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Open AccessArticle

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

1
National Institute for Space Research, Remote Sensing Division, Av. dos Astronautas 1758, São Jose dos Campos 12227-010, Brazil
2
Núcleo de Estudos em Manguezais, Rio de Janeiro State University, Rua São Francisco Xavier 524, Sala 4023E, Rio de Janeiro 20550-900, Brazil
3
Golder Associates, Via Antonio Banfo, 43, 10155 Turin, Italy
4
PETROBRAS R&D Center, Rio de Janeiro 20550-900, Brazil
5
AMAP, IRD, CNRS, CIRAD, INRA, Univ Montpellier, F-34000 Montpellier, France
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(4), 637; https://doi.org/10.3390/rs10040637
Received: 7 March 2018 / Revised: 12 April 2018 / Accepted: 17 April 2018 / Published: 20 April 2018
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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. View Full-Text
Keywords: discrete return lidar; mangrove; aboveground biomass; uncertainty discrete return lidar; mangrove; aboveground biomass; uncertainty
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MDPI and ACS Style

Rocha de Souza Pereira, F.; Kampel, M.; Gomes Soares, M.L.; Estrada, G.C.D.; Bentz, C.; Vincent, G. Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data. Remote Sens. 2018, 10, 637.

AMA Style

Rocha de Souza Pereira F, Kampel M, Gomes Soares ML, Estrada GCD, Bentz C, Vincent G. Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data. Remote Sensing. 2018; 10(4):637.

Chicago/Turabian Style

Rocha de Souza Pereira, Francisca; Kampel, Milton; Gomes Soares, Mário L.; Estrada, Gustavo C.D.; Bentz, Cristina; Vincent, Gregoire. 2018. "Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data" Remote Sens. 10, no. 4: 637.

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