Abstract
Combined use of new geospatial techniques and non-parametric multivariate statistical methods enables monitoring and quantification of the biomass of large areas of forest ecosystems with acceptable reliability. The main objective of the present study was to estimate the aboveground forest biomass (AGB) in the Sierra Madre Occidental (SMO) in the state of Durango, Mexico, using the M5 model tree (M5P) technique and the analysis of medium-resolution satellite-based multi-spectral data, and field data collected from a network of 201 permanent forest growth and soil research sites (SPIFyS). Research plots were installed by systematic sampling throughout the study area in 2011. The digital levels of the images were converted to apparent reflectance (ToA) and surface reflectance (SR). The M5P technique that constructs tree-based piecewise linear models was used. The fitted model with SR and tree abundance by species group as predictive variables (ASG) explained 73% of the observed AGB variance (the root mean squared error (RMSE) = 39.40 Mg·ha−1). The variables that best discriminated the AGB, in order of decreasing importance, were the normalized difference vegetation index (NDVI), tree abundance of other broadleaves species (OB), Band 4 of Landsat 5 TM (Thematic Mapper) satellite and tree abundance of pines (Pinus). The results demonstrate the potential usefulness of the M5P method for estimating AGB based in the surface reflectance values (SR).
Highlights
The Sierra Madre Occidental (SMO) mountain range is of great ecological interest because of its environmental heterogeneity, which is attributed to the broad physiographical and climatic diversity in the area [1]
The decision tree generated by the M5P technique for to apparent reflectance (ToA), surface reflectance values (SR) and SR with Abundance by Species Group (ASG) variables were implemented in WEKA software, using the pixel level values extracted from the images of the 201
Categorization of the trees continued following the path determined by the responses to the questions at the internal nodes, until reaching a terminal node, where the predetermined label will be that assigned to the classification pattern—in this case, the pixel values for aboveground forest biomass (AGB) estimation
Summary
The Sierra Madre Occidental (SMO) mountain range is of great ecological interest because of its environmental heterogeneity, which is attributed to the broad physiographical and climatic diversity in the area [1]. The state of Durango generates between 25% and 30% of the national timber production, producing a total of 1.5 millionm of roundwood per year, and boasts forest reserves that are important sources of environmental services [3]. The emergence of geospatial techniques is becoming increasingly relevant for estimating and monitoring emergence of geospatial techniques is becoming increasingly relevant for estimating and monitoring forest biomass in short periods of time because of its low cost and acceptable accuracy [4,5,6,7]. Because forest biomass in short periods of time because of its low cost and acceptable accuracy [4,5,6,7]. M5P is used for numeric of M5 algorithm for inducing trees of regression models [11]. M5P is used for numeric prediction and at prediction and at each leaf it stores a linear regression model that predicts the class value of instances each leaf it stores a linear regression model that predicts the class value of instances that reach the leaf
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