Declining Effect of Precipitation on the Normalized Difference Vegetation Index of Grasslands in the Inner Mongolian Plateau, 1982–2010

Impacts of climate change on vegetation pattern: Mathematical modeling and data analysis

Climate change has become increasingly severe, threatening ecosystem stability and, in particular, biodiversity. As a typical indicator of ecosystem evolution, vegetation growth is inevitably affected by climate change, and therefore has a great potential to provide valuable information for addressing such ecosystem problems. However, the impacts of climate change on vegetation growth, especially the spatial and temporal distribution of vegetation, are still lacking of comprehensive exposition. To this end, this review systematically reveals the influences of climate change on vegetation dynamics in both time and space by dynamical modeling the interactions of meteorological elements and vegetation growth. Moreover, we characterize the long-term evolution trend of vegetation growth under climate change in some typical regions based on data analysis. This work is expected to lay a necessary foundation for systematically revealing the coupling effect of climate change on the ecosystem.

The Sensitivity of Vegetation Dynamics to Climate Change across the Tibetan Plateau

Vegetation dynamics are key processes which present the ecology system’s response to climate change. However, vegetation sensitivity to climate change remains controversial. This study redefined vegetation sensitivity to precipitation (VSP) and vegetation sensitivity to temperature (VST) by the coefficient of determination (R2) obtained by a linear regression analysis between climate and the normalized difference vegetation index (NDVI), as well as by using an analysis of variance to explore the significant differences between them in different seasons from 1982 to 2013, and exploring the general changed rules of VSP/VST on a timescale. Moreover, the variations in VSP and VST across the Tibetan Plateau were plotted by regression analysis. Finally, we used structural equation modeling (SEM) to verify the hypothesis that the respondence of VSP and VST to the NDVI was regulated by the hydrothermal conditions. Our results showed that: (1) the annual VSP increased in both spring and winter (R2 = 0.32, p < 0.001; R2 = 0.25, p < 0.001, respectively), while the annual VST decreased in summer (R2 = 0.21, p < 0.001); (2) the threshold conditions of seasonal VSP and seasonal VST were captured in the 4–12 mm range (monthly precipitation) and at 0 °C (monthly average temperature), respectively; (3) the SEM demonstrated that climate change has significant direct effects on VSP only in spring and winter and on VST only in summer (path coefficient of −0.554, 0.478, and −0.428, respectively). In summary, our findings highlighted that climate change under these threshold conditions would lead to a variation in the sensitivity of the NDVI to seasonal precipitation and temperature.

Bamboo Forest Mapping in China Using the Dense Landsat 8 Image Archive and Google Earth Engine

It is of great significance to understand the extent and distribution of bamboo for its valuable ecological services and economic benefits. However, it is challenging to map bamboo using remote sensing images over a large area because of the similarity between bamboo and other vegetation types, the availability of clear optical images, huge workload of image processing, and sample collection. In this study, we use the Landsat 8 times series images archive to map bamboo forests in China via the Google Earth engine. Several spectral indices were calculated and used as classification features, including the normalized difference vegetation index (NDVI), the normalized difference moisture index (NDMI) and textural features of the gray-level co-occurrence matrix (GLCM). We found that the bamboo forest covered an area of 709.92 × 104 hectares, with the provinces of Fujian, Jiangxi, and Zhejiang containing the largest area concentrations. The bamboo forest map was accurate and reliable with an average producer’s accuracy of 89.97%, user’s accuracy of 78.45% and kappa coefficient of 0.7789. In addition, bamboo was mainly distributed in forests with an elevation of 300–1200 m above sea level, average annual precipitation of 1200–1500 mm and average day land surface temperature of 19–25 °C. The NDMI is particularly useful in differentiating bamboo from other vegetation because of the clear difference in canopy moisture content, whilst NDVI and elevation are also helpful to improve the bamboo classification accuracy. The bamboo forest map will be helpful for bamboo forest industry planning and could be used for evaluating the ecological service of the bamboo forest.