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Pargal S., Fararoda R., Rajashekar G., Balachandran N., Réjou-Méchain Maxime, Barbier Nicolas, Jha C. S., Pélissier Raphaël, Dadhwal V. K., Couteron Pierre. (2017). Inverting aboveground biomass-canopy texture relationships in a landscape of forest mosaic in the Western Ghats of India using very high resolution cartosat imagery. Remote Sensing, 9 (3), art. 228 [20 p.]. ISSN 2072-4292

Fichier PDF disponible http://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers17-05/010069496.pdf

Lien direct chez l'éditeur doi:10.3390/rs9030228

Titre
Inverting aboveground biomass-canopy texture relationships in a landscape of forest mosaic in the Western Ghats of India using very high resolution cartosat imagery
Année de publication2017
Type de documentArticle référencé dans le Web of Science WOS:000398720100042
AuteursPargal S., Fararoda R., Rajashekar G., Balachandran N., Réjou-Méchain Maxime, Barbier Nicolas, Jha C. S., Pélissier Raphaël, Dadhwal V. K., Couteron Pierre.
SourceRemote Sensing, 2017, 9 (3), p. art. 228 [20 p.]. p. art. 228 [20 p.] ISSN 2072-4292
RésuméLarge scale assessment of aboveground biomass (AGB) in tropical forests is often limited by the saturation of remote sensing signals at high AGB values. Fourier Transform Textural Ordination (FOTO) performs well in quantifying canopy texture from very high-resolution (VHR) imagery, from which stand structure parameters can be retrieved with no saturation effect for AGB values up to 650 Mgha(-1). The method is robust when tested on wet evergreen forests but is more demanding when applied across different forest types characterized by varying structures and allometries. The present study focuses on a gradient of forest types ranging from dry deciduous to wet evergreen forests in the Western Ghats (WG) of India, where we applied FOTO to Cartosat-1a images with 2.5 m resolution. Based on 21 1-ha ground control forest plots, we calibrated independent texture-AGB models for the dry and wet zone forests in the area, as delineated from the distribution of NDVI values computed from LISS-4 multispectral images. This stratification largely improved the relationship between texture-derived and field-derived AGB estimates, which exhibited a R-2 of 0.82 for a mean rRMSE of ca. 17%. By inverting the texture-AGB models, we finally mapped AGB predictions at 1.6-ha resolution over a heterogeneous landscape of ca. 1500 km(2) in the WG, with a mean relative per-pixel propagated error <20% for wet zone forests, i.e., below the recommended IPCC criteria for Monitoring, Reporting and Verification (MRV) methods. The method proved to perform well in predicting high-resolution AGB values over heterogeneous tropical landscape encompassing diversified forest types, and thus presents a promising option for affordable regional monitoring systems of greenhouse gas (GhG) emissions related to forest degradation.
Plan de classementEtudes, transformation, conservation du milieu naturel [082] ; Télédétection [126] ; Bioclimatologie [072]
Descr. géo.INDE ; GHATS OUEST ; ZONE TROPICALE
LocalisationFonds IRD [F B010069496]
Identifiant IRDfdi:010069496
Lien permanenthttp://www.documentation.ird.fr/hor/fdi:010069496

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