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

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 evapotranspira...

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Bibliographic Details
Published in:Forests 2023-02, Vol.14 (2), p.299
Main Authors: Rosas-Chavoya, Marcela, López-Serrano, Pablito Marcelo, Vega-Nieva, Daniel José, Hernández-Díaz, José Ciro, Wehenkel, Christian, Corral-Rivas, José Javier
Format: Article
Language:English
Subjects:
AGB
Biomass
Carbon
Carbon cycle
Carbon cycle (Biogeochemistry)
Climate change
Drought
Environment
Estimation
Evapotranspiration
Forest ecosystems
Forestry research
Forests
Land surface temperature
Nonlinear response
Normalized difference vegetative index
Plant biomass
Remote sensing
Sensors
Soil temperature
Soils
Statistical models
surface temperature
Temperate forests
Terrestrial ecosystems
Thermal properties
Topography
Variables
Vegetation
Vegetation Temperature Condition Index
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Summary: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.
ISSN:1999-4907
1999-4907
DOI:10.3390/f14020299