Deep learning additive models for simultaneous prediction of stand level above- and belowground biomass in tropical forests
Guideline for Applying the Executable Package:
This package enables the application of the best-developed Deep Learning Additive Models (DLAMs) for simultaneously predicting stand-level above- and belowground biomass and total (AGB, BGB and ABGB, respectively), while ensuring additivity in tropical forests.
This executable package was developed based on the following reference:
Huy, B., Poudel, K.P., Temesgen, H., Salas-Eljatib, C., Truong, N.Q., Khiem, N.Q. (2025). Comparing statistical and deep learning approaches for simultaneous prediction of stand-level above- and belowground biomass in tropical forests. Science of the Total Environment 958 (2025) 177869. DOI: https://doi.org/10.1016/j.scitotenv.2024.177869
Download the article.
For detailed methodology and application, please refer to this publication.
Please follow the steps below to apply the executable package:
- Please download the folder DLAMs executable packages.rar then save and extract it to your computer.
- Choose one of the following subfolders based on your preferred predictive covariates:
- DLAMs 1: Includes seven optimal predictive covariates (stand basal area (G), stand volume (V), mean annual temperature (T), elevation (EL), forest type (FT), average height (Hg), and soil group (SG)).
- DLAMs 5: Includes five predictive covariates (stand basal area (G), stand volume (V), mean annual temperature (T), elevation (EL), average height (Hg)).
- DLAMs 9: Uses only one predictor: stand basal area (G).
- DLAMs 10: Uses only one predictor: stand volume (V).
- Enter your new predictor values into the file named ‘predictors data.csv’ located in this subfolder.
- Units of numeric variables: G (m2/ha), V (m3/ha), T (oC), EL (m), Hg (m)
- Using acronyms for nominal categorical variables: FT (DDF for Dry Dipterocarp Forest or EBLF for Evergreen Broadleaf Forest), and SG (FA for Ferric Acrisols, OA for Orthic Acrisols, and RF for Rhodic Ferrasols).
- Double-click on the file named ‘prediction.exe’ in this subfolder to start the prediction process. Please allow a few minutes for the program to complete its process.
- As a result, a new file named ‘prediction.csv’ will be created in this subfolder, containing simultaneous predictions of stand-level aboveground biomass (AGB, tons/ha), belowground biomass (BGB, tons/ha), and total (ABGB, tons/ha), ensuring additivity.
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An ensemble Deep Learning and XGBoost model using Sentinel-2 imagery for additive predictive mapping of above- and belowground carbon pools in tropical dry dipterocarp forests
Coming soon