Changes in Growing Season Vegetation and Their Associated Driving Forces in China during 2001–2012

Liu, Xianfeng; Zhu, Xiufang; Li, Shuangshuang; Liu, Yanxu; Pan, Yaozhong

doi:10.3390/rs71115517

Cited by 58 publications

(52 citation statements)

References 44 publications

(54 reference statements)

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“…The growing season NDVI increased continuously during the period 2001-2012, in China [3]. The data presented in this paper show that within the TRB, values for the NDVI in the whole growing season as well as the spring, summer, and autumn have tended to …”

Section: Discussionmentioning

confidence: 76%

“…Finally, the vegetation growing season (i.e., between April and October) was divided into spring (April and May), summer (between June and August), and autumn (September and October) [29]. Pixels with a mean growing season NDVI less than 0.05 were masked out from this study as they represent non-vegetated areas including deserts and water bodies [3]; these regions are irrelevant to our discussion of vegetation status.…”

Section: Data Sourcesmentioning

confidence: 99%

“…The land cover type is sensitive to environmental changes that result mainly from climatic fluctuations and human activities. As a result, understanding the factors underlying vegetation dynamics has been identified as a key issue in global climate variability [2], particularly in terrestrial ecosystems [3].…”

Section: Introductionmentioning

confidence: 99%

“…However, a limited number of studies to date have been performed to monitor vegetation change and the responses of this land cover type to climate at various spatial scales, including global [5], national [3,4], and provincial [6], as well as on plateaus [7] and within basins [8,9]. All of these previous studies utilized the time series Normalized Difference Vegetation Index (NDVI) obtained from satellite remote sensing (RS); this index is known to be significantly correlated with biomass, photosynthetic capacity, the leaf area index (LAI), and net primary productivity (NPP) [10].…”

Section: Introductionmentioning

confidence: 99%

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The Driving Force Analysis of NDVI Dynamics in the Trans-Boundary Tumen River Basin between 2000 and 2015

et al. 2017

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Vegetation dynamics in relation to climatic changes and anthropogenic activities is critical for terrestrial ecosystem management. The objective of this study was to investigate spatiotemporal change of vegetation and their driving forces during growing seasons (between April and October and including the spring, summer and autumn) in the Tumen River Basin (TRB) using Normalized Difference Vegetation Index (NDVI) and climate data spanning from 2000 to 2015. A linear regression, Pearson correlation coefficients and the residual trend (RESTREND) was applied for this study. Our results demonstrate that vegetation increased during different periods of the growing season in most of the areas of the TRB over 16 years. Our results demonstrate that vegetation increased during different periods of the growing season in most of the areas of the TRB over 16 years; those in growing season (spring, summer, and autumn) were characterized by the increase in rates by 0.0012/year, 0.0022/year, 0.0011/year, and 0.0019/year, respectively. Forested regions are characterized by the largest increase (0.0021/year) in NDVI compared with other vegetation types across the entire study area. The trends in NDVI across the study area were influenced by both climatic variations and human disturbances. The human activities such as reforestation and agricultural practices are the primary driver, greater than climatic factors, during growing season, including summer and autumn. Temperature and precipitation has had a significant influence on NDVI in a limited area (temp = 0.86%, p < 0.05 and precipitation = 1.93%, p < 0.05) during growing season. The significant role of precipitation on NDVI change throughout growing season and the summer is larger than that of temperature across the TRB, although the influence of the latter becomes most significant during the spring and autumn. The RESTREND method shows that human activity during the growing season, including the spring, summer, and autumn, have led to enhancements in NDVI across more than 70% of the TRB over the last 16 years, with the most significant improvements seen in forested land and farmland. At the same time, a significant reduction in residual (i.e., degraded areas) NDVI values for different growing seasons had characterized farmland and urban land at low altitudes. This study provides important background information regarding the influence of human activities on land degradation and provides a scientific foundation for the development of ecological restoration policies within the TRB. We found that the RESTREND method can be used to detect human drivers of vegetation in the regions with semi-humid and humid monsoon, where the significant correlation between NDVI and climatic factors exists.

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Section: Discussionmentioning

confidence: 76%

Section: Data Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Driving Force Analysis of NDVI Dynamics in the Trans-Boundary Tumen River Basin between 2000 and 2015

et al. 2017

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“…(3) We chose NDVI to represent vegetation cover because it is one of the best-known and most effective indices [25], and is widely recognized as a suitable proxy for vegetation cover [40][41][42]. To evaluate the state of vegetation cover and its variations, we used NDVI values at a relatively coarse spatial resolution (1km) from 2000 and 2010 obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor.…”

Section: Datamentioning

confidence: 99%

A GIS-Based Assessment of Vulnerability to Aeolian Desertification in the Source Areas of the Yangtze and Yellow Rivers

Ren

Dong

et al. 2016

Remote Sensing

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Aeolian desertification is a kind of land degradation that is characterized by aeolian activity, resulting from the responses of land ecosystems to climate change and anthropogenic disturbances. The source areas of the Yangtze and Yellow Rivers are typical regions of China's Tibetan Plateau affected by aeolian desertification. We assessed the vulnerability of these areas to aeolian desertification by combining remote sensing with geographical information system technologies. We developed an assessment model with eight indicators, whose weights were determined by the analytical hierarchy process. Employing this model, we analyzed the spatial distribution of vulnerability to aeolian desertification and its changes from 2000 to 2010, and discuss the implications. Overall, low-vulnerability land was the most widespread, accounting for 64%, 62%, and 71% of the total study area in 2000, 2005, and 2010, respectively. The degree of vulnerability showed regional differences. In the source areas of the Yangtze River, land with high or very high vulnerability accounted for 17.4% of this sub-region in 2010, versus 2.6% in the source areas of the Yellow River. In the Zoige Basin, almost all of the land had very low to low vulnerability. To understand the change in vulnerability to aeolian desertification, we calculated an integrated vulnerability index (IVI). This analysis indicated that the vulnerability to aeolian desertification increased from 2000 to 2005 (IVI increased from 2.1709 to 2.2463), and decreased from 2005 to 2010 (IVI decreased from 2.2463 to 2.0057). Increasing regional temperatures appear to be primarily responsible for the change in vulnerability to aeolian desertification throughout the region. The effects of other factors (climatic variation and human activities) differed among the various sub-regions. The implementation of the ecological restoration project has achieved a noticeable effect since 2005. Our results provide empirical support for effort to protect the ecology of this ecologically fragile region.

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Attribution of the vegetation trends in a typical desertified watershed of northeast China over the past three decades

et al. 2016

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Desertification is a serious environmental problem in north China that should be urgently addressed and prevented. Identifying the causes of vegetation trends that are closely related to desertification is prerequisite for a better control of desertification processes. In this study, the Xiliao River basin, a typical desertified watershed in northeast China, was selected as a case study. To partition the vegetation trends into climate-induced and human-induced trends, a linear regression model, piecewise regression model and a binary nonlinear regression analysis were employed, based on the long-term remote measures of the Normalized Difference Vegetation Index (NDVI) and climate data. The results showed that the spatial pattern of the vegetation trends in the basin was highly heterogonous, although the climatic factors varied similarly. In the north, where natural vegetation dominates, the vegetation exhibited significant browning (À0.0015 year À1 ), and a significant changing point was observed in 2000. In the south, where cropland dominates, the vegetation exhibited significant greening (0.002 year À1 ). Approximately 82.7% of the area with a decreasing NDVI was attributed to climate variability. The main reason for the vegetation degradation was because of a multiyear reduction in precipitation from 1999 to 2011 rather than from an increase in the grazing size. Approximately 67.0% of the area with an increasing NDVI could be attributed to irrigation, which overexploited a large amount of water resources, even though significantly increasing air temperatures can promote vegetation greenness. Our findings have important management implications for grazing and agriculture in the context of controlling desertification and sustaining environment.

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Changes in Growing Season Vegetation and Their Associated Driving Forces in China during 2001–2012

Cited by 58 publications

References 44 publications

The Driving Force Analysis of NDVI Dynamics in the Trans-Boundary Tumen River Basin between 2000 and 2015

The Driving Force Analysis of NDVI Dynamics in the Trans-Boundary Tumen River Basin between 2000 and 2015

A GIS-Based Assessment of Vulnerability to Aeolian Desertification in the Source Areas of the Yangtze and Yellow Rivers

Attribution of the vegetation trends in a typical desertified watershed of northeast China over the past three decades

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