Assessing aboveground carbon density with field observations and biophysical and spectral predictors in the northmost Neotropical dry forest

Bibliographic Data

ID19586558
AuthorsAdrián Bojórquez (0000-0001-7059-7189, Sonora Institute of Technology, corresponding author), Guillermo López-Castro (0009-0007-5350-7592, Basque Centre for Climate Change), Jaime Garatuza-Payán (0000-0003-2357-910X, Sonora Institute of Technology), Zulia M Sánchez-Mejía (0000-0001-8444-3885, Sonora Institute of Technology), Tonantzin Tarín (0000-0001-5125-579X, Sonora Institute of Technology), Enrico A Yépez (0000-0003-4746-573X, Sonora Institute of Technology), Juan C Álvarez-Yépiz, Juan C Álvarez‐Yépiz (0000-0002-1825-8371, Sonora Institute of Technology)
Year2025
Volume20
Pages100844
Publication date2025-06-01
Open AccessYes
TypeARTICLE
VenueTrees Forests and People (JOURNAL)
Journal identifiersISSN: 2666-7193 • E-ISSN: 2666-7193
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.tfp.2025.100844
OpenAlexW4409053126
LanguageEN
Citations received1
References cited45

We assessed biophysical and spectral drivers of carbon density in tropical dry forests. • We used National Forest Inventory data to estimate aboveground carbon storage. • We spatially predicted carbon density comparing biophysical and remote sensing predictors. • Biophysical drivers were better predictors of aboveground carbon density than spectral indices. • Our maps should aid REDD+ and restoration programs to improve forest management. The tropical dry forest ecosystem is the most widespread terrestrial tropical vegetation in Mexico and is highly threatened by anthropic disturbance and climate change. Accurate estimates of aboveground biomass and corresponding carbon stocks can influence forest management strategies and help direct or evaluate the effectiveness of REDD+ programs. Here, we assess the aboveground carbon density estimated with field observations and biophysical and spectral predictors in the northmost tropical dry forest of the Americas occurring in the state of Sonora in northwestern Mexico. Our top candidate model with biophysical predictors (tree and structural richness, slope, cation exchange capacity and soil depth) showed the best fit and lower prediction error ( pR 2 = 0.4, RMSE = 0.458) of the spatial distribution of aboveground carbon density in this forest. The effect of structural richness and soil depth was stronger; therefore, these appear to be the most important drivers of aboveground carbon spatial variation across the region. The total aboveground carbon storage predicted with this model in the entire region was 19 305 499.5 Mg C ha −1 (mean = 11.8, sd = 6), with higher aboveground carbon density estimated toward more tropical latitudes. A comprehensive assessment of aboveground carbon density in the tropical dry forest requires a synergistic approach combining field observations and biophysical drivers in lieu of more advanced remote sensing techniques, such as LiDAR that are still not available or validated in many tropical regions. We develop regional aboveground carbon density (ACD) maps for the northmost neotropical dry forests in the Americas occurring in Sonora, Mexico. Biophysical variables such as forest structure (structural richness, tree richness), soil properties (soil depth and cation exchange capacity), and slope steepness strongly control spatial ACD at a regional scale. Our maps aid to REDD+ programs, which may contribute to generating better management strategies and restoration programs that can contribute to climate change mitigations

Agroforestry · Atmospheric sciences · Biology · Carbon fibers · Dry forest · Geography · Remote sensing · Tropical and subtropical dry broadleaf forests · Environmental Science · Forest ecology and management · Mathematics · Remote Sensing and LiDAR Applications · Remote Sensing in Agriculture · Ecology · Forestry · Geology

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