Modeling the Underlying Drivers of Natural Vegetation Occurrence in West Africa with Binary Logistic Regression Method

Beatrice Asenso Barnieh, Li Jia, Massimo Menenti, Min Jiang, Jie Zhou, Yelong Zeng and Ali Bennour
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Beatrice Asenso Barnieh: State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Li Jia: State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Massimo Menenti: State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Min Jiang: State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Jie Zhou: Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Yelong Zeng: State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Ali Bennour: State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

Sustainability, 2021, vol. 13, issue 9, 1-37

Abstract: The occurrence of natural vegetation at a given time is determined by interplay of multiple drivers. The effects of several drivers, e.g., geomorphology, topography, climate variability, accessibility, demographic indicators, and changes in human activities on the occurrence of natural vegetation in the severe drought periods and, prior to the year 2000, have been analyzed in West Africa. A binary logistic regression (BLR) model was developed to better understand whether the variability in these drivers over the past years was statistically significant in explaining the occurrence of natural vegetation in the year 2000. Our results showed that multiple drivers explained the occurrence of natural vegetation in West Africa at p < 0.05. The dominant drivers, however, were site-specific. Overall, human influence indicators were the dominant drivers in explaining the occurrence of natural vegetation in the selected hotspots. Human appropriation of net primary productivity (HANPP), which is an indicator of human socio-economic activities, explained the decreased likelihood of natural vegetation occurrence at all the study sites. However, the impacts of the remaining significant drivers on natural vegetation were either positive (increased the probability of occurrence) or negative (decreased the probability of occurrence), depending on the unique environmental and socio-economic conditions of the areas under consideration. The study highlights the significant role human activities play in altering the normal functioning of the ecosystem by means of a statistical model. The research contributes to a better understanding of the relationships and the interactions between multiple drivers and the response of natural vegetation in West Africa. The results are likely to be useful for planning climate change adaptation and sustainable development programs in West Africa.

Keywords: West Africa; natural vegetation; underlying drivers; climate; human activities; binary logistic regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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