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Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images / Fabien Hubert Wagner in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part B (November 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 145 - part B (November 2018) . - pp 362 - 377 Titre : Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images Type de document : Article/Communication Auteurs : Fabien Hubert Wagner, Auteur ; Matheus Pinheiro Ferreira, Auteur ; Alber Sanchez, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 362 - 377 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Brésil [Termes IGN] détection de contours [Termes IGN] forêt tropicale [Termes IGN] houppier [Termes IGN] identification de plantes [Termes IGN] image multibande [Termes IGN] image Worldview [Termes IGN] morphologie mathématique [Termes IGN] segmentation d'image Résumé : (auteur) Mapping tropical tree species at landscape scales to provide information for ecologists and forest managers is a new challenge for the remote sensing community. For this purpose, detection and delineation of individual tree crowns (ITCs) is a prerequisite. Here, we present a new method of automatic tree crown delineation based only on very high resolution images from WorldView-2 satellite and apply it to a region of the Atlantic rain forest with highly heterogeneous tropical canopy cover – the Santa Genebra forest reserve in Brazil. The method works in successive steps that involve pre-processing, selection of forested pixels, enhancement of borders, detection of pixels in the crown borders, correction of shade in large trees and, finally, segmentation of the tree crowns. Principally, the method uses four techniques: rolling ball algorithm and mathematical morphological operations to enhance the crown borders and ease the extraction of tree crowns; bimodal distribution parameters estimations to identify the shaded pixels in the gaps, borders, and crowns; and focal statistics for the analysis of neighbouring pixels. Crown detection is validated by comparing the delineated ITCs with a sample of ITCs delineated manually by visual interpretation. In addition, to test if the spectra of individual species are conserved in the automatic delineated crowns, we compare the accuracy of species prediction with automatic and manual delineated crowns with known species. We find that our method permits detection of up to 80% of ITCs. The seven species with over 10 crowns identified in the field were mapped with reasonable accuracy (30.5–96%) given that only WorldView-2 bands and texture features were used. Similar classification accuracies were obtained using both automatic and manual delineation, thereby confirming that species’ spectral responses are preserved in the automatic method and thus permitting the recognition of species at the landscape scale. Our method might support tropical forest applications, such as mapping species and canopy characteristics at the landscape scale. Numéro de notice : A2018-536 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.09.013 Date de publication en ligne : 08/10/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.09.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91541 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018121	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018123	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018122	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series / Meng Lu in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 117 (July 2016) . - pp 227 – 236 Titre : Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series Type de document : Article/Communication Auteurs : Meng Lu, Auteur ; Edzer J. Pebesma, Auteur ; Alber Sanchez, Auteur ; Jan Verbesselt, Auteur Année de publication : 2016 Article en page(s) : pp 227 – 236 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Amazonie [Termes IGN] Brésil [Termes IGN] corrélation automatique de points homologues [Termes IGN] déboisement [Termes IGN] détection de changement [Termes IGN] image Aqua-MODIS [Termes IGN] image Terra-MODIS [Termes IGN] points de rupture [Termes IGN] série temporelle Résumé : (auteur) Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over large datasets. Our study case illustrates the detection of breakpoints in MODIS imagery time series for land cover change in the Brazilian Amazon using the BFAST (Breaks For Additive Season and Trend) change detection framework. BFAST includes an Empirical Fluctuation Process (EFP) to alarm the change and a change point time locating process. We extend the EFP to account for the spatial autocorrelation between spatial neighbors and assess the effects of spatial correlation when applying BFAST on satellite image time series. In addition, we evaluate how sensitive EFP is to the assumption that its time series residuals are temporally uncorrelated, by modeling it as an autoregressive process. We use arrays as a unified data structure for the modeling process, R to execute the analysis, and an array database management system to scale computation. Our results point to BFAST as a robust approach against mild temporal and spatial correlation, to the use of arrays to ease the modeling process of spatio-temporal change, and towards communicable and scalable analysis. Numéro de notice : A2016-586 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.03.007 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.03.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81727 [article]