Estimating biomass of mixed and uneven-aged forests using spectral data and a hybrid model combining regression trees and linear models

doi:10.3832/ifor1504-008

Estimating biomass of mixed and uneven-aged forests using spectral data and a hybrid model combining regression trees and linear models

Pablito M López-Serrano⁽¹⁾, Carlos A López-Sánchez⁽²⁾ , Ramón A Díaz-Varela⁽³⁾, José J Corral-Rivas⁽²⁾, Raúl Solís-Moreno⁽⁴⁾, Benedicto Vargas-Larreta⁽⁵⁾, Juan G Álvarez-González⁽⁶⁾

iForest - Biogeosciences and Forestry, Volume 9, Issue 2, Pages 226-234 (2015)
doi: https://doi.org/10.3832/ifor1504-008
Published: Sep 21, 2015 - Copyright © 2015 SISEF

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Geo-mapping

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.

Keywords

Regression Trees, Stepwise Regression, Remote Sensing, ATCOR3, Terrain Features, Image Texture

Authors’ address

(1)

DICAF, Universidad Juárez del Estado de Durango, Boulevard del Guadiana 501, Ciudad Universitaria, Torre de Investigación, 34120 Durango, Dgo (México)

(2)

Instituto de Silvicultura e Industria de la Madera, Universidad Juárez del Estado de Durango, Boulevard del Guadiana 501, Ciudad Universitaria, Torre de Investigación, 34120 Durango, Dgo (México)

(3)

Departamento de Botánica - IBADER, Universidad de Santiago de Compostela, Escuela Politécnica Superior, Lugo (España)

(4)

Facultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Río Papaloapan 132, Valle del Sur Durango, 34120 Durango, Dgo (México)

(5)

División de Estudios de Posgrado e Investigación, Instituto Tecnológico de El Salto, Mesa del Tecnológico s/n, 34942, El Salto, Dgo (México)

(6)

Departamento de Ingeniería Agroforestal, Universidad de Santiago de Compostela, Escuela Politécnica Superior, Lugo (España)

Corresponding author

Carlos A López-Sánchez
calopez@ujed.mx

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

Academic Editor

Davide Travaglini

Paper history

Received: Nov 17, 2014
Accepted: May 17, 2015

First online: Sep 21, 2015
Publication Date: Apr 26, 2016
Publication Time: 4.23 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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