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Evaluating Landsat-9 TIRS-2 calibrations and land surface temperature retrievals against ground measurements using multi-instrument spatial and temporal sampling along transects Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-28 Raquel Niclòs, Martín Perelló, Jesús Puchades, César Coll, Enric Valor
High-resolution TIR data are essential for a wide range of studies, e.g., in environmental monitoring, urban effects, water stress, agricultural productivity, and land/water resources. Landsat series provides high-resolution TIR data, but data accuracy must be ensured to contribute to these fields. After the calibration problems of Landsat 8 (L8) TIRS due to a stray light effect, efforts were made
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Pre-seasonal temperature trend break dominating the trend break in autumn grassland phenology in China Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-29 Ning Qi, Yanzheng Yang, Guijun Yang, Weizhong Li, Chunjiang Zhao, Jun Zhao, Boheng Wang, Shaofeng Su, Pengxiang Zhao
Interannual variations in the end of the growing season (EOS) play a crucial role in assessing carbon and energy cycling within grassland ecosystems. Previous studies have often fixed the trend breakpoint in autumn phenology around the year 2000 to examine the response of the vegetation EOS to long-term climate change. However, the asymmetry of climate change and the diversity of grass species may
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Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2, and Landsat data fusion Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-29 Radoslaw Guzinski, Héctor Nieto, Rubén Ramo Sánchez, Juan Manuel Sánchez, Ihab Jomaa, Rim Zitouna-Chebbi, Olivier Roupsard, Ramón López-Urrea
One of the primary applications of satellite Land Surface Temperature (LST) observations lies in their utilization for modeling of actual evapotranspiration (ET) in agricultural crops, with the primary goals of monitoring and enhancing irrigation practices and improving crop water use productivity, as stipulated by Sustainable Development Goal (SDG) indicator 6.4.1. Evapotranspiration is a complex
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Comparison of 2D and 3D vegetation species mapping in three natural scenarios using UAV-LiDAR point clouds and improved deep learning methods Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-30 Liwei Deng, Bolin Fu, Yan Wu, Hongchang He, Weiwei Sun, Mingming Jia, Tengfang Deng, Donglin Fan
Collaboration between Light Detection and Ranging (LiDAR) point clouds and deep learning has been proven to be an effective approach for vegetation mapping. Current studies have predominantly focused on 2D vegetation mapping, whereas 3D mapping, which directly classifies point clouds at point level, offers a more comprehensive understanding of the stratified structural information of vegetation. However
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Leveraging google earth engine cloud computing for large-scale arctic wetland mapping Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-30 Michael Merchant, Brian Brisco, Masoud Mahdianpari, Laura Bourgeau-Chavez, Kevin Murnaghan, Ben DeVries, Aaron Berg
Climate-driven permafrost degradation and an intensification of the hydrological cycle are rapidly altering the intricate ecohydrological processes of Arctic wetlands, threatening their long-term carbon sequestration capabilities. Addressing this concern through effective management holds immense potential for climate regulation, mitigation, and adaptation efforts. As such, there is growing need for
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Dense orchard landscape mapping based on image merging with skeleton prediction Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-28 Shijia Pan, Zijie Niu, Juntao Deng, Wen Gao, Yuncai Yan, Mingu Zhou, Wenting Han
To address the difficulty of individual vine-plant segmentation in large orchards, this study proposes a segmentation model for individual canopies that combines the characteristics of tree deciduousness and natural growth patterns. This enables the landscape mapping of orchards under intensive planting conditions. In this study, winter and summer unmanned aerial vehicle (UAV) remote-sensing images
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Present-day land subsidence over Semarang revealed by time series InSAR new small baseline subset technique Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-25 Arif Aditiya, Takeo Ito
Over the last two decades, Semarang, located in the central region of Java island, Indonesia, has been undergoing significant land subsidence. Mainly attributed to reservoir compaction and fluid extraction, this subsidence has pronounced effects in coastal regions. However, the existing study on the details of subsidence and its application to accurately depict present circumstances has been insufficient
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Adaptive multi-object tracking based on sensors fusion with confidence updating Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-25 Junting Liu, Deer Liu, Weizhen Ji, Chengfeng Cai, Zhen Liu
Multi-object tracking (MOT) systems typically rely on object detection results for tracking, so the accuracy of the MOT system is significantly affected by the error of the detector. Changes in error usually lead to unstable tracking. Regarding this problem, we proposed an adaptive MOT method based on detection confidence. At first, we use a simple data fusion method to combine the detection results
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Not-so-random forests: Comparing voting and decision tree ensembles for characterizing partial harvest events Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-18 Valerie J. Pasquarella, Luca L. Morreale, Christopher F. Brown, John B. Kilbride, Jonathan R. Thompson
Ensemble-based change detection can improve map accuracies by combining information from multiple datasets. There is a growing literature investigating ensemble inputs and applications for forest disturbance detection and mapping. However, few studies have evaluated ensemble methods other than Random Forest classifiers, which rely on uninterpretable “black box” algorithms with hundreds of parameters
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Towards intelligent ground filtering of large-scale topographic point clouds: A comprehensive survey Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-21 Nannan Qin, Weikai Tan, Haiyan Guan, Lanying Wang, Lingfei Ma, Pengjie Tao, Sarah Fatholahi, Xiangyun Hu, Jonathan Li
With the fast development of 3D data acquisition techniques, topographic point clouds have become easier to acquire and have promoted many geospatial applications. Ground filtering (GF), as one of the most fundamental and challenging tasks for the post-processing of large-scale topographic point clouds, has been extensively studied but has yet to be well solved. To reveal future superior solutions
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Analysis of the performance of polarimetric PSI over distributed scatterers with Sentinel-1 data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-22 Jiayin Luo, Juan M. Lopez-Sanchez, Francesco De Zan
Sentinel−1 (S1) data enables effective monitoring of displacements using persistent scatterer interferometry (PSI). S1 includes VV and VH polarization channels, allowing us to apply polarimetric techniques to PSI. In short, polarimetric PSI (PolPSI) exploits the available polarization channels to enhance the identification and processing of measurement points including persistent scatterers (PS) and
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Long-term vertical-land-motion investigation with space and terrestrial geodetic techniques near San Leon, Texas, USA Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-22 Xiaojun Qiao, Tianxing Chu, Philippe Tissot, Seneca Holland
Monitoring vertical land motion (VLM) along coastlines, which influences the dynamics of sea level changes in relation to the land, is a challenging task due to its inherent high spatiotemporal variability and limited availability of observations. This study aimed to investigate the rates, patterns, and drivers of land subsidence near the coastal town of San Leon, TX, United States, since the 1990s
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Division of the tropical savanna fire season into early and late dry season burning using MODIS active fires Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-22 Tom Eames, Roland Vernooij, Jeremy Russell-Smith, Cameron Yates, Andrew Edwards, Guido R. van der Werf
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Disaggregation of remote sensing and model-based data for 1 km daily seamless soil moisture Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-17 Luyao Zhu, Hongquan Wang, Tianjie Zhao, Wenjie Li, Yongjun Li, Cheng Tong, Xiaodong Deng, Huafeng Yue, Ke Wang
High-resolution soil moisture (SM) products are crucial for managing water in agricultural regions, irrigation scheduling, and land–atmosphere model simulations. Most satellite-based SM products have spatial resolutions in the tens of kilometers, which cannot satisfy the aforementioned applications, and there is an urgent need to disaggregate them to fine spatial resolutions. Currently, disaggregation
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Coarse-to-fine matching via cross fusion of satellite images Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-17 Liangzhi Li, Ling Han, Kyle Gao, Hongjie He, Lanying Wang, Jonathan Li
The registration of multimodal satellite images is essential for a prerequisite for accruing complementary observational data. Nevertheless, the differential imaging nuances amongst non-linear radiometric multimodal images precipitate a complexity in keypoint detection, rendering it a great challenge. This complexity exacerbates the difficulty encountered in matching multimodal satellite images. In
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Georeferencing of UAV imagery for nearshore bathymetry retrieval Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-18 Diogo Santos, Tiago Abreu, Paulo A. Silva, Paulo Baptista
In dynamic coastal areas where rapid changes in the seabed can have a significant impact on day–to–day operations, as is the case of Figueira da Foz harbor inlet, it is important to have updated knowledge of the bathymetry. As in energetic wave conditions it is not possible to carry out traditional bathymetric surveys, this work proposes a methodology that allows, using UAVs, to estimate the bathymetry
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Proposition of UAV multi-angle nap-of-the-object image acquisition framework based on a quality evaluation system for a 3D real scene model of a high-steep rock slope Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-18 Mingyu Zhao, Jianping Chen, Shengyuan Song, Yongchao Li, Fengyan Wang, Sicong Wang, Dianze Liu
The 3D real scene model of high-steep rock slope established based on UAV image provides convenience for non-contact identification and interpretation of rock mass structures. The quality of 3D models directly influences the interpretability of rock mass structures. However, quantitative studies on the relationship between these two aspects are scarce. Therefore, this study investigates the influence
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Ten deep learning techniques to address small data problems with remote sensing Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-18 Anastasiia Safonova, Gohar Ghazaryan, Stefan Stiller, Magdalena Main-Knorn, Claas Nendel, Masahiro Ryo
Researchers and engineers have increasingly used Deep Learning (DL) for a variety of Remote Sensing (RS) tasks. However, data from local observations or via ground truth is often quite limited for training DL models, especially when these models represent key socio-environmental problems, such as the monitoring of extreme, destructive climate events, biodiversity, and sudden changes in ecosystem states
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Detection of large-scale Spartina alterniflora removal in coastal wetlands based on Sentinel-2 and Landsat 8 imagery on Google Earth Engine Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-18 Yukui Min, Liyue Cui, Jinyuan Li, Yue Han, Zhaojun Zhuo, Xiaolan Yin, Demin Zhou, Yinghai Ke
The invasion of Spartina alterniflora has posed significant threats to the ecosystem health and biodiversity in coastal wetlands in China. In recent years, China has enacted large-scale S. alterniflora removal projects. Accurate monitoring of the S. alterniflora removal dynamics is crucial for evaluating the effectiveness of the removal projects and coastal wetland management. In this study, we presented
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An adaptive multi-perceptual implicit sampling for hyperspectral and multispectral remote sensing image fusion Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-15 Chunyu Zhu, Rongyuan Dai, Liwei Gong, Liangbo Gao, Na Ta, Qiong Wu
Hyperspectral and multispectral remote sensing image fusion (HMIF) can effectively enhance image spatial-spectral information representation. However, existing deep learning algorithms usually use neighboring pixels for interpolation in the sampling process, which ignores the correlation of different receptive field pixels. To solve the above problem, this study proposes an adaptive multi-perceptual
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Assessing damage to agricultural fields from military actions in Ukraine: An integrated approach using statistical indicators and machine learning Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-14 Nataliia Kussul, Sofiia Drozd, Hanna Yailymova, Andrii Shelestov, Guido Lemoine, Klaus Deininger
The ongoing full-scale Russian invasion of Ukraine has led to widespread damage of agricultural lands, jeopardizing global food security. Timely detection of impacted fields enables quantification of production losses, guiding recovery policies and monitoring military actions. This study presents a robust methodology to automatically identify agricultural areas damaged by wartime ground activities
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Predicting the transmission trend of respiratory viruses in new regions via geospatial similarity learning Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-15 Yunxiang Zhao, Mingda Hu, Yuan Jin, Fei Chen, Xin Wang, Boqian Wang, Junjie Yue, Hongguang Ren
The outbreak and spread of COVID-19 remind us again of the devastating attack that human-to-human transmitted respiratory infectious diseases (H-HRIDs) bring to global economics and public health. Predicting the trend of H-HRIDs yields important suggestions for making response strategies. Existing methods predict the future trend of H-HRIDs based on the prophase epidemiological data. However, it is
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SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-15 Haojia Yu, Han Hu, Bo Xu, Qisen Shang, Zhendong Wang, Qing Zhu
Most urban applications necessitate building footprints in the form of concise vector graphics with sharp boundaries rather than pixel-wise raster images. This need contrasts with the majority of existing methods, which typically generate over-smoothed footprint polygons. Editing these automatically produced polygons can be inefficient, if not more time-consuming than manual digitization. This paper
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Contribution of urban trees in reducing land surface temperature: Evidence from china's major cities Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-15 Andong Guo, Tingting He, Wenze Yue, Wu Xiao, Jun Yang, Maoxin Zhang, Mengmeng Li
Urban trees mitigate urban heat by altering evapotranspiration processes and providing shade to their surrounding environment. Nevertheless, the impact through which three-dimensional tree characteristics alleviate Land Surface Temperature (LST) remain uncertain, especially for climatic zone differences. In this study, we investigated the potential of trees to mitigate LST in 35 Chinese major cities
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Assessment of the large-scale extraction of photovoltaic (PV) panels with a workflow based on artificial neural networks and algorithmic postprocessing of vectorization results Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-16 Miguel-Ángel Manso-Callejo, Calimanut-Ionut Cira, José-Juan Arranz-Justel, Izar Sinde-González, Tudor Sălăgean
Having a complete and high-quality geospatial catalogue of existing large-scale photovoltaic (PV) panels is very important nowadays, due to the rapid increase in the use of this type of installations. This catalogue could be used to estimate, with a higher level of granularity, the energy that can be produced from solar radiation forecasts, to operate the electricity system efficiently by adjusting
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Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-16 Zobaer Ahmed, Lawton Nalley, Kristofor Brye, V. Steven Green, Michael Popp, Aaron M. Shew, Lawson Connor
Accurately identifying and systematically mapping winter-time cover crops and their phenological characteristics offer significant benefits to agricultural producers and policymakers, as cover crops are one of several potential solutions to climate change mitigation. We present a methodological framework for identifying and mapping the presence of winter-time cover crops at the field level and aggregated
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Marginal agricultural land identification in the Lower Mississippi Alluvial Valley based on remote sensing and machine learning model Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-16 Prakash Tiwari, Krishna P. Poudel, Jia Yang, Bruno Silva, Yun Yang, Mark McConnell
Marginal agricultural lands are considered unsuitable for conventional crop production but could be utilized for biofuel production, groundwater recharge, afforestation, and other land use purposes. Effective management of these lands requires accurate identification and understanding of their spatial distribution. We used random forest regression (RFR) with remotely sensed vegetation indices, soil
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Characterization of an antarctic penguin colony ecosystem using high-resolution UAV hyperspectral imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-15 Alejandro Román, Antonio Tovar-Sánchez, Beatriz Fernández-Marín, Gabriel Navarro, Luis Barbero
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A RGB-Thermal based adaptive modality learning network for day–night wildfire identification Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-11 Xue Rui, Ziqiang Li, Xueyu Zhang, Ziyang Li, Weiguo Song
Wildfires have long been a danger to the atmosphere and ecological environment. With the advancement of deep learning technology and sensor equipment, wildfire identification methods based on optical (RGB) or thermal infrared (TIR) images have made significant progress. However, single-modal identification technologies often experience serious performance degradation in challenging scenarios. Therefore
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Patch-based M3C2: Towards lower-uncertainty and higher-resolution deformation analysis of 3D point clouds Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-09 Yihui Yang, Volker Schwieger
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A lightweight and scalable greenhouse mapping method based on remote sensing imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-10 Wei Chen, Qingpeng Wang, Dongliang Wang, Yameng Xu, Yingxuan He, Lan Yang, Hongzhao Tang
Seeking a low-cost, high-efficiency greenhouse mapping technology has immense significance. While greenhouse extraction methods using deep learning have been proposed, the challenge of extracting dense small objects remains an unresolved problem. The inherent downscaling strategy in general-purpose semantic segmentation (SS) models renders them unsuitable for such tasks. In contrast, the dramatically
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Comparing Object-Based and Pixel-Based Machine Learning Models for Tree-Cutting Detection with PlanetScope Satellite Images: Exploring Model Generalization Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-08 Vahid Nasiri, Paweł Hawryło, Piotr Janiec, Jarosław Socha
Despite utilizing various remote sensing datasets, precise tree-cutting detection remains challenging due to spatial and spectral resolution constraints in satellite imagery, complex landscapes, data integration issues, and the need for accurate multi-temporal reference datasets. This study investigates the utilization of PlanetScope (PS) satellite images, along with pixel-based (PBIA) and object-based
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Early detection of pine shoot beetle attack using vertical profile of plant traits through UAV-based hyperspectral, thermal, and lidar data fusion Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-07 Qinan Lin, Huaguo Huang, Jingxu Wang, Ling Chen, Huaqiang Du, Guomo Zhou
Pine shoot beetle (PSB) is one of the most damaging forest insects of Yunnan pine plantations in southwest China. However, the subtle symptoms of heterogeneous tree crowns make it difficult to accurately detect at early stage of PSB attack. Here, we evaluated the potential of a combination of plant traits (PTs) and vegetation indices (VIs) to distinguish different levels of tree damage by integrating
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Automatic detection of charcoal kilns on Very High Resolution images with a computer vision approach in Somalia Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-08 Astrid Verhegghen, Laura Martinez-Sanchez, Michele Bolognesi, Michele Meroni, Felix Rembold, Petar Vojnoviæ, Marijn van der Velde
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Assessing spatiotemporal bikeability using multi-source geospatial big data: A case study of Xiamen, China Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-08 Shaoqing Dai, Wufan Zhao, Yanwen Wang, Xiao Huang, Zhidong Chen, Jinghan Lei, Alfred Stein, Peng Jia
This study focuses on the development of a new framework for evaluating bikeability in urban environments with the aim of enhancing sustainable urban transportation planning. To close the research gap that previous studies have disregarded the dynamic environmental factors and trajectory data, we propose a framework that comprises four sub-indices: safety, comfort, accessibility, and vitality. Utilizing
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Branching the limits: Robust 3D tree reconstruction from incomplete laser point clouds Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-08 Weixi Wang, Yaoyu Li, Hongsheng Huang, Linping Hong, Siqi Du, Linfu Xie, Xiaoming Li, Renzhong Guo, Shengjun Tang
Accurate individual tree reconstruction based on laser point clouds is vital for precise biomass estimation, virtual geographic environment modeling, and simulation. However, mobile laser 3D scanning systems often capture tree point clouds obscured by leaves, resulting in missing or incomplete branch data, posing significant challenges to detailed tree reconstruction. In this paper, a novel method
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Quantized compression of SAR data: Bounds on signal fidelity, InSAR PS candidates identification and surface motion accuracy Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-05 Man Wai Yip, A. Alexander G. Webb, Pablo J. González
Satellite radar imaging has been used as a remote sensing tool for studying Earth’s surface. High spatial resolution achieved by Synthetic Aperture Radar (SAR) allows to classify landcover and, using radar interferometry, to measure topography and surface deformation at millimeter scale. However, the handling of voluminous SAR data has presented a significant challenge over the past decade, leading
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Lithological mapping of geological remote sensing via adversarial semi-supervised segmentation network Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-03 Sheng Wang, Xiaohui Huang, Wei Han, Jun Li, Xiaohan Zhang, Lizhe Wang
Geological remote sensing interpretation (GRSI), which aims to recognize multiple geological elements based on their characteristics on satellite remote sensing images, is vital in large-scale regional lithological mapping. However, due to the influence of long-term geological movements, the spatial distribution of geological elements (such as lithology, glaciers, and soils) on the image is often complex
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Sentinel-1 InSAR-derived land subsidence assessment along the Texas Gulf Coast Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-03 Xiaojun Qiao, Tianxing Chu, Philippe Tissot, Seneca Holland
Mapping large-scale coastal subsidence is significant in providing valuable support to decision-making stakeholders to recognize impacts of potential natural disasters. However, this task presents significant challenges due to its highly complex nature of the spatial–temporal variability. Recent advances in the observation capability of synthetic aperture radar (SAR) missions and the processing algorithms
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An attention model with multiple decoders for solving p-Center problems Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-04 Xu Chen, Shaohua Wang, Huilai Li, Haojian Liang, Ziqiong Li, Hao Lu
The p-Center Problem (PCP) is a classical discrete facility location problem (FLP) with broad real-world application scenarios, such as public facilities and urban emergency services facilities. Most of the current methods for solving PCP are exact and heuristic. However, as the problem conditions are added or the problem scale is increased, the complexity of exact and heuristic methods for solving
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Continuous burned area monitoring using bi-temporal spectral index time series analysis Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-04 Vangelis Fotakidis, Irene Chrysafis, Giorgos Mallinis, Nikos Koutsias
Extreme wildfires are a major agent of disturbance in present-day forest ecosystems and, have serious impacts on society in terms of human casualties and economic losses. Prioritizing and implementing relief and recovery measures after fires require explicit spatial information on the disaster extent. Information for detecting and monitoring ecosystem disturbance due to wildfires is obtainable using
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Analysing gender differences in the perceived safety from street view imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-31 Qinyu Cui, Yan Zhang, Guang Yang, Yiting Huang, Yu Chen
The relationship between the built environment and human perception of safety is well recognised in a growing literature of urban studies. However, there is a lack of attention to gender differences in perceptions of place, particularly in studies that assess perceived safety using street view images (SVIs). This limitation hinders the comprehensive assessment of safety perceptions. Traditional analyses
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The Segment Anything Model (SAM) for remote sensing applications: From zero to one shot Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-01 Lucas Prado Osco, Qiusheng Wu, Eduardo Lopes de Lemos, Wesley Nunes Gonçalves, Ana Paula Marques Ramos, Jonathan Li, José Marcato
Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis. SAM is known for its exceptional generalization capabilities and zero-shot learning, making it a promising approach to processing aerial and orbital images
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Sentinel-2 and Landsat-8 potentials for high-resolution mapping of the shifting agricultural landscape mosaic systems of southern Cameroon Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-11-01 Christin Steve Keyamfe Nwagoum, Martin Yemefack, Francis Brice Silatsa Tedou, Fritz Tabi Oben
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Stratifying forest overstory and understory using the Global Ecosystem Dynamic Investigation laser scanning data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-30 Zengxin Yun, Guang Zheng, L. Monika Moskal, Jiarui Li, Peng Gong
Most multi-layer natural forest structures, usually containing obvious vertical structures including overstory, understory, and grass, show significant differences in structure, phenology variations, and photosynthetic capacity. However, it is still challenging to extract the waveforms of overstory and understory in forests with varied canopy covers and topographic conditions using the Global Ecosystem
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A feature enhancement framework for landslide detection Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-27 Ruilong Wei, Chengming Ye, Tianbo Sui, Huajun Zhang, Yonggang Ge, Yao Li
Accurate landslide detection is essential for disaster mitigation and relief. In this study, we develop a feature enhancement framework that integrates attention and multiscale mechanisms with U-Net (AMU-Net) for landslide detection. The framework has four steps. First, the attention module in the convolutional block enhances the feature response of landslides when extracting high-level feature representations
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Forest height estimation combining single-polarization tomographic and PolSAR data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-27 Yihao Zhang, Xing Peng, Qinghua Xie, Yanan Du, Bing Zhang, Xiaomin Luo, Shaobo Zhao, Zhentao Hu, Xinwu Li
Forest height is of great significance for forest resource management and forest carbon sink estimation. Tomographic synthetic aperture radar (TomoSAR) technology provides an effective means for the accurate inversion of this parameter. Several multi-polarization synthetic aperture radar (SAR) images are generally required to obtain forest height. However, it is common that only a small number of single-polarization
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Equalizing urban agriculture access in Glasgow: A spatial optimization approach Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-26 Amy Russell, Ziqi Li, Mingshu Wang
Glasgow, Scotland, United Kingdom, has long-term issues with inequalities in health and food security, as well as large areas of vacant and derelict land. Urban agriculture projects can increase access to fresh food, improve mental health and nutrition, and empower and bring communities together. We investigated the distribution of urban agriculture in Glasgow and found that the current configuration
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Building and road detection from remote sensing images based on weights adaptive multi-teacher collaborative distillation using a fused knowledge Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-24 Ziyi Chen, Liai Deng, Jing Gou, Cheng Wang, Jonathan Li, Dilong Li
Knowledge distillation is one effective approach to compress deep learning models. However, the current distillation methods are relatively monotonous. There are still rare studies about the combination of distillation strategies using multiple types of knowledge and employing multiple teacher models. Besides, how to optimize the weights among different teacher models is still an open problem. To address
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Developing effective wildfire risk mitigation plans for the wildland urban interface Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-26 Alan T. Murray, Jiwon Baik, Vanessa Echeverri Figueroa, Darlene Rini, Max A. Moritz, Dar A. Roberts, Stuart H. Sweeney, Leila M.V. Carvalho, Charles Jones
The wildland urban interface (WUI) is a transition zone between mostly undeveloped, vegetated lands and more densely populated areas with buildings and other infrastructure. This interface is of great interest and concern in wildfire prone areas like California, as significant increases in scale, frequency and intensity of fires have resulted in devastating impacts to life-safety, property and other
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An approach to detect gas flaring sites using sentinel-2 MSI and NOAA-20 VIIRS images Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-24 Chenglin Hu, Xiuying Zhang, Xuewen Xing, Qian Gao
Detecting gas flaring activities during oil production on a regional scale is necessary, since it emits harmful gases and bring serious global environmental impacts. This study developed a new algorithm to detect gas flare sites (GFs) on daytime Sentinel-2 MSI images and Nighttime NOAA-20 VIIRS images. The algorithm includes three steps: Thermal Anomaly Index (TAI) on single-temporal MSI images was
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An improved surface water extraction method by integrating multi-type priori information from remote sensing Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-23 Bingyu Zhao, Jianjun Wu, Xinyi Han, Feng Tian, Mengxue Liu, Meng Chen, Jingyu Lin
Surface water mapping based on historical, neighbourhood, and other priori information has shown improved accuracy. However, the accuracy can be compromised due to the lack of consideration for water dynamics in the proximity period and the limited utilization of quantitative methods for integrating multiple types of priori information. In this study, an unsupervised surface water extraction method
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An improved active layer thickness retrieval method over Qinghai-Tibet permafrost using InSAR technology: With emphasis on two-dimensional deformation and unfrozen water Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-23 Jiachen Li, Qijie Wang, Ya Zhang, Sha Yang, Guanyou Gao
The increasing warming and humidification have caused the dramatic degradation of Qinghai-Tibet Plateau permafrost. The active layer thickness (ALT) is particularly crucial to be monitored with a wide range as an indispensable variable to characterize permafrost status. Interferometric synthetic aperture radar (InSAR) technology has recently been widely used for ALT retrieval. However, these studies
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Spatial optimization of cotton cultivation in Xinjiang: A climate change perspective Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-24 Yaqiu Zhu, Liang Sun, Qiyou Luo, Haoyu Chen, Yadong Yang
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An improved fusion of Landsat-7/8, Sentinel-2, and Sentinel-1 data for monitoring alfalfa: Implications for crop remote sensing Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-22 Jiang Chen, Zhou Zhang
Precision agriculture management with remote sensing big data provides a promising solution to monitor crops. Alfalfa is an important forage crop for various livestock around the world. Unlike corn and soybean, alfalfa growth is difficult to describe using a typical phenological curve since it is characterized by monthly harvest and rapid regrowth. Limited by the availability of high spatio-temporal
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Comparison of different machine learning algorithms for predicting maize grain yield using UAV-based hyperspectral images Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-21 Yahui Guo, Yi Xiao, Fanghua Hao, Xuan Zhang, Jiahao Chen, Kirsten de Beurs, Yuhong He, Yongshuo H. Fu
Timely and accurately predicting maize grain yields will contribute to making adaptive measures to improve management practice and to adjust consumption patterns for ensuring food security. Unmanned aerial vehicles (UAV) are widely used to obtain high-temporal and high-spatial resolution remote sensing images of crops, enabling a possible sensor performance comparison. To date, few studies have compared
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Robust surface crack detection with structure line guidance Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-21 Yongjun Zhang, Yixin Lu, Yansong Duan, Dong Wei, Xianzhang Zhu, Bin Zhang, Bohui Pang
Crack detection plays a pivotal role in civil engineering applications, where vision-based methods find extensive use. In practice, crack images are sourced from Unmanned Aerial Vehicles (UAV) and handheld photography, and the balance between the utilization of global and local information is the key to detecting cracks from images of different sources: the former tends to eliminate interferences with
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Reproducing computational processes in service-based geo-simulation experiments Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-19 Zhiyi Zhu, Min Chen, Lingzhi Sun, Zhen Qian, Yuanqing He, Zaiyang Ma, Fengyuan Zhang, Yongning Wen, Songshan Yue, Guonian Lü
Geo-simulation experiments (GSEs) are experiments allowing the simulation and exploration of Earth’s surface (such as hydrological, geomorphological, atmospheric, biological, and social processes and their interactions) with the usage of geo-analysis models (hereafter called ‘models’). Computational processes represent the steps in GSEs where researchers employ these models to analyze data by computer
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Bridging the gap: Enhancing visual indoor mapping through semantic association and reference alignment Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-16 Xiaohang Shao, Chun Liu, Hangbin Wu, Yanyi Li, Fanjin Cheng, Junyi Wei
In global navigation satellite system-denied environments, indoor point-cloud maps are valuable sources of detailed and inaccessible information. Dense visual point clouds can be produced quickly and inexpensively using stereo RGB cameras and simultaneous localization and mapping (SLAM) techniques. However, visual point-cloud mapping is affected by complex visual observation conditions, and a lack
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Mapping of land-based aquaculture regions in Southeast Asia and its Spatiotemporal change from 1990 to 2020 using time-series remote sensing data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2023-10-11 Junyao Zhang, Xiaomei Yang, Zhihua Wang, Yueming Liu, Xiaoliang Liu, Yaxin Ding
Aquaculture, a crucial component of global food production, faces sustainability challenges due to increased competition for natural resources, environmental pollution, and subpar management practices. Understanding spatial–temporal changes in aquaculture distribution can offer insights into guiding its scientific and sustainable development. While Southeast Asia constitutes a key player in global