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 (R2 = 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.
Highlights
Global climate change greatly influences humans’ living environment through a variety of impacts—such as forest decline, land degradation and desertification, ecosystem degradation, and vegetation zone migration—that directly affect human living standards and quality of life [1]
NDVI3g dataset as a remote sensing data source; we built an Fractional vegetation coverage (FVC) model based on the dimidiate pixel model and analyzed the dynamics of the temporal and spatial distributions of the vegetation cover and change trendsdistributions using the intensity analysis method from
To derive more details details concerning the variation of vegetation coverage in Inner Mongolia over the past 30 years, we concerning the variation of vegetation coverage in Inner Mongolia over the past 30 years, we identified identified the areas with different levels of vegetation coverage in each decade
Summary
Global climate change greatly influences humans’ living environment through a variety of impacts—such as forest decline, land degradation and desertification, ecosystem degradation, and vegetation zone migration—that directly affect human living standards and quality of life [1]. The other is based on spatial data mining and knowledge discovery (SDMKD) technology and includes decision trees, artificial neural networks, and other methods In both general methods, pixel decomposition is widely used in scientific research because the calculations are simple and reliable and the input parameters obtained. Against the background of global climate change, elucidating the patterns of vegetation coverage variation and exploring the driving effects of climate factors have important theoretical and practical significance for the evaluation of environmental quality in terrestrial ecosystems and the regulation of ecological processes. NDVI3g dataset as a remote sensing data source; we built an FVC model based on the dimidiate pixel model and analyzed the dynamics of the temporal and spatial distributions of the vegetation cover and change trendsdistributions using the intensity analysis method from 1982 to.
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