scholarly journals SRP, une base de calage 3D de très haute précision sur le continent africain

2021 ◽  
Vol 223 ◽  
pp. 129-142
Author(s):  
Laure Chandelier ◽  
Laurent Coeurdevey ◽  
Pascal Favé ◽  
Alexis Barot ◽  
Mathilde Jaussaud
Keyword(s):  
Reference Points ◽  
Haute Résolution ◽  
Sentinel 2

La SRP (« Space Reference Points ») est une base mondiale, précise, dense et homogène de points 3D géoréférencés qui est réalisée à partir de l’archive d’images SPOT6/7. Ce projet, mené en partenariat entre l’Institut national de l’information géographique et forestière (IGN) et Airbus Defense and Space (ADS), permet le calage géométrique automatique d’images très haute résolution avec une précision de l’ordre de 3m partout dans le monde. La SRP sur l’Afrique a été produite au cours de l’année 2019. Les contrôles qualité confirment le respect des spécifications attendues pour ce produit. Les particularités des paysages rencontrés sur ce continent ont conduit à intégrer de nouvelles fonctionnalités à la chaîne de production. Tout d’abord, la sélection des images SPOT6/7 a été enrichie sur la zone intertropicale en prenant en compte les masques de nuage fournis avec les produits, permettant d’obtenir une densité de points SRP optimale pour la zone. Ensuite, un prototype de socle de calage exploitant des ortho-images Sentinel-2 a montré la capacité de cette méthodologie à assurer la spécification de localisation à 3m sur un archipel d’îles (ici le Cap Vert). Afin de valider pleinement le produit, l’article présente deux tests d’exploitation sur le Nigéria pour des productions 2D et sur la ville de Marrakech pour des productions 3D. Ils démontrent la capacité de la SRP à caler différents types d’images et à atteindre la cible de précision de la base. La SRP est destinée, dès 2021, à assurer le calage d’images dans différents projets et notamment, de façon massive, dans le segment sol Pléiades Neo.

scholarly journals Crop Area Mapping in Southern and Central Malawi With Google Earth Engine

2021 ◽  
Vol 3 ◽  
Author(s):  
Seth Peterson ◽  
Greg Husak
Keyword(s):  
Spatial Resolution ◽  
Test Data ◽  
Google Earth ◽  
Reference Points ◽  
Sub Saharan Africa ◽  
High Temporal Resolution ◽  
Smallholder Farms ◽  
Google Earth Engine ◽  
Crop Area ◽  
Sentinel 2

Agriculture in sub-Saharan Africa consists primarily of smallholder farms of rainfed crops. Historically, satellite data were too coarse to account for the heterogeneity in these landscapes. Sentinel-2 data have improved spectral resolution and much higher spatial resolution (10 m) than previously available satellites with global coverage, such as Landsat or MODIS, making mapping smallholder farms possible. Spectral mixture analysis was used to convert the Sentinel-2 signal into fractions of green vegetation, non-photosynthetic vegetation, soil, and shade endmembers. Very high spatial resolution imagery in Google Earth Pro was used to identify locations of crop and natural vegetation classes, with over 20,000 reference points interpreted. The high temporal resolution of Sentinel-2 (5 days repeat) allows for classification of landcover based on the phenological signal, with natural areas having smoothly varying amounts of photosynthetic vegetation annually, while cropped areas show more abrupt changes, and also the presence of bare soil due to agricultural activity at some point during the year. We summarized the endmember values using monthly medians, extracted values for the reference data points, randomly split them into training and test data sets, and input the training data into the random forests algorithm in Google Earth Engine to map crop area. We divided southern and central Malawi into tiles, and found crop/no crop classification accuracies on the test data for each tile to be between 87 and 93%. The 10 m map of crop area was aggregated to the district level and showed an R2 of 0.74 with ground-based statistics from the Malawi government and 0.79 with a remotely sensed product developed by the USGS.

scholarly journals ASSESSMENT OF CLASSIFICATION ACCURACIES OF SENTINEL-2 AND LANDSAT-8 DATA FOR LAND COVER / USE MAPPING

2016 ◽  
Vol XLI-B8 ◽  
pp. 1055-1059 ◽  
Author(s):  
Raziye Hale Topaloğlu ◽  
Elif Sertel ◽  
Nebiye Musaoğlu
Keyword(s):  
Land Cover ◽  
Reference Points ◽  
Sensor Data ◽  
Support Vector ◽  
Landsat 8 ◽  
Geometric Correction ◽  
Classification Methods ◽  
Error Matrix ◽  
Barren Land ◽  
Sentinel 2

This study aims to compare classification accuracies of land cover/use maps created from Sentinel-2 and Landsat-8 data. Istanbul metropolitan city of Turkey, with a population of around 14 million, having different landscape characteristics was selected as study area. Water, forest, agricultural areas, grasslands, transport network, urban, airport- industrial units and barren land- mine land cover/use classes adapted from CORINE nomenclature were used as main land cover/use classes to identify. To fulfil the aims of this research, recently acquired dated 08/02/2016 Sentinel-2 and dated 22/02/2016 Landsat-8 images of Istanbul were obtained and image pre-processing steps like atmospheric and geometric correction were employed. Both Sentinel-2 and Landsat-8 images were resampled to 30m pixel size after geometric correction and similar spectral bands for both satellites were selected to create a similar base for these multi-sensor data. Maximum Likelihood (MLC) and Support Vector Machine (SVM) supervised classification methods were applied to both data sets to accurately identify eight different land cover/ use classes. Error matrix was created using same reference points for Sentinel-2 and Landsat-8 classifications. After the classification accuracy, results were compared to find out the best approach to create current land cover/use map of the region. The results of MLC and SVM classification methods were compared for both images.

scholarly journals Fusion tardive d'images SPOT-6/7 et de données multi-temporelles Sentinel-2 pour la détection de la tâche urbaine

2018 ◽  
pp. 87-97
Author(s):  
Arnaud Le Bris ◽  
Cyril Wendl ◽  
Nesrine Chehata ◽  
Anne Puissant ◽  
Tristan Postadjian
Keyword(s):  
A Priori ◽  
De Se ◽  
Haute Résolution ◽  
Sentinel 2

La fusion d'images multispectrales à très haute résolution spatiale (THR) avec des séries temporelles d'images moins résolues spatialement mais comportant plus de bandes spectrales permet d'améliorer la classification de l'occupation du sol. Elle tire en effet le meilleur parti des points forts géométriques et sémantiques de ces deux sources. Ce travail s'intéresse à un processus d'extraction automatique de la tache urbaine fondé sur la fusion tardive de classifications calculées respectivement à partir d'images satellitaires Sentinel-2 et SPOT-6/7. Ces deux sources sont d'abord classées indépendamment selon 5 classes, respectivement par forêts aléatoires et réseaux de neurones convolutifs. Les résultats sont alors fusionnés afin d'extraire les bâtiments le plus finement possible. Cette étape de fusion inclut une fusion au niveau pixellaire suivie d'une étape de régularisation spatiale intégrant un terme lié au contraste de l'image. Le résultat obtenu connaît ensuite une seconde fusion afin d'en déduire une tache urbaine : une mesure a priori de se trouver en zone urbaine est calculée à partir des objets bâtiments détectés précédemment et est fusionnée avec une classification binaire dérivée de la classification originale des données Sentinel-2.  

scholarly journals ASSESSMENT OF CLASSIFICATION ACCURACIES OF SENTINEL-2 AND LANDSAT-8 DATA FOR LAND COVER / USE MAPPING

2016 ◽  
Vol XLI-B8 ◽  
pp. 1055-1059 ◽  
Author(s):  
Raziye Hale Topaloğlu ◽  
Elif Sertel ◽  
Nebiye Musaoğlu
Keyword(s):  
Land Cover ◽  
Reference Points ◽  
Sensor Data ◽  
Support Vector ◽  
Landsat 8 ◽  
Geometric Correction ◽  
Classification Methods ◽  
Error Matrix ◽  
Barren Land ◽  
Sentinel 2

This study aims to compare classification accuracies of land cover/use maps created from Sentinel-2 and Landsat-8 data. Istanbul metropolitan city of Turkey, with a population of around 14 million, having different landscape characteristics was selected as study area. Water, forest, agricultural areas, grasslands, transport network, urban, airport- industrial units and barren land- mine land cover/use classes adapted from CORINE nomenclature were used as main land cover/use classes to identify. To fulfil the aims of this research, recently acquired dated 08/02/2016 Sentinel-2 and dated 22/02/2016 Landsat-8 images of Istanbul were obtained and image pre-processing steps like atmospheric and geometric correction were employed. Both Sentinel-2 and Landsat-8 images were resampled to 30m pixel size after geometric correction and similar spectral bands for both satellites were selected to create a similar base for these multi-sensor data. Maximum Likelihood (MLC) and Support Vector Machine (SVM) supervised classification methods were applied to both data sets to accurately identify eight different land cover/ use classes. Error matrix was created using same reference points for Sentinel-2 and Landsat-8 classifications. After the classification accuracy, results were compared to find out the best approach to create current land cover/use map of the region. The results of MLC and SVM classification methods were compared for both images.

scholarly journals Potentiel de l'imagerie optique satellitaire à haute résolution pour détecter les dommages engendrés par des épisodes pluvieux extrêmes

2020 ◽  
pp. 66-74
Author(s):  
Arnaud Cerbelaud ◽  
Axelle Favro ◽  
Laure Roupioz ◽  
Gwendoline Blanchet ◽  
Xavier Briottet ◽  
...  
Keyword(s):  
Nous Avons ◽  
Haute Résolution ◽  
Sentinel 2

De nombreuses données satellites peuvent aujourd'hui être combinées afin de couvrir des surfaces très importantes avec une très haute résolution spatiale (THR) ainsi qu'une haute fréquence de revisite. Le potentiel de ces images pour évaluer et cartographier les dommages engendrés par des pluies extrêmes, en particulier ceux causés par le ruissellement pluvial, a été jusqu'à présent peu étudié. Cette étude propose une méthode pour détecter de la manière la plus exhaustive possible ces dommages à partir de données satellitaires THR et HR acquises au plus près, avant et après, d'un événement de pluie intense. Pour ce faire, nous avons utilisé des images Pléiades (0,7 m) et Sentinel-2 (10 m) acquises au-dessus de la région de l'Aude (France), fortement touchée par des intempéries le 15 octobre 2018. Notre intérêt a porté sur les zones agricoles qui ont fait l'objet de 1119 demandes d'indemnisation en calamités agricoles pour cet événement. Plusieurs indices et filtres spectraux ont été appliqués sur un échantillon d'images Sentinel-2 sélectionnées avant et après l'épisode orageux. Ce travail exploratoire révèle que certains types de dommages agricoles sont bien détectés alors que d'autres, même clairement visibles sur les images Pléiades, sont plus difficiles à distinguer avec les indices et filtres sélectionnés sur les images Sentinel-2. Il démontre également le potentiel de ces méthodes pour discriminer les différents degrés de dégâts relevés sur les parcelles agricoles. Cette étude confirme l'importance de combiner information spectrale, temporelle et contextuelle pour détecter à l'aide de l'imagerie optique les dommages engendrés par des pluies extrêmes, en particulier ceux causés par le ruissellement pluvial. Ces travaux préliminaires ouvrent la voie au développement de nouvelles méthodes de détection, l'utilisation de nouveaux indices ainsi que sur l'intelligence artificielle.

scholarly journals Utilisation conjointe de séries temporelles d'images optiques et radar pour le suivi des surfaces agricoles

2020 ◽  
Author(s):  
Jordi Inglada
Keyword(s):  
Haute Résolution ◽  
Sentinel 2

Dans un contexte de pression croissante sur les terres agricoles et leur production \cite{foley11_solut}, \cite{godfray10_food_secur}, la cartographie des cultures est nécessaire pour fournir des informations précises pour leur conduite efficace et durable. L'imagerie de télédétection en général, et plus particulièrement les séries temporelles d'images à haute résolution comme celles fournies par les satellites Sentinel-1 et Sentinel-2 sont un atout majeur pour ce type d'application.

scholarly journals Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa

2019 ◽  
Vol 11 (24) ◽  
pp. 2928 ◽  
Author(s):  
Pinki Mondal ◽  
Xue Liu ◽  
Temilola E. Fatoyinbo ◽  
David Lagomasino
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Satellite Data ◽  
Learning Algorithms ◽  
Google Earth ◽  
The Gambia ◽  
Reference Points ◽  
Machine Learning Algorithms ◽  
Mangrove Forests ◽  
Sentinel 2

Creating a national baseline for natural resources, such as mangrove forests, and monitoring them regularly often requires a consistent and robust methodology. With freely available satellite data archives and cloud computing resources, it is now more accessible to conduct such large-scale monitoring and assessment. Yet, few studies examine the reproducibility of such mangrove monitoring frameworks, especially in terms of generating consistent spatial extent. Our objective was to evaluate a combination of image processing approaches to classify mangrove forests along the coast of Senegal and The Gambia. We used freely available global satellite data (Sentinel-2), and cloud computing platform (Google Earth Engine) to run two machine learning algorithms, random forest (RF), and classification and regression trees (CART). We calibrated and validated the algorithms using 800 reference points collected using high-resolution images. We further re-ran 10 iterations for each algorithm, utilizing unique subsets of the initial training data. While all iterations resulted in thematic mangrove maps with over 90% accuracy, the mangrove extent ranges between 827–2807 km2 for Senegal and 245–1271 km2 for The Gambia with one outlier for each country. We further report “Places of Agreement” (PoA) to identify areas where all iterations for both methods agree (506.6 km2 and 129.6 km2 for Senegal and The Gambia, respectively), thus have a high confidence in predicting mangrove extent. While we acknowledge the time- and cost-effectiveness of such methods for the landscape managers, we recommend utilizing them with utmost caution, as well as post-classification on-the-ground checks, especially for decision making.

Reference Coordinate System Requirements for Geophysics

1975 ◽  
Vol 26 ◽  
pp. 87-92
Author(s):  
P. L. Bender
Keyword(s):  
Coordinate System ◽  
Polar Motion ◽  
Main Part ◽  
Measurement Techniques ◽  
Reference Points ◽  
Angular Motion ◽  
Coordinate Systems ◽  
Axis Of Rotation ◽  
The Earth ◽  
Fixed System

AbstractFive important geodynamical quantities which are closely linked are: 1) motions of points on the Earth’s surface; 2)polar motion; 3) changes in UT1-UTC; 4) nutation; and 5) motion of the geocenter. For each of these we expect to achieve measurements in the near future which have an accuracy of 1 to 3 cm or 0.3 to 1 milliarcsec.From a metrological point of view, one can say simply: “Measure each quantity against whichever coordinate system you can make the most accurate measurements with respect to”. I believe that this statement should serve as a guiding principle for the recommendations of the colloquium. However, it also is important that the coordinate systems help to provide a clear separation between the different phenomena of interest, and correspond closely to the conceptual definitions in terms of which geophysicists think about the phenomena.In any discussion of angular motion in space, both a “body-fixed” system and a “space-fixed” system are used. Some relevant types of coordinate systems, reference directions, or reference points which have been considered are: 1) celestial systems based on optical star catalogs, distant galaxies, radio source catalogs, or the Moon and inner planets; 2) the Earth’s axis of rotation, which defines a line through the Earth as well as a celestial reference direction; 3) the geocenter; and 4) “quasi-Earth-fixed” coordinate systems.When a geophysicists discusses UT1 and polar motion, he usually is thinking of the angular motion of the main part of the mantle with respect to an inertial frame and to the direction of the spin axis. Since the velocities of relative motion in most of the mantle are expectd to be extremely small, even if “substantial” deep convection is occurring, the conceptual “quasi-Earth-fixed” reference frame seems well defined. Methods for realizing a close approximation to this frame fortunately exist. Hopefully, this colloquium will recommend procedures for establishing and maintaining such a system for use in geodynamics. Motion of points on the Earth’s surface and of the geocenter can be measured against such a system with the full accuracy of the new techniques.The situation with respect to celestial reference frames is different. The various measurement techniques give changes in the orientation of the Earth, relative to different systems, so that we would like to know the relative motions of the systems in order to compare the results. However, there does not appear to be a need for defining any new system. Subjective figures of merit for the various system dependon both the accuracy with which measurements can be made against them and the degree to which they can be related to inertial systems.The main coordinate system requirement related to the 5 geodynamic quantities discussed in this talk is thus for the establishment and maintenance of a “quasi-Earth-fixed” coordinate system which closely approximates the motion of the main part of the mantle. Changes in the orientation of this system with respect to the various celestial systems can be determined by both the new and the conventional techniques, provided that some knowledge of changes in the local vertical is available. Changes in the axis of rotation and in the geocenter with respect to this system also can be obtained, as well as measurements of nutation.

Cognitive Reflection Effects on Time Discounting

2018 ◽  
Vol 39 (2) ◽  
pp. 99-106 ◽  
Author(s):  
Michał Białek ◽  
Przemysław Sawicki
Keyword(s):  
Individual Differences ◽  
Individual Difference ◽  
Delay Discounting ◽  
Reference Point ◽  
Reference Points ◽  
Cognitive Reflection Test ◽  
Process Information ◽  
Time Discounting ◽  
Cognitive Reflection ◽  
The Way

Abstract. In this work, we investigated individual differences in cognitive reflection effects on delay discounting – a preference for smaller sooner over larger later payoff. People are claimed to prefer more these alternatives they considered first – so-called reference point – over the alternatives they considered later. Cognitive reflection affects the way individuals process information, with less reflective individuals relying predominantly on the first information they consider, thus, being more susceptible to reference points as compared to more reflective individuals. In Experiment 1, we confirmed that individuals who scored high on the Cognitive Reflection Test discount less strongly than less reflective individuals, but we also show that such individuals are less susceptible to imposed reference points. Experiment 2 replicated these findings additionally providing evidence that cognitive reflection predicts discounting strength and (in)dependency to reference points over and above individual difference in numeracy.

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