Assessing Vegetation Change Patterns in Talassemtane National Park (Morocco) Using Temporal Segmentation Algorithm and Cloud Computing Techniques

Arid regions worldwide are facing environmental deterioration due to the climate change impacts. Assessing the spatial and temporal patterns of vegetation change and their relation with human activities is essential to address the challenges posed by this climate emergency. In this research, cloud computing techniques based on Google Earth Engine (GEE) allowed to identify the dynamics of vegetation change in Talassemtane National Park (TNP) using spectral-temporal segmentation algorithm LandTrendr (LT) for the period 1990–2020. NDVI was chosen as an appropriate index to detect vegetation trends, and yearly reflectance values were computed after cloud and shadow masking. Results showed over the past 30 years that the TNP has undergone a vegetation change of 16,329 ha where 61.02% corresponds to vegetation disturbance and 39.58% corresponds to vegetation gain. The main forested areas of the TNP showed significantly higher rates of vegetation increase compared to areas close to villages and cultivation areas with a respective percentage of 21.4% and 53.9% for vegetation disturbance versus 52% and 36.2% for vegetation gain. Computed years of change showed that the history of vegetation patterns has witnessed two main periods: a period that ends around 2007 when vegetation loss rates exceed vegetation gain rates and a second period beginning in 2008 (four years after the creation of the TNP) where vegetation gain exceeded vegetation loss. Hence, the recovery of vegetation within the protected areas is successful, which shows the effectiveness of these biodiversity conservation strategies.