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Exploitation of time series Sentinel-2 data and different machine learning algorithms for detailed tree species classification

Yanbiao Xi, Chunying Ren, Qingjiu Tian, Yongxing Ren, Xinyu Dong, Zhichao Zhang
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The dense time series and a wide swath of Sentinel-2 data provided the opportunity to map tree species accurately and in a timely manner over a large area.  ...  In this study, five machine learning algorithms were compared to identify the composition of tree species with multi-temporal Sentinel-2 images in the JianShe forest farm, Northeast China.  ...  ACKNOWLEDGMENT We thank the National Earth System Science Data Center for providing geographic information data.  ... 
doi:10.1109/jstars.2021.3098817 fatcat:54so3pca5rfjxfuom6cwh44nay


R. Gimenez, G. Lassalle, R. Hédacq, A. Elger, D. Dubucq, A. Credoz, C. Jennet, S. Fabre
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Feature selection is found to be a necessary step to perform classification with time series of MS images.  ...  Species that are difficult to distinguish from the HS image, namely Salix and Populus, are well separated using Sentinel-2 images (precision around 70%).  ...  Déliot (ONERA) and L. Poutier (ONERA) for their implication in image acquisitions and preprocessing. This work was co-funded by TOTAL R&D in the frame of DEMETER Project.  ... 
doi:10.5194/isprs-archives-xliii-b3-2021-559-2021 fatcat:r43ppsyjwnbdzionxib5zclqmq

Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France

Emile Ndikumana, Dinh Ho Tong Minh, Nicolas Baghdadi, Dominique Courault, Laure Hossard
2018 Remote Sensing  
We revealed that even with classical machine learning approaches (K nearest neighbors, random forest, and support vector machines), good performance classification could be achieved with F-measure/Accuracy  ...  The aim of this paper is to provide a better understanding of the capabilities of Sentinel-1 radar images for agricultural land cover mapping through the use of deep learning techniques.  ...  The authors wish to thank Dino Ienco (UMR TETIS-IRSTEA) and Raffaele Gaetano (UMR TETIS-CIRAD) for insight discussion on the deep learning.  ... 
doi:10.3390/rs10081217 fatcat:ifbsx5xj2bcwxbpbcbi5rchu4y


E. Elmoussaoui, A. Moumni, A. Lahrouni
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
This research aims to use the potential of the combination of multi-sensor data to detect, map and identify argan tree from other forest species using three Machine Learning algorithms: Support Vector  ...  The exploited datasets included Sentinel-1 (S1), Sentinel-2 (S2) time series, Shuttle Radar Topographic Missing Digital Elevation Model (DEM) layer and Ground truth data.  ...  ACKNOWLEDGEMENTS The authors would like to thank the Center of Forestry Research CRF (Centre de Recherches Forestières) for their technical support and accompaniment during field campaigns.  ... 
doi:10.5194/isprs-archives-xlvi-4-w5-2021-211-2021 fatcat:ih7hl2yhbfhtpexjwflyslekuu

DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images

Xueliang Wang, Honge Ren
2021 Forests  
In the present paper, a novel deep learning approach for hyperspectral imagery is proposed to improve accuracy for the classification of tree species.  ...  by the Sentinel-2 satellite.  ...  Data Data captured by HJ-1A and Sentinel-2 were used for tree species classification.  ... 
doi:10.3390/f13010033 fatcat:5p7fps4tcbepfpdschqnm5syvu

Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data

Abdulhakim Mohamed Abdi
2019 GIScience & Remote Sensing  
The results are discussed within the scope of recent studies involving machine learning and Sentinel-2 data and key knowledge gaps identified.  ...  The satellite imagery used for the classification were multi-temporal scenes from Sentinel-2 covering spring, summer, autumn and winter conditions.  ...  Acknowledgements Open access funding for this article was provided by Lund University. I would like to sincerely thank all three reviewers  ... 
doi:10.1080/15481603.2019.1650447 fatcat:fpmgve3bffcffcazwwacivsqr4

Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

Kourosh Ahmadi, Bahareh Kalantar, Vahideh Saeidi, Elaheh K. G. Harandi, Saeid Janizadeh, Naonori Ueda
2020 Remote Sensing  
The present study considers the use of a Bayesian additive regression trees (BART) algorithm as a non-parametric classifier using Sentinel-2A data and topographic variables to estimate the forest stand  ...  These results were compared with those of three other popular machine learning (ML) algorithms, such as generalised linear model (GLM), K-nearest neighbours (KNN), and support vector machine (SVM).  ...  Acknowledgments: The authors would like to thank the RIKEN AIP, Japan for providing all facilities during the research. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12183019 doaj:4c89b85e8b7e4cd4a842be1eac97859f fatcat:v6cctg6n4ze3fj24r5lxucbsfe

Argan Tree (Argania spinosa (L.) Skeels) Mapping Based on Multisensor Fusion of Satellite Imagery in Essaouira Province, Morocco

Aicha Moumni, Tarik Belghazi, Brahim Maksoudi, Abderrahman Lahrouni, Stelios M. Potirakis
2021 Journal of Sensors  
The support vector machine (SVM) algorithm was used for the classification of the two types of data. We first classified the optical data for tree species identification and mapping.  ...  The satellite-based remote sensing time series of Sentinel missions (Sentinel-1 and Sentinel-2) are a perfect tool to map the type, location, and extent of forest cover over large areas at local or global  ...  Acknowledgments The authors would like to thank the Center of Forestry Research CRF (Centre de Recherches Forestières) for their technical support and accompaniment during field campaigns.  ... 
doi:10.1155/2021/6679914 fatcat:idnb2hzndrhflbjbapk73useua

The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review [chapter]

Crismeire Isbaex, Ana Margarida Coelho
2021 Forest Biomass - From Trees to Energy  
The Sentinel data have great potential for studies on LCLU classification and forest biomass estimates.  ...  The Sentinel-2 (S2) systematically acquires optical imagery and provides global monitoring data with high spatial resolution (10–60 m) images.  ...  Acknowledgements The work was supported by Programa Operativo de Cooperação Transfronteiriço Espanha-Portugal (POCTEP); project CILIFO -Centro Ibérico para la Investigación y Lucha contra Incendios Forestales and  ... 
doi:10.5772/intechopen.93363 fatcat:v4bzh7jg4vdv7lli5bn2sbezoq


N. Zaabar, S. Niculescu, M. K. Mihoubi
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Furthermore, to compare the performance of the proposed approach, an OBIA based on machines learning algorithms mainly Random Forest (RF) and Support Vector Machine (SVM), was provided.  ...  In this regard, a Sentinel-2 image was used, to perform the classification, using spectral index combinations.  ...  In Central Europe, based on Sentinel-2 data, (Immitzer et al, 2016) applied machines learning classifiers to Tree Species Classification.  ... 
doi:10.5194/isprs-archives-xliii-b3-2021-383-2021 fatcat:jhuudy7ov5cvlfn3kw767h26xa

Mapping of Eucalyptus in Natura 2000 Areas Using Sentinel 2 Imagery and Artificial Neural Networks

Andreas Forstmaier, Ankit Shekhar, Jia Chen
2020 Remote Sensing  
This study demonstrates the applicability of multi-spectral imagery for tree-species classification and invasive species control.  ...  This study uses medium resolution, multi-spectral imagery of the Sentinel 2 satellites to map Eucalyptus across Portugal and parts of Spain with a focus on Natura 2000 areas inside Portugal, that are protected  ...  Also we thank the association Movimento Gaio, especially Bernardo and Teresa Markowsky who helped with their knowledge as well as with providing infrastructure and transport services during the field campaign  ... 
doi:10.3390/rs12142176 fatcat:p5iobu2tcrafzjv3nzvflzvgvm

Applying Machine Learning and Time-Series Analysis on Sentinel-1A SAR/InSAR for Characterizing Arctic Tundra Hydro-Ecological Conditions

Michael Allan Merchant, Mayah Obadia, Brian Brisco, Ben DeVries, Aaron Berg
2022 Remote Sensing  
In this study, a time series of dual-polarimetric (VV, VH) Sentinel-1 SAR/InSAR data collected over one growing season, in addition to a digital elevation model (DEM), was used to characterize an Arctic  ...  SAR intensity and coherence patterns based on repeat-pass interferometry were analyzed in terms of ecological structure (i.e., graminoid, or woody) and hydrology (i.e., wet, or dry) using machine learning  ...  Lastly, the guidance and support by Canadian Center for Mapping and Earth Observation (CCMEO) staff was invaluable.  ... 
doi:10.3390/rs14051123 fatcat:slbsg7grbvcb3d4nrtox6nvjaa

Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping

Emiliano Agrillo, Federico Filipponi, Alice Pezzarossa, Laura Casella, Daniela Smiraglia, Arianna Orasi, Fabio Attorre, Andrea Taramelli
2021 Remote Sensing  
The procedure integrates forest habitat data in Italy from the European Vegetation Archive (EVA), with Sentinel-2 imagery processing (vegetation indices time series, spectral indices, and single bands  ...  The results of the proposed methodology open the way to increase the EUNIS habitat categories to be mapped together with their geographical extent, and to test different semi-supervised machine learning  ...  The combination of EO data, observing and measuring ecosystem processes, big data analytics, making use of advanced computational analytic techniques. like RF machine learning algorithm, allowed for demonstrating  ... 
doi:10.3390/rs13071231 fatcat:p63viqeh6vbfbfeubxugw5m6hy

Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery

Arsalan Ghorbanian, Seyed Ali Ahmadi, Meisam Amani, Ali Mohammadzadeh, Sadegh Jamali
2022 Water  
In this regard, the joint use of remote sensing data and machine learning algorithms can assist in producing accurate mangrove ecosystem maps.  ...  This study investigated the potential of artificial neural networks (ANNs) with different topologies and specifications for mangrove classification in Iran.  ...  Acknowledgments: The authors would like to thank the European Space Agency (ESA) for freely providing Sentinel-1 and Sentinel-2 satellite data.  ... 
doi:10.3390/w14020244 fatcat:tkxo2pxkj5g77o4fxwu64keywq

Assessment of Agricultural Water Requirements for Semi-Arid Areas: A Case Study of the Boufakrane River Watershed (Morocco)

Mohammed El Hafyani, Ali Essahlaoui, Kimberley Fung-Loy, Jason A. Hubbart, Anton Van Rompaey
2021 Applied Sciences  
Two classifiers were used, namely Support vector machine (SVM) and Random forest (RF). A validation of the classified parcels showed a high overall accuracy of 89.76% for SVM and 84.03% for RF.  ...  Land use practices were mapped at the thematic resolution of individual crops, using a total of 13 images generated from the Sentinel-2 satellites.  ...  Acknowledgments: The authors would like to thank the Thematic Project 4, Integrated Water Resources Management of the Institutional University Cooperation, and VLIR-UOS for the financial support, equipment  ... 
doi:10.3390/app112110379 fatcat:avv6wcdcsfchzm7royp3wzbi6y
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