# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 2 Materials and Methods
### Data Acquisition and Pre-Processing
#### 2.4.1 Spectral Indices  
In the field of remote sensing, spectral indices serve as key tools within multispectral transformation processes [83]. These indices transform satellite sensor reflectance readings into environmentally significant figures. Thanks to the multispectral capabilities of satellite imagery, these transformations provide insights into various phenomena's current conditions. Vegetation indices, in particular, play a crucial role in both the identification and monitoring of vegetation patterns and in calculating key biophysical attributes of plant covers, including biomass, the leaf area index, and the proportion of absorbed photosynthetically active radiation [83]. Therefore, we decided to include various spectral indices available in the Sentinel hub platform (Table 4) [84]. Throughout the study, tests were carried out to examine the importance of each index.  
These indices were selected based on studies aimed at forest mapping [91; 92]. Their purpose in this study was to enhance organ tree detection and forest mapping. The Normalized Difference Vegetation Index (NDVI) is widely used to distinguish vegetation from non-vegetation by comparing red and near-infrared reflectance [93]. The Enhanced Vegetation Index (EVI) accounts for background canopy effects and atmospheric disturbances, increasing sensitivity in regions with high biomass [94]. The Atmospherically Resistant Vegetation Index (ARVI) reduces atmospheric scattering effects by incorporating the blue reflectance band [95]. Meanwhile, the Green Normalized Difference Vegetation Index (GNDVI) provides greater sensitivity to chlorophyll content by leveraging green reflectance instead of red [96]. The Normalized Difference Water Index (NDWI) highlights vegetation water content by utilizing green and near-infrared reflectance [97]. The Perpendicular Vegetation Index (PVI) minimizes soil background influence through a geometric approach in spectral space [98]. The Soil-Adjusted Vegetation Index (SAVI) compensates for soil brightness variations by incorporating a correction factor into the NDVI formula [99]. Finally, the Soil Composition Index (SCI) emphasizes spectral variations due to differences in soil constituents and thus helps to isolate soil reflectance from vegetation signals, improving forest/non-forest discrimination in heterogeneous landscapes [100]. While NDVI, EVI, ARVI, GNDVI, and SAVI mainly differ in their sensitivity to soil background and atmospheric effects, NDWI provides additional insights into vegetation and soil water  
\begin{table}
\begin{tabular}{c c c} \hline
**Abbreviation** & **Name** & **Formula** \\ \hline ARVI & Atmospherically Resistant Vegetation Index & \(\frac{\text{BSA}-\text{B4}-\text{J}\times(\text{B4}-\text{B2})}{\text{B8A}+ \text{B4}-\text{J}\times(\text{B4}-\text{B2})}\) \\ EVI & Enhanced Vegetation Index & \(2.5\times(\text{B8}-\text{B4})\) \\ \cline{3-3} GNDVI & Green Normalized Difference Vegetation Index & \(\frac{\text{BSA}+\text{B4}}{\text{B8A}+ \text{B4}}\) \\ NDVI & Normalized Difference Vegetation Index & \(\frac{\text{BSA}-\text{B4}}{\text{B8A}+ \text{B4}}\) \\ NDWI & Normalized Difference Water Index & \(\frac{\text{BSA}+\text{B8}}{\text{B3}+ \text{B8}}\) \\ PVI & Perpendicular Vegetation Index & \(\frac{\text{BSA}-\text{B4}}{\text{B2}+ \text{B4}}-b\) \\ SAVI & Soil Adjusted Vegetation Index & \((\frac{\text{BSA}-\text{B4}}{\text{B4}})\times(1+L)\) \\ SCI & Soil Composition Index & \(\frac{\text{BSA}+\text{B4}+\text{B4}+L}{\text{B1}+ \text{B8}}\) \\ \hline \end{tabular}
\end{table}
Table 4: Spectral indices selected [84].  
content, while SCI helps assess soil composition. Collectively, these indices enable a more accurate and robust classification of forested areas [101].