Abstract
Grassland degradation is one of the most pressing challenges in natural environment and anthropogenic society. However, there is yet no effective approach for monitoring the spatio-temporal pattern of large-scale grassland degradation. In particular, the research on grassland changes in the harsh natural environment such as the Qinghai-Tibet Plateau is still in its infancy due to complexity, and it is extremely difficult for humans to reach these remote areas. The annual changes in the grassland biomass might be the results of climate fluctuations or grassland degradation. To test the hypothesis, the impact of inter-annual climate fluctuations needs to be considered when monitoring the grassland degradation based on spatio-temporal change of grassland biomass. In this paper, we propose a Novel Climate Use Efficiency index (NCUE) by considering rainfall, temperature, sunlight time, wind speed, surface temperature, accumulated temperature, time lag effect, light, temperature and water suitability and their coordination climatic factors that mainly affect vegetation growth comprehensively, to monitor grassland change suitable for cold and dry climate characteristics of the Qinghai-Tibet Plateau, and to reduce the effect of inter-annual variability of grassland productivity caused by climate fluctuation. As a consequence, grassland degradation monitoring could be more accurate and objective than existing ecological indicators. Our experiments show that the slope of NCUE over 31 years from 1982 to 2012 is 0.0028, showing a recovery trend in grassland. Degradation and restoration of grassland exist at the same time, and their proportions are 20.49% and 23.89%, respectively. By comparing with in-situ measurements in 2013 and 2009, 68% consistency was achieved with our prediction, and the 70% consistency is achieved by comparing with the positive and negative change trend of accumulated NDVI during the growing season. Moreover, the comparative analysis of land use/cover changes (LUCC) from 1990 to 2010 shows 69% of consistency. The ratio of the area of grassland significantly degradation and recovered predicted by NCUE change trend is 1.41% and 1.43%, respectively. It occupies a very small area of the study area. Yet, that predicted by NDVI change trend is 42.17% and 31.90%, respectively, and about 70% of the area is detected as drastic changes. It shows that NDVI is sensitive to climate fluctuations, while NCUE reduces the impact of climate fluctuations, reflecting change of grassland being affected by human activities and long-term climate change. The novel NCUE has great potential and utility to minify the impact of climate fluctuation and reflect grassland changes over space and time quantitatively. Such ecological index provides a new understanding of spatial and temporal patterns of grassland degradation in the Three River Headwaters Region (TRHR) at the same time.
Highlights
Grasslands are one of the most important parts of natural ecosystems.Grassland degradation refers to significant changes in the composition, structure, and function of grassland ecosystems influenced by human activities or climate-related natural factors
The main scientific contribution of this paper are as follows: (1) Propose a Novel Rainfall Use Efficiency index (NCUE) for monitoring grassland dynamic which is suitable for cold and arid climate characteristics of the Qinghai-Tibet Plateau to reduce the effect of inter-annual variability of grassland productivity caused by climate fluctuation; (2) Construct an Integrated Meteorological Factor (IMF) based on the analysis of domi nate climatic elements affecting vegetation growth; (3) Reveal the spatial temporal characteristics of grassland change from 1982 to 2012 in the Three River Headwaters Region (TRHR) by trend analysis of Novel Climate Use Efficiency index (NCUE) time series
The percentage of significantly increased NDVI is 42.17%, which cover the largest area of the TRHR, while the percentage of insignificantly increased is 13.52%; the percentage of significantly decreased NDVI from 1982 to 2012 is 31.90%, cover the secondary largest area of the TRHR, and the per centage of insignificantly decreased NDVI is 12.41%
Summary
Grasslands are one of the most important parts of natural ecosystems.Grassland degradation refers to significant changes in the composition, structure, and function of grassland ecosystems influenced by human activities or climate-related natural factors. With the increasing use of remotely sensed imagery and products such as AVHRR, MODIS and SPOT VEGETATION (de Jong et al, 2011; Li et al, 2020b), more attention has been paid to characterize the changes of grassland productivity through trend analysis of long-term sequence vegetation indices (such as the Normalized Difference Vegetation Index (NDVI) as a proxy for the net primary productivity (NPP) or above-ground biomass) (Bai et al, 2008; Fensholt et al, 2012; Shen et al, 2018) These methods do not take into account reducing the effects of climate fluc tuations on grassland productivity over the years and may not be able to reflect the real grassland condition. There are other methods developed for monitoring grassland degradation by considering climate impacts include LNS (Local NPP Scaling) (An et al, 2017; Prince, 2012; Prince et al, 2009; Wessels et al, 2008), and timeseries analysis using nonlinear seasonal-trend analysis (Prince, 2012; Eckert et al, 2015; Shen et al, 2018), the residual trend analysis method derived from the RUE (Burrell et al, 2017; Cai et al, 2015; Evans and Geerken, 2004; Leroux et al, 2017; Li et al, 2012; Wessels et al, 2007; Xu et al, 2010) and the simulation of the potential NPP through global vegetation physiological and biochemical models such as the LundPotsdam-Jena dynamic vegetation model(LPJ) (Seaquist et al, 2008; Zika and Erb, 2009)
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