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Automated detection of individual Juniper tree location and forest cover changes using Google Earth Engine / Sudeera Wickramarathna in Annals of forest research, vol 64 n° 1 (2021)  

Public [article]  inAnnals of forest research > vol 64 n° 1 (2021) . - pp 61 - 72 Titre : Automated detection of individual Juniper tree location and forest cover changes using Google Earth Engine Type de document : Article/Communication Auteurs : Sudeera Wickramarathna, Auteur ; Jamon Van Den Hoek, Auteur ; Bogdan Mihai Strimbu, Auteur Année de publication : 2021 Article en page(s) : pp 61 - 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] canopée [Termes IGN] classification dirigée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] couvert forestier [Termes IGN] croissance des arbres [Termes IGN] détection d'arbres [Termes IGN] détection de changement [Termes IGN] extraction de la végétation [Termes IGN] Google Earth Engine [Termes IGN] image à très haute résolution [Termes IGN] image multibande [Termes IGN] juniperus (genre) [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] Normalized Difference Water Index [Termes IGN] Oregon (Etats-Unis) [Termes IGN] réflectance Résumé : (auteur) Tree detection is the first step in the appraisal of a forest, especially when the focus is monitoring the growth of tree canopy. The acquisition of annual very high-resolution aerial images by the National Agriculture Imagery Program (NAIP) and their accessibility through Google Earth Engine (GEE) supports the delineation of tree canopies and change over time in a cost and time-effective manner. The objectives of this study are to develop an automated method to detect the crowns of individual western Juniper (Juniperus occidentalis) trees and to assess the change of forest cover from multispectral 1-meter resolution NAIP images collected from 2009 to 2016, in Oregon, USA. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Vegetation Index (RVI), were calculated from the NAIP images, in addition to the red-green-blue-near infrared bands. To identify the most suitable approach for individual tree crown identification, we created two training datasets: one considering yearly images separately and one merging all images, irrespective of the year. We segmented individual tree crowns using a random forest algorithm implemented in GEE and seven rasters, namely the reflectance of four spectral bands as recorded by the NAIP images (i.e., the red-green-blue-near infrared) and three calculated indices (i.e., NDVI, NDWI, and RVI). We compared the estimated location of the trees, computed as the centroid of the crown, with the visually identified treetops, which were considered as validation locations. We found that tree location errors were smaller when years were analyzed individually than by merging the years. Measurements of completeness (74%), correctness (94%), and mean accuracy detection (82 %) show promising performance of the random forest algorithm in crown delineation, considering that only four original input bands were used for crown segmentation. The change in the calculated crown area for western juniper follows a sinusoidal curve, with a decrease from 2011 to 2012 and an increase from 2012 to 2014. The proposed approach has the potential to estimate individual tree locations and forest cover area dynamics at broad spatial scales using regularly collected airborne imagery with easy-to-implement methods. Numéro de notice : A2021-779 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15287/afr.2020.2145 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.15287/afr.2020.2145 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98846 [article]