Innovative evaluation of field and spatial remote sensing data for analysis of vegetation bio-types in arid rangelands, Taznakht, Moroccan anti-atlas

Citation
T. Bennouna et al., Innovative evaluation of field and spatial remote sensing data for analysis of vegetation bio-types in arid rangelands, Taznakht, Moroccan anti-atlas, ARID SOIL R, 14(1), 2000, pp. 69-85
Citations number
31
Categorie Soggetti
Environment/Ecology
Journal title
ARID SOIL RESEARCH AND REHABILITATION
ISSN journal
08903069 → ACNP
Volume
14
Issue
1
Year of publication
2000
Pages
69 - 85
Database
ISI
SICI code
0890-3069(200001/03)14:1<69:IEOFAS>2.0.ZU;2-H
Abstract
In Morocco, as in many other regions of north Africa, desertification is af fecting the most sensitive environments such as the rangelands. Demographic expansion, cereal growing, and overgrazing constitute the principal factor s of degradation in such regions. The use of satellite data provides an eff icient tool for observation and continuous measurement of the biosphere. Ou r objective is to propose a method for the characterization and mapping of rangelands in arid and desert areas, based on the biophysical reality of th e environment (field data). A highly detailed study based on field surveys of the Taznakht basin (Moraccan Anti-Atlas) was carried out to determine ra ngeland typology. Close relationships were demonstrated between the abiotic environment and the vegetation. The relevant bio-pedo-morphological classe s at each site, corresponding to the different types of rangeland, were ide ntified. The cartographic accuracy of these classes was considerably increa sed by combining the stratification obtained by Visual interpretation Assis ted by Computer of the photofacies of a Satellite Pour l'Observation de la Tene (SPOT) image obtained during the dry period, with a supervised classif ication of each stratum based on maximum likelihood. This methodological ap proach was used to develop a simple, robust, and generally applicable model for the efficient correlation of field and remote sensing data.