Abstract
Tree species identification and their geospatial distribution mapping are crucial for forest monitoring and management. The satellite-based remote sensing time series of Sentinel missions (Sentinel-1 and Sentinel-2) are a perfect tool to map the type, location, and extent of forest cover over large areas at local or global scale. This study is focused on the geospatial mapping of the endemic argan tree (Argania spinosa (L.) Skeels) and the identification of two other tree species (sandarac gum and olive trees) using optical and synthetic aperture radar (SAR) time series. The objective of the present work is to detect the actual state of forest species trees, more specifically the argan tree, in order to be able to study and analyze forest changes (degradation) and make new strategies to protect this endemic tree. The study was conducted over an area located in Essaouira province, Morocco. The support vector machine (SVM) algorithm was used for the classification of the two types of data. We first classified the optical data for tree species identification and mapping. Second, the SAR time series were used to identify the argan tree and distinguish it from other species. Finally, the two types of satellite images were combined to improve and compare the results of classification with those obtained from single-source data. The overall accuracy (OA) of optical classification reached 86.9% with a kappa coefficient of 0.84 and declined strongly to 37.22% (kappa of 0.29) for SAR classification. The fusion of multisensor data (optical and SAR images) reached an OA of 86.51%. A postclassification was performed to improve the results. The classified images were smoothed, and therefore, the quantitative and qualitative results showed an improvement, in particular for optical classification with a highest OA of 89.78% (kappa coefficient of 0.88). The study confirmed the potential of the multitemporal optical data for accurate forest cover mapping and endemic species identification.
Highlights
The monitoring of forest cover plays a crucial role in biodiversity, feedstock, and water cycle, etc
In order to visually compare the quality of the classified images, we present in Figure 7 the images obtained for the scenarios reached good overall accuracy (OA)
The use of remote sensing data has obvious advantages over land cover classification. The accuracy of these data depends on the quality of information extraction from them, and the combination of optical remote sensing, which includes radiometric indices, with synthetic aperture radar (SAR) remote sensing is generally accepted as a means of improving classification [89]
Summary
The monitoring of forest cover plays a crucial role in biodiversity, feedstock, and water cycle, etc. Machine learning algorithms are techniques that have been successfully used based on remote sensing data for tree type classification [1]. Current satellite sensors, such as Sentinel missions, facilitated the tree species mapping at local as well as national scale. It is endemic only to Morocco, where it grows in arid and semiarid climates with 150 to 400 mm of rainfall per year. It is a slow-growing spiny tree that can reach a maximum height of around 10 m (Figure 1). Leaves may be shed entirely or partially in response to summer stress
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