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

Abstract

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.

Grassland Conservation Effectiveness of National Nature Reserves in Northern China

“…In addition, our results also showed that the greening pixels were located mainly in the northeast and southwest, whereas central and eastern Inner Mongolia were dominated by browning pixels. Both the CV NDVI results and NDVI slope results were consistent with previous studies derived from SPOT, MODIS, and GIMMS NDVI [44,45,66]. Furthermore, the rank of CV NDVI among different NNRs in descending order was FEP, WAP, IWP, GMP, DEP, WVP, and PRP (see Table 6).…”

“…In this study, the MOD13Q1 NDVI was obtained from the Google Earth Engine for the period from 2000 to 2020 in the growing season ranging from April to October. April to October was considered the growing season for the IMAR, as previous research has noted that the vegetation in the IMAR reinvigorates in April and languishes in October [43,44]. In order to assure the reliability of the pixels, we have used the summary QA band to filter the growing season images, leaving only the remote sensing images with confidence.…”

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

“…In addition, as Inner Mongolia is a major livestock province in China, the impact of grazing intensity on vegetation change should not be neglected. We chose the number of livestock as a representative driver to examine its influence [55].…”

Influence of Variations of Hydrothermal Conditions on Normalized Difference Vegetation Index in Typical Temperature Zones Along the East Coast of China

“…Compared with the correlation study of NDVI with mean temperature and precipitation on the annual scale, the response of the NDVI to the temperature and precipitation on the monthly scale could reveal the influence of hydrothermal changes on the NDVI more deeply (Pei et al, 2019;Guo et al, 2020). On the one hand, the annual-scale dependency between the NDVI and climate factors reflects the long-term change trend relationship between the two, which failed to "peel off" other factors, such as urbanization, land utilization/change, and solar radiation in the course of the year, which affect vegetation.…”