Estimation of Rangeland Production in the Arid Oriental Region (Morocco) Combining Remote Sensing Vegetation and Rainfall Indices: Challenges and Lessons Learned
[en] This study aimed at investigating the potential of vegetation indices and precipitation-related variables derived from remote sensing to assess rangeland production in the arid environment of the Moroccan Oriental region and identifying the challenges linked to that particular biome. Vegetation indices (VIs) and the Standardized Precipitation Index (SPI) computed at various aggregation periods were first integrated into a Random Forest model. In a second step, we studied in more detail the linear relationship between rangeland biomass and one of the spectral indices (ARVI) for the various vegetation formations present in the area. We concluded that, mostly due to the presence of alfa steppes (Stipa tenacissima), and especially to a large proportion of non-photosynthetic vegetation, it is not possible to accurately estimate rangeland production with a global model in this region. We recommend separating Stipa tenacissima from the other species in models and focusing on methods aimed at studying dry and non-photosynthetic vegetation to improve the quality of the prediction for alfa steppes.
Disciplines :
Agriculture & agronomy
Author, co-author :
Lang, Marie ; Université de Liège - ULiège > DER Sc. et gest. de l'environnement (Arlon Campus Environ.) > Eau, Environnement, Développement
Mahyou, Hamid; Institut National de la Recherche Agronomique Maroc > Centre Régional de la Recherche Agronomique d’Oujda
Tychon, Bernard ; Université de Liège - ULiège > DER Sc. et gest. de l'environnement (Arlon Campus Environ.) > Eau, Environnement, Développement
Language :
English
Title :
Estimation of Rangeland Production in the Arid Oriental Region (Morocco) Combining Remote Sensing Vegetation and Rainfall Indices: Challenges and Lessons Learned
Alternative titles :
[en] Belgique
Publication date :
26 May 2021
Journal title :
Remote Sensing
eISSN :
2072-4292
Publisher :
Multidisciplinary Digital Publishing Institute (MDPI), Arlon, Switzerland
Special issue title :
Deep Learning Methods for Crop Monitoring and Crop Yield Prediction
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