Modeling the underlying drivers of natural vegetation occurrence in west africa with binary logistic regression method

Modeling the underlying drivers of natural vegetation occurrence in west africa with binary logistic regression method

Barnieh, Beatrice Asenso (Chinese Academy of Sciences; University of Chinese Academy of Sciences)
Jia, Li (Chinese Academy of Sciences)
Menenti, M. (TU Delft Optical and Laser Remote Sensing; Chinese Academy of Sciences)
Jiang, Min (Chinese Academy of Sciences)
Zhou, Jie (Central China Normal University)
Zeng, Yelong (Chinese Academy of Sciences; University of Chinese Academy of Sciences)
Bennour, Ali (Chinese Academy of Sciences; University of Chinese Academy of Sciences)

2021

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.

Binary logistic regression
Climate
Human activities
Natural vegetation
Underlying drivers
West Africa

http://resolver.tudelft.nl/uuid:07bd1f96-4d83-4d5b-b289-ee72f72eea3f

https://doi.org/10.3390/su13094673

2071-1050

Sustainability, 13 (9)

Institutional Repository

journal article

© 2021 Beatrice Asenso Barnieh, Li Jia, M. Menenti, Min Jiang, Jie Zhou, Yelong Zeng, Ali Bennour