PT Journal
AU Laybros, A
   Aubry-Kientz, M
   Féret, J
   Bedeau, C
   Brunaux, O
   Derroire, G
   Vincent, G
TI Quantitative airborne inventories in dense tropical forest using imaging spectroscopy
SO Remote Sensing
JI Remote Sens.
PY 2020
BP 1577
VL 12
IS 10
DI 10.3390/rs12101577
DE Hyperspectral; LiDAR; Species diversity; Tropical forest; Cost effectiveness; Discriminant analysis; Infrared devices; Infrared radiation; Logistic regression; Remote sensing; Tropics; Classification accuracy; Classification performance; Linear discriminant analysis; Operational applications; Regularized discriminant analysis; Remote sensing technology; Short wave infrared bands; Visible and near infrared; Forestry
AB 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). © 2020 by the authors.
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