Quantitative Contributions of Climate Change and Human Activities to Vegetation Changes in the Upper White Nile River

Abstract

Vegetation changes in the Upper White Nile River (UWNR) are of great significance to the maintenance of local livelihoods, the survival of wildlife, and the protection of species habitats. Based on the GIMMS NDVI3g and MODIS normalized difference vegetation index (NDVI) data, the temporal and spatial characteristics of vegetation changes in the UWNR from 1982 to 2020 were analyzed by a Theil-Sen median trend analysis and Mann-Kendall test. The future trend of vegetation was analyzed by the Hurst exponential method. A partial correlation analysis was used to analyze the relationship of the vegetation and climate factors, and a residual trend analysis was used to quantify the influence of climate change and human activities on vegetation change. The results indicated that the average NDVI value (0.75) of the UWNR from 1982 to 2020 was relatively high. The average coefficient of variation for the NDVI was 0.059, and the vegetation change was relatively stable. The vegetation in the UWNR increased 0.013/10 year on average, but the vegetation degradation in some areas was serious and mainly classified as agricultural land. The results of a future trend analysis showed that the vegetation in the UWNR is mainly negatively sustainable, and 62.54% of the vegetation will degrade in the future. The NDVI of the UWNR was more affected by temperature than by precipitation, especially on agricultural land and forestland, which were more negatively affected by warming. Climate change and human activities have an impact on vegetation changes, but the spatial distributions of the effects differ. The relative impact of human activities on vegetation change accounted for 64.5%, which was higher than that of climate change (35.5%). Human activities, such as the large proportion of agriculture, rapid population growth and the rapid development of urbanization were the main driving forces. Establishing a cross-border drought joint early warning mechanism, strengthening basic agricultural research, and changing traditional agricultural farming patterns may be effective measures to address food security and climate change and improve vegetation in the UWNR.

Effects of Human Activities on Urban Vegetation: Explorative Analysis of Spatial Characteristics and Potential Impact Factors

“…Because of the close correlations between LUCC types and human activity [12,36], each socio-economic factor was individually matched to five LUCC types with the linear correlation analysis and then one LUCC type with the highest relevance to the factor was selected. This close relevance was calculated based on the absolute value of the Pearson correlation coefficient [5,37]. The equation is shown following:…”

Assessing the Spatiotemporal Evolution of Anthropogenic Impacts on Remotely Sensed Vegetation Dynamics in Xinjiang, China

“…The direct quantification of the impacts of anthropogenic activities on vegetation is difficult due to the lack of long-term serial observation data on the impacts of anthropogenic activities on the NDVI [13,22]. Residual analysis can separate NDVI changes caused by natural factors and NDVI changes caused by human activities and is widely used in the study of the impact of natural factors and human activities on vegetation changes [13,15,[19][20][21][22][23]. The basic concept of residual analysis is as follows.…”

“…Using NDVI data, previous studies have focused on the relationship between vegetation dynamics and human activities at a variety of spatiotemporal scales. For example, Ma et al [19] studied the quantitative contribution of climate change and human activities to vegetation change in the Upper White Nile River and believed that the large proportion of agriculture, rapid population growth, and rapid urbanization were the main anthropogenic driving forces for vegetation change from 1982 to 2020. Liu and Xin [20] analyzed the impact of climate change and human activities on the changes in desert vegetation in China.…”

“…Currently, residual analysis is considered to be the most commonly used and most effective method to separate the relative influence of human and natural factors on vegetation [21]. The previous NDVI prediction models, dominated by natural factors, generally take temperature and precipitation as the primary indicators and lack consideration of other natural factors, such as solar radiation and soil moisture [13,19,20,22,23]. However, studies have confirmed that soil moisture and vegetation change cycles in arid areas have similar changing trends [24].…”

“…Therefore, it is most important to accurately quantify the contribution of natural factors to the actual NDVI in the residual analysis method, because this process subsequently determines the level of anthropogenic contribution [21]. In addition, existing studies mostly analyzed human activities as a whole [15,19,20], and lacked analysis of the contribution of different types of human activities to vegetation change. The contribution of different types of human activities to vegetation dynamics varies from place to place [10,27,28].…”