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Open AccessArticle

The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest

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CIRAD, Forêts et Sociétés, F-34398 Montpellier, France
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Forêts et Sociétés, University Montpellier, CIRAD, 34398 Montpellier, France
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LISAH, University Montpellier, INRA, IRD, Montpellier SupAgro, 34398 Montpellier, France
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Department SIAFEE AgroParisTech, 75231 Paris, France
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Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK
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Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
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UMR CNRS LETG 6554, Laboratory of Geography and Remote Sensing COSTEL, Université de Rennes 2, 35043 Rennes, France
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Centre de Recherche et de Documentations sur les Amériques (CREDA), UMR 7227, Université Sorbonne Nouvelle, Paris 3, 75006 Paris, France
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UMR CNRS ESO (Espaces et Sociétés), Le Mans Université, 72000 Le Mans, France
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Author to whom correspondence should be addressed.
Forests 2018, 9(6), 303; https://doi.org/10.3390/f9060303
Received: 13 April 2018 / Revised: 24 May 2018 / Accepted: 25 May 2018 / Published: 29 May 2018
In the agricultural frontiers of Brazil, the distinction between forested and deforested lands traditionally used to map the state of the Amazon does not reflect the reality of the forest situation. A whole gradient exists for these forests, spanning from well conserved to severely degraded. For decision makers, there is an urgent need to better characterize the status of the forest resource at the regional scale. Until now, few studies have been carried out on the potential of multisource, freely accessible remote sensing for modelling and mapping degraded forest structural parameters such as aboveground biomass (AGB). The aim of this article is to address that gap and to evaluate the potential of optical (Landsat, MODIS) and radar (ALOS-1 PALSAR, Sentinel-1) remote sensing sources in modelling and mapping forest AGB in the old pioneer front of Paragominas municipality (Para state). We derived a wide range of vegetation and textural indices and combined them with in situ collected AGB data into a random forest regression model to predict AGB at a resolution of 20 m. The model explained 28% of the variance with a root mean square error of 97.1 Mg·ha−1 and captured all spatial variability. We identified Landsat spectral unmixing and mid-infrared indicators to be the most robust indicators with the highest explanatory power. AGB mapping reveals that 87% of forest is degraded, with illegal logging activities, impacted forest edges and other spatial distribution of AGB that are not captured with pantropical datasets. We validated this map with a field-based forest degradation typology built on canopy height and structure observations. We conclude that the modelling framework developed here combined with high-resolution vegetation status indicators can help improve the management of degraded forests at the regional scale. View Full-Text
Keywords: forest degradation; multisource remote sensing; modelling aboveground biomass; random forest; Brazilian Amazon forest degradation; multisource remote sensing; modelling aboveground biomass; random forest; Brazilian Amazon
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Bourgoin, C.; Blanc, L.; Bailly, J.-S.; Cornu, G.; Berenguer, E.; Oszwald, J.; Tritsch, I.; Laurent, F.; Hasan, A.F.; Sist, P.; Gond, V. The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest. Forests 2018, 9, 303.

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