Remote Sensing and Phytoecological Methods for Mapping and Assessing Potential Ecosystem Services of the Ouled Hannèche Forest in the Hodna Mountains, Algeria

Abstract

Regardless of their biogeographic origins or degree of artificialization, the world’s forests are a source of a wide range of ecosystem services (ES). However, the quality and quantity of these services depend on the type of forest studied and its phytogeographic context. Our objective is to transpose the concept of ES, in particular, the assessment of forest ES, to the specific Mediterranean context of the North African mountains, where this issue is still in its infancy and where access to the data needed for assessment remains difficult. Our work presents an introductory approach, allowing us to set up methodological and scientific milestones based on open-access remote sensing data and already tested geospatial processing associated with phytoecological surveys to assess the ES provided by forests in an Algerian study area. Specifically, several indicators used to assess (both qualitatively and quantitatively) the potential ES of the Ouled Hannèche forest, a forest located in the Hodna Mountains, are derived from LANDSAT 8 OLI images from 2017 and an ALOS AW3D30 DSM. The qualitative ES typology is jointly based on an SVM classification of topographically corrected LANDSAT images and a geomorphic-type classification using the geomorphon method. NDVI is a quantitative estimator of many plant ecosystem functions related to ES. It highlights the variations in the provision of ES according to the types of vegetation formations present. It serves as a support for estimating spectral heterogeneity through Rao’s quadratic entropy, which is considered a relative indicator of biodiversity at the landscape scale. The two previous variables (the multitemporal NDVI and Rao’s Q), completed by the Shannon entropy method applied to the geomorphon classes as a proxy for topo-morphological heterogeneity, constitute the input variables of a quantitative map of the potential supply of ES in the forest determined by Spatial Multicriteria Analysis (SMCA). Ultimately, our results serve as a useful basis for land-use planning and biodiversity conservation.