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

iForest - Biogeosciences and Forestry

López-Serrano Pablito M¹  Díaz-Varela Ramón A²  Álvarez-González Juan G³  Vargas-Larreta B⁴  Solís-Moreno R⁵  López-Sánchez Carlos A⁶  Corral-Rivas José J⁶ 

[1] DICAF, Universidad Juárez del Estado de Durango, Boulevard del Guadiana 501, Ciudad Universitaria, Torre de Investigación, 34120 Durango, Dgo (México);Departamento de Botánica - IBADER, Universidad de Santiago de Compostela, Escuela Politécnica Superior, Lugo (España);Departamento de Ingeniería Agroforestal, Universidad de Santiago de Compostela, Escuela Politécnica Superior, Lugo (España);División de Estudios de Posgrado e Investigación, Instituto Tecnológico de El Salto, Mesa del Tecnológico s/n, 34942, El Salto, Dgo (México);Facultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Río Papaloapan 132, Valle del Sur Durango, 34120 Durango, Dgo (México);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);

关键词: Regression Trees;    Stepwise Regression;    Remote Sensing;    ATCOR3;    Terrain Features;    Image Texture;   

DOI  :  10.3832/ifor1504-008

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown