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Combining readily available population and land cover maps to generate non-residential built-up labels to train Sentinel-2 image segmentation models Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-17 Diogo Duarte, Cidália C. Fonte
The localization of non-residential buildings over wide geographical areas is used as input within several contexts such as disaster management, regional and national planning, policy making and evaluation, among others. While the built-up environment has been continuously and globally mapped, given the efforts on producing synoptic land cover information; little attention has been given to the land
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An intercomparison of national and global land use and land cover products for Fiji Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-17 Kevin P. Davies, John Duncan, Renata Varea, Diana Ralulu, Solomoni Nagaunavou, Nathan Wales, Eleanor Bruce, Bryan Boruff
Here, a methodology to generate national-scale annual 10 m spatial resolution land use and land cover maps for Fiji (Fiji LULC) is presented. A training dataset of 13,419 points with a LULC label across three years from 2019 to 2021 was generated alongside a nationally representative test dataset of 834 points. These data were used to train a random forests model to convert an image stack of pre-processed
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The illusion of success: Test set disproportion causes inflated accuracy in remote sensing mapping research Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-16 Yuanjun Xiao, Zhen Zhao, Jingfeng Huang, Ran Huang, Wei Weng, Gerui Liang, Chang Zhou, Qi Shao, Qiyu Tian
In remote sensing mapping studies, selecting an appropriate test set to accurately evaluate the results is critical. An imprecise accuracy assessment can be misleading and fail to validate the applicability of mapping products. Commencing with the WHU-Hi-HanChuan dataset, this paper revealed the impact of sample size ratios in test sets on accuracy metrics by generating a series of test sets with varying
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Tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in Beijing Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-15 Xiao Zhang, Liangyun Liu, Wenhan Zhang, Linlin Guan, Ming Bai, Tingting Zhao, Zhehua Li, Xidong Chen
Impervious surfaces are important indicators of human activity, and finding ways to quantify the gain and loss of impervious surfaces is important for sustainable urban development. However, most relevant studies assume that the transformation of natural surfaces to impervious surfaces is irreversible; thus, the losses of impervious surfaces are often ignored. Here, we propose a novel framework taking
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White blanket, blue waters: Tracing El Niño footprints in Canada Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-15 Afshin Amiri, Silvio Gumiere, Hossein Bonakdari
The El Niño Southern Oscillation (ENSO) significantly influences global climate patterns, with one of the strongest warm phases (El Niño) occurring in 2023, altering precipitation and temperature regimes. In this study, the spatiotemporal variability in snow cover across Canadian provinces from December 2023 to February 2024 relative to long-term averages is explored. The NOAA-OISST, NOAA-CSFV2, and
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A high temporal resolution NDVI time series to monitor drought events in the Horn of Africa Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-15 Riccardo D’Ercole, Daniele Casella, Giulia Panegrossi, Paolo Sanò
This study investigates the reconstruction of climatological patterns and vegetation dynamics in the Horn of Africa region using high temporal resolution (i.e. daily) Normalized Difference Vegetation Index (NDVI) datasets. The analysis compares a straight-forward processing approach to derive a daily vegetation index from a geostationary (SEVIRI) satellite with existing NDVI series from geostationary
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Using UAV hyperspectral imagery and deep learning for Object-Based quantitative inversion of Zanthoxylum rust disease index Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-15 Kai Zhang, Jie Deng, Congying Zhou, Jiangui Liu, Xuan Lv, Ying Wang, Enhong Sun, Yan Liu, Zhanhong Ma, Jiali Shang
Zanthoxylum rust (ZR) poses a significant threat to Zanthoxylum bungeanum Maxim.(ZBM) production, impacting both the yield and quality. The lack of current research on ZR using unmanned aerial vehicle (UAV) remote sensing poses a challenge to achieving precise management of individual ZBM plant. This study acquired six UAV hyperspectral images to create a ZR inversion dataset . This dataset, to our
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DeLA: An extremely faster network with decoupled local aggregation for large scale point cloud learning Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-15 Weikang Yang, Xinghao Lu, Binjie Chen, Chenlu Lin, Xueye Bao, Weiquan Liu, Yu Zang, Junyu Xu, Cheng Wang
With advances in data collection technology, the volume of recent remote sensing point cloud datasets has grown significantly, posing substantial challenges for point cloud deep learning, particularly in neighborhood aggregation operations. Unlike simple pooling, neighborhood aggregation incorporates spatial relationships between points into the feature aggregation process, requiring repeated relationship
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Multispectral imaging and terrestrial laser scanning for the detection of drought-induced paraheliotropic leaf movement in soybean Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-15 Erekle Chakhvashvili, Lina Stausberg, Juliane Bendig, Lasse Klingbeil, Bastian Siegmann, Onno Muller, Heiner Kuhlmann, Uwe Rascher
Plant foliage is known to respond rapidly to environmental stressors by adjusting leaf orientation at different timescales. One of the most fascinating mechanisms is paraheliotropism, also known as light avoidance through leaf movement. The leaf orientation (zenith and azimuth angles) is a parameter often overlooked in the plant and remote sensing community due to its challenging measurement procedures
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MGFNet: An MLP-dominated gated fusion network for semantic segmentation of high-resolution multi-modal remote sensing images Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-15 Kan Wei, JinKun Dai, Danfeng Hong, Yuanxin Ye
The heterogeneity and complexity of multimodal data in high-resolution remote sensing images significantly challenges existing cross-modal networks in fusing the complementary information of high-resolution optical and synthetic aperture radar (SAR) images for precise semantic segmentation. To address this issue, this paper proposes a multi-layer perceptron (MLP) dominated gate fusion network (MGFNet)
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Accuracy fluctuations of ICESat-2 height measurements in time series Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-15 Xu Wang, Xinlian Liang, Weishu Gong, Pasi Häkli, Yunsheng Wang
The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission, spanning the past five years, has collected extensive three-dimensional Earth observation data, facilitating the understanding of environmental changes on a global scale. Its key product, Land and Vegetation Height (ATL08), offers global land and vegetation height data for carbon budget and cycle modeling. Consistent measurement accuracy
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Estimation of long time-series fine-grained asset wealth in Africa using publicly available remote sensing imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-13 Mengjie Wang, Xi Li
Traditional methods for measuring asset wealth face limitations due to data scarcity, making it challenging to apply them on a large scale and over long periods with fine granularity. Publicly available satellite images, such as nighttime light imagery, have become an important alternative data source for estimating asset wealth. This study thoroughly exploited the spatial neighborhood information
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ICESat-2 data denoising and forest canopy height estimation using Machine Learning Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-13 Dan Kong, Yong Pang
Supervised classification methods can distinguish between noise and signal in ice, cloud, and land elevation satellite-2 (ICESat-2) data across various feature perspectives and autonomously optimize parameters. Nevertheless, model generalization remains a significant limitation for practical applications. This study focuses on developing a universal denoising model for ICESat-2 using machine learning
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A multi-domain dual-stream network for hyperspectral unmixing Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-12 Jiwei Hu, Tianhao Wang, Qiwen Jin, Chengli Peng, Quan Liu
Hyperspectral unmixing is of vital importance within the realm of hyperspectral analysis, which is aimed to decide the fractional proportion (abundances) of fundamental spectral signatures (endmembers) at a subpixel level. Unsupervised unmixing techniques that employ autoencoder (AE) network have gained significant attention for its exceptional feature extraction capabilities. However, traditional
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Recovering NDVI over lake surfaces: Initial insights from CYGNSS data enhanced by ERA-5 inputs Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-11 Yinqing Zhen, Qingyun Yan
The escalating water pollution in many lakes has led to more frequent occurrences of algal bloom disasters in recent decades. The severity of these disasters can be assessed through remote sensing techniques, specifically using the Normalized Difference Vegetation Index (NDVI) for measurement. However, NDVI observations using optical sensors are often affected by cloud and fog in areas with numerous
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IPS Monitor – A habitat suitability monitoring tool for invasive alien plant species in Germany Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-11 Fabian Sittaro, Michael Vohland
Invasive alien plant species (IPS) are one of the major threats to biodiversity and ecosystem services. As the dynamics of biological invasions by non-native plant species are expected to intensify with climate change, there is an increasing need to provide accessible information on the distribution of IPS to improve environmental management programmes. Monitoring the probability of occurrence of IPS
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Urban flood mapping by fully mining and adaptive fusion of the polarimetric and spatial information of Sentinel-1 images Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-11 Qi Zhang, Xiangyun Hu
Highly destructive flood disasters have occurred frequently recently. Related to this, accurate mapping of flood areas is a necessary undertaking that helps to understand the temporal and spatial evolution patterns of floods. Thus, this paper proposes a novel, unsupervised multi-scale machine learning (ML) approach for urban flood mapping with SAR images from the perspective of information mining and
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Efficient multi-modal high-precision semantic segmentation from MLS point cloud without 3D annotation Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-11 Yuan Wang, Pei Sun, Wenbo Chu, Yuhao Li, Yiping Chen, Hui Lin, Zhen Dong, Bisheng Yang, Chao He
Quick and high-precision semantic segmentation from Mobile Laser Scanning (MLS) point clouds faces huge challenges such as large amounts of data, occlusion in complex scenes, and the high annotation cost associated with 3D point clouds. To tackle these challenges, this paper proposes a novel efficient and high-precision semantic segmentation method Mapping Considering Semantic Segmentation (MCSS) for
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Estimating medium-term regional monthly economic activity reductions during the COVID-19 pandemic using nighttime light data Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-11 Ma. Flordeliza P. Del Castillo, Toshio Fujimi, Hirokazu Tatano
Economic impact estimates of the initial lockdowns due to the COVID-19 pandemic showed a significant reduction in economic activities globally. However, the succeeding impacts and their spatiotemporal distribution within countries remain unknown. Studies showed that nighttime light data (NTL) has effectively revealed the spatiotemporal dimensions of the economic effects of COVID-19. Thus, this study
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Integrated assessment of land use and carbon storage changes in the Tulufan-Hami Basin under the background of urbanization and climate change Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-09 Meiling Huang, Yusuyunjiang Mamitimin, Abudukeyimu Abulizi, Rebiya Yimaer, Bahejiayinaer Tiemuerbieke, Han Chen, Tongtong Tao, Yunfei Ma
Precise forecasting of land use modifications and carbon storage (CS) alterations is essential for effective regulatory measures and ecological quality enhancement. However, there are limited studies on land use dynamics and its impact on CS in the arid regions of Northwest China. Therefore, this study explores land use and CS changes in the Tulufan-Hami Basin from 2000 to 2050. The SD-FLUS and InVEST
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Cloud probability distribution of typical urban agglomerations in China based on Sentinel-2 satellite remote sensing Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-09 Jing Ling, Rui Liu, Shan Wei, Shaomei Chen, Luyan Ji, Yongchao Zhao, Hongsheng Zhang
Cloud distribution significantly impacts global climate change, ecosystem health, urban environments, and satellite remote sensing observations. However, past research has primarily focused on the meteorological characteristics of clouds with limitations in scale and resolution, leading to an insufficient understanding of large-scale cloud distribution and its relationship with land surface cover and
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Coupling between evapotranspiration, water use efficiency, and evaporative stress index strengthens after wildfires in New Mexico, USA Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-08 Ryan C. Joshi, Annalise Jensen, Madeleine Pascolini-Campbell, Joshua B. Fisher
Examine the effects of evapotranspiration (ET), water use efficiency (WUE), and evaporative stress index (ESI) on wildfire temperature and extent. Compare land cover type proportions in burned area with land cover type proportions in New Mexico.
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Integration of ecological knowledge with Google Earth Engine for diverse wetland sampling in global mapping Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-07 Xuanlin Huo, Zhenguo Niu, Linsong Liu, Yuhang Jing
Accurate wetland extraction using remote sensing technology poses significant challenges due to the complex hydrological dynamics, diverse landscapes, and varied wetland types. Constructing a reliable sample set is a critical first step in overcoming these challenges for large-scale wetland mapping. To meet the demand for global wetland mapping, this study (1) proposes a multi-level wetland classification
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Quantitative assessment of spatiotemporal variations and drivers of gross primary productivity in tropical ecosystems at higher resolution Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-06 Ruize Xu, Jiahua Zhang, Fang Chen, Bo Yu, Shawkat Ali, Hidayat Ullah, Ali Salem Al-Sakkaf
Climate change significantly impacts vegetation gross primary productivity (GPP), yet uncertainties persist in the carbon cycle of tropical terrestrial ecosystems due to incomplete consideration of productivity drivers and lag effects. To address this, we developed a remote sensing-based process model by integrating high-resolution vegetation indices and multi-layer soil hydrological module, to simulate
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Bayesian modeling of incompatible spatial data: A case study involving Post-Adrian storm forest damage assessment Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-06 Lu Zhang, Andrew O. Finley, Arne Nothdurft, Sudipto Banerjee
Modeling incompatible spatial data, i.e., data with different spatial resolutions, is a pervasive challenge in remote sensing data analysis. Typical approaches to addressing this challenge aggregate information to a common coarse resolution, i.e., compatible resolutions, prior to modeling. Such pre-processing aggregation simplifies analysis, but potentially causes information loss and hence compromised
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Optimizing rural waste management: Leveraging high-resolution remote sensing and GIS for efficient collection and routing Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-11-06 Xi Cheng, Jieyu Yang, Zhiyong Han, Guozhong Shi, Deng Pan, Likang Meng, Zhuojun Zeng, Zhanfeng Shen
Accurate assessment of distribution patterns and dynamic insights into rural populations is pivotal for comprehending domestic waste generation, recycling, and transportation in rural territories. Given that the dispersion of rural inhabitants exhibits minimal variation and maintains stability, this research endeavors to establish a pragmatic model for rural domestic waste collection and routing, leveraging
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Experimental observations of marginally detectable floating plastic targets in Sentinel-2 and Planet Super Dove imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-30 Dimitris Papageorgiou, Konstantinos Topouzelis
Remote sensing applications are garnering much attention as a promising solution for detection, tracking and monitoring of floating marine litter (FML). With an increasing number of studies portraying the technical feasibility of FML detection, we attempt here to experimentally observe a minimum detectable abundance fraction of floating plastic (white HDPE sheets), in a Sentinel-2 and PlanetScope SuperDove
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Mitigating terrain shadows in very high-resolution satellite imagery for accurate evergreen conifer detection using bi-temporal image fusion Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-30 Xiao Zhu, Tiejun Wang, Andrew K. Skidmore, Stephen J. Lee, Isla Duporge
Very high-resolution (VHR) optical satellite imagery offers significant potential for detailed land cover mapping. However, terrain shadows, which appear dark and lack texture and detail, are especially acute at low solar elevations. These shadows hinder the creation of spatially complete and accurate land cover maps, particularly in rugged mountainous environments. While many methods have been proposed
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Optimizing UAV-based uncooled thermal cameras in field conditions for precision agriculture Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-30 Quanxing Wan, Magdalena Smigaj, Benjamin Brede, Lammert Kooistra
Unoccupied aerial vehicles (UAVs) equipped with thermal cameras show great promise for precision agriculture, but challenges persist in analyzing land surface temperature (LST). This study explores the influence of ambient environmental conditions and intrinsic characteristics of uncooled thermal cameras on the accuracy of temperature measurements obtained through UAV-based thermal cameras. The research
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Advancing complex urban traffic forecasting: A fully attentional spatial-temporal network enhanced by graph representation Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-28 Guangyue Li, Jinghan Wang, Zilong Zhao, Yang Chen, Luliang Tang, Qingquan Li
Accurate urban traffic forecasting is essential for intelligent transportation systems (ITS). However, the majority of existing forecasting methodologies predominantly concentrate on point-based forecasts (e.g., traffic detector forecasts). A limited number of them pay attention to the urban bidirectional road segments and the complex road network topology. To advance accurate traffic forecasting in
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UAV measurements and AI-driven algorithms fusion for real estate good governance principles support Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-28 Pawel Tysiac, Artur Janowski, Marek Walacik
The paper introduces an original method for effective spatial data processing, particularly important for land administration and real estate governance. This approach integrates Unmanned Aerial Vehicle (UAV) data acquisition and processing with Artificial Intelligence (AI) and Geometric Transformation algorithms. The results reveal that: (1) while the separate applications of YOLO and Hough Transform
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Identify and map coastal aquaculture ponds and their drainage and impoundment dynamics Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-26 Duanrui Wang, Dehua Mao, Ming Wang, Xiangming Xiao, Chi-Yeung Choi, Chunlin Huang, Zongming Wang
Sustainable management of coastal aquaculture ponds could achieve win-win between food and economic benefits and ecological conservation including waterbird. In this study, 5790 Harmonized Landsat and Sentinel-2 images from July 2021 to June 2022 and 498 Sentinel-1 images from July 2021, August 2021, and June 2022 as supplementary data were collected to calculate multiple water indices. Based on Otsu
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An enhanced method for reconstruction of full SIF spectrum for near-ground measurements Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-25 Feng Zhao, Mateen Tariq, Weiwei Ma, Zhenfeng Wu, Yanshun Zhang
Recently the applications of remotely sensed Solar-Induced chlorophyll Fluorescence (SIF) in the study of photosynthesis, stress conditions, and gross primary production have increased significantly. The full SIF spectrum spans over a spectral region of 650 ∼ 850 nm with two characteristic peaks around 685 nm and 740 nm. Over recent decades, many retrieval algorithms have been developed to estimate
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Automated tree crown labeling with 3D radiative transfer modelling achieves human comparable performances for tree segmentation in semi-arid landscapes Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-24 Decai Jin, Jianbo Qi, Nathan Borges Gonçalves, Jifan Wei, Huaguo Huang, Yaozhong Pan
Mapping tree crowns in arid or semi-arid areas, which cover around one-third of the Earth’s land surface, is a key methodology towards sustainable management of trees. Recent advances in deep learning have shown promising results for tree crown segmentation. However, a large amount of manually labeled data is still required. We here propose a novel method to delineate tree crowns from high resolution
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Uncovering the drivers of gender inequality in perceptions of safety: An interdisciplinary approach combining street view imagery, socio-economic data and spatial statistical modelling Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-24 Yu Zhu, Fengmin Su, Xin Han, Qiaoting Fu, Jie Liu
The perception of safety significantly impacts residents’ urban living and socio-economic development. However, the phenomenon and drivers of gender differences in safety perceptions have not received sufficient emphasis, resulting in the gradual exacerbation of gender inequality in urban environments. To address this issue, we explored a research methodology that integrates visual perception with
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Network invulnerability modeling of daily necessity supply based on cascading failure considering emergencies and dynamic demands Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-24 Hao Huang, Wenchu Zhang, Zipei Zhen, Haochen Shi, Miaoxi Zhao
Confronting the escalating challenge of emergencies, the urban supply network of daily necessity is an important defense line for human well-being. This study introduces a groundbreaking approach that leverages mobile signaling data, departing from static regional data, to model large-scale and high-precision urban supply-demand network. Moreover, a significant stride in assessing network invulnerability
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Divergent dynamics of coastal wetlands in the world’s major river deltas during 1990–2019 Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-24 Yongchao Liu, Jialin Li, Xinxin Wang, Chao Sun, Peng Tian, Gaili He
Coastal wetlands provide vital dynamic ecosystem services. They have become increasingly important after being linked to several sustainable developmental goals, resulting in a focus on their protection, management, and restoration. Therefore, there is an increasing need to detect and compare coastal wetland spatiotemporal dynamics in deltas at a global scale. In this study, we mapped and characterized
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Increasing irrigation-triggered landslide activity caused by intensive farming in deserts on three continents Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-23 Zijing Liu, Haijun Qiu, Yaru Zhu, Wenchao Huangfu, Bingfeng Ye, Yingdong Wei, Bingzhe Tang, Ulrich Kamp
Population growth and agricultural intensification lead to stress on landscapes that are highly sensitive to land-use changes. An increase in irrigation-triggered landslides (ITL) in dry climates has negative impacts on local communities. However, evolution and global impacts of ITL are little-known. Here, we use Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR), vectorization, and
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A satellite-derived bathymetry method combining depth invariant index and adaptive logarithmic ratio: A case study in the Xisha Islands without in-situ measurements Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-23 Xiangtao Zhao, Chao Qi, Jianhua Zhu, Dianpeng Su, Fanlin Yang, Jinshan Zhu
Accurate bathymetric data is crucial for various aspects such as marine resource exploitation and marine ecological conservation. Currently, satellite-derived bathymetry (SDB) based on empirical and physical models has been widely utilized in constructing underwater terrain in shallow seas. However, the application of such SDB models is limited in remote island reef areas lacking in-situ measurement
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SLRCNN: Integrating sparse and low-rank with a CNN denoiser for hyperspectral and multispectral image fusion Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-23 Li Li, Hongjie He, Nan Chen, Xujie Kang, Baojie Wang
Fusion of hyperspectral image (HSI) and multispectral image (MSI) is a prevalent scheme to generate a HSI with enhanced spatial resolution. Current methods often fail to sufficiently leverage the effective spectral and spatial priors existing in the observed HSI and MSI to further enhance the fusion performance. To address this limitation, this paper proposes a novel HSI-MSI fusion approach, which
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Dynamic analysis of landscape drivers in the thermal environment of Guanzhong plain urban agglomeration Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-22 Long Chen, Heng Li, Chunxiao Zhang, Wenhao Chu, Jonathan Corcoran, Tianbao Wang
Climate change caused by rapid urbanization in the Guanzhong region of China is becoming an increasingly significant problem. Previous empirical studies have confirmed that landscape patterns inextricably linked with the thermal environment, but static results based on a single temporal cross section of image data provide only a partial understanding. In this paper, we constructed a dynamic framework
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Spatiotemporal variations of surface albedo in Central Asia and its influencing factors and confirmatory path analysis during the 21st century Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-21 Shuai Yuan, Yongqiang Liu, Yongnan Liu, Kun Zhang, Yongkang Li, Reifat Enwer, Yaqian Li, Qingwu Hu
Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices
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Evaluation of average leaf inclination angle quantified by indirect optical instruments in crop fields Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-21 Kaiyuan Li, Chongya Jiang, Kaiyu Guan, Genghong Wu, Zewei Ma, Ziyi Li
Average leaf inclination angle (θ¯L) is an important canopy structure variable that influences light regime, photosynthesis, and evapotranspiration of plants. θ¯L can be measured through direct methods (e.g., protractor), which are labor-intensive and time-consuming, or through indirect optical instruments, which are more efficient than the direct methods. However, uncertainties of different indirect
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Object detection in aerial images using DOTA dataset: A survey Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-20 Ziyi Chen, Huayou Wang, Xinyuan Wu, Jing Wang, Xinrui Lin, Cheng Wang, Kyle Gao, Michael Chapman, Dilong Li
In recent years, the Dataset for Object deTection in Aerial images (DOTA) dataset has played a pivotal role in advancing object detection in aerial images (ODAI). Despite its significance, there hasn’t been a comprehensive review summarizing its research developments. Addressing this gap, this paper offers the first comprehensive overview on the subject. Within this review, we begin by examining prevalent
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Big geo-data unveils influencing factors on customer flow dynamics within urban commercial districts Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-19 Xia Peng, Yue-yan Niu, Bin Meng, Yingchun Tao, Zhou Huang
Commercial districts, as the epicenters of urban commerce and economic activity, largely reflect an area’s prosperity through their customer flow. However, previous research, which often relied on statistical and survey data, has typically not captured the full scope of customer flow dynamics throughout urban commercial districts and has not adequately measured the specific impacts of business district
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Simulating SAR constellations systems for rapid damage mapping in urban areas: Case study of the 2023 Turkey-Syria earthquake Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-19 Riccardo Vitale, Pietro Milillo
This study evaluates the feasibility of using Synthetic Aperture Radar (SAR) constellations for rapid damage mapping in the aftermath of the 2023 Turkey-Syria earthquake. We specifically address the data acquisition latency challenges associated with X- and L-Band SAR constellations, including those operated by U.S. Capella Space, UMBRA Space, European ICEYE, and the Italian/Argentinian SIASGE constellation
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Attribution of forest disturbance types based on the Dynamic World class probability data: A case study of Myanmar Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-19 Zhe Li, Tetsuji Ota, Nobuya Mizoue
Attribution of forest disturbance types using satellite remote sensing is practicable and several methods have been developed to automate the procedure. However, limited by commonly used data and the methodology, achieving accurate and rapid attribution of forest disturbance types over broad spatial extents remains challenging. In this study, we developed a method for attributing forest disturbance
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Robust multi-stage progressive autoencoder for hyperspectral anomaly detection Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-19 Qing Guo, Yi Cen, Lifu Zhang, Yan Zhang, Shunshi Hu, Xue Liu
Recently, Autoencoders (AEs) have demonstrated remarkable performance in the field of hyperspectral anomaly detection, owing to their powerful capability in handling high-dimensional data. However, they often overlook the inherent global distribution characteristics and long-range dependencies in hyperspectral images (HSI). This oversight makes it challenging to accurately characterize and describe
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A fast hybrid approach for continuous land cover change monitoring and semantic segmentation using satellite time series Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-17 Wenpeng Zhao, Rongfang Lyu, Jinming Zhang, Jili Pang, Jianming Zhang
Land cover change detection and classification, including both inter-class changes (land cover conversion, LCC) and intra-class changes (land cover modification, LCM), is critical for understanding the Earth’s dynamic processes and promoting sustainability. However, previous studies have predominantly focused on LCC, with less emphasis on LCM. Land cover classification remains challenging, and its
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An event logic graph for geographic environment observation planning in disaster chain monitoring Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-17 Yunbo Zhang, Wenjie Chen, Bingshu Huang, Zongran Zhang, Jie Li, Ruishan Gao, Ke Wang, Chuli Hu
Effective geographic environment observation planning is the key to obtain disaster monitoring and warning information. The previous researches can only make observation plans for a single disaster at some specific stages. They are difficult to apply to the dynamic evolution of the disaster chain. Timely and comprehensive geographic environment observation planning is urgently needed to provide high-value
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Dual-branch multi-modal convergence network for crater detection using Chang’e image Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-17 Feng Lin, Xie Hu, Yiling Lin, Yao Li, Yang Liu, Dongmei Li
Knowledge about the impact craters on rocky planets is crucial for understanding the evolutionary history of the universe. Compared to traditional visual interpretation, deep learning approaches have improved the efficiency of crater detection. However, single-source data and divergent data quality limit the accuracy of crater detection. In this study, we focus on valuable features in multi-modal remote
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Optimal algorithm for distributed scatterer InSAR phase estimation based on cross-correlation complex coherence matrix Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-17 Dingyi Zhou, Zhifang Zhao
Low scattering terrain areas introduce complex phase interference, which reduces the accuracy of deformation signal estimation in InSAR(Interferometric Synthetic Aperture Radar) techniques. Existing covariance matrix-based InSAR phase calculation methods often fail to account for translational offset relations between scatterers leading to inaccuracies, and pixels with zero spatial coherence exist
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A mixed convolution and distance covariance matrix network for fine classification of corn straw cover types with fused hyperspectral and multispectral data Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-17 Wenliang Chen, Kun Shang, Yibo Wang, Wenchao Qi, Songtao Ding, Xia Zhang
Effective management of corn straw and stubble is critical in conservation tillage, as it impacts soil health and productivity. However, accurate classification of different types of straw cover has been hindered by their similar spectral and spatial characteristics and the low spatial resolution of hyperspectral satellite imagery. Moreover, traditional convolution neural network (CNN)-based methods
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DeepAAT: Deep Automated Aerial Triangulation for Fast UAV-based mapping Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-17 Zequan Chen, Jianping Li, Qusheng Li, Zhen Dong, Bisheng Yang
Automated Aerial Triangulation (AAT), aiming to restore image poses and reconstruct sparse points simultaneously, plays a pivotal role in earth observation. AAT has evolved into a fundamental process widely applied in large-scale Unmanned Aerial Vehicle (UAV) based mapping. However classic AAT methods still face challenges like low efficiency and limited robustness. This paper introduces DeepAAT, a
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Classification of protected grassland habitats using deep learning architectures on Sentinel-2 satellite imagery data Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-16 Gabriel Díaz-Ireland, Derya Gülçin, Aida López-Sánchez, Eduardo Pla, John Burton, Javier Velázquez
This study examines the effectiveness of five deep learning models—ViTb-19, SwinV2-t, VGG-16, ResNet-50, and DenseNet-121—in distinguishing different vegetation types in the protected grasslands of Castilla y León region, Spain, following the guidelines of the Natura 92/43/CEE directive. Among the models, ResNet-50 achieved the highest weighted overall accuracy (OA) of 0.95, closely followed by SwinV2-t
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The question answering system GeoQA2 and a new benchmark for its evaluation Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-16 Sergios-Anestis Kefalidis, Dharmen Punjani, Eleni Tsalapati, Konstantinos Plas, Maria-Aggeliki Pollali, Pierre Maret, Manolis Koubarakis
We present the question answering engine GeoQA2 which is able to answer geospatial questions over the union of knowledge graphs YAGO2 and YAGO2geo. We also present the dataset GeoQuestions1089 which consists of 1089 natural language questions, their corresponding SPARQL or GeoSPARQL queries and their answers over the union of the same knowledge graphs. We use this dataset to compare the effectiveness
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Quantification and mapping of medicinally important Quercitrin compound using hyperspectral imaging and machine learning Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-16 Ayushi Gupta, Prashant K. Srivastava, Karuna Shanker, K. Chandra Sekar
Precise spatial mapping of individual species using hyperspectral data is crucial for effective forest management and policy-making. This study focuses on Rhododendron arboreum, known for its medicinal properties attributed to the flavonoid Quercitrin. Sample data and spectroradiometer data were collected from the complex terrain of the Kumaon region in the Himalayas. Hyperspectral data, which includes
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A critical review of literature on remote sensing grass quality during the senescence phenological stage Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-14 Anita Masenyama, Onisimo Mutanga, Mbulisi Sibanda, Timothy Dube
This article provides a critical review of progress, challenges, emerging gaps as well as future recommendations on the remote sensing of grass quality during the senescence phenological stage. The study adopted a critical approach and analysed nineteen peer-reviewed articles which were retrieved from Scopus, Web of Science, and Institute of Electrical and Electronics Engineers using key search words
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A rangeland management-oriented approach to map dry savanna − Woodland mosaics Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-14 Vera De Cauwer, Marie-Pascale Colace, John Mendelsohn, Telmo Antonio, Cornelis Van Der Waal
Tropical savannas have a patchy vegetation structure and heterogeneous composition that complicates their mapping and management. Land managers need detailed vegetation information, especially as tropical savannas often support extensive ranching systems or wildlife-based tourism and face specific challenges such as bush thickening, drought, bushfires and, in Africa, browsing by large game. Since existing
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How can geostatistics help us understand deep learning? An exploratory study in SAR-based aircraft detection Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2024-10-14 Lifu Chen, Zhenhuan Fang, Jin Xing, Xingmin Cai
Deep Neural Networks (DNNs) have garnered significant attention across various research domains due to their impressive performance, particularly Convolutional Neural Networks (CNNs), known for their exceptional accuracy in image processing tasks. However, the opaque nature of DNNs has raised concerns about their trustworthiness, as users often cannot understand how the model arrives at its predictions