# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 1 Introduction  
The organ tree (_Argania spinosa_ (L.) [PERSON]) is a rare species endemic to southwestern Morocco [1]. Argan trees play a critical role in the environmental, social, and economic landscape. Known for their resilience in arid conditions, the organ trees contribute to biodiversity by supporting a rich network of plant and animal life while also enhancing soil stability [2]. Beyond their ecological significance, they also serve as a source of livelihood and income for local communities through valuable products like organ oil and timber [3]. The organ oil industry significantly contributes to the Moroccan economy, as highlighted in Table 1, which presents the market size of Moroccan and global organ oil in 2019, along with forecasts for 2030 [4, 5]. The growing global demand for organ oil across the cosmetics, skincare, and culinary sectors further underscores its increasing economic importance. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Moroccan Market Size** & **Global Market Size** \\ \hline Revenue, 2019 (USSM) & \$37.4 & \$233.0 \\ Forecast, 2030 (USSM) & \$97.0 & \$712.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Argan oil market analysis: global and Morocco size and forecast (2019–2030) [4, 5]. The organ forest considered in this study is located in the Souss region, centered around the city of Agadir (Morocco), an area distinguished by its agricultural sector, which is a leading economic sector alongside tourism. This organ forest is one of the largest in the country [6] and includes a section that was declared a Biosphere Reserve by UNESCO in 1998, dedicated to protecting this unique habitat [7]. However, accurately assessing the precise distribution and abundance of organ trees in the region remains challenging [8]. At present, the organ forest has been threatened by deforestation due to several factors [9]. On the one hand, agricultural expansion, urban encroachment [6], massive logging for producing firewood and charcoal, not to mention climate change and drought [8; 10]. As a result, the organ forests have lost approximately 50% of their total area over the past century (Table 2), with an estimated annual loss of 600 hectares [11]. This has raised alarm bells regarding the possible extinction of this magnificent tree in the future [12]. It is therefore crucial for resource conservation that significant efforts are made to create accurate maps, essential for the monitoring and proper management of land use and land cover (LULC) in this strategic zone, given its role in food security and Morocco's environmental and economic development [13]. In recent years, remote sensing technologies and satellite imagery have provided the capacity to collect accurate, comprehensive, and wide-ranging data, facilitating studies related to forest mapping and the accurate monitoring of forest habitat change and deforestation [14; 15; 16; 17]. In particular, multispectral and multitemporal data from the European Space Agency's Sentinel-2 satellites have contributed to the success of this technology in these applications [18; 19; 20]. Tree detection studies using Sentinel-2 data fall into two main categories: those relying solely on single-date multispectral information and those incorporating both multispectral and multi-temporal data [21]. Two primary methods are used to assess spectral band significance. The first calculates feature importance scores from classifiers, offering a quantitative approach to select optimal bands, though interpretability and classification performance can sometimes pose challenges [22]. The second method involves individual classification experiments with each spectral feature, providing a clearer understanding of their impact [23; 24]. Incorporating multi-temporal data is crucial due to the \"same spectra but different objects, same objects but different spectra\" phenomenon, caused by tree phenological cycles, which can limit detection accuracy when using single-date data [21; 25].