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Studying long term relationship between carbon Emissions, Soil, and climate Change: Insights from a global Earth modeling Framework Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-11 Huimin Han, Zeeshan Zeeshan, Bandeh Ali Talpur, Touseef Sadiq, Uzair Aslam Bhatti, Emad Mahrous Awwad, Muna Al-Razgan, Yazeed Yasid Ghadi
The persistent increase in greenhouse gas (GHG) emissions, notably carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), since the mid-20th century has been a key driver of significant climate alterations. This study investigates the complex feedback mechanisms that both influence and are influenced by global climate dynamics, soil processes, and GHG emissions. Our statistical approach incorporates
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Addressing the spatial disparity of COVID-19 vaccination services: A spatial optimisation approach Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-11 Huanfa Chen, Xiaowei Gao, Kangdi Chen, Honghan Bei, Roberto Murcio
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A texture feature extraction method considering spatial continuity and gray diversity Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-10 Haishuo Wei, Kun Jia, Qiao Wang, Fengcheng Ji, Biao Cao, Jianbo Qi, Wenzhi Zhao, Kai Yan, Guoqiang Wang, Baolin Xue, Xing Yan
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Annual winter crop distribution from MODIS NDVI timeseries to improve yield forecasts for Europe Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-09 Lorenzo Seguini, Anton Vrieling, Michele Meroni, Andrew Nelson
Crop yield forecasts allow policy makers to anticipate market behaviour and regulate prices. Annual updates on which crops are grown where can improve crop yield forecast accuracy. Existing efforts to map crops across the European Union resulted in late-season map availability or short time series that do not meet forecasting requirements. We propose a new approach to retrieve annual winter crop maps
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Deep Siamese Network for annual change detection in Beijing using Landsat satellite data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-08 Hanqing Bao, Vinzenz H.D. Zerres, Lukas W. Lehnert
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Spatiotemporal monitoring of grasshopper habitats using multi-source data: Combined with landscape and spatial heterogeneity Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-08 Jing Guo, Wenjiang Huang, Yingying Dong, Kejian Lin, Yantao Zhou, Ning Wang, Rui Hua, Zhuoqing Hao, Xiaolong Ding, Fei Zhao
Grasshoppers, as pernicious pests, pose a formidable threat to the advancement of agriculture and animal husbandry. Their presence can elicit a cascade of environmental challenges, underscoring the pressing need for effective control measures. However, grasshopper development is an intricate process influenced by diverse environmental factors with varying weights across regions, making it difficult
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Examining the potential and effectiveness of water indices using multispectral sentinel-2 data to detect soil moisture as an indicator of mudflow occurrence in arid regions Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-07 Zahraa Al-Ali, Ammar Abulibdeh, Talal Al-Awadhi, Midhun Mohan, Noura Al Nasiri, Mohammed Al-Barwani, Sara Al Nabbi, Meshal Abdullah
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How can integrated Space–Air–Ground observation contribute in aboveground biomass of shrub plants estimation in shrub-encroached Grasslands? Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-07 Bin Sun, Rong Rong, Hanwen Cui, Ye Guo, Wei Yue, Ziyu Yan, Han Wang, Zhihai Gao, Zhitao Wu
Shrub encroachment in grassland has become an ecological issue of mounting concern. Accordingly, an accurate estimation of aboveground biomass (AGB) of shrub vegetation is the basis for a sound assessment and in-depth understanding of carbon cycling in shrub-encroached grassland ecosystems. Yet the relatively low stature of plants in the shrub community, coupled with the high spatial heterogeneity
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Data-driven anatomy of hierarchical migration patterns in the United States Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-07 Xurui Yan, Haoying Han, Xing Su, Chao Fan
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New three red-edge vegetation index (VI3RE) for crop seasonal LAI prediction using Sentinel-2 data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-06 Kun Qiao, Wenquan Zhu, Zhiying Xie, Shanning Wu, Shaodan Li
Leaf area index (LAI) serves as a pivotal parameter in crop monitoring, significantly impacting agricultural applications. Empirical models are one of the commonly used methods for estimating LAI, they are often dependent on vegetation indices (VIs), predominantly derived from low-to-moderate spatial resolution satellite sensors. A critical limitation of these VIs is their tendency to saturate at elevated
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Different data-driven prediction of global ionospheric TEC using deep learning methods Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-06 Jun Tang, Mingfei Ding, Dengpan Yang, Cihang Fan, Nasim Khonsari, Wenfei Mao
Ionospheric Total Electron Content (TEC) is a crucial parameter for monitoring the ionosphere and space weather disasters. Its accurate prediction is vital for precise applications of Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS). This study proposes a novel method for ionospheric TEC prediction that considers multiple TEC-related factors. We present
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Uncertainty analysis for forest height inversion using L / P band PolInSAR datasets and RVoG model over kryclan forest site Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-06 Han Zhao, Tingwei Zhang, Yongjie Ji, Wangfei Zhang
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Towards religious landscape: Protection of Tibetan Buddhist heritage in Aba Prefecture Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-06 Xiaoyi Zu, Chen Gao, Yi Wang
Aba Prefecture is a prominent Tibetan inhabited area with profound Tibetan Buddhist sites that need to be protected. This study demonstrates that the value of religious sites lies in their terrain-based nature with perceptions by people, and advocates to interpret and preserve them from the religious landscape perspective. Using geographic data and online geo-tagged images, geographical and visual
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Surface urban heat island analysis based on local climate zones using ECOSTRESS and Landsat data: A case study of Valencia city (Spain) Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-06 Letian Wei, José A. Sobrino
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Kudzu invasion and its influential factors in the southeastern United States Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-06 Ming Shen, Maofeng Tang, Wenzhe Jiao, Yingkui Li
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CDasXORNet: Change detection of buildings from bi-temporal remote sensing images as an XOR problem Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-06 Shanxiong Chen, Wenzhong Shi, Mingting Zhou, Min Zhang, Yue Yu, Yangjie Sun, Linjie Guan, Shuangping Li
The up-to-date building information is significant to urban planning and economic assessment. Automatic building change detection (BCD) from bi-temporal remote sensing images is essential for updating building status efficiently. Nevertheless, BCD remains challenging due to the complex building appearance, the diverse imaging conditions, and the building’s positional inconsistencies between the bi-temporal
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The influence of changing moisture content on laboratory acquired spectral feature parameters and mineral classification Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-05 Tingxuan Jiang, Harald van der Werff, Frank van Ruitenbeek, Arjan Dijkstra, Caroline Lievens, Mark van der Meijde
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Advanced utilization of satellite and governmental data for determining the coverage and condition of green areas in Poland: An experimental statistics supporting the Statistics Poland Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-04 Ewa Panek-Chwastyk, Katarzyna Dąbrowska-Zielińska, Anna Markowska, Marcin Kluczek, Marek Pieniążek
Statistics Poland utilizes Earth Observation and in-situ data to assess green areas, aiming to derive vital information on natural capital statistics and human well-being. This is essential for informed decision-making and sustainable development. Improving regional well-being statistics is a priority due to Poland's low rankings in environmental well-being indicators. To overcome challenges in obtaining
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Tracking annual dynamics of carbon storage of salt marsh plants in the Yellow River Delta national nature reserve of china based on sentinel-2 imagery during 2017–2022 Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-04 Chen Chen, Yi Ma, Dingfeng Yu, Yabin Hu, Lirong Ren
Coastal salt marshes play a key role in coastal carbon sequestration. Understanding the spatial distribution and annual changes of carbon storage of salt marsh plants at a local scale is helpful for accurate protection and restoration. However, the annual carbon storage of salt marsh plants has not yet been obtained in the Yellow River Delta National Nature Reserve (YRDNNR), China. Here, we developed
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Estimating snow depth based on dual polarimetric radar index from Sentinel-1 GRD data: A case study in the Scandinavian Mountains Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-04 Tianwen Feng, Chunlin Huang, Guanghui Huang, Donghang Shao, Xiaohua Hao
The sensitivity of synthetic aperture radar (SAR) polarization information to snow depth changes provides new opportunities for regional snow depth retrieval in mountains with thick snow cover. However, interference from soil signals can affect the accurate quantification of snow volume scattering signals. The aim of this study was to develop a dual-polarimetric radar snow depth estimation (DpRSE)
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Unraveling near real-time spatial dynamics of population using geographical ensemble learning Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-03 Yimeng Song, Shengbiao Wu, Bin Chen, Michelle L. Bell
Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues
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Assessment of soil salinity using explainable machine learning methods and Landsat 8 images Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-03 Samet Aksoy, Elif Sertel, Ribana Roscher, Aysegul Tanik, Nikou Hamzehpour
The aim of this study is to comparatively analyze the performance of machine learning (ML) algorithms for modeling soil salinity using field-based electrical conductivity (EC) data and Landsat-8 OLI satellite images with derived environmental covariates. We also aim to interpret and explain the ML models with and without over-sampling methods using Shapley (SHAP) values, an explainable ML approach
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A strategy for tracing interactions in online collaborative geographic experiments Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-03 Hengyue Li, Zaiyang Ma, Zhong Zheng, Fengyuan Zhang, Songshan Yue, Yongning Wen, Guonian Lü, Min Chen
Online collaborative geographic experiments have many advantages in communication, resource sharing, and task coordination; thus, they play a vital role in comprehensive geographic problem solving for interdisciplinary experts. In these collaborative experiments, different experts usually possess different knowledge backgrounds and are responsible for specific tasks, making it difficult to fully understand
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Where is my attention? An explainable AI exploration in water detection from SAR imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-02 Lifu Chen, Xingmin Cai, Zhenhong Li, Jin Xing, Jiaqiu Ai
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Application of an improved U-Net with image-to-image translation and transfer learning in peach orchard segmentation Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-02 Jiayu Cheng, Yihang Zhu, Yiying Zhao, Tong Li, Miaojin Chen, Qinan Sun, Qing Gu, Xiaobin Zhang
Peach cultivation holds a significant economic importance, and obtaining the spatial distribution of peach orchards is helpful for yield prediction and precision agriculture. In this study, we introduce a new U-Net semantic segmentation model, utilizing ResNet50 as a backbone network, augmented with an Efficient Multi-Scale Attention (EMA) mechanism module and a LayerScale adaptive scaling parameter
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Individual tree detection in large-scale urban environments using high-resolution multispectral imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-02 Jonathan Ventura, Camille Pawlak, Milo Honsberger, Cameron Gonsalves, Julian Rice, Natalie L.R. Love, Skyler Han, Viet Nguyen, Keilana Sugano, Jacqueline Doremus, G. Andrew Fricker, Jenn Yost, Matt Ritter
Systematic maps of urban forests are useful for regional planners and ecologists to understand the spatial distribution of trees in cities. However, manually-created urban forest inventories are expensive and time-consuming to create and typically do not provide coverage of private land. Toward the goal of automating urban forest inventory through machine learning techniques, we performed a comparative
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Urban inundation mapping by coupling 1D − 2D models and model comparison Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-05-01 Yao Li, Frank Badu Osei, Tangao Hu, Yao Shi, Alfred Stein
Urban inundation mapping is important for early warning, management, and drainage network planning. Urban flood simulations typically utilize a 1D model focused on pipe nodes, whereas 2D simulations are required over a land area. In this paper, our focus is on their coupling, so that a dynamic 1D-2D model is obtained. We couple the Storm Water Management Model (SWMM), a widely utilized urban drainage
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Fusion of satellite and street view data for urban traffic accident hotspot identification Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-30 Wentong Guo, Cheng Xu, Sheng Jin
As the number of vehicles and the volume of traffic swell in urban centers, cities have experienced a concomitant increase in traffic accidents. Proactively identifying accident-prone hotspots in urban environments holds the promise of preventing traffic mishaps, thereby curtailing the incidence of accidents and reducing property damage. This research introduces the Two-Branch Contextual Feature-Guided
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Interpreting regional characteristics of Tibetan-Qiang houses in Northwestern Sichuan by Deep Learning and Image Landscape Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-30 Xiaoyi Zu, Chen Gao, Yi Wang
This paper presents a framework for interpreting regional features of houses in the Tibetan-Qiang region by Deep Learning (DL) and Image Landscape (IL), which learns the typical features from online building photos in different subordinate areas of the whole region through a set of datasets and DL models. The contribution of this framework is taking online building images as a proxy of rural building
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Large-scale land use/land cover extraction from Landsat imagery using feature relationships matrix based deep-shallow learning Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-29 Peng Dou, Huanfeng Shen, Chunlin Huang, Zhiwei Li, Yujun Mao, Xinghua Li
Deep learning has demonstrated its effectiveness in capturing high-level features, with convolutional neural networks (CNNs) excelling in remote sensing classification. However, CNNs encounter challenges when applied to Landsat images with limited multi-spectral bands, as they struggle to learn stable features from the spectral domain and integrate them with spatial features to enhance accuracy. Additionally
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Temperature scaling unmixing framework based on convolutional autoencoder Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-29 Jin Xu, Mingming Xu, Shanwei Liu, Hui Sheng, Zhiru Yang
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Feature disentanglement based domain adaptation network for cross-scene coastal wetland hyperspectral image classification Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-29 Ziqi Xin, Zhongwei Li, Mingming Xu, Leiquan Wang, Guangbo Ren, Jianbu Wang, Yabin Hu
At present, domain adaptation (DA) methods have made noteworthy advancements in cross-scene hyperspectral image (HSI) classification. Their success largely hinges on the alignment of distributions between source and target domains, which is a critical step in extracting domain-invariant features. However, this intense focus on domain-invariant feature extraction frequently leads to the neglect of
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Comparative analysis of SAOCOM and Sentinel-1 data for surface soil moisture retrieval using a change detection method in a semiarid region (Douro River’s basin, Spain) Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-27 Benedetta Brunelli, Francesco Mancini
The growing interest in low-frequency SAR for soil parameter retrieval has led to the development of new active L-band satellites, that will provide novel surface soil moisture products and retrieval possibilities; however, due to data unavailability so far, limited applications have investigated the use of change detection models using L-band satellite SAR data. Since July 2020, high revisit time
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Satellite hyperspectral imagery reveals scale dependence of functional diversity patterns in a Qinghai-Tibetan alpine meadow Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-27 Yi-Wei Zhang, Yanpei Guo, Yuhao Feng, Zhenhua Zhang, Rong Tang, Yun-Hao Bai, Hong-Tu Zhang, Yi-Wei Lin, Jiangling Zhu, Tiejun Wang, Zhiyao Tang
Knowing how functional diversity varies across environmental gradients is crucial in investigating biodiversity formation and community assembly processes. The majority of studies concerning functional diversity are based on one fixed plot size, resulting in weakened sensitivity of the spatial patterns to sampling resolution. This weakness may obscure the true mechanisms behind community assemblage
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Evaluation of tree stump measurement methods for estimating diameter at breast height and tree height Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-27 Milan Koreň, Ľubomír Scheer, Róbert Sedmák, Marek Fabrika
The estimation of diameter at breast height (DBH) and tree height from stump dimensions plays a crucial role in assessing the economic and environmental impacts resulting from illegal logging or natural disasters. In this study, we assessed tree stump dimension measurements using a tape measure, nadir color photographs, and the UAV orthophoto. We explored challenges in stump measurement and evaluated
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Temporally transferable crop mapping with temporal encoding and deep learning augmentations Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-26 Vu-Dong Pham, Gideon Tetteh, Fabian Thiel, Stefan Erasmi, Marcel Schwieder, David Frantz, Sebastian van der Linden
Detailed maps on the spatial and temporal distribution of crops are key for a better understanding of agricultural practices and for food security management. Multi-temporal remote sensing data and deep learning (DL) have been extensively studied for deriving accurate crop maps. However, strategies to solve the problem of transferring crop classification models over time, e.g., training the model with
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Semantic segmentation of large-scale point cloud scenes via dual neighborhood feature and global spatial-aware Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-26 Tao Liu, Tianen Ma, Ping Du, Dehui Li
As a core task in 3D scene information extraction, point cloud semantic segmentation is crucial for understanding 3D scenes and environmental perception. While extracting local geometric structural features from point clouds, existing research often overlooks the long-range dependencies present in the scene, making it challenging to fully uncover the long-range contextual features hidden within point
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Characterizing urban actively populated area growth in the Yangtze River Delta using nighttime light data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-26 Ting Yu, Chun Liu, Weiyue Li, Wei Huang, Hangbin Wu, Zhanyong Fan
Understanding the spatiotemporal evolution of urban actively populated area (UAPA) in urban agglomerations is crucial for sustainable development. This study fills a gap in previous research that overlooked urban population dynamics, leading to inaccurate UAPA characterizations. We utilized nighttime light (NTL) data, which reflects both urban construction status and population dynamics, to characterize
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An accurate snow cover product for the Moroccan Atlas Mountains: Optimization of the MODIS NDSI index threshold and development of snow fraction estimation models Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-26 Mostafa Bousbaa, Abdelghani Boudhar, Christophe Kinnard, Haytam Elyoussfi, Ismail Karaoui, Youssra Eljabiri, Hafsa Bouamri, Abdelghani Chehbouni
In semi-arid Mediterranean areas, a significant proportion of the population living downstream depends on water resources from snowmelt and precipitation as their main source of water. Consequently, snow-covered mountain regions can be considered as a vital water tower, providing a steady supply of water, and contributing significantly to streamflow and groundwater recharge. Given the scarcity of ground-based
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Class imbalance: A crucial factor affecting the performance of tea plantations mapping by machine learning Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-26 Yuanjun Xiao, Jingfeng Huang, Wei Weng, Ran Huang, Qi Shao, Chang Zhou, Shengcheng Li
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Revisiting spatial optimization in the era of geospatial big data and GeoAI Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-26 Kai Cao, Chenghu Zhou, Richard Church, Xia Li, Wenwen Li
Spatial optimization is an interdisciplinary field dedicated to the scientific and rational allocation of resources spatially, which has received tremendous attention across various disciplines including geography, operations research, management science, and computer science. Spatial optimization provides important theoretical foundations and solutions for determining optimal spatial arrangements
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BiGNN: Bipartite graph neural network with attention mechanism for solving multiple traveling salesman problems in urban logistics Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-25 Haojian Liang, Shaohua Wang, Huilai Li, Liang Zhou, Xueyan Zhang, Shaowen Wang
The multiple traveling salesman problems (MTSP), which arise from real world problems, are essential in urban logistics. Variations such as MinMax-MTSP and Bounded-MTSP aim to distribute workload evenly among salesmen and impose constraints on visited cities, respectively. Branch-and-bound (B&B) provides an exact algorithm solution for these problems. The Learn to Branch (L2B) approach guides branch
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Deep learning based crop-type mapping using SAR and optical data fusion Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-25 Masoumeh Hamidi, Saeid Homayouni, Abdolreza Safari, Hadiseh Hasani
Accurate crop maps are essential in various applications related to food security management activities. Remote Sensing is the primary data source for land-use and land-cover monitoring applications. However, high spectral, spatial, and temporal variations of crop types during their phonological stages make crop mapping challenging. According to the complementary behaviors of optical and radar data
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Multi-factor weighted image fusion method for high spatiotemporal tracking of reservoir drawdown area and its vegetation dynamics Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-25 Shiqiong Li, Lei Cheng, Liwei Chang, Chenhao Fu, Zhida Guo, Pan Liu
Reservoir drawdown areas (RDAs) with distinct dry-wet cycles and vegetation dynamics have emerged as significant hotspots for carbon-related activities. However, high-resolution spatiotemporal tracking of the variations and vegetation dynamics of RDAs remains challenging because they often change dramatically and are controlled by both human activities and natural factors. Herein, a modified image
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Stripe noise removal for the thermal infrared spectrometer of the SDGSAT-1 Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-25 Mingxin Dai, Jianing Yu, Zhuoyue Hu, Lu Zou, Ji Bian, Qiyao Wang, Xiaofeng Su, Fansheng Chen
Stripe noise is present in the on-orbit images captured by the Sustainable Development Goals Satellite-1 (SDGSAT-1) Thermal Infrared Spectrometer (TIS). Removing these stripes lays the crucial groundwork for subsequent remote sensing data applications. This paper proposes a high-performance method for removing stripe noise in SDGSAT-1 TIS large dynamic range imaging. Based on the fixation properties
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Reconstructing fresh green leaf spectra in the SWIR-2 region (2001–2500 nm) collected in a humid environment by referring to publicly available green leaf spectral databases Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-25 Lino Garda Denaro, Hsin-Ju Li, Jie-Yun Chong, Cho-ying Huang
Leaf spectra (reflectance and transmittance) are commonly measured using a portable spectroradiometer and an integrating sphere or contact probe with an artificial light source. However, spectral data may be obscured due to water vapor and low signal-to-noise ratios, especially in the shortwave infrared-2 region (SWIR-2, 2001–2500 nm). Therefore, we proposed a spectral reconstruction approach to retrieve
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Deforestation detection from spaceborne full-waveform laser altimetry, incorporating terrain effects: A case study in Porto Velho, Brazil Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-24 Min Ji, Huan Xie, Jürgen Oberst, Qi Xu, Yuan Sun, Sicong Liu, Changda Liu, Xiaohua Tong
Forest structure, notably the canopy height above the ground, can be obtained from the full-waveform data acquired by satellite laser altimetry. On sloped terrain, the laser pulse can simultaneously reflect from both the vegetation and the ground within their intersecting vertical extents, causing their signals to overlap. This overlap complicates the separation of canopy and ground in the analysis
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UrbanSegNet: An urban meshes semantic segmentation network using diffusion perceptron and vertex spatial attention Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-24 Wenjie Zi, Jun Li, Hao Chen, Luo Chen, Chun Du
Urban meshes semantic segmentation is essential for comprehending the 3D real-world environments, as it plays a vital role across various application domains, including digital twins, 3D navigation, and smart cities. Nevertheless, the inherent topological complexities of urban meshes impede the precise representation of dependencies and local structures, yielding compromised segmentation accuracy,
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Spatio-temporal modeling of satellite-observed CO2 columns in China using deep learning Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-23 Zhonghua He, Gaofeng Fan, Xiang Li, Fang-Ying Gong, Miao Liang, Ling Gao, Minqiang Zhou
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Indoor view-based visibility analysis using UAV and TLS point clouds with Line-of-Sight correction Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-23 Maolin Chen, Aobo An, Jianping Pan, Fengyun Mu
Visibility analysis is a crucial geographic information processing method, used to assess the observable spatial range from a particular location under specific conditions. It has broad applications, such as urban planning, landscape design and environmental research. Laser scanning provides detailed and accurate 3D data for visibility analysis, called point cloud. However, certain deficiencies still
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Integrated study of water levels and water storage variations using GNSS-MR and remote Sensing: A case study of Sarez Lake, the world's Highest-Altitude dammed lake Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-23 Rui Tu, Xiaolei Wang, Nan Xu, Junqiang Han, Tao Wang, Weisheng Wang, Feng Zhao, Bayindalai, Gulayozov Majid Shonazarovich
Sarez Lake, recognized as the world's highest-altitude dammed lake, necessitates meticulous monitoring of dam deformation, lake water levels, and water storage fluctuations to ensure its safety. In pursuit of this critical objective, a Global Navigation Satellite System (GNSS) monitoring network has been established around the lake to compute real-time station coordinates for deformation monitoring
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A TomoSAR regularization-based method for height change detection in urban areas Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-22 Hossein Armeshi, Mahmod Reza Sahebi, Hossein Aghababaei
Over the past years, urban change detection has always been a challenging issue for researchers. Due to the two-dimensional intrinsic property of satellite imageries and the inherent less intuitive interpretability of radar images, height change detection and SAR imagery-based change detection have been even more complicated tasks. However, TomoSAR-based height change detection is a novel topic on
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Oil spill detection and classification through deep learning and tailored data augmentation Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-20 Ngoc An Bui, Youngon Oh, Impyeong Lee
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A communication-efficient distributed deep learning remote sensing image change detection framework Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-20 Hongquan Cheng, Jie Zheng, Huayi Wu, Kunlun Qi, Lihua He
With the introduction of deep learning methods, the computation required for remote sensing change detection has significantly increased, and distributed computing is applied to remote sensing change detection to improve computational efficiency. However, due to the large size of deep learning models, the time-consuming gradient transfer during distributed model training weakens the acceleration effectiveness
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A graph-based deep learning framework for field scale wheat yield estimation Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-19 Dong Han, Pengxin Wang, Kevin Tansey, Yue Zhang, Hongmei Li
Accurate estimation of crop yield at the field scale plays a pivotal role in optimizing agricultural production and food security. Conventional studies have mainly focused on employing data-driven models for crop yield estimation at the regional scale, while large challenges may occur when attempting to apply these methods at the field scale. This is primarily due to the inherent complexity of obtaining
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Does the Chinese coastal ports disruption affect the reliability of the maritime network? Evidence from port importance and typhoon risk Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-18 Naixia Mou, Huanqing Xu, Yong Liu, Guoqing Li, Lingxian Zhang, César Ducruet, Xianghao Zhang, Yanci Wang, Tengfei Yang
Traditional studies typically employed random and deliberate attack methods to explore port failure, overlooking real-world factors. In this research, we focus on exploring the reliability of the Maritime Silk Road (MSR) container shipping networks after the failure of Chinese coastal ports due to the impact of typhoons. This article analyzes AIS trajectory data and typhoon occurrence data through
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Robust loop closure detection and relocalization with semantic-line graph matching constraints in indoor environments Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-18 Xiqi Wang, Shunyi Zheng, Xiaohu Lin, Qiyuan Zhang, Xiaojian Liu
Loop closure detection (LCD) plays an essential role in the Simultaneous Localization and Mapping (SLAM) process, effectively reducing cumulative trajectory errors. However, conventional LCD methods often encounter challenges when dealing with variations in illumination, changes in viewpoint, and environments with weak textures. This is due to their reliance on low-level geometric or image features
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Monitoring cyanobacterial blooms in China’s large lakes based on MODIS from both Terra and Aqua satellites with a novel automatic approach Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-18 Yichen Du, Junsheng Li, Bing Zhang, Kai Yan, Huan Zhao, Chen Wang, Yunchang Mu, Fangfang Zhang, Shenglei Wang, Mengqiu Wang
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Accelerate spatiotemporal fusion for large-scale applications Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-18 Yunfei Li, Liangli Meng, Huaizhang Sun, Qian Shi, Jun Li, Yaotong Cai
Spatiotemporal fusion (STF) can provide dense satellite image series with high spatial resolution. However, most spatiotemporal fusion approaches are time-consuming, which seriously limits their applicability in large-scale areas. To address this problem, some efforts have been paid for accelerating STF approaches with help of graphics processing units (GPUs), whose effect is dramatic. However, this
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Explainable artificial intelligence framework for urban global digital elevation model correction based on the SHapley additive explanation-random forest algorithm considering spatial heterogeneity and factor optimization Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-17 Chuanfa Chen, Yan Liu, Yanyan Li, Dongxing Chen
Satellite global digital elevation models (GDEMs) suffer from positive biases in urban areas due to building artifacts. While various machine learning (ML)-based methods have been proposed to remove these biases, their generalizability is limited by spatial heterogeneity and redundancy in prediction factors across different regions. Therefore, to investigate the spatial heterogeneity of prediction