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Integrated Evaluation and error decomposition of four gridded precipitation products using dense rain gauge observations over the Yunnan-Kweichow Plateau, China Eur. J. Remote Sens. (IF 4.0) Pub Date : 2024-03-11 Tianjian Lu, Qingquan Xiao, Hanyu Lu, Jintong Ren, Yongyi Yuan, Xiaoshan Luo, Zhijie Zhang
Evaluating the precision and applicability of high-quality precipitation products in the distinctive terrain and intricate climate of the Yunnan-Kweichow Plateau (YKP) is pivotal for climate resear...
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Growth unveiled: decoding the start of grassland seasons in Austria Eur. J. Remote Sens. (IF 4.0) Pub Date : 2024-03-05 Aleksandar Dujakovic, Andreas Schaumberger, Andreas Klingler, Konrad Mayer, Clement Atzberger, Anja Klisch, Francesco Vuolo
The start of the growing season (SOS) in grasslands is a critical factor that significantly affects grassland dynamics, production and quality. In the context of grassland fodder production, the ex...
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UAV image matching from handcrafted to deep local features Eur. J. Remote Sens. (IF 4.0) Pub Date : 2024-02-21 Xueman zhang, Feng Xiao, Maoteng Zheng, Zhong Xie
Local feature matching between images is a challenging task, particularly when there are significant appearance variations, such as extreme viewpoint changes. In this work, we present LoFTRS, a dee...
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Using bi-temporal ALS and NFI-based time-series data to account for large-scale aboveground carbon dynamics: the showcase of mediterranean forests Eur. J. Remote Sens. (IF 4.0) Pub Date : 2024-02-18 Juan Guerra-Hernández, Adrian Pascual, Frederico Tupinambá-Simões, Sergio Godinho, Brigite Botequim, Alfonso Jurado-Varela, Vicente Sandoval-Altelarrea
New remote-sensed biomass change products will transform our capacity to monitor and validate large-scale carbon dynamic in the next decade. In this study, we evaluated the use of multitemporal Air...
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Real-time identification of collapsed buildings triggered by natural disasters using a modified object-detection network with quasi-panchromatic images Eur. J. Remote Sens. (IF 4.0) Pub Date : 2024-02-18 Jiayi Ge, Qiao Wang, Hong Tang
During disaster response, it is very important to obtain the information of collapsed building distribution accurately and quickly. However, limited by some practical factors, existed methods often...
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Comparative Evaluation of LiDAR systems for transport infrastructure: case studies and performance analysis Eur. J. Remote Sens. (IF 4.0) Pub Date : 2024-02-16 Rabia Rashdi, Iván Garrido, Jesús Balado, Pablo Del Río-Barral, Juan Luis Rodríguez-Somoza, Joaquín Martínez-Sánchez
Mobile laser scanners are vital for intelligent transport infrastructure, capturing detailed 3D road representations, but their accuracy depends on factors like sensor positioning and environment. ...
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YOLOSeaShip: a lightweight model for real-time ship detection Eur. J. Remote Sens. (IF 4.0) Pub Date : 2024-01-31 Xiaoliang Jiang, Jianchen Cai, Ban Wang
With the rapid advancements in computer vision, ship detection models based on deep learning have been more and more prevalent. However, most network methods use expensive costs with high hardware ...
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Validation of photosynthetically active radiation by OLCI on Sentinel-3 against ground-based measurements in the central Mediterranean and possible aerosol effects Eur. J. Remote Sens. (IF 4.0) Pub Date : 2024-01-30 Mattia Pecci, Simone Colella, Tatiana Di Iorio, Daniela Meloni, Francesco Monteleone, Giandomenico Pace, Damiano Massimiliano Sferlazzo, Alcide Giorgio di Sarra
Instantaneous determinations of photosynthetically active radiation (PAR) over the sea from the Ocean and Land Color Instrument (OLCI) on Sentinel-3 are compared with in-situ measurements at the is...
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PRISMA hyperspectral imagery for mapping alteration zones associated with Kuhpanj porphyry copper deposit, Southern Iran Eur. J. Remote Sens. (IF 4.0) Pub Date : 2024-01-22 Maryam Esmaeili, Nader Fathianpour, Saeed Soltani-Mohammadi
Hyperspectral images have been extensively employed to map alterations related to various ore deposits, particularly those associated with porphyry copper deposits. The present study aims to evalua...
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Upgrade and extension of LSA-SAF land surface albedo archive from EPS Metop/AVHRR: description and quality assessment Eur. J. Remote Sens. (IF 4.0) Pub Date : 2024-01-21 Anthéa Delmotte, Daniel Juncu, Xavier Ceamanos, Isabel F. Trigo, Sandra Gomes
ETAL is the operational EPS Ten-Day Albedo product, produced by the EUMETSAT Satellite Application Facility for Land Surface Analysis (LSA SAF). By back-processing the full catalogue of EPS-Metop r...
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Mitigating the impact of dense vegetation on the Sentinel-1 surface soil moisture retrievals over Europe Eur. J. Remote Sens. (IF 4.0) Pub Date : 2024-01-10 Samuel Massart, Mariette Vreugdenhil, Bernhard Bauer-Marschallinger, Claudio Navacchi, Bernhard Raml, Wolfgang Wagner
The C-band Synthetic Aperture Radar (SAR) on board of the Sentinel-1 satellites have a strong potential to retrieve Surface Soil Moisture (SSM). Using a change detection model to Sentinel-1 backsca...
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Regional estimates of gross primary production applying the Process-Based Model 3D-CMCC-FEM vs. Remote-Sensing multiple datasets Eur. J. Remote Sens. (IF 4.0) Pub Date : 2024-01-09 D. Dalmonech, E. Vangi, M. Chiesi, G. Chirici, L. Fibbi, F. Giannetti, G. Marano, C. Massari, A. Nolè, J. Xiao, A. Collalti
Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of carbon fluxes and stocks in forests. Yet, their predictive capacity should be demonst...
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Combining multiple UAV-Based indicators for wheat yield estimation, a case study from Germany Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-12-22 Shovkat Khodjaev, Lena Kuhn, Ihtiyor Bobojonov, Thomas Glauben
Unmanned aircraft vehicles (UAV) are widely used for yield estimations in agricultural production. Many significant improvements have been made towards the usage of hyperspectral and thermal sensor...
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Mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and H2O library Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-12-22 Jiří Šandera, Přemysl Štych
Permanent grasslands play a very important role in the landscape. The loss of permanent grasslands and their subsequent conversion into arable land create erosion-prone agricultural areas in the la...
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Predictions of Spartina alterniflora leaf functional traits based on hyperspectral data and machine learning models Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-12-22 Wei Li, Xueyan Zuo, Zhijun Liu, Leichao Nie, Huazhe Li, Junjie Wang, Zhiguo Dou, Yang Cai, Xiajie Zhai, Lijuan Cui
Investigating the functional traits of Spartina alterniflora can provide insights towards understanding its invasion mechanism, and developing a method leaves can improve its management in coastal ...
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Asymmetry of leaf internal structure affects PLSR modelling of anatomical traits using VIS-NIR leaf level spectra Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-12-18 Eva Neuwirthová, Zuzana Lhotáková, Lucie Červená, Petr Lukeš, Petya Campbell, Jana Albrechtová
Leaf traits can be used to elucidate vegetation functional responses to global climate change. Pigments, water and leaf mass per area are the most used traits. However, detailed anatomical traits s...
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A review of research on remote sensing images shadow detection and application to building extraction Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-12-13 Xueyan Dong, Jiannong Cao, Weiheng Zhao
Buildings are one of the most important habitats for humans, and therefore, accurate identification and extraction of building information in remote sensing images are crucial. Buildings in remote ...
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A study of high-resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancement Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-12-11 Kun Wang, Ling Han, Juan Liao
This paper takes landslide as a special research object. For the problems of landslide detection in remote sensing images, deep learning and playback method is adopted. Using the You Only Look Once...
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On-orbit geometric calibration and preliminary accuracy verification of GaoFen-14 (GF-14) optical two linear-array stereo camera Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-12-03 Bincai Cao, Wang Jianrong, Hu Yan, Lv Yuan, Yang Xiuce, Lu Xueliang, Li Gang, Wei Yongqiang, Liu Zhuang
The GaoFen-14 (GF-14) satellite is China’s most recent high-resolution earth observation satellite system. It is equipped with a two linear-array stereo camera and is intend for topographic mapping...
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GAUSS: Guided encoder - decoder Architecture for hyperspectral Unmixing with Spatial Smoothness Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-11-18 H.M.K.D. Wickramathilaka, D. Fernando, D. Jayasundara, D. Wickramasinghe, D.Y.L. Ranasinghe, G.M.R.I. Godaliyadda, M.P.B. Ekanayake, H.M.V.R. Herath, L. Ramanayake, N. Senarath, H.M.H.K. Weerasooriya
This study introduces GAUSS (Guided encoder-decoder Architecture for hyperspectral Unmixing with Spatial Smoothness), a novel autoencoder-based architecture for hyperspectral unmixing (HU). GAUSS c...
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Future of urban remote sensing and new sensors Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-11-23 Tu Nguyen, Nam P. Nguyen, Claudio Savaglio, Ying Zhang, Braulio Dumba
Published in European Journal of Remote Sensing (Ahead of Print, 2023)
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Cloud climatology of northwestern Mexico based on MODIS data Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-11-15 A. Karen Ramírez-López, Noel Carbajal, Luis F. Pineda-Martínez, José Tuxpan-Vargas
The geographical regions of northwestern Mexico consisting of the Pacific Ocean, the Baja California Peninsula with its mountain range along it, the Gulf of California, and the coastal zone with it...
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A New ground open water detection scheme using Sentinel-1 SAR images Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-11-15 Songxin Tan
The detection of groundwater is essential not only for scientific research but also for agricultural purposes. This research aims to improve the accuracy and reliability of detecting ground standin...
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Modelling in-ground wood decay using time-series retrievals from the 5th European climate reanalysis (ERA5-Land) Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-11-07 Brendan N. Marais, Marian Schönauer, Philip Bester van Niekerk, Jonas Niklewski, Christian Brischke
This article presents models to predict the time until mechanical failure of in-ground wooden test specimens resulting from fungal decay. Historical records of decay ratings were modelled by remote...
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Tree species classification on images from airborne mobile mapping using ML.NET Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-11-07 Maja Michałowska, Jacek Rapiński, Joanna Janicka
Deep learning is a powerful tool for automating the process of recognizing and classifying objects in images. In this study, we used ML.NET, a popular open-source machine learning framework, to dev...
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Wind field reconstruction based on dual-polarized synthetic aperture radar during a tropical cyclone Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-11-01 Zhengzhong Lai, Mengyu Hao, Weizeng Shao, Wei Shen, Yuyi Hu, Xingwei Jiang
A wind field reconstruction method for dual-polarized (vertical-vertical [VV] and vertical-horizontal [VH]) Sentinel-1 (S-1) synthetic aperture radar (SAR) images collected during tropical cyclones...
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Spatial and temporal evolution of air pollution and verification of the environmental Kuznets curve in the Yangtze River Basin during 1980—2019 Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-10-30 Peipei He, Jingru Lv, Lijie He, Kaifeng Ma, Qingfeng Hu, Xin Liu
Continuously high concentrations of haze pollution can hinder urban economic development. In order to improve the quality of the environment in the Yangtze River Economic Belt, it is necessary to i...
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Deep convolutional transformer network for hyperspectral unmixing Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-10-30 Fazal Hadi, Jingxiang Yang, Ghulam Farooque, Liang Xiao
Hyperspectral unmixing (HU) is considered one of the most important ways to improve hyperspectral image analysis. HU aims to break down the mixed pixel into a set of spectral signatures, often comm...
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Uncertainty assessment of Sentinel-2-retrieved vegetation spectral indices over Europe Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-10-19 S. De Petris, F Sarvia, E. Borgogno-Mondino
Vegetation spectral indices (VIs) from multispectral remotely sensed imagery provide useful information in several sectors, especially if longing for change detection analyses or land monitoring. I...
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Synthetic aperture radar polarised backscattering behaviour in partially inundated agricultural fields Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-10-19 Keisuke Hoshikawa, Porntip Phontusang, Roengsak Katawatin
A methodology for classifying rainfed paddy fields based on their hydrological conditions is lacking. This study analysed the behaviour of synthetic aperture radar (SAR) backscatter coefficients at...
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Sensitivity of surface soil moisture retrieval to satellite-derived vegetation descriptors over wheat fields in the Kairouan plain Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-10-06 Emna Ayari, Mehrez Zribi, Zohra Lili-Chabaane, Zeineb Kassouk, Lionel Jarlan, Nemesio Rodriguez-Fernandez, Nicolas Baghdadi
Soil moisture estimation is a key component in hydrological processes and irrigation amounts' estimation. The synergetic use of optical and radar data has been proven to retrieve the surface soil m...
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Analysing the spatial context of the altimetric error pattern of a digital elevation model using multiscale geographically weighted regression Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-09-25 Zuleide Ferreira, Ana Cristina Costa, Pedro Cabral
Many freely available Digital Elevation Models (DEM) have increasingly been used worldwide due to the difficulty in acquiring accurate elevation data in some regions, emphasizing the need to invest...
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Crop water use dynamics over arid and semi-arid croplands in the lower Colorado River Basin Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-09-25 Neda Abbasi, Hamideh Nouri, Pamela Nagler, Kamel Didan, Sattar Chavoshi Borujeni, Armando Barreto-Muñoz, Christian Opp, Stefan Siebert
Numerous studies have evaluated the application of Remote Sensing (RS) techniques for mapping actual evapotranspiration (ETa) using Vegetation-Index-based (VI-based) and surface energy balance meth...
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Detecting semi-arid forest decline using time series of Landsat data Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-09-25 Elham Shafeian, Fabian Ewald Fassnacht, Hooman Latifi
Detecting forest decline is crucial for effective forest management in arid and semi-arid regions. Remote sensing using satellite image time series is useful for identifying reduced photosynthetic ...
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Geochemical analysis of SAR backscattering (Sentinel-1) on global ocean oil spill cases Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-09-21 José Milton Neves de Souza Júnior, Luís Felipe Ferreira de Mendonça, Heverton da Silva Costa, Juliana Costi, Rodrigo Nogueira Vasconcelos, André Telles da Cunha Lima, Sidnei João Siqueira Sant’anna, José Marques Lopes, Milton José Porsani, de José Vivas Garica Miranda, Carlos Alessandre Domingos Lentini
The oil spill is one of the most impactful sources of marine pollution on the ocean surface, detected by the SAR sensors as dark areas, regions with low backscatter values. Due to the complex mix...
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Let the loss impartial: a hierarchical unbiased loss for small object segmentation in high-resolution remote sensing images Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-09-05 Qianpeng Chong, Mengying Ni, Jianjun Huang, Guangyi Wei, Ziyi Li, Jindong Xu
ABSTRACT The progress in optical remote sensing technology presents both a possibility and challenge for small object segmentation task. However, the gap between human vision cognition and machine behavior still poses an inherent constrains to the interpretation of small but key objects in large-scale remote sensing scenes. This paper summarizes this gap as a bias of the machine against small object
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County-level corn yield prediction using supervised machine learning Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-09-05 Shahid Nawaz Khan, Abid Nawaz Khan, Aqil Tariq, Linlin Lu, Naeem Abbas Malik, Muhammad Umair, Wesam Atef Hatamleh, Farah Hanna Zawaideh
ABSTRACT The main objectives of this study are (1) to compare several machine learning models to predict county-level corn yield in the study area and (2) to compare the feasibility of machine learning models for in-season yield prediction. We acquired remotely sensed vegetation indices data from moderate resolution imaging spectroradiometer using the Google Earth Engine (GEE). Vegetation indices for
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Leveraging involution and convolution in an explainable building damage detection framework Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-09-05 Seyd Teymoor Seydi, Mahdi Hasanlou, Jocelyn Chanussot, Pedram Ghamisi
ABSTRACT Timely and accurate building damage mapping is essential for supporting disaster response activities. While RS satellite imagery can provide the basis for building damage map generation, detection of building damages by traditional methods is generally challenging. The traditional building damage mapping approaches focus on damage mapping based on bi-temporal pre/post-earthquake dataset extraction
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Object-oriented polarimetric SAR image classification via the combination of a pixel-based classifier and a region growing technique Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-08-23 Lei Huang, Jinqing You, Xiongjian Zhu, Tao Shuai, Yongfu Liao
ABSTRACT Land-cover type interpretation by the use of remote sensing image classification techniques is always a hot topic. In this paper, an object-oriented method is presented for fully polarimetric synthetic aperture radar (SAR) image classification. Differing from most of the traditional object-oriented classification algorithms, the proposed method employs an innovative classification strategy
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Forecasting monthly soil moisture at broad spatial scales in sub-Saharan Africa using three time-series models: evidence from four decades of remotely sensed data Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-08-23 Solomon G. Tesfamichael, Yegnanew A. Shiferaw, Tsehaie Woldai
ABSTRACT Soil moisture is a critical environmental variable that determines primary productivity and contributes to climatic processes. It is, therefore, important to forecast soil moisture to inform expectations of derivative outputs reliably. While forecasting soil moisture continues to advance, there is a need to extend it to different geoclimatic regions, including in sub-Saharan Africa, where
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Hyperspectral image classification using improved multi-scale block local binary pattern and bi-exponential edge-preserving smoother Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-07-27 Xiaoqing Wan, Shuanghao Chen
ABSTRACT In this paper, a multi-strategy fusion (MSF) framework, based on improved MBLBP and bi-exponential edge-preserving smoother (BEEPS), is proposed for hyperspectral image (HSI) classification. First, MBLBP operator is adopted to characterize the overall structural information of HSI, where the averaging strategy allocates same weights for the pixels in a local sub-region, so that the edges tend
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Winter remote sensing images are more suitable for forest mapping in Jiangxi Province Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-07-27 Ruilin Wang, Meng Wang, Xiaofang Sun, Junbang Wang, Guicai Li
ABSTRACT Jiangxi Province boasts the second-highest forest coverage in China. Its forests play a crucial role in providing essential ecosystem services and maintaining the ecological health of the region. High-resolution and high-precision forest mapping are significant in the timely and accurate monitoring of dynamic forest changes to support sustainable forest management. This study used Sentinel-2
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Development of an algorithm for identification of sown biodiverse pastures in Portugal Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-07-26 Tiago G. Morais, Nuno R. Rodrigues, Ivo Gama, Tiago Domingos, Ricardo F.M. Teixeira
ABSTRACT Sown biodiverse pastures (SBP) are a pasture system developed in Portugal. Until 2014, farmers were supported in installing and maintaining SBP, but tracking their locations has been lacking. To survey the country, remote sensing tools with machine learning were used. Here, we developed the first algorithm that combines remote sensing data with machine learning algorithms to identify SBP
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Performance evaluation of multiple satellite rainfall data sets in central highlands of Abbay Basin, Ethiopia Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-07-14 Mulugojjam Taye, Daniel Mengistu, Dejene Sahlu
ABSTRACT This study evaluates four satellite-based precipitation datasets with gauged rainfall observations at a daily and wet season time scales. Satellite Precipitation Estimators from Climate Hazards Group Infrared Precipitation Stations (CHIRPSv2), the Climate prediction center (CPC) morphing technique (CMORPH), the Integrated Multi-satellite Retrieval for GPM (IMERG-06) and Precipitation Estimation
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Fanet: A deep learning framework for black and odorous water extraction Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-07-14 Guizhou Zheng, Yingying Zhao, Zixuan Pan, Zhixing Chen, Zhonghang Qiu, Tingting Zheng
ABSTRACT Black and odorous water (BOW) is a common issue in rapidly urbanizaing developing countries. Existing methods for extracting BOW from remote sensing images focus mainly on spectral information and ignores important spatial characteristics like texture, context and orientation. Deep learning has emerged as a powerful approach for BOW extraction, but its effectiveness is hindered by limited
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Prediction of land use and land cover change in two watersheds in the Senegal River basin (West Africa) using the Multilayer Perceptron and Markov chain model Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-07-06 Mame Henriette Astou Sambou, Jean Albergel, Expédit Wilfrid Vissin, Stefan Liersch, Hagen Koch, Zoltan Szantoi, Wassim Baba, Moussé Landing Sane, Ibrahima Toure
ABSTRACT Land use and Land cover change (LULCC) is a major global problem, and projecting change is critical for policy decision-making. Understanding LULCCs at the watershed level is essential for transboundary river basin management. The present study aims to analyse the past and future LULCCs in two significant watersheds of the Senegal River basin (SRB) in West Africa: Bafing and Faleme. This study
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Estimation of the occurrence, severity, and volume of heartwood rot using airborne laser scanning and optical satellite data Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-07-04 Endre Hansen, Julius Wold, Michele Dalponte, Terje Gobakken, Lennart Noordermeer, Hans Ole Ørka
ABSTRACT Rot in commercial timber reduces the value of the wood substantially and estimating the occurrence, severity, and volume of heartwood rot would be a useful tool in decision-making to minimize economic losses. Remotely sensed data has recently been used for mapping rot on a single-tree level, and although the results have been relatively poor, some potential has been shown. This study applied
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TrmGLU-Net: transformer-augmented global-local U-Net for hyperspectral image classification with limited training samples Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-06-23 Bing Liu, Yifan Sun, Ruirui Wang, Anzhu Yu, Zhixiang Xue, Yusong Wang
ABSTRACT In recent years, deep learning methods have been widely used for the classification of hyperspectral images. However, their limited availability under the condition of small samples remains a serious issue. Moreover, the current mainstream approaches based on convolutional neural networks do well in local feature extraction but are also restricted by its limited receptive field. Hence, these
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Using geospatial data to identify land grabbing. Detecting spatial reconfigurations during the implementation of the Nacala Development Corridor in Mozambique with remote sensing and land conflicts databases Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-06-15 Ricardo Gellert Paris, Andreas Rienow
ABSTRACT The contemporary food system pushes agriculture to a globalized value-chain, affecting landscapes, resource access, and institutional arrangements. Institutions operating in Africa adopt development corridors to integrate multisector investments and induce export-driven primary sector, leading to massive land deals, also known as land-grabbing. Organizations struggle to monitor land deals
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Monitoring biological degradation of historical stone using hyperspectral imaging Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-06-14 Eva Matoušková, Kateřina Kovářová, Michal Cihla, Jindřich Hodač
ABSTRACT Stone is one of the most common materials used as a building material in central Europe for centuries. Historical objects are endangered by degradation procedures coming from physical, chemical and biological weathering agents.The weathering process itself should be analysed in detail in order to prevent historical objects by application of proper restoration cleaning techniques. Within our
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High-resolution monitoring of landslides with UAS photogrammetry and digital image correlation Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-06-02 Francesco Mugnai, Andrea Masiero, Riccardo Angelini, Irene Cortesi
ABSTRACT Periodically monitoring landslides is a key factor for supporting the realisation of hazard warning systems and risk reduction in the corresponding neighbourhood areas. Although satellite remote sensing solutions can be considered for low spatial resolution monitoring, this approach is still inappropriate for high spatial resolution investigations. Ground-based Radar Interferometry is also
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Capturing deprived areas using unsupervised machine learning and open data: a case study in São Paulo, Brazil Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-05-19 Lorraine Trento Oliveira, Monika Kuffer, Nina Schwarz, Julio C. Pedrassoli
ABSTRACT Managing the rapid growth of deprived areas (commonly known as slums, informal settlements, etc.) in cities of Low- to Middle-Income Countries (LMICs) demands detailed and consistent information that is often unavailable. Recent Earth Observation (EO) mapping approaches with supervised classification models overlook the diversity of deprived areas and require resource-intensive training sets
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Machine learning-based segmentation of aerial LiDAR point cloud data on building roof Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-05-11 Emon Kumar Dey, Mohammad Awrangjeb, Fayez Tarsha Kurdi, Bela Stantic
ABSTRACT Three-dimensional (3D) reconstruction of a building can be facilitated by correctly segmenting different feature points (e.g. in the form of boundary, fold edge, and planar points) over the building roof, and then, establishing relationships among the constructed feature lines and planar patches using the segmented points. Present machine learning-based segmentation approaches of Light Detection
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Planet care from space Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-04-14 Maria Teresa Melis, Maria Antonietta Dessena, Mirco Boschetti, Gabriele Candiani, Giacomo Deiana, Laura Pioli
Published in European Journal of Remote Sensing (Ahead of Print, 2023)
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Deep hierarchical transformer for change detection in high-resolution remote sensing images Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-04-06 Bing Liu, Anzhu Yu, Xibing Zuo, Ruirui Wang, Chunping Qiu, Xuchu Yu
ABSTRACT Deep learning instantiated by convolutional neural networks has achieved great success in high-resolution remote-sensing image change detection. However, such networks have a limited receptive field, being unable to extract long-range dependencies in a scene. As the transformer model with self-attention can better describe long-range dependencies, we introduce a hierarchical transformer model
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Estimating the effect of water shortage on olive trees by the combination of meteorological and Sentinel-2 data Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-03-31 Piero Battista, Edoardo Bellini, Marta Chiesi, Sergi Costafreda-Aumedes, Luca Fibbi, Luisa Leolini, Marco Moriondo, Bernardo Rapi, Riccardo Rossi, Francesco Sabatini, Fabio Maselli
ABSTRACT Relative soil water content (RSWC) is widely used to characterize the impact of water stress (WS) on vegetation. In bi-layer ecosystems, such as olive groves, this impact must be primarily estimated for the tree component, which, having greater rooting depth, responds more slowly to WS than understory grass. This complicates the application of methods for RSWC prediction, which must be properly
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Assessing the effectiveness of UAV data for accurate coastal dune habitat mapping Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-03-28 Charmaine Cruz, Jerome O’Connell, Kevin McGuinness, James R. Martin, Philip M. Perrin, John Connolly
ABSTRACT Coastal dunes are considered some of the most threatened and vulnerable habitats in the European Union. Mapping the spatial distribution of these habitats is an essential task for their conservation. Advances in Unoccupied Aerial Vehicles (UAVs) facilitate the flexible acquisition of high-resolution imagery for identifying detailed spatial distributions of habitats within dune systems. This
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Extraction of cropland field parcels with high resolution remote sensing using multi-task learning Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-03-15 Leilei Xu, Peng Yang, Juanjuan Yu, Fei Peng, Jia Xu, Shiran Song, Yongxing Wu
ABSTRACT Parcel-level farmland information contains rich spatial distribution and boundary details, which is crucial for digital agriculture and agricultural resource surveys. However, the spatial complexity and heterogeneity of features resulting from high resolution makes it difficult to obtain parcel-level information quickly and accurately. In addition, existing methods do not sufficiently take
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Detecting diseases in apple tree leaves using FPN–ISResNet–Faster RCNN Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-03-09 Jingwei Hou, Chen Yang, Yonghong He, Bo Hou
ABSTRACT Apple leaf diseases typified by small disease spots are generally difficult to detect in images. This study proposes a deep learning model called the feature pyramid networks (FPNs) –inception squeeze-and-excitation ResNet (ISResNet)–Faster RCNN (region with convolutional neural network) model to improve the accuracy of detecting apple leaf diseases. Apple leaf diseases were identified, evaluated
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UAV DTM acquisition in a forested area – comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1) Eur. J. Remote Sens. (IF 4.0) Pub Date : 2023-03-01 Martin Štroner, Rudolf Urban, Tomáš Křemen, Jaroslav Braun
ABSTRACT In this paper, we evaluated the results in terms of accuracy and coverage of the LiDAR-UAV system DJI Zenmuse L1 and Digital Aerial Photogrammetric system (DAP – UAV) DJI Zenmuse P1 in a forested area under leaf-off conditions on three sites with varying terrain ruggedness/tree type combinations. Detailed reference clouds were obtained using terrestrial scanning by Leica P40. Our results show