# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 1 Introduction
The multi-temporal analysis allows for better tracking of these phenological stages, enhancing detection accuracy [26]. However, cloud contamination can interfere with this process [27]. Integrating multi-source data has been suggested, but this approach requires extensive preprocessing and sensor calibration [28; 29]. In summary, using multispectral and multi-temporal Sentinel-2 data significantly improves tree detection accuracy, with results strongly dependent on information selection strategies [21; 26]. In studies on tree detection and forest mapping using Sentinel-2 data, various classification algorithms have been employed, encompassing both machine learning and deep learning techniques [18], with supervised classification generally preferred over unsupervised approaches [30]. Before undertaking any remote sensing task, it is essential to identify the most appropriate approach for data analysis, especially with Sentinel-2. There are \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Early 20 th Century** & **2016** \\ \hline Area (ha) & 1,500,000 & 829,087 \\ Density (trees/ha) & 100 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Changes in organ area and density (argan trees per ha): historical data from the early 20 th century to 2016 [11]. three primary approaches: pixel-based [31], patch-based [32], and object-based [31], which differ primarily in the basic unit of analysis. Pixel-based and patch-based approaches often employ machine learning algorithms for LULC classification, with Random Forest (RF) and Support Vector Machine (SVM) being the most frequently used. These algorithms have shown promising results, often achieving an Overall Accuracy (OA) exceeding 80% [18]. Other studies with similar objectives have utilized other machine learning algorithms, such as Maximum Likelihood Classification (MLP), Artificial Neural Networks (ANN), Decision Trees, k-Nearest Neighbors (KNN), and Bayes models [18]. It is worth noting that pixel-based methods, while commonly used, can be prone to noise. Patch-based or object-based approaches are generally preferable, especially for change detection tasks [33; 34]. Object-based methods, in particular, often take advantage of deep learning algorithms, which were first introduced to remote sensing in 2014 [20]. Since then, deep learning has gained significant attention due to its success in this domain [35]. Among the most commonly used deep learning models are Convolutional Neural Networks (CNN), which have a demonstrated OA exceeding 90% in LULC classification and mapping tasks using Sentinel-2 data [17; 36]. Overall, deep learning algorithms outperform traditional machine learning algorithms in terms of accuracy due to their superior feature extraction capabilities, non-linear modeling, and high-level semantic segmentation [37; 38]. Nevertheless, the efficiency of machine learning models remains relevant, as they typically require less training data compared to deep learning models, making them well-suited for smaller datasets [39]. Finally, regardless of the type of algorithm chosen for a remote sensing task, factors such as data preprocessing and algorithm parameterization can significantly enhance accuracy [18]. Finally, it is important to briefly review the role of remote sensing in detecting changes in the same geographical area over time, a key focus of this study [40]. Change detection methods using multispectral satellite data can be categorized into four main types [41]: Algebra-Based Methods [42], Statistics-Based Methods [43; 44], Transformation-Based Methods [42], and Deep-Learning-Based Methods [17; 45]. For deforestation mapping, two primary approaches are commonly used. The first involves classifying images for each time period into multiple land-cover labels (e.g., trees, bare soil, urban areas, water) using machine learning classifiers [46] or deep learning algorithms [47; 48]. By comparing these classification maps, deforestation maps are generated.