Estimating biomass of mixed and uneven-aged forests using spectral data and a hybrid model combining regression trees and linear models
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Title: | Estimating biomass of mixed and uneven-aged forests using spectral data and a hybrid model combining regression trees and linear models |
Author: | López Serrano, Pablito Marcelo López Sánchez, Carlos Antonio Díaz Varela, Ramón Alberto Corral Rivas, José Javier Solís Moreno, Raúl Vargas Larreta, Benedicto Álvarez González, Juan Gabriel |
Affiliation: | Universidade de Santiago de Compostela. Departamento de Botánica Universidade de Santiago de Compostela. Departamento de Enxeñaría Agroforestal |
Subject: | Regression trees | Stepwise regression | Remote sensing | ATCOR3 | Terrain features | Image texture | |
Date of Issue: | 2015 |
Publisher: | Italian Society of Silviculture and Forest Ecology (SISEF) |
Citation: | López-Serrano PM, López-Sánchez CA, Díaz-Varela RA, Corral-Rivas JJ, Solís-Moreno R, Vargas-Larreta B, Álvarez-González JG (2015). Estimating biomass of mixed and uneven-aged forests using spectral data and a hybrid model combining regression trees and linear models. iForest 9: 226-234. - doi: 10.3832/ifor1504-008 |
Abstract: | The Sierra Madre Occidental mountain range (Durango, Mexico) is of great ecological interest because of the high degree of environmental heterogeneity in the area. The objective of the present study was to estimate the biomass of mixed and uneven-aged forests in the Sierra Madre Occidental by using Landsat-5 TM spectral data and forest inventory data. We used the ATCOR3 ® atmospheric and topographic correction module to convert remotely sensed imagery digital signals to surface reflectance values. The usual approach of modeling stand variables by using multiple linear regression was compared with a hybrid model developed in two steps: in the first step a regression tree was used to obtain an initial classification of homogeneous biomass groups, and multiple linear regression models were then fitted to each node of the pruned regression tree. Cross-validation of the hybrid model explained 72.96% of the observed stand biomass variation, with a reduction in the RMSE of 25.47% with respect to the estimates yielded by the linear model fitted to the complete database. The most important variables for the binary classification process in the regression tree were the albedo, the corrected readings of the short-wave infrared band of the satellite (2.08-2.35 µm) and the topographic moisture index. We used the model output to construct a map for estimating biomass in the study area, which yielded values of between 51 and 235 Mg ha-1. The use of regression trees in combination with stepwise regression of corrected satellite imagery proved a reliable method for estimating forest biomass. |
Publisher version: | https://doi.org/10.3832/ifor1504-008 |
URI: | http://hdl.handle.net/10347/22342 |
DOI: | 10.3832/ifor1504-008 |
E-ISSN: | 1971-7458 |
Rights: | Copyright © 2015 SISEF 2015. 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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Except where otherwise noted, this item's license is described as Copyright © 2015 SISEF 2015. 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