Dynamics of Fractional Vegetation Coverage and Its Relationship with Climate and Human Activities in Inner Mongolia, China

Abstract

Long-term remote sensing normalized difference vegetation index (NDVI) datasets have been widely used in monitoring vegetation changes. In this study, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g dataset was used as the data source, and the dimidiate pixel model, intensity analysis, and residual analysis were used to analyze the changes of vegetation coverage in Inner Mongolia-from 1982 to 2010-and their relationships with climate and human activities. This study also explored vegetation changes in Inner Mongolia with respect to natural factors and human activities. The results showed that the estimated vegetation coverage exhibited a high correlation (0.836) with the actual measured values. The increased vegetation coverage area (49.2% of the total area) was larger than the decreased area (43.3%) from the 1980s to the 1990s, whereas the decreased area (57.1%) was larger than the increased area (35.6%) from the 1990s to the early 21st century. This finding indicates that vegetation growth in the 1990s was better than that in the other two decades. Intensity analysis revealed that changes in the average annual rate from the 1990s to the early 21st century were relatively faster than those in the 1980s-1990s. During the 1980s-1990s, the gain of high vegetation coverage areas was active, and the loss was dormant; in contrast, the gain and loss of low vegetation coverage areas were both dormant. In the 1990s to the early 21st century, the gains of high and low vegetation coverage areas were both dormant, whereas the losses were active. During the study period, areas of low vegetation coverage were converted into ones with higher coverage, and areas of high vegetation coverage were converted into ones with lower coverage. The vegetation coverage exhibited a good correlation (R 2 = 0.60) with precipitation, and the positively correlated area was larger than the negatively correlated area. Human activities not only promote the vegetation coverage, but also have a destructive effect on vegetation, and the promotion effect during 1982 to 2000 was larger than from 2001 to 2010, while, the destructive effect was larger from 2000 to 2010.

Human Activity Influences on Vegetation Cover Changes in Beijing, China, from 2000 to 2015

“…The Pearson's correlation coefficient was 0.773 (p < 0.01, RMSE = 0.091), which meets the accuracy requirements ( Figure 5), indicating that the DPM was reliable in extracting the FVC. This is also consistent with previous findings [3].…”

“…It confirmed that China has been undergoing significant large-scale policy-driven ecological restoration [26]. However, the effects on a large scale do not necessarily occur on a small scale, and previous studies have concluded the relationship between vegetation transitions and natural or human-induced factors at a global, national, or regional level; the provincial and/or city scale is lacking [3,27,28]. Furthermore, there are also very few investigations of the spatial heterogeneity of human activity's influential effects.…”

“…Vegetation is the most basic part of the ecosystem [3]. In the long geological history of the earth, carbon stocks in vegetation are almost the only source of oxygen in the atmosphere, determining the trend of the earth's environment.…”

Temporal and Spatial Characteristics of EVI and Its Response to Climatic Factors in Recent 16 years Based on Grey Relational Analysis in Inner Mongolia Autonomous Region, China

“…It is a water conservation area of Songhua River and functions as an important ecological barrier of northern China [11,12]. The ecological environment in IMAR is characterized by distinctive geographical differences, fragile ecological conditions, and complex ecological types [12][13][14], where vegetation types show a northeast-southwest pattern of surface cover of forests, steppes and deserts [12,15]. Quantitative assessment of the dynamic changes and driving forces of the vegetation ecosystem in Inner Mongolia will help people to understand the feedback between the global climate change and vegetation ecosystems [12,[14][15][16], which is of great theoretical and practical significance for evaluating the environmental quality of terrestrial ecosystems and regulating ecological processes [1,16,17].…”

Time Series of Landsat Imagery Shows Vegetation Recovery in Two Fragile Karst Watersheds in Southwest China from 1988 to 2016

“…In the present study, the FVC was retrieved from a sub-pixel model, which assumes the remotely sensed response in a mixed pixel is composed of a linearly weighted combination of vegetated and non-vegetated components, with the most widely used remote sensing spectral response being NDVI [43], since NDVI is the optimal indicator of vegetation growth and strongly correlated with vegetation cover [44]. The calculation formula of the sub-pixel model is calculated as [45]:…”