Spatial and temporal variations of vegetation cover and the relationships with climate factors in Inner Mongolia based on GIMMS NDVI3g data

Tong, Siqin; Zhang, Jiquan; Bao, Yuhai; Wurina,; Te, Rigele; Weilisi,; Lianxiao,

doi:10.1007/s40333-017-0016-4

Cited by 46 publications

(24 citation statements)

References 23 publications

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“…The slowest increase is in SWC but also very impressive, which also had an increasing rate of 0.0029/a ( 2 = 0.66, < 0.001). The areas are the extremely arid deserts and Gobi but afforestation was also mainly implemented in the area to improve the vegetation during the past decades, which is confirmed by the previous study [43].…”

Section: Resultssupporting

confidence: 81%

Vegetation Dynamics and Their Response to Climatic Variability in China

Zhang

Cui

Shi

et al. 2017

Advances in Meteorology

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Based on SPOT VEGETATION data and meteorological data, NDVI (Normalized Difference Vegetation Index) and its response to temperature and precipitation in China and its different regions were investigated over the period 1998-2013 by using the maximum value composite and linear regression methods. The results showed that NDVI presented significant increase (0.0046/a) for all of China and all the regions over the last 16 years. Meanwhile, annual mean temperature of China presented a slightly increasing trend, while the annual precipitation showed a slightly decreasing trend over the last 16 years. Nevertheless, there were differences between temperature and precipitation in the subregions of China. The Annual NDVI had better relationships with precipitation ( = 0.126) compared to temperature ( = −0.094), and NDVI also had a good correlation with precipitation rather than temperature in different subregions of China. Additionally, human activities also made a difference to the trends of NDVI in some regions. This study is conductive to the effects of climate change on vegetation activity in future research.

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Section: Resultssupporting

confidence: 81%

Vegetation Dynamics and Their Response to Climatic Variability in China

Zhang

Cui

Shi

et al. 2017

Advances in Meteorology

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“…In this context, Tong et al (2017) report similar results from the eastern part of Inner Mongolia, where NDVI-values increased from 1984 to 2013. The correlation between NDVI and precipitation and temperature, however, cannot be confirmed for the study area, which is most-likely connected to seasonal variation in precipitation totals in eastern Inner Mongolia and the annual cycle (Liu et al, 2019;Tong et al, 2017;Zhang et al, 2019).…”

Section: Environmental Transformation and Land Degradationsupporting

confidence: 74%

“…Although Ren et al (2012) highlighted the variability of rainfall and temperature as the most important driving factor of vegetation dynamics in Inner Mongolia, the results from the NDVI long-term series cannot confirm a direct relationship between climatic variability and NDVI between March and May. Maximum temperature in the study area increased strongly during the past 20 years (Tong et al, 2017) and no particular trend but multiannual variation can be observed in the precipitation totals (Zhang et al, 2019). No significant relationship between NDVI and maximum temperature (Lauenroth and Sala, 1992) and NDVI and precipitation development can be detected from Pearson's correlation test (Lu et al, 2019).…”

Section: Environmental Transformation and Land Degradationmentioning

confidence: 68%

Monitoring landcover change and desertification processes in northern China and Mongolia using historical written sources and vegetation indices

Kempf

2021

Preprint

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Abstract. Fighting land degradation of semi-arid and climate-sensitive grasslands are among the most urgent tasks of current eco-political agenda. Northern China and Mongolia are particularly prone to surface transformations caused by heavily increased livestock numbers during the 20th century. Extensive overgrazing and resource exploitation amplify regional climate change effects and trigger intensified surface transformation, which forces policy-driven interventions to prevent desertification. In the past, the region has been subject to major shifts in environmental and socio-cultural parameters, what makes it difficult to measure the extent of the regional anthropogenic impact and global climate change. This article analyses historical written sources, palaeoenvironmental data, and Normalized Difference Vegetation Index (NDVI) temporal series from the Moderate Resolution Imaging Spectroradiometer (MODIS) to compare landcover change during the Little Ice Age (LIA) and the reference period 2000–2018. Results show that decreasing precipitation and temperature records led to increased land degradation during the late 17th century. However, modern landcover data shows enhanced expansion of bare lands contrasting an increase in precipitation (Ptotal) and maximum temperature (Tmax). Vegetation response during the early growing season (March–May) and the late grazing season (September) does not relate to Ptotal and Tmax and generally low NDVI values indicate no major grassland recovery over the past 20 years.

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“…To further decrease the effects of atmospheric and aerosol scattering, we developed a monthly NDVI dataset using the maximum value composite (MVC) method for each month, as in previous studies [23]. We produced the growing season NDVI dataset by averaging the monthly NDVI during April-October for each year, as several studies suggest vegetation growth begins in April and senescence in October across Inner Mongolia [24,25].…”

Section: Datasetmentioning

confidence: 99%

Revealing the Fingerprint of Climate Change in Interannual NDVI Variability among Biomes in Inner Mongolia, China

et al. 2020

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An understanding of the response of interannual vegetation variations to climate change is critical for the future projection of ecosystem processes and developing effective coping strategies. In this study, the spatial pattern of interannual variability in the growing season normalized difference vegetation index (NDVI) for different biomes and its relationships with climate variables were investigated in Inner Mongolia during 1982-2015 by jointly using linear regression, geographical detector, and geographically weighted regression methodologies. The result showed that the greatest variability of the growing season NDVI occurred in typical steppe and desert steppe, with forest and desert most stable. The interannual variability of NDVI differed monthly among biomes, showing a time gradient of the largest variation from northeast to southwest. NDVI interannual variability was significantly related to that of the corresponding temperature and precipitation for each biome, characterized by an obvious spatial heterogeneity and time lag effect marked in the later period of the growing season. Additionally, the large slope of NDVI variation to temperature for desert implied that desert tended to amplify temperature variations, whereas other biomes displayed a capacity to buffer climate fluctuations. These findings highlight the relationships between vegetation variability and climate variability, which could be used to support the adaptive management of vegetation resources in the context of climate change.

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Spatial and temporal variations of vegetation cover and the relationships with climate factors in Inner Mongolia based on GIMMS NDVI3g data

Cited by 46 publications

References 23 publications

Vegetation Dynamics and Their Response to Climatic Variability in China

Vegetation Dynamics and Their Response to Climatic Variability in China

Monitoring landcover change and desertification processes in northern China and Mongolia using historical written sources and vegetation indices

Revealing the Fingerprint of Climate Change in Interannual NDVI Variability among Biomes in Inner Mongolia, China

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