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Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors / Pauline Perbet in International Journal of Remote Sensing IJRS, vol 40 n°19 (February 2019)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 40 n°19 (February 2019) . - pp 7439 - 7458 Titre : Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors Type de document : Article/Communication Auteurs : Pauline Perbet, Auteur ; Michelle Fortin, Auteur ; Anouk Ville, Auteur ; Martin Béland, Auteur Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 7439 - 7458 Note générale : bibliographie This work was supported by the Natural Sciences and Engineering Research Council of Canada. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse vectorielle [Termes IGN] déboisement [Termes IGN] défrichement [Termes IGN] détection de changement [Termes IGN] forêt tropicale [Termes IGN] image captée par drone [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Indonésie [Termes IGN] Malaisie [Termes IGN] surveillance forestière Résumé : (auteur) Malaysia and Indonesia have been affected by deforestation caused in great part by the proliferation of oil palm plantations. To survey this loss of forest, several studies have monitored these southeast Asian nations with satellite remote sensing alert systems. The methods used have shown potential for this approach, but they are limited by imagery with coarse spatial resolution, low revisit times, and cloud cover. The objective of this research is to improve near real-time operational deforestation detection by combining three sensors: Sentinel-1, Sentinel-2 and Landsat-8. We used Change Vector Analysis to detect changes between non-affected forest and images under analysis. The results were validated using 166 plots of undisturbed forest and confirmed deforestation events throughout Sabah Malaysian State, and from 70 points from drone pictures in Sumatra, Indonesia. Sentinel-2 and Landsat-8 yielded sufficient results in terms of accuracy (less than 11% of commission and omission error). Sentinel-1 had lower accuracy (14% of commission error and 28% of omission error), probably resulting from geometric distortions and speckle noise. During the high cloud-cover season optical sensors took about twice the time to detect deforestation compared to Sentinel-1 which was not affected by cloud cover. By combining the three sensors, we detected deforestations about 8 days after forest clearing events. Deforestations were only detectable during approximately the first 100 days, before bare soils were often coved by legume crop. Our results indicate that near real-time deforestation detection can reveal most events, but the number of false detections could be improved using a multiple event detection process. Numéro de notice : A2019-321 Affiliation des auteurs : ENSG+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2019.1579390 Date de publication en ligne : 17/02/2019 En ligne : https://doi.org/10.1080/01431161.2019.1579390 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93295 [article]