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Google Earth Engine applications / Lalit Kumar (2019)  

Public Titre : Google Earth Engine applications Type de document : Monographie Auteurs : Lalit Kumar, Éditeur scientifique ; Onisimo Mutanga, Éditeur 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 IGN] base de données d'images [Termes IGN] Google Earth Engine [Termes IGN] image 3D [Termes IGN] image aérienne [Termes IGN] image satellite [Termes IGN] information géographique numérique [Termes IGN] informatique en nuage [Termes IGN] moteur de recherche [Termes IGN] surveillance écologique [Termes IGN] système d'information environnementale [Termes 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

Remote sensing of above ground biomass / Lalit Kumar (2019)  

Public Titre : Remote sensing of above ground biomass Type de document : Monographie Auteurs : Lalit Kumar, Auteur ; Onisimo Mutanga, Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 264 p. ISBN/ISSN/EAN : 978-3-03921-210-1 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] changement climatique [Termes IGN] coefficient de corrélation [Termes IGN] données lidar [Termes IGN] image Terra-MODIS [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] régression [Termes IGN] série temporelle Résumé : (Editeur) Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local–regional–global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring. Numéro de notice : 26325 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03921-210-1 Date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.3390/books978-3-03921-210-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95159

Markov land cover change modeling using pairs of time-series satellite images / Priyakant Sinha in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 11 (November 2013) . - pp 1037 - 1051 Titre : Markov land cover change modeling using pairs of time-series satellite images Type de document : Article/Communication Auteurs : Priyakant Sinha, Auteur ; Lalit Kumar, Auteur Année de publication : 2013 Article en page(s) : pp 1037 - 1051 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] automate cellulaire [Termes IGN] chaîne de Markov [Termes IGN] flore locale [Termes IGN] image Landsat-MSS [Termes IGN] image Landsat-TM [Termes IGN] image multitemporelle [Termes IGN] Nouvelle-Galles du Sud [Termes IGN] occupation du sol [Termes IGN] prédiction Résumé : (Auteur) Models of change processes created with the Markov chain model (MCM) can be used in the interpolation of temporal data and in short-term change projections. However, there are two major issues associated with the use of Markov models for land-cover change projections: the stationarity of change and the impact of neighboring cells on the change areas. This study addressed these two issues using an investigation of five time-series land-cover datasets generated between 1972 and 2009 for the Liverpool region of NSW, Australia. Four short- term transition matrices were computed, and the results were used to predict land-cover distributions for the near future. The issue of neighborhood effects was addressed by incorporating spatial components in a Cellular Automata (CA)-based MCM, and the results were compared with those derived from a normal MCM. Given the marginal improvements in the simulation achieved with CA-MCM rather than MCM, and because of the ability of CA-MCM to incorporate spatial variants, CA-MCM was determined to be the more suitable method for predicting land-cover changes for the year 2019. The land-cover projection indicated that future land-cover changes will likely continue to affect the natural vegetation, which will in turn likely decrease through the continued conversion of natural to agricultural lands over the years. Numéro de notice : A2013-598 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.11.1037 En ligne : https://doi.org/10.14358/PERS.79.11.1037 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32734 [article]

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Independent two-step thresholding of binary images in inter-annual land cover change/no-change identification / Priyakant Sinha in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 81 (July 2013) . - pp 31 - 43 Titre : Independent two-step thresholding of binary images in inter-annual land cover change/no-change identification Type de document : Article/Communication Auteurs : Priyakant Sinha, Auteur ; Lalit Kumar, Auteur Année de publication : 2013 Article en page(s) : pp 31 - 43 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse diachronique [Termes IGN] détection de changement [Termes IGN] écart type [Termes IGN] image binaire [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] occupation du sol [Termes IGN] segmentation binaire [Termes IGN] seuillage d'image Résumé : (Auteur) Binary images from one or more spectral bands have been used in many studies for land-cover change/no-change identification in diverse climatic conditions. Determination of appropriate threshold levels for change/no-change identification is a critical factor that influences change detection result accuracy. The most used method to determine the threshold values is based on the standard deviation (SD) from the mean, assuming the amount of change (due to increase or decrease in brightness values) to be symmetrically distributed on a standard normal curve, which is not always true. Considering the asymmetrical nature of distribution histogram for the two sides, this study proposes a relatively simple and easy ‘Independent Two-Step’ thresholding approach for optimal threshold value determination for spectrally increased and decreased part using Normalized Difference Vegetation Index (NDVI) difference image. Six NDVI differencing images from 2007 to 2009 of different seasons were tested for inter-annual or seasonal land cover change/no-change identification. The relative performances of the proposed and two other methods towards the sensitivity of distributions were tested and an improvement of ~3% in overall accuracy and of ~0.04 in Kappa was attained with the Proposed Method. This study demonstrated the importance of consideration of normality of data distributions in land-cover change/no-change analysis. Numéro de notice : A2013-387 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.03.010 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.03.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32525 [article]

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