Driving forces of recent vegetation changes in the Sahel: Lessons learned from regional and local level analyses

Abstract

A wide range of environmental and societal issues such as food security policy implementation requires accurate information on biomass productivity and its underlying drivers at both regional and local scales. While many studies in West Africa are conducted with coarse resolution earth observation data, few have tried to relate vegetation trends to explanatory factors, as is generally done in land use and land cover change (LULCC) studies at finer scales. In this study we proposed to make a bridge between vegetation trend analysis and LULCC studies to improve the understanding of the various factors that influence the biomass production changes observed in satellite time series (using integrated Normalized Difference Vegetation Index [NDVI] as a proxy). The study was conducted in two steps. In the first step we analyzed MODIS NDVI linear trends together with TRMM growing season rainfall over the Sahel region from 2000 to 2015. A classification scheme was proposed that enables better specification of the relative role of the main drivers of biomass production dynamics. We found that 16% of the Sahel is re-greening—but found strong evidence that rainfall is not the only important driver of biomass increase. Moreover, a decrease found in 5% of the Sahel can be chiefly attributed to factors other than rainfall (88%). In the second step, we focused on the “Degré Carré de Niamey” site in Niger. Here, the observed biomass trends were analyzed in relation to land cover changes and a set of potential drivers of LULCC using the Random Forest algorithm. We observed negative trends (29% of the Niger site area) mainly in tiger bush areas located on lateritic plateaus, which are particularly prone to pressures from overgrazing and overlogging. The significant role of accessibility factors in biomass production trends was also highlighted. Our methodological framework may be used to highlight changing areas and their major drivers to identify target areas for more detailed studies. Finer-scale assessments of the long-term vulnerability of populations can then be made to substantiate food security management policies. (Résumé d'auteur

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

“…between geographic variables described above, these linear hypotheses usually lead to biased results. Second, previous research commonly explored the relationships between vegetation growth and climatic factors from a time series, ignoring their spatial differences [26][27][28][29][30]. Thirdly, previous studies have not quantitatively assessed the interaction between two or multiple factors, which are commonly used to quantitatively check whether two or multiple environmental determinants work independently or not.…”

Effects of Climate Factors and Human Activities on the Ecosystem Water Use Efficiency throughout Northern China

“…To explore the underlying drivers of the changes in WUE, we selected the main driver by analyzing the importance of every factor to the WUE. Here, we calculated the variable importance with the internal importance measurement of the random forest (RF) algorithm to identify the drivers with the most important contributions to changing trends in the WUE [57]. The RF is an effective algorithm for optimizing learning accuracy without obviously complicating the calculation [58], and it has been widely used in geography and ecology [57,59,60].…”

“…The WUE trends (WUE-K) were treated as the target variables to be explained. The mean value of the climate factors represented the general climate conditions over a period of time, while the trends of the slope reflected the changes in the climate conditions over many years [57]. One RF model was set up for each of the four regions in this study.…”

Vegetation Cover Change and Relative Contributions of Associated Driving Factors in the Ecological Conservation and Development Zone of Beijing, China

“…The residual analysis method proposed by Evans and Geerken [33] in 2004 was considered to be robust and has been widely accepted to separate the effects of climate change and human activities on vegetation cover change [17]. Based on the residual analysis method, we established the multiple regression models of the NDVI, temperature and precipitation for every pixel in order to obtain the predictive value of the NDVI (NDVI P ), which is regarded as the influence of the climatic factors on the changes in the NDVI.…”

“…For example, Faramarzi et al [16] created the NDVI maps of 1986, 2001, and 2013 to evaluate the vegetation change in a semiarid rangeland in western Iran, and the authors found that the amount of precipitation seemed to be one of the most important factors that affected the vegetation in the study area. Leroux et al [17] successfully combined vegetation trend analysis with land use and land cover change (LULCC) studies to analyze the driving force of the biomass production changes that were represented by the NDVI. Muriithi et al [18] investigated the trends in the average annual NDVI before and after the presumed onset of rapid horticulture in the central highlands of Kenya, and the authors further analyzed the relationship between the average annual NDVI and specific driving factors, such as population density, large-scale commercial farms, and mean annual rainfall in sub-watersheds.…”