Abstract
By using the Global Inventory Modeling and Mapping Studies (GIMMS) third-generation normalized difference vegetation index (NDVI3g) data, this paper explores the spatiotemporal variations in vegetation and their relationship with temperature and precipitation between 1982 and 2015 in the Inner Mongolia region of China. Based on yearly scale data, the vegetation changes in Inner Mongolia have experienced three stages from 1982 to 2015: the vegetation activity kept a continuous improvement from 1982–1999, then downward between 1999–2009, and upward from 2009 to 2015. On the whole, the general trend is increasing. Several areas even witnessed significant vegetation increases: in the east and south of Tongliao and Chifeng, north of Xing’anmeng, north and west of Hulunbir, and in the west of Inner Mongolia. Based on monthly scale data, one-year and half-year cycles exist in normalized difference vegetation index (NDVI) and temperature but only a one-year cycle in precipitation. Finally, based on the one-year cycle, the relationship between NDVI and climatic were studied; NDVI has a significant positive correlation with temperature and precipitation, and temperature has a greater effect in promoting vegetation growth than precipitation. Moreover, based on a half-year changing period, NDVI is only affected by temperature in the study region. Those findings can serve as a critical reference for grassland managers or policy makers to make informed decisions on grassland management.
Highlights
Vegetation is one of the most critical elements of terrestrial ecosystems and plays an important role in material cycling and energy flow [1,2,3,4]
Vegetation changes are constantly affected by differences in factors such as precipitation, temperature, and human activities [6,7]
Some prior research results confirm that changes in normalized difference vegetation index (NDVI) time series could indicate variations in vegetation conditions proportionally to the absorption of radiation used for photosynthesis [12]
Summary
Vegetation is one of the most critical elements of terrestrial ecosystems and plays an important role in material cycling and energy flow [1,2,3,4]. Long-time series data of vegetation cover are often used to detect the dynamic vegetation changes and extract change characteristics and patterns [8,9,10] Vegetation indices, such as the normalized difference vegetation index (NDVI), are important characteristic parameters for revealing surface vegetation features [11]. The NDVI is very sensitive to the physical characteristics of vegetation: sensor observation angles, solar radiation, and different soil backgrounds [13,14]. Several vegetation properties, such as the length of the growing season, onset date of greenness, and date of maximum photosynthetic activity, can be derived from the NDVI time series to monitor vegetation changes [15,16,17,18]
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