Estimating Above-Ground Biomass from Land Surface Temperature and Evapotranspiration Data at the Temperate Forests of Durango, Mexico

Abstract

The study of above-ground biomass (AGB) is important for monitoring the dynamics of the carbon cycle in forest ecosystems. The emergence of remote sensing has made it possible to analyze vegetation using land surface temperature (LST), Vegetation Temperature Condition Index (VTCI) and evapotranspiration (ET) information. However, relatively few studies have evaluated the ability of these variables to estimate AGB in temperate forests. The aim of the present study was to evaluate the relationship of LST, VTCI and ET with AGB in temperate forests of Durango, Mexico, regarding each season of the year and to develop a AGB estimation model using as predictors LST, VCTI and ET, together with topographic, reflectance and Gray-Level Co-Occurrence Matrix (GLCM) texture variables. A semi-parametric model was generated to analyze the linear and non-linear responses of the predictive variables of AGB using a generalized linear model (GAM). The results show that the best predictors of AGB were longitude, latitude, spring LST, ET, elevation VTCI, NDVI (Normalized Difference Vegetation Index), slope and GLCM mean (R2 = 0.61; RMSE = 28.33 Mgha−1). The developed GAM model was evaluated with an independent dataset (R2 = 0.58; RMSE = 31.21 Mgha−1), suggesting the potential of this modeling approach to predict AGB for the analyzed temperate forest ecosystems.

Forest Canopy Fuel Loads Mapping Using Unmanned Aerial Vehicle High-Resolution Red, Green, Blue and Multispectral Imagery

“…Conversely, the greatest random pattern was observed in P3, presenting the lowest CFL values (median value of 10.71 kg/m 2 ). These differences limit the use of linear and allometric model regression because tree spatial distribution patterns prevent generalizable models from being obtained [89,90]. Other studies have identified that mixed forests tend to present random distributions following a disturbance occurrence and they are reverted to clusters over time [91]; therefore, these may be the cause of differences in the spatial distribution patterns of trees in our study area.…”

UAV-LiDAR Integration with Sentinel-2 Enhances Precision in AGB Estimation for Bamboo Forests

“…Models are the bridge between ground-truthed AGB linked remote sensing data to achieve large-scale forest AGB spatiotemporal estimation, so the general practice of forest AGB remote sensing estimation is achieved by establishing regression relationships between ground-truthed data and feature variables extracted from remote sensing data [36]. Therefore, the screening of feature variables and model selection of remote sensing data becomes the key to ensuring the accuracy of AGB estimation [37,38]. Boruta is a commonly used feature screening algorithm, which is capable of automatically identifying and selecting important features [39].…”