Variables Selection for Aboveground Biomass Estimations Using Satellite Data: A Comparison between Relative Importance Approach and Stepwise Akaike’s Information Criterion

Rita Libertad Adame-Campos,Jean-François Mas,Jaime Paneque-Gálvez,Adrian Ghilardi,Yan Gao

doi:10.3390/ijgi8060245

Rita Libertad Adame-Campos, Jean-François Mas + Show 3 more

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https://doi.org/10.3390/ijgi8060245

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Abstract

It is still a major challenge to select appropriate variables from remote sensing sensors, which implicates finding reliable selection methods that can maximize the performance of chosen variables in regression models. In this study, we compare the performance of stepwise variable selection based on Akaike information criterion and an approach that integrates relative importance techniques and the decomposition criteria of R 2 using two different remote sensing data: SPOT-5 and RapidEye images, with the purpose of selecting suitable variables in multiple linear regression models to estimate aboveground biomass. The obtained accuracy of the regression models was evaluated by triple cross-validation. We carried out this study in a mixed pine–oak forest of central Mexico where intensive wood extraction occurs and therefore different levels of degradation are found. We estimated aboveground biomass from field inventory data at the plot level (n = 52) and used well-established allometric equations. The results showed that a better fit was obtained with the explanatory variables selected from the RapidEye image ( R 2 = 0.437 with stepwise variable selection based on the Akaike information criterion approach and R 2 = 0.420 with relative importance techniques) and the approach that integrates the relative importance can generate better regression models to estimate forest biomass with a reduced number of variables and less error in the estimates.

Highlights

Forest ecosystems are recognized as containing the largest proportion of air and underground terrestrial biomass reserves [1,2]
Remote sensing sensors such as SPOT-5 and RapidEye have a high spatial resolution (10 and 5 m, respectively, in multispectral mode and 2.5 for SPOT-5 in panchromatic mode), and a medium high temporal resolution that allows several images to be captured within a year to characterize the information of vegetation phenology. Those sensors are potentially useful for the estimation of the forest structure parameters due to their relatively large imaging swath and accessible cost [43]. Considering that this relative importance analysis for variable selection can be potentially useful for the estimation of the aboveground biomass (AGB) and other forest parameters with reduced uncertainty and collinearity between variables, the objectives of the present study are (1) to compare the performance of a stepwise method based on the Akaike information criterion (AIC) variable selection method (STEPWISE-AIC) and an approach that integrates relative importance techniques (RI) to select variables in multiple linear regression models in terms of its adjustment and uncertainty to explain and predict AGB
No studies were found in which the multispectral bands of the RapidEye image were included as predictive variables for biomass estimation, studies such as Ojoyi et al [82] estimated the AGB with a vegetation index that included those bands as predictive variables and Wallner et al [16] estimated the parameters of the forest structure using the visible spectral bands and the vegetation indices

Summary

Introduction

Forest ecosystems are recognized as containing the largest proportion of air and underground terrestrial biomass reserves [1,2]. Several studies have been carried out to estimate AGB using satellite images with data sampled in the field Those studies can be roughly grouped into four groups: studies that combine spectral responses and image textures to improve the performance of AGB estimation [8,9,10]; studies with adequate algorithms for estimating biomass at different scales [11,12,13,14,15]; studies using vegetation stratification methods to improve the accuracy of AGB estimation [16,17]; and some other works that use methods for the selection of the suitable predictive and explanatory variables of AGB [18,19,20,21]. The methods for selecting suitable variables from satellite images and the development of adequate estimation models for specific studies are still poorly understood [6]

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