Estimating biomass of mixed and uneven-aged forests using spectral data and a hybrid model combining regression trees and linear models
| Autor: | Carlos A. López-Sánchez, Juan Gabriel Álvarez-González, Benedicto Vargas-Larreta, Raúl Solís-Moreno, Pablito M. López-Serrano, José Javier Corral-Rivas, Ramón Alberto Díaz-Varela |
|---|---|
| Přispěvatelé: | Universidade de Santiago de Compostela. Departamento de Botánica, Universidade de Santiago de Compostela. Departamento de Enxeñaría Agroforestal |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
010504 meteorology & atmospheric sciences
Mean squared error Regression trees 0211 other engineering and technologies ATCOR3 02 engineering and technology 01 natural sciences Remote Sensing Linear regression Statistics Satellite imagery Image texture lcsh:Forestry Stepwise Regression 021101 geological & geomatics engineering 0105 earth and related environmental sciences Nature and Landscape Conservation Mathematics Biomass (ecology) Forest inventory Ecology Image Texture Linear model Regression Trees Forestry Stepwise regression Remote sensing Regression Terrain Features Terrain features lcsh:SD1-669.5 |
| Zdroj: | iForest-Biogeosciences and Forestry, Vol 9, Iss 1, Pp 226-234 (2016) Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela instname |
| ISSN: | 1971-7458 |
| Popis: | 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. This research was supported by SEPPROMEP (Project: Seguimiento y Evaluación de Sitios Permanentes de Investigación Forestal y el Impacto Socioeconómico del anejo Forestal en el Norte de México) SI |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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