The Relationship of Vegetation Greenness Period and Climate Precipitation Change in the North-South Transect of Eastern China

Zhi, Wang; Liu, Shirong; Zhou, Ling; Guo, Zhihua; Park, Sun; Li, Hong

doi:10.1016/j.proenv.2011.09.046

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…However, in most Chinese agricultural zones, greenness changes were predominantly affected by temperature as opposed to cropland percentage and cropland changes. Previous studies also suggested that in most parts of China, vegetation greenness is more sensitive to temperature rather than precipitation [77,78], which was consistent with our findings. Hua et al [24] found that in southern China, the relationship between NDVI and temperature was positive, suggesting that warmer climate could enhance vegetation activity.…”

Section: Implications Of the Relationship Between Vegetation Greenness And Cropland Changes As Well As Climatesupporting

confidence: 93%

Observed Vegetation Greening and Its Relationships with Cropland Changes and Climate in China

Zhang

Liang

Xiao

2020

Land

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Chinese croplands have changed considerably over the past decades, but their impacts on the environment remain underexplored. Meanwhile, understanding the contributions of human activities to vegetation greenness has been attracting more attention but still needs to be improved. To address both issues, this study explored vegetation greening and its relationships with Chinese cropland changes and climate. Greenness trends were first identified from the normalized difference vegetation index and leaf area index from 1982–2015 using three trend detection algorithms. Boosted regression trees were then performed to explore underlying relationships between vegetation greening and cropland and climate predictors. The results showed the widespread greening in Chinese croplands but large discrepancies in greenness trends characterized by different metrics. Annual greenness trends in most Chinese croplands were more likely nonlinearly associated with climate compared with cropland changes, while cropland percentage only predominantly contributed to vegetation greening in the Sichuan Basin and its surrounding regions with leaf area index data and, in the Northeast China Plain, with vegetation index data. Results highlight both the differences in vegetation greenness using different indicators and further impacts on the nonlinear relationships with cropland and climate, which have been largely ignored in previous studies.

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Section: Implications Of the Relationship Between Vegetation Greenness And Cropland Changes As Well As Climatesupporting

confidence: 93%

Observed Vegetation Greening and Its Relationships with Cropland Changes and Climate in China

Zhang

Liang

Xiao

2020

Land

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“…This might differ from other areas where grassland vegetation is very sensitive to precipitation. 33,47 The canonical analysis between climate factors and vegetation change does not show significant correlation at 95% confidence level (not shown). Table 6 shows some significant autocorrelations among temperature (W 1 , W 2 , W 3 , and W 4 ), precipitation (W 5 , W 6 , and W 7 ), evaporation (W 8 ), and SCI (W 9 ), particularly, negative correlations between temperature and W 9 and between W 8 and W 9 , and positive correlations between precipitation and W 9 and between temperature and W 8 .…”

Section: Between Climate Change and Grassland Vegetationmentioning

confidence: 94%

Interrelation among climate factors, snow cover, grassland vegetation, and lake in the Nam Co basin of the Tibetan Plateau

Chen

Liang

Xie

et al. 2014

J. Appl. Remote Sens

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Taking Nam Co basin as an example, we examine the interrelationship among vegetation growth, lake expansion, snow cover, and climate change, based on meteorological data and multisource remote sensing datasets. Results show that the climate has become warmer and wetter during the period of time from 1961 to 2010, with rates of þ0.04°C∕year (P < 0.001) for annual mean temperature and þ1.66 mm∕year (P ¼ 0.007) for annual precipitation, while the snow-covered index experienced a decreasing trend (−31.94 km 2 · day∕year, P ¼ 0.129) from 2003 to 2010. In response, the vegetation growth was deteriorative in most parts of the basin. Conversely, both the lake's area and water level increased (þ2.15 km 2 ∕year and þ0.12 m∕year, respectively). Although the enhanced vegetation index in the basin negatively correlates well with the lake's area (R 2 ¼ 0.75, P ¼ 0.001), the correlation shows gradual decrease as distance away from the lake's shoreline, from 25 km (zone A), to 25-50 km (zone B), and to 50-95 km (zone C). Two main factors might have contributed to this: (1) lake expansion buried grassland vegetation in zone A and (2) more gravel buildup and soil erosion due to runoff from snow melted water in zone A than in zones B and C. This study provides a scientific basis for the evaluation of changes in alpine grassland, lake, snow cover, and their responses to climate change.

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“…The Normalized Difference Vegetation Index (NDVI) is closely related to vegetation coverage, biomass, and productivity and has been widely used as an indicator of ecosystem quality in recent years. [41,42]. Therefore, we used the vegetation coverage coefficient as an indicator to modify the ESV at the grid scale according to the correspondence between vegetation coverage and NDVI.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Spatial Association between Land Use Change and Ecosystem Services Value: A Case Study in Xi’an, China

Shao

Yuan

et al. 2020

Sustainability

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The impact of land use and land cover (LULC) change on ecosystem services value (ESV) varies in different spatial locations. Although many studies have focused on quantifying the effect of LULC change on ESV, few have considered the spatial heterogeneity of the relationship between LULC change and ESV. Therefore, this study examines the relationship between ESV and LULC change from a spatial perspective in Xi’an City. We divide the study area into 10,522 grid cells, based on land cover data from 2000 to 2018, and we identify the spatial-temporal dynamics of LULC change. Next, we employ the Benefits Transfer Method (BTM) to evaluate the ESV, and the ESV is corrected by the normalized difference vegetation index (NDVI). A geographically weighted regression (GWR) model and ordinary least squares (OLS) regression model are used to assess the spatial association of LULC change and ESV. The results show that the total ESV loss is 6.57 billion yuan (Chinese yuan), and the loss rate is 12.18%. The distribution of ESV shows an obvious spatial heterogeneity, and the low-value area of ESV expands eastward from the main urban area. More than 50% of total ESV is provided by woodland. From 2000 to 2018, the land use pattern in Xi’an underwent a significant change with the developed land increasing by 64.09%, whereas farmland decreased by 12.49%. Based on the GWR model, the relationship between LULC change and ESV in Xi’an showed a significant negative association and spatial heterogeneity. Our study results provide a new way to effectively identify the relationship between LULC change and ESV, and in turn, to fully understand the ecological trends at the regional scale, laying a foundation for regional sustainable development.

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The Relationship of Vegetation Greenness Period and Climate Precipitation Change in the North-South Transect of Eastern China

Cited by 6 publications

References 7 publications

Observed Vegetation Greening and Its Relationships with Cropland Changes and Climate in China

Observed Vegetation Greening and Its Relationships with Cropland Changes and Climate in China

Interrelation among climate factors, snow cover, grassland vegetation, and lake in the Nam Co basin of the Tibetan Plateau

Quantifying the Spatial Association between Land Use Change and Ecosystem Services Value: A Case Study in Xi’an, China

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