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

Abstract

As a result of the unique geographical characteristics, pastoral lifestyle, and economic conditions in Mongolia, its fragile natural ecosystems are highly sensitive to climate change and human activities. The normalized difference vegetation index (NDVI) was employed in this study as an indicator of the growth status of vegetation. The Sen's slope, Mann-Kendall test, and geographical detector modelling methods were used to assess the spatial and temporal changes of the NDVI in response to variations in natural conditions and human activities in Mongolia from 1982 to 2015. The corresponding individual and interactive driving forces, and the optimal range for the maximum NDVI value of vegetation distribution were also quantified. The area in which vegetation was degraded was roughly equal to the area of increase, but different vegetation types behaved differently. The desert steppe and the Gobi Desert both in arid regions have degraded significantly, whereas the meadow steppe and alpine steppe showed a significant upward trend. Precipitation can satisfactorily account for vegetation distribution. Changes of livestock quantity was the dominant factor influencing the changes of most vegetation types. The interactions of topographic factors and climate factors have significant effects on vegetation growth. In the region of annual precipitation between 331 mm and 596 mm, forest vegetation type and pine sandy soil type were found to be most suitable for the growth of vegetation in Mongolia. The findings of this study can help us to understand the appropriate range or type of environmental factors affecting vegetation growth in Mongolia, based on which we can apply appropriate interventions to effectively mitigate the impact of environmental changes on vegetation.With the continuous development of earth observation system technology and the continuous enrichment of statistical models, the research content and methods of the relationships between vegetation, climate change, and human activities have become increasingly diverse. The normalized density vegetation index (NDVI), derived from infrared channel and near-infrared channel remote sensing data, is a good indicator of vegetation growth status and spatial distribution density of vegetation, and is linearly related to vegetation distribution density [6,7]. A large number of ecological studies have been carried out related to vegetation change and its influencing factors using NDVI and statistical models. Temperature and precipitation, as important factors in the natural environment, are often used as research priorities to assess their impact on vegetation. However, the dominant factors contributing to NDVI in different regions vary considerably. The variety in vegetation and its connection to climate change have been investigated by Du et al. [5] and Zheng et al. [8] in China. These studies identified precipitation as the key climatic factor governing variation in NDVI. However, Guo et al. [9] found that temperature was the dominant factor on vegetation (NDVI) growth ...

Spatio-Temporal Vegetation Dynamic and Persistence under Climatic and Anthropogenic Factors

“…To detect the vegetation dynamics and its response to climatic factors, one of the most commonly used method is to use satellite derived Normalized Difference Vegetation Index (NDVI) [25,26] because NDVI is highly correlated with leaf area, potential photosynthesis of vegetation, photosynthetically active biomass, and chlorophyll abundance [15,27]. The NDVI, quantifies vegetation by measuring the difference between infrared and near infrared channel remote sensing data.…”

“…The NDVI, quantifies vegetation by measuring the difference between infrared and near infrared channel remote sensing data. NDVI is linearly related to vegetation distribution density [25,28]. Despite the fact that NDVI has several limitations, including noisy canopy background signal in the regions of sparse vegetation and saturation in highly vegetated canopy, this index is largely used from local to global scale in vegetation dynamics studies [16,22] due to its simplicity and robustness [23].…”

Relationship between multi-scale climate factors and performance of ecological engineering on the Loess Plateau, China

“…Geographical conditions such as climatic, topography, and soil texture factors, are highly significant to vegetation growth (Meng et al 2020;Sun et al 2020); however, climate may be the most important factor. The relationship between climate and vegetation is a constant regardless of natural and human disturbances (Meng et al 2020). In this study, rainfall, soil moisture, and surface runoff for most areas on the Loess Plateau show a positive increases after 2000 (Fig.…”

Spatio-Temporal Characteristics and Driving Factors of the Foliage Clumping Index in the Sanjiang Plain from 2001 to 2015

“…Theil−Sen trend analysis, first proposed by Sen et al [50], is also known as Sen's slope. It is a non-parametric statistical method, which is robust for calculating the variation trend of a time series, and it is not sensitive to abnormal data [30]. The computational formula used to calculate Sen's slope is shown below:…”

“…Its research area ranges from the national scale [27] to the regional scale [28]. It has been widely used to analyse vegetation cover variation [29][30][31][32][33][34], and the spatial differentiation of vegetation can be ascertained effectively. For the CI, GDM not only can detect the influence of the spatial distribution characteristics of numerical data such as the average annual temperature and of qualitative data such as soil type on CI, but it can also determine the interactions among different driving factors on the spatial differentiation of the CI.…”