Publications des scientifiques de l'IRD

Rocha de Souza Pereira F., Kampel M., Gomez Soares M.L., Calderucio Duque Estrada G., Bentz C., Vincent Grégoire. (2018). Reducing uncertainty in mapping of mangrove aboveground biomass using airborne discrete return Lidar data. Remote Sensing, 10, art. no 637 [21 p. en ligne]. ISSN 2072-4292.

Titre du document
Reducing uncertainty in mapping of mangrove aboveground biomass using airborne discrete return Lidar data
Année de publication
2018
Type de document
Article référencé dans le Web of Science WOS:000435187500149
Auteurs
Rocha de Souza Pereira F., Kampel M., Gomez Soares M.L., Calderucio Duque Estrada G., Bentz C., Vincent Grégoire
Source
Remote Sensing, 2018, 10, art. no 637 [21 p. en ligne] ISSN 2072-4292
Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass (AGB) maps of mangrove forests. AGB was estimated in 34 small plots scatted over a 50 km2 mangrove forest in Rio de Janeiro, Brazil. Plot AGB was computed using either species-specific or non-species-specific allometric models. A total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors (Auto-PLS)) were tested to predict the recorded AGB. The models developed using species-specific allometric models were distinctly more accurate (R2(calibration) = 0.89, R2(validation) = 0.80, root-mean-square error (RMSE, calibration) = 11.20 t·ha−1, and RMSE(validation) = 14.80 t·ha−1). The use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. It was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the AGB in mangrove forests. Conversely this study underlines the potential bias affecting the estimates of AGB in other forested landscapes where only non-species-specific allometric equations are available.
Plan de classement
Etudes, transformation, conservation du milieu naturel [082] ; Télédétection [126]
Localisation
Fonds IRD [F B010072532]
Identifiant IRD
fdi:010072532
Contact