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Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine / Aparna R. Phalke in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 104 - 122 Titre : Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine Type de document : Article/Communication Auteurs : Aparna R. Phalke, Auteur ; Mutlu Ozdogan, Auteur ; Prasad S. Thenkabail, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 104 - 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] Asie centrale [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] Corine Land Cover [Termes descripteurs IGN] Europe (géographie politique) [Termes descripteurs IGN] Google Earth Engine [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] photo-interprétation [Termes descripteurs IGN] Russie [Termes descripteurs IGN] surface cultivée [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) Accurate and timely information on croplands is important for environmental, food security, and policy studies. Spatially explicit cropland datasets are also required to derive information on crop type, crop yield, cropping intensity, as well as irrigated areas. Large area – defined as continental to global – cropland mapping is challenging due to differential manifestation of croplands, wide range of cultivation practices and limited reference data availability. This study presents the results of a cropland extent mapping of 64 countries covering large parts of Europe, Middle East, Russia and Central Asia. To cover such a vast area, roughly 160,000 Landsat scenes from 3351 footprints between 2014 and 2016 were processed within the Google Earth Engine (GEE) platform. We used a pixel-based Random Forest (RF) machine learning algorithm with a set of satellite data inputs capturing diverse spectral, temporal and topographical characteristics across twelve agroecological zones (AEZs). The reference data to train the classification model were collected from very high spatial resolution imagery (VHRI) and ancillary datasets. The result is a binary map showing cultivated/non-cultivated areas ca. 2015. The map produced an overall accuracy of 93.8% with roughly 14% omission and commission errors for the cropland class based on a large set of independent validation samples. The map suggests the entire study area has a total 546 million hectares (Mha) of net croplands (nearly 30% of global net cropland areas) occupying 18% of the study land area. Comparison between national cropland area estimates from United Nations Food and Agricultural Organizations (FAO) and those derived from this work also showed an R-square value of 0.95. This Landsat-derived 30-m cropland product (GFSAD30) provided 10–30% greater cropland areas compared to UN FAO in the 64 Countries. Finally, the map-to-map comparison between GFSAD30 with several other cropland products revealed that the best similarity matrix was with the 30 m global land cover (GLC30) product providing an overall similarity of 88.8% (Kappa 0.7) with producer’s cropland similarity of 89.2% (errors of omissions = 10.8%) and user’s cropland similarity of 81.8% (errors of commissions = 8.1%). GFSAD30 captured the missing croplands in GLC30 product around significantly irrigated agricultural areas in Germany and Belgium and rainfed agriculture in Italy. This study also established that the real strengths of GFSAD30 product, compared to other products, were: 1. identifying precise location of croplands, and 2. capturing fragmented croplands. The cropland extent map dataset is available through NASA’s Land Processes Distributed Active Archive Center (LP DAAC) at https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001, while the training and reference data as well as visualization are available at the Global Croplands website, GEE code is accessible at: https://code.earthengine.google.com/1666e8bed34e0ce2b2aaf1235ad8c6bd. Numéro de notice : A2020-547 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.022 date de publication en ligne : 18/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95773 [article]

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A worldwide 3D GCP database inherited from 20 years of massive multi-satellite observations / Laure Chandelier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 15 - 23 Titre : A worldwide 3D GCP database inherited from 20 years of massive multi-satellite observations Type de document : Article/Communication Auteurs : Laure Chandelier , Auteur ; Laurent Coeurdevey, Auteur ; Sébastien Bosch, Auteur ; Pascal Favé, Auteur ; Roland Gachet, Auteur ; Alain Orsoni, Auteur ; Thomas Tilak , Auteur ; Alexis Barot, Auteur Congrès : Congrès: ISPRS 2020, virtual Congress Imaging today foreseeing tomorrow (31 aout - 2 septembre 2020; Nice, France), Commanditaire Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 15 - 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] base de données d'images [Termes descripteurs IGN] compensation par bloc [Termes descripteurs IGN] données localisées de référence [Termes descripteurs IGN] formatage [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image multi sources [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] image SPOT 6 [Termes descripteurs IGN] image SPOT 7 [Termes descripteurs IGN] image SPOT-HRS [Termes descripteurs IGN] informatique en nuage [Termes descripteurs IGN] Institut national de l'information géographique et forestière (France) [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] spatiotriangulation Résumé : (auteur) High location accuracy is a major requirement for satellite image users. Target performance is usually achieved thanks to either specific on-board satellite equipment or an auxiliary registration reference dataset. Both methods may be expensive and with certain limitations in terms of performance. The Institut national de l’information géographique et forestière (IGN) and Airbus Defence and Space (ADS) have worked together for almost 20 years, to build reference data for improving image location using multi-satellite observations. The first geometric foundation created has mainly used SPOT 5 High Resolution Stereoscopic (HRS) imagery, ancillary Ground Control Points (GCP) and Very High Resolution (VHR) imagery, providing a homogenous location accuracy of 10m CE90 almost all over the world in 2010. Space Reference Points (SRP) is a new worldwide 3D GCP database, built from a plethoric SPOT 6/7 multi-view archive, largely automatically processed, with cloud-based technologies. SRP aims at providing a systematic and reliable solution for image location (Unmanned Aerial Vehicle, VHR satellite imagery, High Altitudes Pseudo-Satellite…) and similar topics thanks to a high-density point distribution with a 3m CE90 accuracy. This paper describes the principle of SRP generation and presents the first validation results. A SPOT 6/7 smart image selection is performed to keep only relevant images for SRP purpose. The location of these SPOT 6/7 images is refined thanks to a spatiotriangulation on the worldwide geometric foundation, itself improved where needed. Points making up the future SRP database are afterward extracted thanks to classical feature detection algorithms and with respect to the expected density. Different filtering methods are applied to keep the best candidates. The last step of the processing chain is the formatting of the data to the delivery format, including metadata. An example of validation of SRP concept and specification on two tests sites (Spain and China) is then given. As a conclusion, the on-going production is shortly presented. Numéro de notice : A2020-474 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-15-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-15-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95613 [article]

Above-ground biomass estimation of arable crops using UAV-based SfM photogrammetry / Maria Luz Gil-Docampo in Geocarto international, vol 35 n° 7 ([15/05/2020])  

Public [article]  inGeocarto international > vol 35 n° 7 [15/05/2020] . - pp 687 - 699 Titre : Above-ground biomass estimation of arable crops using UAV-based SfM photogrammetry Type de document : Article/Communication Auteurs : Maria Luz Gil-Docampo, Auteur ; Marcos Arza-García, Auteur ; Juan Ortiz-Sanz, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 687 - 699 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] acquisition d'images [Termes descripteurs IGN] agronomie [Termes descripteurs IGN] biomasse [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] photogrammétrie aérienne [Termes descripteurs IGN] sol arable [Termes descripteurs IGN] structure-from-motion Résumé : (Auteur) Methods of estimating the total amount of above-ground biomass (AGB) in crop fields are generally based on labourious, random, and destructive in situ sampling. This study proposes a methodology for estimating herbaceous crop biomass using conventional optical cameras and structure from motion (SfM) photogrammetry. The proposed method is based on the determination of volumes according to the difference between a digital terrain model (DTM) and digital surface model (DSM) of vegetative cover. A density factor was calibrated based on a subset of destructive random samples to relate the volume and biomass and efficiently quantify the total AGB. In all cases, RMSE Z values less than 0.23 m were obtained for the DTM-DSM coupling. Biomass field data confirmed the goodness of fit of the yield-biomass estimation (R2=0.88 and 1.12 kg/ha) mainly in plots with uniform vegetation coverage. Furthermore, the method was demonstrated to be scalable to multiple platform types and sensors. Numéro de notice : A2020-186 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1552322 date de publication en ligne : 07/02/2019 En ligne : https://doi.org/10.1080/10106049.2018.1552322 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94993 [article]

Assessment of salt marsh change on Assateague Island National Seashore between 1962 and 2016 / Anthony Campbell in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 187 - 194 Titre : Assessment of salt marsh change on Assateague Island National Seashore between 1962 and 2016 Type de document : Article/Communication Auteurs : Anthony Campbell, Auteur ; Yeqiao Wang, Auteur Année de publication : 2020 Article en page(s) : pp 187 - 194 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] approche hiérarchique [Termes descripteurs IGN] Atlantique (océan) [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] cartographie thématique [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] lidar bathymétrique [Termes descripteurs IGN] littoral [Termes descripteurs IGN] marais salant [Termes descripteurs IGN] montée du niveau de la mer [Termes descripteurs IGN] service écosystémique [Termes descripteurs IGN] surveillance du littoral Résumé : (auteur) Salt marshes provide extensive ecosystem services, including high biodiversity, denitrification, and wave attenuation. In the mid-Atlantic, sea level rise is predicted to affect salt marsh ecosystems severely. This study mapped the entirety of Assateague Island with Very High Resolution satellite imagery and object-based methods to determine an accurate salt marsh baseline for change analysis. Topobathy-metric light detection and ranging was used to map the salt marsh and model expected tidal effects. The satellite imagery, collected in 2016 and classified at two hierarchical thematic schemes, were compared to determine appropriate thematic richness. Change analysis between this 2016 map and both a manually delineated 1962 salt marsh extent and image classification of the island from 1994 determined rates off change. The study found that from 1962 to 1994, salt marsh expanded by 4.01 ha/year, and from 1994 to 2016 salt marsh was lost at a rate of -3.4 ha/ year. The study found that salt marsh composition, (percent vegetated salt marsh) was significantly influenced by elevation, the length of mosquito ditches, and starting salt marsh composition. The study illustrates the importance of remote sensing monitoring for understanding site-specific changes to salt marsh environments and the barrier island system. Numéro de notice : A2020-148 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.3.187 date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.3.187 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94777 [article]

Edge-reinforced convolutional neural network for road detection in very-high-resolution remote sensing imagery / Xiaoyan Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 153 - 160 Titre : Edge-reinforced convolutional neural network for road detection in very-high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Xiaoyan Lu, Auteur ; Yanfei Zhong, Auteur ; Zhuo Zheng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 153 - 160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] accentuation de contours [Termes descripteurs IGN] analyse multiéchelle [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] ombre [Termes descripteurs IGN] segmentation d'image Résumé : (auteur) Road detection in very-high-resolution remote sensing imagery is a hot research topic. However, the high resolution results in highly complex data distributions, which lead to much noise for road detection—for example, shadows and occlusions caused by disturbance on the roadside make it difficult to accurately recognize road. In this article, a novel edge-reinforced convolutional neural network, combined with multiscale feature extraction and edge reinforcement, is proposed to alleviate this problem. First, multiscale feature extraction is used in the center part of the proposed network to extract multiscale context information. Then edge reinforcement, applying a simplified U-Net to learn additional edge information, is used to restore the road information. The two operations can be used with different convolutional neural networks. Finally, two public road data sets are adopted to verify the effectiveness of the proposed approach, with experimental results demonstrating its superiority. Numéro de notice : A2020-145 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.3.153 date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.3.153 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94774 [article]

Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data / Xiuyuan Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

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Integration of remote sensing and GIS to extract plantation rows from a drone-based image point cloud digital surface model / Nadeem Fareed in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)  

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Complex deformation at shallow depth during the 30 October 2016 Mw6.5 Norcia earthquake: interferencebetween tectonic and gravity processes? / Arthur Delorme in Tectonics, vol 39 n° 2 (February 2020)    

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Some thoughts on measuring earthquake deformation using optical imagery / Min Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

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3D iterative spatiotemporal filtering for classification of multitemporal satellite data sets / Hessah Albanwan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 1 (January 2020)  

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Context-aware convolutional neural network for object detection in VHR remote sensing imagery / Yiping Gong in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

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Current challenges in operational very high resolution land-cover mapping / Clément Mallet (2020)  

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Streambank topography: an accuracy assessment of UAV-based and traditional 3D reconstructions / Benjamin U. Meinen in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)  

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Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation / Li Mi in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

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Very high resolution land cover mapping of urban areas at global scale with convolutional neural network / Thomas Tilak (2020)  

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