irrigation is a critical component of global water usage, accounting for approximately 70 % of water consumption. As the world’s population continues to grow, the demand for food will increase, making it essential to improve irrigation management by reducing water waste and increasing efficiency. This study aims to develop and validate a fuzzy decision-making system that determines crop irrigation needs based on parameters that affect plant water requirements. These parameters can be monitored using Remote sensing (RS) satellites, enabling large-scale agricultural irrigation monitoring. The study utilized Landsat-8 satellite data and meteorological data. It also employed a fuzzy decision system with inputs of estimated evapotranspiration, Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Surface Temperature (LST), Crop Water Stress Index (CWSI), Stress Index (SI), and Soil Moisture (SM). The output of the fuzzy model is a map that effectively determines the irrigation requirements for agricultural land relatively. The system was tested on six Landsat images of winter wheat crops in Tehran University’s agricultural fields. The estimated evapotranspiration was compared to Reference Evapotranspiration (ET_r) obtained from the FAO-Penman-Monteith equation, resulting in a root mean square error of 0.33 mm. The fuzzy decision system was evaluated by comparing its results with Vegetation Water Content (VWC) measurements during satellite overpass time. The NDVI, CWSI, SI, and SM variables had the highest R² with VWC data (0.71––0.92) on all six dates. This approach has significant implications for improving irrigation management practices, reducing water waste, and increasing crop yields, which can contribute to global food security. The study highlights the potential of RS technology and fuzzy decision-making systems in promoting sustainable agriculture.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104338"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying heat-related risks from urban heat island effects: A global urban expansion perspective","authors":"Ming Hao ,&nbsp;Xue Liu ,&nbsp;Xia Li","doi":"10.1016/j.jag.2024.104344","DOIUrl":"10.1016/j.jag.2024.104344","url":null,"abstract":"<div><div>Quantifying the urban heat island (UHI) effect and its impact on summer heat-related risk is important for both urban environment and human well-being. Existing studies frequently adopt the static (fixed) urban boundary to define urban/rural area in UHI measurement, overlooking the exacerbation of the urbanization-induced warming during long-term urban expansion and the consequent increase in urban heat risks. Here we measured the global surface UHI (SUHI) intensity up to 7,554 urban patches during 2000–2015 using every five-year dynamic urban boundary, followed by a two-stage analysis based on a Distributed Lag Non-linear Model (DLNM) to quantify the additional heat-related risks caused by the urbanization-induced warming. Our results show that the global average SUHI intensity increased by approximately 10 % in 15 years with distinct seasonal and diurnal variations. The global urban expansion from 2000 to 2015 resulted in an average increase of 0.61℃ (95 % CI = 0.56℃-0.66℃) in summer UHI intensity for newly built-up areas. This urbanization-induced warming further leads to a 20 % (95 % CI = 14.8 %-25.2 %) increase in summer heat relative risk (RR) on average, which implied an average increase of 20 % (95 % CI = 14.8 %-25.2 %) in annual heat-related mortality for the newly built-up areas. Furthermore, over 2.3 % of the world population would experience an RR increase greater than 10 %. This study highlights the importance of dynamic urban boundary for long-time span UHI measurements, providing a deeper understanding of the role of urbanization-induced warming on urban heat risk.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104344"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Satellite images reveal rapid development of global water-based photovoltaic over the past 20 years","authors":"He Ren ,&nbsp;Zhen Yang ,&nbsp;Fashuai Li ,&nbsp;Maoxin Zhang ,&nbsp;Yuwei Chen ,&nbsp;Tingting He","doi":"10.1016/j.jag.2025.104354","DOIUrl":"10.1016/j.jag.2025.104354","url":null,"abstract":"<div><div>Water-based photovoltaics (WPV) have emerged as a promising solution to land-use conflicts associated with solar photovoltaic systems. Accurate monitoring of the spatiotemporal distribution of WPV is essential for evaluating its development potential, environmental impacts, and informing policy decisions. Satellite remote sensing data offer a feasible approach for WPV mapping and monitoring. However, conventional image classification and deep learning methods often limited by sample size requirements, computational costs, and technical complexity, which hinder their widespread applicability. To address these challenges, this study proposes a novel index, the normalized difference photovoltaic index (NDPI), for WPV detection. We generated a global WPV map for the year 2023 using Sentinel-2 MSI imagery and NDPI. Additionally, by integrating NDPI with Landsat time series data, we determined the installation dates of WPV systems and evaluated their development trends from 2000 to 2023. Our results show that: (i) The NDPI demonstrated excellent performance in WPV detection, with overall accuracy for spatial location and installation dates of WPV was 0.935 and 0.927, respectively, and Kappa coefficients of 0.870 and 0.921. (ii) Global WPV coverage in 2023 reached 589.17 km², with Asia being the primary contributor, accounting for over 97 %. China emerged as the leading country, with a WPV area of 472.92 km², significantly exceeding other nations (&lt; 50 km²). (iii) WPV experienced significant growth from 2000 to 2023, particularly after 2015. The increase in WPV area (434.57 km²) from 2015 to 2023 was nearly three times the total area covered in the previous 15 years. The proposed NDPI provides a universal approach for global WPV spatiotemporal monitoring and the update of basic information. It also provides potential for assessing the environmental impacts of WPV across its operational lifecycle.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104354"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}