Machine learning and ensemble analysis for AGB

This page contains code for the article "Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis" published in Journal of Environmental Management (https://doi.org/10.1016/j.jenvman.2022.114639).

Machine learning and ensemble analysis estimate for predicting above-ground biomass (AGB)

The code contains following:

1. Feature selection algorithm (Boruta and VIF)
2. Modelling techniques (GAMM, kNN, SVM, ANN and RF)
3. Spatial autocorrelation
4. Sensitivity analysis
5. Data analysis (includes analysing figures from manuscript and supplementary materials)

Code 1-4 are coded in 'R', whereas 5 is coded in 'python'. Most of the spatial analysis/mapping that are not included above were done using either ArcGIS or ERDAS Imagine.

Coded by:
Chandrakant Singh
Stockholm Resilience Centre, Stockholm University
Contact: chandrakant.singh@su.se