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

• Published in: IForest (Viterbo) Vol. 9; no. 2; pp. 226 - 234
• Main Authors: López-Serrano, PM, López-Sánchez, CA, Díaz-Varela, RA, Corral-Rivas, JJ, Solís-Moreno, R, Vargas-Larreta, B, Álvarez-González, JG
• Format: Journal Article
• Language: English
• Published: Potenza The Italian Society of Silviculture and Forest Ecology (SISEF) 01-04-2016; Italian Society of Silviculture and Forest Ecology (SISEF)
• Subjects: Albedo; ATCOR3; Atmospheric correction; Atmospheric models; Biomass; Classification; Digital imaging; Ecosystems; Forest biomass; Forests; Heterogeneity; Image Texture; Landsat; Landsat 5; Landsat satellites; Methods; Moisture effects; Moisture index; Mountains; Regression analysis; Regression models; Regression Trees; Remote Sensing; Satellite imagery; Short wave radiation; Stepwise Regression; Terrain Features; Topography; Trees; Variables; Vegetation; Mexico
• ISSN: 1971-7458; 1971-7458
• DOI: 10.3832/ifor1504-008

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.