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

Autor: Milton Kampel, Gustavo Calderucio Duque Estrada, Mário Luiz Gomes Soares, Cristina Maria Bentz, Grégoire Vincent, Francisca Rocha de Souza Pereira
Přispěvatelé: National Institute for Space Research [Sao José dos Campos] (INPE), State University of Rio de Janeiro, Petrobras Research and Development Center, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD [France-Sud]), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])
Jazyk: angličtina
Rok vydání: 2018
Předmět:
010504 meteorology & atmospheric sciences
Mean squared error
Calibration (statistics)
Science
0211 other engineering and technologies
Tree allometry
02 engineering and technology
[SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics
Phylogenetics and taxonomy
01 natural sciences
[SDV.EE.ECO]Life Sciences [q-bio]/Ecology
environment/Ecosystems
discrete return lidar
mangrove
aboveground biomass
uncertainty
ESTIMATING FOREST BIOMASS
TROPICAL RAIN-FOREST
CARBON STOCKS
SCANNING LIDAR
HEIGHT
LASER
MODELS
RADAR
AREA
TREE
Partial least squares regression
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
15. Life on land
[SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics
Random forest
Lidar
General Earth and Planetary Sciences
Environmental science
Allometry
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
Scale (map)
Zdroj: Remote Sensing
Remote Sensing, MDPI, 2018, 10 (4), pp.637. ⟨10.3390/rs10040637⟩
Remote Sensing, Vol 10, Iss 4, p 637 (2018)
Remote Sensing, 2018, 10 (4), pp.637. ⟨10.3390/rs10040637⟩
Remote Sensing 4 (10), . (2018)
Remote Sensing; Volume 10; Issue 4; Pages: 637
ISSN: 2072-4292
Popis: International audience; 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 km 2 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 (R 2 (calibration) = 0.89, R 2 (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.
Databáze: OpenAIRE
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