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Open AccessArticle

Quantitative Airborne Inventories in Dense Tropical Forest Using Imaging Spectroscopy

1
AMAP, IRD, CNRS, INRA, Université Montpellier, CIRAD, 34000 Montpellier, France
2
Pôle RDI, ONF Guyane, 97300 Cayenne, French Guiana, France
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TETIS, INRAE, University of Montpellier, 500 rue François Breton, 34093 Montpellier CEDEX 5, France
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Cirad, UMR EcoFoG (AgroParistech, CNRS, INRAE, Université des Antilles, Université de la Guyane), 97379 Kourou, French Guiana, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1577; https://doi.org/10.3390/rs12101577
Received: 22 April 2020 / Revised: 10 May 2020 / Accepted: 12 May 2020 / Published: 15 May 2020
(This article belongs to the Section Forest Remote Sensing)
Tropical forests have exceptional floristic diversity, but their characterization remains incomplete, in part due to the resource intensity of in-situ assessments. Remote sensing technologies can provide valuable, cost-effective, large-scale insights. This study investigates the combined use of airborne LiDAR and imaging spectroscopy to map tree species at landscape scale in French Guiana. Binary classifiers were developed for each of 20 species using linear discriminant analysis (LDA), regularized discriminant analysis (RDA) and logistic regression (LR). Complementing visible and near infrared (VNIR) spectral bands with short wave infrared (SWIR) bands improved the mean average classification accuracy of the target species from 56.1% to 79.6%. Increasing the number of non-focal species decreased the success rate of target species identification. Classification performance was not significantly affected by impurity rates (confusion between assigned classes) in the non-focal class (up to 5% of bias), provided that an adequate criterion was used for adjusting threshold probability assignment. A limited number of crowns (30 crowns) in each species class was sufficient to retrieve correct labels effectively. Overall canopy area of target species was strongly correlated to their basal area over 118 ha at 1.5 ha resolution, indicating that operational application of the method is a realistic prospect (R2 = 0.75 for six major commercial tree species). View Full-Text
Keywords: tropical forest; species diversity; hyperspectral; LiDAR tropical forest; species diversity; hyperspectral; LiDAR
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MDPI and ACS Style

Laybros, A.; Aubry-Kientz, M.; Féret, J.-B.; Bedeau, C.; Brunaux, O.; Derroire, G.; Vincent, G. Quantitative Airborne Inventories in Dense Tropical Forest Using Imaging Spectroscopy. Remote Sens. 2020, 12, 1577. https://doi.org/10.3390/rs12101577

AMA Style

Laybros A, Aubry-Kientz M, Féret J-B, Bedeau C, Brunaux O, Derroire G, Vincent G. Quantitative Airborne Inventories in Dense Tropical Forest Using Imaging Spectroscopy. Remote Sensing. 2020; 12(10):1577. https://doi.org/10.3390/rs12101577

Chicago/Turabian Style

Laybros, Anthony; Aubry-Kientz, Mélaine; Féret, Jean-Baptiste; Bedeau, Caroline; Brunaux, Olivier; Derroire, Géraldine; Vincent, Grégoire. 2020. "Quantitative Airborne Inventories in Dense Tropical Forest Using Imaging Spectroscopy" Remote Sens. 12, no. 10: 1577. https://doi.org/10.3390/rs12101577

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