样式: 排序: IF: - GO 导出 标记为已读
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
Vectorizing historical maps with topological consistency: A hybrid approach using transformers and contour-based instance segmentation Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-17 Xue Xia, Tao Zhang, Magnus Heitzler, Lorenz Hurni
Reducing the complexity of the workflow for historical map vectorization is essential to promote the widespread utilization of historical spatial data. Traditional pixel-wise segmentation followed by vectorization workflows suffer from tedious post-processing steps. To address this challenge, we introduce an innovative pure vector-based workflow. This workflow predicts object contours in vector format
-
Spatio-temporal dynamics of plastic mulch use in crop rotation at parcel and regional scales Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-17 Elsy Ibrahim, Anne Gobin
-
Focused information learning method for change detection based on segmentation with limited annotations Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-16 H. Ahn, S. Chung, S. Park, D. Kim
Recent advancements have significantly improved the field of segmentation-based change detection, particularly in the context of remote-sensing images. However, change detection datasets generally lack segmentation annotations, and the required labeling process is resource-intensive. We propose an improved change detection method based on segmentation to address this challenge. First, change detection
-
Remote sensing crop water productivity and water use for sustainable agriculture during extreme weather events in South Africa Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-16 Kudzai S. Mpakairi, Timothy Dube, Mbulisi Sibanda, Onisimo Mutanga
The impact of climate variability and extreme weather events on agricultural productivity in arid environments has become a focal point in contemporary research. Monitoring crop water productivity (CWP) is critical and urgently required especially in the arid regions where agriculture consumes an above-average portion of the available fresh water resources. In this context, this study aimed to demonstrate
-
Topographic knowledge-aware network for automatic small-scale impact crater detection from lunar digital elevation models Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-16 Yang Juntao, Zhang Shuowei, Li Lin, Kang Zhizhong, Ma Yuechao
Impact craters represent the most prevalent and prominent topographical features on the surfaces of planets. They provide crucial insights into the internal and surface-level geological activities of planets but are difficult to identify from digital topographic data due to heterogeneous planetary surfaces and lack of distinguishing features. Previous studies, which implemented convolutional neural
-
SADNet: Space-aware DeepLab network for Urban-Scale point clouds semantic segmentation Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-15 Wenxiao Zhan, Jing Chen
Semantic segmentation of urban-scale point clouds can effectively assist people in understanding and perceiving 3D urban scenes. Although a considerable number of deep learning models for the semantic segmentation of point clouds have been proposed, some methods are plagued by information loss caused by sampling and insufficient perception of the spatial relationship between points. To address this
-
Mapping tobacco planting areas in smallholder farmlands using Phenological-Spatial-Temporal LSTM from time-series Sentinel-1 SAR images Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-13 Mengmeng Li, Xiaomin Feng, Mariana Belgiu
-
Correcting land surface temperature from thermal imager by considering heterogeneous emissivity Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-12 Wenjie Yan, Jiawei Jiang, Lanwu He, Wenli Zhao, Richard Nair, Xu Wang, Yujiu Xiong
It is fundamental to obtain accurate land surface temperature (LST) to study surface energy process. Infrared thermal imagers are commonly used for deriving LST on the basis of radiance measurements. However, when deriving LST from brightness temperature of a blackbody in thermal imagers, thermal imagers only allow setting a fixed land surface emissivity (LSE). This causes uncertainty in retrieving
-
MCDNet: Multilevel cloud detection network for remote sensing images based on dual-perspective change-guided and multi-scale feature fusion Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-11 Junwu Dong, Yanhui Wang, Yang Yang, Mengqin Yang, Jun Chen
Cloud detection plays a crucial role in the preprocessing of optical remote sensing images. While extensive deep learning-based methods have shown strong performance in detecting thick clouds, their ability to identify thin and broken clouds is often inadequate due to their sparse distribution, semi-transparency, and similarity to background regions. To address this limitation, we introduce a multilevel
-
Hierarchical local global transformer for point clouds analysis Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-11 Dilong Li, Shenghong Zheng, Ziyi Chen, Xiang Li, Lanying Wang, Jixiang Du
Transformer networks have demonstrated remarkable performance in point cloud analysis. However, achieving a balance between local regional context and global long-range context learning remains a significant challenge. In this paper, we propose a Hierarchical Local Global Transformer Network (LGTNet), designed to capture local and global contexts in a hierarchical manner. Specifically, we employ serial
-
Semi-supervised object detection with uncurated unlabeled data for remote sensing images Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-10 Nanqing Liu, Xun Xu, Yingjie Gao, Yitao Zhao, Heng-Chao Li
Annotating remote sensing images (RSIs) poses a significant challenge, primarily due to its labor-intensive nature. Semi-supervised object detection (SSOD) methods address this challenge by generating pseudo-labels for unlabeled data, assuming that all classes present in the unlabeled dataset are also represented in the labeled data. However, real-world scenarios may lead to a mixture of out-of-distribution
-
Interpreting differences in access and accessibility to urban greenspace through geospatial analysis Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-09 Gang Lin, Yongze Song, Dong Xu, Mohammad Shahidul Hasan Swapan, Peng Wu, Weitao Hou, Zhuoyao Xiao
-
High-resolution mapping of GDP using multi-scale feature fusion by integrating remote sensing and POI data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-09 Nan Wu, Jining Yan, Dong Liang, Zhongchang Sun, Rajiv Ranjan, Jun Li
High-resolution spatial distribution maps of GDP are essential for accurately analyzing economic development, industrial layout, and urbanization processes. However, the currently accessible GDP gridded datasets are limited in number and resolution. Furthermore, high-resolution GDP mapping remains a challenge due to the complex sectoral structure of GDP, which encompasses agriculture, industry, and
-
Harmonizing atmospheric ozone column concentrations over the Tibetan Plateau from 2005 to 2022 using OMI and Sentinel-5P TROPOMI: A deep learning approach Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-09 Changjiang Shi, Zhijie Zhang, Shengqing Xiong, Wangang Chen, Wanchang Zhang, Qian Zhang, Xingmao Wang
-
Indoor localization and trajectory correction with point cloud-derived backbone map Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-09 Zhenqi Zheng, Xiao Sun, Zhichao Wen, Xuan Wang, Wenlei Fan, Hongji Yan, You Li
As a commonly used indoor internet-of-things (IoT) positioning method, fingerprinting is frequently carried out through the fusion of inertial and WiFi or magnetic data. Nevertheless, signals like magnetic intensity and WiFi received signal strength tend to fluctuate and cannot always provide high accuracy. Thus, other available data can be introduced to maintain positioning continuity and reliability
-
Evident influence of water availability on the relationship between solar-induced chlorophyll fluorescence and gross primary productivity in the alpine grasslands of the Tibetan Plateau Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-08 Zhoutao Zheng, Nan Cong, Guang Zhao, Bo Zhao, Yixuan Zhu, Yangjian Zhang, Juntao Zhu, Tao Zhang, Ning Chen, Jie Gao, Yu Zhang, Yihan Sun
Solar-induced chlorophyll fluorescence (SIF) is strongly physiologically associated with vegetation photosynthesis, and has been broadly employed to monitor gross primary productivity (GPP). However, for some understudied ecosystems, such as the alpine grasslands of the Tibetan Plateau (TP), it is highly uncertain about how SIF performs in tracking GPP. Here, by collecting eddy covariance-derived GPP
-
Wetting or greening? Probing the global trends in Vegetation Condition Index (VCI) Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-07 Guoying Yin, Wei He, Xiangyu Liu, Yu Xia, Hongyan Zhang
Vegetation Condition Index (VCI), as a widely used drought index for monitoring vegetation drought stress and estimating drought trends, is constructed by normalizing the long-term satellite-based Normalized Difference Vegetation Index (NDVI) data. However, under global greening, vegetation across different regions has shown an increasing trend in greenness, which may cause VCI to inherit the greening
-
Inferring freeway traffic volume with spatial interaction enhanced betweenness centrality Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-06 Beibei Zhang, Shifen Cheng, Peixiao Wang, Feng Lu
Freeway traffic volume is strongly correlated with the intensity of regional socioeconomic spatial interactions and the road network structure. Although existing studies have proposed indicators of betweenness centrality (BC) integrated into regional spatial interactions, the socio-economic drivers of freeway traffic volume formation have been neglected. More importantly, existing studies have not
-
Pixel-associated autoencoder for hyperspectral anomaly detection Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-06 Pei Xiang, Shahzad Ali, Jiajia Zhang, Soon Ki Jung, Huixin Zhou
Autoencoders (AEs) are central to hyperspectral anomaly detection, given their impressive efficacy. However, the current methodologies often neglect the global pixel similarity of the hyperspectral image (HsI), thereby limiting reconstruction accuracy. This study introduces an innovative pixel-associated AE approach that leverages pixel associations to augment hyperspectral anomaly detection. First
-
Browsing target extraction and spatiotemporal preference mining from the complex virtual trajectories Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-05 Guangsheng Dong, Xiangning Mou, Hongping Zhang, Rui Li, Huayi Wu, Jie Jiang, Fangning Li, Wensen Yu
Public Map Service Platforms (PMSPs) aggregate and disseminate the earth observation data. Leveraging spatiotemporal preference patterns derived from browsing targets within complex virtual trajectories on PMSPs aids in constructing user-profiles and comprehending their intentions. However, complex virtual trajectories, characterized by numerous trajectory points and overlapping pyramidal spatial structures
-
Integrating UAV hyperspectral data and radiative transfer model simulation to quantitatively estimate maize leaf and canopy nitrogen content Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-05 Jiating Li, Yufeng Ge, Laila A. Puntel, Derek M. Heeren, Geng Bai, Guillermo R. Balboa, John A. Gamon, Timothy J. Arkebauer, Yeyin Shi
Crop nitrogen (N) content reflects crop nutrient status and plays an important role in precision nutrient management. Accurate crop N content estimation from remote sensing has been well documented. However, the robustness (i.e., the ability of a model to perform consistently across various conditions) of these methods under varied soil conditions or different growth stages has rarely been considered
-
A spatiotemporal attention-augmented ConvLSTM model for ocean remote sensing reflectance prediction Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-05 Gaoxiang Zhou, Jun Chen, Ming Liu, Lingfei Ma
Remote sensing reflectance () is an essential parameter in ocean color remote sensing and a fundamental input for the estimation of ocean color elements. Predicting has the potential to enable simultaneous prediction of multiple marine environmental parameters, facilitating multi-perspective analysis of marine environmental changes. This paper proposes a spatiotemporal attention-augmented ConvLSTM-based
-
A vector-based coastline shape classification approach using sequential deep learning model Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-05 Aji Gao, Tinghua Ai, Huafei Yu, Tianyuan Xiao, Yuejun Chen, Jingzhong Li, Haosheng Huang
Coastlines play a crucial role in coastal dynamics, and classifying their shape is an essential requirement for coastal analysis. With the development of Coastal Management Systems (CMS), structured and high-resolution vector-format coastlines have become increasingly available compared to remote sensing image coastlines. However, due to the challenges of accurate description and ambiguous classification
-
Fusing multimodal data of nature-economy-society for large-scale urban building height estimation Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-05 Shouhang Du, Hao Liu, Jianghe Xing, Shihong Du
The building height holds significant importance for comprehensively understanding urban morphology, enhancing urban planning, and fostering sustainable development. Although many methods using optical and SAR images have been presented for building height estimation, these methods fall short in capturing the influences of economic and social attributes on building height. In this study, we introduced
-
The unsuPervised shAllow laNdslide rapiD mApping: PANDA method applied to severe rainfalls in northeastern appenine (Italy) Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-05 Davide Notti, Martina Cignetti, Danilo Godone, Davide Cardone, Daniele Giordan
Shallow landslides, frequently triggered by extreme events such as heavy rainfall, snowmelt, or earthquakes, affect vast areas with remarkable density. In the immediate aftermath of such events, it becomes crucial to rapidly assess landslides distribution and pinpoint the most severely affected areas to prioritize damage assessments and guide field survey operations effectively. Once the emergency
-
Connecting spaceborne lidar with NFI networks: A method for improved estimation of forest structure and biomass Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-05 Paul B. May, Ralph O. Dubayah, Jamis M. Bruening, George C. Gaines III
Spaceborne lidar provides a unique opportunity to supplement the field plot measurements of national forest inventories (NFIs) by providing dense measurements of vertical canopy structure. For full waveform instruments such as the Global Ecosystem Dynamics Investigation (GEDI), measurements take the form of reflected energy as a function of height within an observed footprint. Many forest attributes
-
A multimodal data fusion model for accurate and interpretable urban land use mapping with uncertainty analysis Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-04 Xiaoqin Yan, Zhangwei Jiang, Peng Luo, Hao Wu, Anning Dong, Fengling Mao, Ziyin Wang, Hong Liu, Yao Yao
Urban land use patterns can be more accurately mapped by fusing multimodal data. However, many studies only consider socioeconomic and physical attributes within land parcels, neglecting spatial interaction and uncertainty caused by multimodal data. To address these issues, we constructed a multimodal data fusion model (MDFNet) to extract natural physical, socioeconomic, and spatial connectivity ancillary
-
A deep learning framework for 3D vegetation extraction in complex urban environments Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-04 Jiahao Wu, Qingyan Meng, Liang Gao, Linlin Zhang, Maofan Zhao, Chen Su
Accurate extraction of three-dimensional (3D) vegetation is essential for monitoring urban ecological environments and carbon sinks. Two-dimensional vegetation data in cities has been widely researched. However, large-scale urban vegetation height inventories are lacking. This study proposes a novel framework for 3D extraction of urban vegetation, which can be widely applied based on remote sensing
-
The impact of clear-sky biases of land surface temperature on monthly evapotranspiration estimation Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-03 Xin Pan, Zhanchuan Wang, Suyi Liu, Zi Yang, Rufat Guluzade, Yuanbo Liu, Jie Yuan, Yingbao Yang
Remotely sensed land surface temperature (LST) is critical for retrieving evapotranspiration (ET). However, due to cloud contamination, LST is often limited to clear-sky conditions and the differences between clear-sky and all-sky LST will lead to clear-sky biases of LST. Consequently, the accuracy of ET varies drastically under different weather conditions. To evaluate the impact of clear-sky biases
-
Coupling effect of impoundment and irrigation on landslide movement in Maoergai Reservoir area revealed by multi-platform InSAR observations Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-03 Jiantao Du, Zhenhong Li, Chuang Song, Wu Zhu, Roberto Tomás
The mountainous areas of Southwest China are crucial regions for hydropower resource development. The unique geological environment and numerous constructed hydropower stations make landslides the primary geological hazard for the region. Therefore, the deformation monitoring and mechanism analysis of reservoir landslides have attracted extensive attention from the academic community. The Maoergai
-
A novel algorithm for estimating phytoplankton algal density in inland eutrophic lakes based on Sentinel-3 OLCI images Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-01 Honglei Guo, Wenyu Liu, Heng Lyu, Huaiqing Liu, Jiafeng Xu, Yunmei Li, Xianzhang Dong, Yuxin Zhu, Yiling Zheng, Song Miao
As one of the optically active components, phytoplankton are common photosynthetic organisms in oceans, nearshore, and inland water bodies. The variations in phytoplankton algal density play a crucial role in understanding primary productivity, carbon cycling, and early warning of algal blooms. In this study, three typical eutrophic lakes in China, Lake Taihu, Lake Chaohu, and Lake Dianchi, were taken
-
CSDFormer: A cloud and shadow detection method for landsat images based on transformer Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-01 Jiayi Li, Qunming Wang
Cloud and shadow (CS) detection is crucial prerequisite for application of remote sensing images. Current deep learning-based detection algorithms mainly employ Convolutional Neural Networks (CNNs). However, the local receptive field in CNNs cannot effectively capture global contextual information, which hinders accurate characterization of the dependency between clouds and shadows. In vision Transformers
-
A framework for automated landslide dating utilizing SAR-Derived Parameters Time-Series, An Enhanced Transformer Model, and Dynamic Thresholding Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-04-01 Wandi Wang, Mahdi Motagh, Zhuge Xia, Simon Plank, Zhe Li, Aiym Orynbaikyzy, Chao Zhou, Sigrid Roessner
Determining the timing of landslide occurrence is crucial for establishing an accurate, comprehensive and systematic landslide inventory while assessing the potential for reducing landslide risk. Unfortunately, many existing landslide inventories lack temporal information such as the precise time of landslide events. Optical and Synthetic Aperture Radar (SAR) sensors are the most commonly used remote
-
A building change detection framework with patch-pairing single-temporal supervised learning and metric guided attention mechanism Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-30 Song Gao, Kaimin Sun, Wenzhuo Li, Deren Li, Yingjiao Tan, Jinjiang Wei, Wangbin Li
Building change detection (CD) aims to detect changes in buildings from bi-temporal pairwise images obtained at different times. Typically, a deep learning-based building CD algorithm requires bi-temporal samples with significant building changes for training. However, obtaining such bi-temporal samples is challenging because building changes have a low probability of occurrence. Fortunately, it is
-
Mapping open-pit mining area in complex mining and mixed land cover zone using Landsat imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-30 Yongkai Wang, Kai Qin, Zilong Zhang, Qin He, Jason Cohen
The monitoring of open-pit mining and reclamation activities is an important part of ecological protection across various countries. Among the many methods of monitoring open-pit mining areas, satellite remote sensing is the most widely used and effective. However, few works have been conducted to map open-pit mines over a larger geographical scale, or have considered complex land cover types, or a
-
Active thickness estimation and failure simulation of translational landslide using multi-orbit InSAR observations: A case study of the Xiongba landslide Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-29 Wu Zhu, Luyao Yang, Yiqing Cheng, Xiaoyu Liu, Ruixuan Zhang
The active thickness of the translational landslides plays a pivotal role in evaluating its hazards and simulating its instability. Existing techniques have difficulties in estimating the accurate active thickness due to the limitations of observation conditions and imaging geometry, leading to deviations in failure simulations. To overcome these challenges, this study proposes an enhanced method that
-
Second-order texton feature extraction and pattern recognition of building polygon cluster using CNN network Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-29 Pengcheng Liu, Ziqin Shao, Tianyuan Xiao
-
L-band microwave-retrieved fuel temperature predicts million-hectare-scale destructive wildfires Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-29 Ju Hyoung Lee, Sander Veraverbeke, Brendan Rogers, Yann H. Kerr
The 2014 Northwest Territories fires are one of the largest wildfires in history. However, it is difficult to explain what caused such devastating wildfires simply with meteorological conditions and hydrological drought. There is a lack of large-scale Near-Real-Time (NRT) observations that characterize fuel conditions. To fill this research gap, we provide the new earth observations that the -scale
-
Automated classification of valid and invalid satellite derived bathymetry with random forest Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-28 Matthew B. Sharr, Christopher E. Parrish, Jaehoon Jung
Recent decades have seen rapid growth in algorithms and workflows for generating bathymetry from multispectral satellite imagery, with the output typically referred to as satellite derived bathymetry (SDB). An inherent challenge is that, while SDB algorithms generally output a value for every pixel in each input scene, the value of any particular raster cell may not represent a valid depth. Typically
-
Multi-feature supported dam height measurement method for large hydraulic projects using high resolution remote sensing imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-28 Runsheng Ma, Yating Wei, Qiang Zhao, Shuangming Zhao, Zhiwen Yang, Fang Shangguan, Jixuan Li, Zhaowen Wu, Zhijuan Shen, Wen Zhang, Linyi Li, Lingkui Meng
Most studies on building height estimation using remote sensing imagery mainly focus on urban buildings with relatively regular shape and relatively flat terrain, and pay little attention to large buildings with complex terrain like dams. A new dam height measurement method was proposed in this paper, which used shadow measurement data and metadata from multiple image sources. The method not only considers
-
Revealing early pest source points and spreading laws of Pantana phyllostachysae Chao in Moso bamboo (Phyllostachys pubescens) forests from Sentinel-2A/B images Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-28 Anqi He, Zhanghua Xu, Bin Li, Yifan Li, Huafeng Zhang, Guantong Li, Xiaoyu Guo, Zenglu Li
-
Fusion of GaoFen-5 and Sentinel-2B data for lithological mapping using vision transformer dynamic graph convolutional network Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-28 Yanni Dong, Zhenzhen Yang, Quanwei Liu, Renguang Zuo, Ziye Wang
Lithological identification and mapping using remote sensing (RS) imagery are challenging. Traditional lithological mapping relies mainly on multispectral data and machine learning methods. However, inadequate spectral information and inappropriate classification algorithms are major problems for RS geological applications. Moreover, satellite hyperspectral images (HSI) at low spatial resolution and
-
Evaluating the spatial representativeness of ground-based observations for satellite total ozone products Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-28 Chunguang Lyu, Wenmin Zhang, Chi Zhang, Yunfei Shi, Yue Zhang, Yuping Wang
External verification based on ground-based data is commonly used to assess the accuracy of satellite products for total ozone. However, the spatial representativeness of ground-based observations at the pixel scale and the effects of the spatial mismatch between ground observations and satellite pixels are often overlooked. Therefore, this study focused on an Ozone Monitoring Instrument (OMI) and
-
Graph-infused hybrid vision transformer: Advancing GeoAI for enhanced land cover classification Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-28 Muhammad Hassaan Farooq Butt, Jian Ping Li, Muhammad Ahmad, Muhammad Adnan Farooq Butt
Hyperspectral Image Classification (HSIC) is a challenging task due to the high-dimensional nature of Hyperspectral Imaging (HSI) data and the complex relationships between spectral and spatial information. This paper proposes a Graph-Infused Hybrid spatial–spectral Transformer (GFormer) for HSIC. The GFormer combines the power of graph and spatial–spectral transformer to capture both spectral relationships
-
CRformer: Multi-modal data fusion to reconstruct cloud-free optical imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-27 Yu Xia, Wei He, Qi Huang, Guoying Yin, Wenbin Liu, Hongyan Zhang
Cloud contamination is a common problem in Earth observation that hinders various remote sensing applications. To address this problem, recent studies have employed deep neural networks and multi-modal data fusion to reconstruct cloud-free optical imagery. However, this task faces many challenges, such as: (1) the scarcity of suitable multi-modal datasets; (2) the ineffective use of feature correlations;
-
Estimating carbon emissions from thermal power plants based on thermal characteristics Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-26 Kairui Li, Hong Fan, Peiwen Yao
Carbon emissions from thermal power plants (TPPs) are a significant source of anthropogenic carbon and a priority for carbon reduction efforts. The accurate estimating of carbon emissions from TPPs is essential research in the field of carbon reduction. In this study, we take TPPs located in the United States as examples to discuss the relationship between the thermal characteristics of the power plant
-
Assessing topographic effects on forest responses to drought with multiple seasonal metrics from Sentinel-2 Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-25 Yirong Sang, Feng Tian, Hongxiao Jin, Zhanzhang Cai, Luwei Feng, Yujie Dou, Lars Eklundh
Topography determines run-off direction, redistributes groundwater, and affects land surface solar radiation loads and the associated evaporative forcing, consequently, topography can modulate the impact of drought and heat waves on ecosystems. This topographic modulation effect, which typically occurs at the local scale, is often overlooked when assessing ecosystem drought responses using moderate-to-coarse
-
A novel machine learning-based approach for improving global correction of AIRS-derived water vapor satellite product Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-25 Jiafei Xu, Zhizhao Liu
Precise precipitable water vapor (PWV) observations play a crucially important role in weather and climate research. The Atmospheric Infrared Sounder (AIRS) on-board the Aqua spacecraft is a hyperspectral instrument that offers operational PWV products using infrared (IR) channels. However, the observational accuracy of AIRS-sensed cloudy-sky PWV products is much poorer than that of PWV retrievals
-
Dynamic clustering transformer network for point cloud segmentation Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-23 Dening Lu, Jun Zhou, Kyle (Yilin) Gao, Jing Du, Linlin Xu, Jonathan Li
Point cloud segmentation is one of the most important tasks in LiDAR remote sensing with widespread scientific, industrial, and commercial applications. The research thereof has resulted in many breakthroughs in 3D object and scene understanding. Existing methods typically utilize hierarchical architectures for feature representation. However, the commonly used sampling and grouping methods in hierarchical
-
Dynamics of the 2021 unrest at Changbaishan Tianchi volcano from ALOS-2/PALSAR-2 and seismic data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-23 Lianhuan Wei, Ying Sun, Xingyu Pan, Guoming Liu, Elisa Trasatti, Cristiano Tolomei, Guido Ventura, Christian Bignami, Meng Ao, Shanjun Liu
The Changbaishan Tianchi intraplate volcano is one of the most active and hazardous volcanoes of NE Asia, characterized by a summit caldera formed after the 946 CE ‘Millennium’ Plinian eruption. From December 2020 to June 2021, the frequency and magnitude of earthquakes at Tianchi were significantly higher than during background periods, with hundreds of earthquakes (46 events per month in average)
-
GFSegNet: A multi-scale segmentation model for mining area ground fissures Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-22 Peng Chen, Peixian Li, Bing Wang, Xingcheng Ding, Yongliang Zhang, Tao Zhang, TianXiang Yu
Precise identification of ground fissures is of paramount importance for the safety and environmental management of coal mining areas. However, the surface environment in coal mining regions is complex, and, to date, the efficiency of artificial fissure detection has been relatively low. Therefore, we have proposed a ground fissure automatic identification model based on an encoder–decoder architecture
-
A semi-analytical approach for estimating inland water inherent optical properties and chlorophyll a using airborne hyperspectral imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-22 Chao Niu, Kun Tan, Xue Wang, Peijun Du, Chen Pan