# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 4 Discussion  
This study aimed to map the Admine organ forest located in the Souss region of Morocco for the periods 2017/2018 and 2022/2023, calculate the deforestation rate over this six-year period, and provide specialists with accurate insights to inform mitigation strategies for this UNESCO World Biosphere Reserve habitat. Using remote sensing and machine learning tools, we addressed the challenge of limited organ-specific data through efficient algorithms capable of analyzing large datasets with minimal computational requirements. Additionally, we sought to enhance the precision of these tools by conducting extensive experiments to identify the optimal scenarios for mapping and deforestation analysis. The experiments provided valuable insights into the performance of different machine-learning algorithms, data levels, and spectral information. The Decision Tree, Random Forest, and XGBoost machine learning algorithms consistently performed well, in line with prior studies [18], but the LightGBM was selected due to the outstanding results, with OAs greater than 98.0% in all cases. Additionally, the comparison between Sentinel-2 Levels 1C and 2A demonstrated minimal differences, primarily attributed to the negligible contribution of Band 10, the key differentiator between these data levels. These results suggest that both data levels are suitable for this task, offering flexibility in data selection for future studies. Figure 11: Map of changes and deforestation for the study area (patch size: 9 \(\times\) 9). The superior classification results for 2022/2023 compared to 2017/2018 can be attributed to the lack of available ground truth data for 2017/2018. Consequently, the 2022/2023 dataset is inherently more reliable than the 2017/2018 dataset. Experiments also highlighted the importance of selecting optimal temporal windows for Argan tree detection (Figure 7). Variations in classification results were observed depending on the observation date, underscoring the need for a precise temporal analysis when mapping vegetation in dynamic ecosystems [126; 127]. An analysis of classification performance throughout the year, as illustrated in Figure 7, reveals some periods with higher accuracy; however, the difference between the average OA for each season is minimal (Table 14), suggesting that seasonal variations have a limited impact on classification performance and organ tree detection accuracy. This can be attributed to the severe seven-year drought that Morocco is facing, which has affected the study area [128]. The decreasing rainfall and its seasonal irregularity (Figure 12), as well as the relatively stable temperature throughout the year, which results in limited seasonal fluctuations in the region [129]. On the other hand, the experiments demonstrated that combining spectral and temporal data significantly improves OA (Table 12). Specifically, models trained on time-series data consistently outperformed those trained on single-date data, indicating that temporal information helps the models better learn the characteristics of Argan trees, ultimately leading to improved classification accuracy. The organ tree is distinguished by its medium-density distribution, adaptation to semi-arid regions, and typical growth in areas surrounded by bare soil. Given these conditions, it was initially expected that incorporating additional spectral information, such as spectral indices, could enhance accuracy--provided an appropriate resampling method was applied. In this study, all Sentinel-2 bands were resampled to a 10-m resolution. While resampling higher-resolution bands does not introduce new information to lower-resolution data, the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Season** & **Average OA** & **Average OA** \\  & **2017/2018** & **2022/2023** \\ \hline \hline Autumn (September to November) & 0.960 & 0.976 \\ Winter (December to February) & 0.958 & 0.979 \\ Spring (March to May) & 0.957 & 0.967 \\ Summer (June to August) & 0.950 & 0.975 \\ \hline \hline \end{tabular}
\end{table}