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

Li, Yanan; Wu, Dan; Yang, Liangyan; Zhou, Tiancai

doi:10.3390/app11188766

Cited by 8 publications

(8 citation statements)

References 42 publications

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“…Similarly, this phenomenon has been mentioned in previous studies, such as Hsu et al [1] who found that changes in the inter-annual variability of precipitation had negligible effects on the mean ANPP. On the contrary, Li et al [55] highlighted that the NDVI is adaptable to the significant increase in temperature but is sensitive to the decrease in precipitation on the Inner Mongolia Plateau, possibly due to regional differences. In addition, VST and VSP in the different seasons showed significant dynamic change at the scale of space-time (Figures 3 and 4).…”

Section: Discussionmentioning

confidence: 97%

“…In fact, R 2 is often used to study the influence of climate on vegetation dynamics. For example, it has been used to analyze the effects between the NDVI and climate, and reflect the effect of the climate on net primary production [52,55]. In addition, compared with the previous calculation method of VSP and VST by least squares [1,38], the spatial heterogeneity of VSP and VST was reduced in the updated method because it fragmented the space by dividing the Tibetan Plateau into many small plots, on average, to discuss the sensitivity of vegetation to precipitation and temperature.…”

Section: Discussionmentioning

confidence: 99%

“…We used it to test the direct and indirect effects on the variations in VSP and VST and to describe the hypothetical causal relationships [54]. By selecting the appropriate variables and models based on certain statistical criteria [55], the standard estimate results express the influence on the seasonal VSP (and VST) using a path coefficient generated by the AMOS statistical tool (17.0.2, Amos Development Corporation, Crawfordville, FL, USA).…”

Section: Sensitivity Analysismentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%

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

et al. 2022

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“…The maximum NDVI performed better than other NDVI metrics, indicating that the differentiation between bamboo and other vegetation is more obvious during the growing seasons. It may be related to the fact that NDVI is sensitive to changes in precipitation and temperature [79,80]. Additionally, elevation was ranked fourth in the importance score [25].…”

Section: Feature Importancementioning

confidence: 99%

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

Song

Liu

et al. 2022

Remote Sensing

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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.

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“…Climate change and its impact on terrestrial ecosystems have attracted widespread attention (Bao et al, 2014). Globally, CO 2 fertilization is the main driver of the vegetative greenness, with other factors including anthropogenic warming, precipitation, and solar radiation that are dominant drivers of the vegetation growth at regional scales (Li et al, 2021;Bai et al, 2022). In high latitudes and high altitudes where temperature is usually a limiting factor, global warming has a positive impact on vegetation growth, especially for ecosystems at high latitudes, and increasing air temperature promotes photosynthesis of vegetation (Wu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Increasing precipitation promoted vegetation growth in the Mongolian Plateau during 2001–2018

et al. 2023

Front. Environ. Sci.

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Taking the Mongolian Plateau as the research area, this paper studied the vegetation growth from 2001 to 2018. We quantified the vegetation growth changes based on changes in gross primary productivity (GPP) and leaf area index (LAI) and their relationships to climate variables using correlation analysis, partial correlation analysis and multiple correlation analysis. The results showed that from 2001 to 2018 both GPP and LAI showed an increasing trend, with great heterogeneities among different areas and land cover types. The largest increase of GPP and LAI occurred in the northeast plateau with the land cover types of forest and cropland. The main driving factor of vegetation growth was precipitation, while temperature was significantly negatively correlated with vegetation growth. The CO2 concentration had a significant impact on the GPP in farmland, and the increase of solar radiation had a significant impact on tundra. Our study highlights the importance of precipitation in regulating vegetation growth in the Mongolian Plateau, challenging the prevailing views that the temperature dominates the vegetation growth in the northern ecosystems.

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

Cited by 8 publications

References 42 publications

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

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

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

Increasing precipitation promoted vegetation growth in the Mongolian Plateau during 2001–2018

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