AboveGroundBiomassEstimation

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AboveGroundBiomassEstimation

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## Above Ground Biomass Estimation: implementation of a Sentinel-2 based exploratory workflow

This repository contains the supplementary material required to reproduce the findings of a Sentinel-2 based exploratory workflow for the estimation of Above
Ground Biomass (AGB).

## Description

In the last decades, remote sensing-based studies on AGB have been widely investigated alongside with a variety of sensors, features and Machine Learning (ML) algorithms. Up-to-date and reliable mapping of such measures have been increasingly required by international commitments under the climate convention as well as by sustainable forest management practices.

The proposed approach consists of 5 major steps: 
1. Generation of several Vegetation Indices (VI), biophysical parameters and texture measures (to be performed previous to running this notebook).
2. Feature selection with Mean Decrease in Impurity (MDI), Mean Decrease in Accuracy (MDA), L1 Regularization (LASSO), and Principal Component Analysis (PCA).
3. Feature selection testing with k-Nearest Neighbour (kNN), Random Forest (RF), Extreme Gradient Boosting (XGB), and 3 Artificial Neural Networks (ANN).
4. Hyper-parameters fine-tuning with Grid Search, Random Search and Bayesian Optimization. 
5. Model explanation with the SHapley Additive exPlanations (SHAP) package.

## Software requirements
- Python 3
- Jupyter Notebook
- ArcGIS Pro (2.x) or QGIS (3.x)
- SNAP

## Manual for reproducibility
1. Clone this repository.
2. Add `dataset.shp` file into the `dataset` folder (including `.dbf`, `.fix`, `.prj`, `.qix`, `.shp`, and `.shx` files).
3. Open this repository on the notebook.
4. RUN `main.ipynb` one cell at the time by following the instructions within the file.

NOTE A: `datasets.shp` must be generated by transforming the original images (Sentinel-2 derived measures and DEM) from raster to vector (points) format. This file must include labelled (where the measured Above Ground Biomass is known) and unlabelled points. The predictions should be transformed back to raster format. This step can be done by  using the Raster to Point (Conversion) tool in ArcGIS Pro; subsequently, the Extract multiple values to points tool should be used in order to create the final feature point with associated table containing a total of N columns. These columns represent: features + coordinates + target (AGB).

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