The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-4/W3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4/W3-2020, 365–373, 2020
https://doi.org/10.5194/isprs-archives-XLIV-4-W3-2020-365-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4/W3-2020, 365–373, 2020
https://doi.org/10.5194/isprs-archives-XLIV-4-W3-2020-365-2020

  23 Nov 2020

23 Nov 2020

DECISIONAL TREE MODELS FOR LAND COVER MAPPING AND CHANGE DETECTION BASED ON PHENOLOGICAL BEHAVIORS. APPLICATION CASE: LOCALIZATION OF NON-FULLY-EXPLOITED AGRICULTURAL SURFACES IN THE EASTERN PART OF THE HAOUZ PLAIN IN THE SEMI-ARID CENTRAL MOROCCO

B. Sebbar, A. Moumni, and A. Lahrouni B. Sebbar et al.
  • Laboratoire de Modélisation Moléculaire et Ecophysiologie, Centre Geber, Faculté des Sciences de Semlalia, 40000 Marrakech, Morocco

Keywords: remote sensing, NDVI profiles, Decision Tree classifier, Land-cover mapping, change detection

Abstract. Great effort has been recently employed for the development of a modern and competitive agriculture in Morocco, growth in the agricultural sector is determined largely through the realization of thousands of new projects, and the support of the smallholder farmers at a national scale. Modernization of irrigation systems, and enlargement of the extent and spatial distribution of irrigated areas holds the key to increase annual productions. In this context, we established a unique procedure for monitoring the agricultural surfaces not fully exploited in terms of potential and production, in the semi-arid zone of the Haouz plain, central Morocco. We derived Normalized Difference Vegetation Index (NDVI) time series from Sentinel-2 (S2) and Landsat 8 (L8) high spatial resolution satellite images from 2016 to 2018. Seasonal phenological changes and land-cover dynamics, in addition to elevation models and landscape slopes, helped determine periods and thresholds suitable for classes separability, and establish a set of rules to be implemented in a Decision tree classifier model for a detailed land-cover mapping of the last three years. The agricultural zone was successfully separated from mountains and hills, and the derived maps of the three years yielded satisfying result with an OAthat reached above 91% for quite detailed landscape-type information. The outputs of this work hold promise to provide valuable information for planners, decision-makers and regional offices, to help smallholder farmers. Although this approach has been developed at regional-scale, it holds the potential to be adapted to larger scales, with the appropriate selection of land-cover types, and carful adjustment in the threshold values.