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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 Affiliation des auteurs : non IGN 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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Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine / Thuan Sarzynski in Remote sensing, vol 12 n° 7 (April 2020)  

Public [article]  inRemote sensing > vol 12 n° 7 (April 2020) Titre : Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine Type de document : Article/Communication Auteurs : Thuan Sarzynski, Auteur ; Xingli Giam, Auteur ; Luis Carrasco, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] Elaeis guineensis [Termes descripteurs IGN] Google Earth Engine [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] Sumatra [Termes descripteurs IGN] surveillance agricole [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) Monitoring the expansion of commodity crops in the tropics is crucial to safeguard forests for biodiversity and ecosystem services. Oil palm (Elaeis guineensis) is one such crop that is a major driver of deforestation in Southeast Asia. We evaluated the use of a semi-automated approach with random forest as a classifier and combined optical and radar datasets to classify oil palm land-cover in 2015 in Sumatra, Indonesia, using Google Earth Engine. We compared our map with two existing remotely-sensed oil palm land-cover products that utilized visual and semi-automated approaches for the same year. We evaluated the accuracy of oil palm land-cover classification from optical (Landsat), radar (synthetic aperture radar (SAR)), and combined optical and radar satellite imagery (Combined). Combining Landsat and SAR data resulted in the highest overall classification accuracy (84%) and highest producer’s and user’s accuracy for oil palm classification (84% and 90%, respectively). The amount of oil palm land-cover in our Combined map was closer to official government statistics than the two existing land-cover products that used visual interpretation techniques. Our analysis of the extents of disagreement in oil palm land-cover indicated that our map had comparable accuracy to one of them and higher accuracy than the other. Our results demonstrate that a combination of optical and radar data outperforms the use of optical-only or radar-only datasets for oil palm classification and that our technique of preprocessing and classifying combined optical and radar data in the Google Earth Engine can be applied to accurately monitor oil-palm land-cover in Southeast Asia. Numéro de notice : A2020-455 Affiliation des auteurs : non IGN Nature : Article DOI : 10.3390/rs12071220 date de publication en ligne : 10/04/2020 En ligne : https://doi.org/10.3390/rs12071220 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95554 [article]

Regional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification / Viktor Myroniuk in Remote sensing, vol 12 n°1 (January 2020)  

Public [article]  inRemote sensing > vol 12 n°1 (January 2020) . - 24 p. Titre : Regional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification Type de document : Article/Communication Auteurs : Viktor Myroniuk, Auteur ; Mykola Kutia, Auteur ; Arbi J. Sarkissian, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bande infrarouge [Termes descripteurs IGN] carte forestière [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] Global Forest Change map [Termes descripteurs IGN] Google Earth Engine [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] plaine [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surveillance forestière [Termes descripteurs IGN] Ukraine Résumé : (auteur) Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15% Numéro de notice : A2020-225 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs12010187 date de publication en ligne : 05/01/2020 En ligne : https://doi.org/10.3390/rs12010187 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94940 [article]

National scale identification and characterization of braided rivers in New Zealand using Google Earth Engine / Alexis Jean (2020) 

Public Titre : National scale identification and characterization of braided rivers in New Zealand using Google Earth Engine Type de document : Mémoire Auteurs : Alexis Jean, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2020 Importance : 56 p. Format : 21 x 30 cm Note générale : Bibliographie Rapport de projet pluridisciplinaire, cycle ING2 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] détection automatique [Termes descripteurs IGN] Google Earth Engine [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Normalized Difference Water Index [Termes descripteurs IGN] Nouvelle-Zélande [Termes descripteurs IGN] rivière Index. décimale : PROJET Rapports de projet - stage des ingénieurs de 2e année Résumé : (Auteur) La Nouvelle-Zélande est l’un des derniers pays à avoir des rivières tressées sur son territoire. Ces rivières tirent leur nom de la morphologie particulière de leurs cours d’eau qui est en forme de tresse. Les rivières tressées de par leurs caractéristiques évoluent rapidement dans le temps. Afin de les protéger de toutes interactions anthropiques, il est donc nécessaire de délimiter une zone de protection. Pour faciliter leurs études, un procédé de détection automatique et de caractérisation des rivières sera étudié. Ce procédé s’appuiera sur les données multi-temporelles de Sentinel-2 et utilisera les services de Google Earth Engine, une plate-forme d’analyse géospatiale basée sur le cloud computing, dans le but de réaliser les différents calculs nécessaires. Note de contenu : 1. Introduction 1.1 Background 1.2 Challenge 1.3 Research objective & questions 1.4 Internship outline 2. Literature review: remote sensing techniques 2.1 Pre-processing 2.2 Water detection 2.3 Post-processing 2.4 Conclusion 3. Automatic surface water detection 3.1 Study area 3.2 Data resources 3.3 Method 3.4 Ground truth comparison 4. River characteristics 4.1 River width Conclusion Numéro de notice : 26367 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire de projet pluridisciplinaire Organisme de stage : University of Glasgow Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95816

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Google Earth Engine applications / Lalit Kumar (2019)  

Public Titre : Google Earth Engine applications Type de document : Monographie Auteurs : Lalit Kumar, Editeur scientifique ; Onisimo Mutanga, Editeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 420 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-03897-885-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Information géographique [Termes descripteurs IGN] base de données d'images [Termes descripteurs IGN] Google Earth Engine [Termes descripteurs IGN] image 3D [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] information géographique numérique [Termes descripteurs IGN] informatique en nuage [Termes descripteurs IGN] moteur de recherche [Termes descripteurs IGN] surveillance écologique [Termes descripteurs IGN] système d'information environnementale [Termes descripteurs IGN] traitement de données localisées Résumé : (éditeur) In a rapidly changing world, there is an ever-increasing need to monitor the Earth's resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth's surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales. Note de contenu : 1- Google Earth Engine applications since inception: usage, trends, and potential 2- Global estimation of biophysical variables from Google Earth Engine platform 3- An operational before-after-control-impact (BACI) designed platform for vegetation monitoring at planetary scale 4- Mapping vegetation and land use types in Fanjingshan national nature reserve using Google Earth Engine 5- A dynamic Landsat derived Normalized Difference Vegetation Index (NDVI) product for the conterminous United States 6- High spatial resolution visual band imagery outperforms medium resolution spectral imagery for ecosystem assessment in the semi-arid Brazilian Sert˜ao 7- Assessing the spatial and occupation dynamics of the Brazilian pasturelands based on the automated classification of MODIS images from 2000 to 2016 8- Towards global-scale seagrass mapping and monitoring using Sentinel-2 on Google Earth Engine: The case study of the Aegean and Ionian Seas 9- BULC-U: Sharpening resolution and improving accuracy of land-use/land-cover classifications in Google Earth Engine 10- Monitoring the impact of land cover change on surface urban heat island through Google Earth Engine: Proposal of a global methodology, first applications and problems 11- Regional crop gross primary productivity and yield estimation using fused Landsat-MODIS data 12- The first wetland inventory map of Newfoundland at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform 13- A cloud-based multi-temporal ensemble classifier to map smallholder farming systems 14- Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine 15- SnowCloudHydro — A new framework for forecasting streamflow in snowy, data-scarce regions 16- Flood prevention and emergency response system powered by Google Earth Engine 17- Leveraging the Google Earth Engine for drought assessment using global soil moisture data 18- Multitemporal cloud masking in the Google Earth Engine 19- Historical and operational monitoring of surface sediments in the lower Mekong basin using Landsat and Google Earth Engine cloud computing 20- Mapping mining areas in the Brazilian Amazon using MSI/Sentinel-2 imagery (2017) 21- Estimating satellite-derived bathymetry (SDB) with the Google Earth Engine and Sentinel-2 22- Mean composite fire severity metrics computed with Google Earth Engine offer improved accuracy and expanded mapping potential Numéro de notice : 25887 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03897-885-5 En ligne : https://doi.org/10.3390/books978-3-03897-885-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95788

A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform / Bangqian Chen in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)  

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