High resolution biomass mapping in tropical forests with LiDAR-derived Digital Models: Poás Volcano National Park (Costa Rica)

doi:10.3832/ifor1744-009

High resolution biomass mapping in tropical forests with LiDAR-derived Digital Models: Poás Volcano National Park (Costa Rica)

Alfredo Fernández-Landa⁽¹⁾ , José Antonio Navarro^(1-2), Sonia Condés⁽²⁾, Nur Algeet-Abarquero^(1-3), Miguel Marchamalo⁽³⁾

iForest - Biogeosciences and Forestry, Volume 10, Issue 1, Pages 259-266 (2017)
doi: https://doi.org/10.3832/ifor1744-009
Published: Feb 23, 2017 - Copyright © 2017 SISEF

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Tropical forests play a key role in global carbon cycle. Reducing Emissions from Deforestation and forest Degradation (REDD+) program requires reliable mechanisms for Monitoring, Reporting and Verification (MRV). In this regard, new methods must be developed using updated technologies to assess carbon stocks. The combination of LiDAR technology and in situ forest networks allows the estimation of biomass with high resolution in low data environments, such as tropical countries. However, the evaluation of current LiDAR methods of biomass inventory, and the development of new methodologies to reduce uncertainty and increase accuracy, is still needed. Our aim is to evaluate new methodologies of spatially explicit LiDAR biomass inventories based on local and general plot-aggregate allometry. For this purpose, 25 field plots were inventoried, covering the structural and ecological variability of Poás Volcano National Park (Costa Rica). Important differences were detected in the estimation of aboveground biomass (92.74 t ha^-1 considering the mean value of plot sample) depending on the chosen tree allometry. We validated the general aboveground biomass plot-aggregate allometry proposed by Asner & Mascaro (2014) in our study area, and we fitted two specific models for Poás forests. Both locals and general models depend on LiDAR top-of-canopy height (TCH), basal area (BA) and wood density. Small deviations in the wood density plot sample (0.60 ± 0.05) indicated that a single wood density constant value could be used throughout the study area. A BA-TCH origin forced linear model was fitted to estimate basal area, as suggested by the general methodology. Poás forest has a larger biomass density for the same THC compared to the rest of the forests previously studied, and shows that the BA-TCH relationship might have different trends in each life zone. Our results confirm that the general plot-aggregate methodology can be easily and reliably applied as aboveground biomass in a new area could be estimated by only measuring BA in field plots to obtain a local BA-TCH regression. For both local and general methods, the estimation of BA is critical. Therefore, the definition of precise basal area field measurement procedures is decisive to achieve reliable results in future studies.

Keywords

Carbon, Remote Sensing, REDD, LiDAR, Plot-level Allometry, Biomass, Basal Area

Authors’ address

(1)

Agresta Soc. Coop, C/ Duque de Fernán Núñez 2, Madrid 28012 (Spain)

(2)

Dept. Natural Systems and Resources, Technical University of Madrid. School of Forestry, Ciudad Universitaria, Madrid 28040 (Spain)

(3)

Dept. of Land Morphology and Engineering, Technical University of Madrid, Ciudad Universitaria, Madrid 28040 (Spain)

Corresponding author

Alfredo Fernández-Landa
afernandez@agresta.org

Citation

Fernández-Landa A, Navarro JA, Condés S, Algeet-Abarquero N, Marchamalo M (2017). High resolution biomass mapping in tropical forests with LiDAR-derived Digital Models: Poás Volcano National Park (Costa Rica). iForest 10: 259-266. - doi: 10.3832/ifor1744-009

Academic Editor

Davide Travaglini

Paper history

Received: Jun 18, 2015
Accepted: Oct 20, 2016

First online: Feb 23, 2017
Publication Date: Feb 28, 2017
Publication Time: 4.20 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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