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Use of automated change detection and VGI sources for identifying and validating urban land use change / Ana-Maria Olteanu-Raimond in Remote sensing, vol 12 n° 7 (April 2020)  

Public [article]  inRemote sensing > vol 12 n° 7 (April 2020) . - n° 1186 Titre : Use of automated change detection and VGI sources for identifying and validating urban land use change Type de document : Article/Communication Auteurs : Ana-Maria Olteanu-Raimond , Auteur ; L. See, Auteur ; M. Schultz, Auteur ; Giles M. Foody, Auteur ; M. Riffler, Auteur ; T. Gasber, Auteur ; Laurence Jolivet , Auteur ; Arnaud Le Bris , Auteur ; Yann Méneroux , Auteur ; Lanfa Liu, Auteur ; Marc Poupée , Auteur ; Marie Gombert, Auteur Année de publication : 2020 Projets : Landsense / Raimond, Ana-Maria Article en page(s) : n° 1186 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] carte d'utilisation du sol [Termes IGN] cartographie collaborative [Termes IGN] changement d'utilisation du sol [Termes IGN] détection automatique [Termes IGN] détection de changement [Termes IGN] données localisées des bénévoles [Termes IGN] estimation de précision [Termes IGN] science citoyenne [Termes IGN] zone urbaine Résumé : (Auteur) Land use and land cover (LULC) mapping is often undertaken by national mapping agencies, where these LULC products are used for different types of monitoring and reporting applications. Updating of LULC databases is often done on a multi-year cycle due to the high costs involved, so changes are only detected when mapping exercises are repeated. Consequently, the information on LULC can quickly become outdated and hence may be incorrect in some areas. In the current era of big data and Earth observation, change detection algorithms can be used to identify changes in urban areas, which can then be used to automatically update LULC databases on a more continuous basis. However, the change detection algorithm must be validated before the changes can be committed to authoritative databases such as those produced by national mapping agencies. This paper outlines a change detection algorithm for identifying construction sites, which represent ongoing changes in LU, developed in the framework of the LandSense project. We then use volunteered geographic information (VGI) captured through the use of mapathons from a range of different groups of contributors to validate these changes. In total, 105 contributors were involved in the mapathons, producing a total of 2778 observations. The 105 contributors were grouped according to six different user-profiles and were analyzed to understand the impact of the experience of the users on the accuracy assessment. Overall, the results show that the change detection algorithm is able to identify changes in residential land use to an adequate level of accuracy (85%) but changes in infrastructure and industrial sites had lower accuracies (57% and 75 %, respectively), requiring further improvements. In terms of user profiles, the experts in LULC from local authorities, researchers in LULC at the French national mapping agency (IGN), and first-year students with a basic knowledge of geographic information systems had the highest overall accuracies (86.2%, 93.2%, and 85.2%, respectively). Differences in how the users approach the task also emerged, e.g., local authorities used knowledge and context to try to identify types of change while those with no knowledge of LULC (i.e., normal citizens) were quicker to choose ‘Unknown’ when the visual interpretation of a class was more difficult. Numéro de notice : A2020-243 Affiliation des auteurs : LASTIG+Ext (2016-2019) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12071186 Date de publication en ligne : 07/04/2020 En ligne : https://doi.org/10.3390/rs12071186 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95217 [article]

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 IGN] carte de la végétation [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] Elaeis guineensis [Termes IGN] Google Earth Engine [Termes IGN] image Landsat [Termes IGN] image radar moirée [Termes IGN] occupation du sol [Termes IGN] Sumatra [Termes IGN] surveillance agricole [Termes 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 Thématique : IMAGERIE 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]