scholarly journals Kalideos OSR MiPy : un observatoire pour la recherche et la démonstration des applications de la télédétection à la gestion des territoires

2014 ◽  
pp. 17-30
Author(s):  
Jean-François Dejoux ◽  
Gérard Dedieu ◽  
Olivier Hagolle ◽  
Danièle Ducrot ◽  
Jean-Claude Menaut ◽  
...  
Keyword(s):  
Traitement Du Signal ◽  
Sentinel 2

Ces dernières années, le CESBIO a mis en place un Observatoire Spatial Régional (OSR), dispositif d'observation couplant mesures de terrain et télédétection dans le sud-ouest de la France. L'OSR se base sur des acquisitions mensuelles de données satellitaires à résolution décamétrique depuis 2002 et sur des sites expérimentaux lourdement instrumentés (mesures en continu de flux d'eau et de carbone) à partir de 2004. Ce dispositif a été reconnu service d'observation par l'INSU/CNRS en 2007 et site Kalideos par le CNES fin 2009 sous le nom de « OSR MiPy ». Le site atelier correspond à l'emprise d'une image Spot, soit environ 50 x 50 km, et couvre une grande diversité de milieux (pédologie, topographie), d'occupation et d'utilisation des sols, de pratiques et de modalités de gestion (agricole, forestière…) et de conditions climatiques (fort gradient de déficits hydriques estivaux).Pour la télédétection, ce site a servi la préparation de SMOS et soutient maintenant en priorité la préparation des missions VENµS et Sentinel-2. Les aspects radar, imagerie thermique et les approches multi-capteurs se développent depuis peu. Le traitement du signal, la physique de la mesure et l'amélioration de la qualité des données constituent le premier axe de recherche. Au niveau thématique, le CESBIO a pour priorité le suivi et la modélisation des agrosystèmes de grandes cultures. L'implication récente d'autres partenaires scientifiques ou de gestionnaires a permis d'initier des travaux sur d'autres aspects, comme la biodiversité, l'aménagement du territoire, le suivi de l'extension urbaine, les risques environnementaux, la santé des forêts, l'enfrichement, la diversité et la productivité des prairies. La valorisation des 10 années d'archives 2002-2011 débute et semble très pertinente pour la caractérisation en haute et en basse résolution des conséquences d'années climatiques atypiques (2003, 2011) sur les éco-agro-systèmes. L'extrapolationdes résultats obtenus sur ce site atelier à toute la région Midi-Pyrénées ou à la chaine des Pyrénées est aussi initiée.

Intérêts de l’étude des interactions précoces mère-bébé par des méthodes automatisées de traitement du signal social : applications à la psychopathologie

2016 ◽  
Vol 200 (6) ◽  
pp. 1203-1220
Author(s):  
David Cohen ◽  
Sylvie Viaux ◽  
Catherine Saint-georges ◽  
Chloé Leclère ◽  
Mohamed Chétouani ◽  
...  
Keyword(s):  
Social Applications ◽  
Traitement Du Signal

Estimación del Factor C de la RUSLE en la microcuenca del rio Lodana, Ecuador, usando imágenes del satélite Sentinel 2

2020 ◽  
Vol ve2020 (2) ◽  
pp. 69-75
Author(s):  
Ángel Claudio Ruiz Vélez ◽  
◽  
Henry Antonio Pacheco Gil ◽  
Keyword(s):  
Del Rio ◽  
Factor C ◽  
Sentinel 2

Post-Fire Assessment of Burned Areas with Landsat-8 and Sentinel-2 Imagery Together with Modis and Viirs Active Fire Products

2020 ◽  
Author(s):  
Cesario Vincenzo Angelino ◽  
Luca Cicala ◽  
Sara Parrilli ◽  
Nicomino Fiscante ◽  
Silvia Liberata Ullo
Keyword(s):  
Landsat 8 ◽  
Burned Areas ◽  
Active Fire ◽  
Sentinel 2

Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images

CATENA ◽  
2021 ◽  
Vol 205 ◽  
pp. 105442
Author(s):  
Xianglin He ◽  
Lin Yang ◽  
Anqi Li ◽  
Lei Zhang ◽  
Feixue Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Sentinel 2

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

2021 ◽  
Vol 10 (4) ◽  
pp. 251
Author(s):  
Christina Ludwig ◽  
Robert Hecht ◽  
Sven Lautenbach ◽  
Martin Schorcht ◽  
Alexander Zipf
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Green Spaces ◽  
Data Sets ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Quality Of Life ◽  
Shafer Theory ◽  
Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

scholarly journals Reply to Comment on “Comparison of Cloud Cover Detection Algorithms on Sentinel–2 Images of the Amazon Tropical Forest”

2021 ◽  
Vol 13 (5) ◽  
pp. 1028
Author(s):  
Alber Hamersson Sanchez ◽  
Michelle Cristina A. Picoli ◽  
Gilberto Camara ◽  
Pedro R. Andrade ◽  
Michel Eustaquio D. Chaves ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Cloud Cover ◽  
Optimal Performance ◽  
Detection Algorithms ◽  
Sentinel 2

In their comments about our paper, the authors remark on two issues regarding our results relating to the MACCS-ATCOR Joint Algorithm (MAJA). The first relates to the sub-optimal performance of this algorithm under the conditions of our tests, while the second corresponds to an error in our interpretation of MAJA’s bit mask. To answer the first issue, we acknowledge MAJA’s capacity to improve its performance as the number of images increases with time. However, in our paper, we used the images we had available at the time we wrote our paper. Regarding the second issue, we misread the MAJA’s bit mask and mistakenly labelled shadows as clouds. We regret our error and here we present the updated tables and images. We corrected our estimation and, consequently, there is an increment in MAJA’s accuracy in the detection of clouds and cloud shadows. However, these increments are not enough to change the conclusion of our original paper.

scholarly journals Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Negar Tavasoli ◽  
Hossein Arefi
Keyword(s):  
Machine Learning ◽  
Carbon Stock ◽  
Vegetation Indices ◽  
Forest Biomass ◽  
Principal Component ◽  
Optical Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Texture Characteristics ◽  
Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

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