Relation between interannual variations in satellite measures of northern forest greenness and climate between 1982 and 1999

Abstract

[1] This paper analyzes the relation between satellite-based measures of vegetation greenness and climate by land cover type at a regional scale (2°Â 2°grid boxes) between 1982 and 1999. We use the normalized difference vegetation index (NDVI) from the Global Inventory Monitoring and Modeling Studies (GIMMS) data set to quantify climate-induced changes in terrestrial vegetation. Climatic conditions are represented with monthly data for land surface air temperature and precipitation. The relation between NDVI and the climate variables is represented using a quadratic specification, which is consistent with the notion of a physiological optimum. The effects of spatial heterogeneity and unobserved variables are estimated with specifications and statistical techniques that allow coefficients to vary among grid boxes. Using this methodology, we are able to estimate statistically meaningful relations between NDVI and climate during spring, summer, and autumn for forests between 40°N and 70°N in North America and Eurasia. Of the variables examined, changes in temperature account for the largest fraction of the change in NDVI between the early 1980s and the late 1990s. Changes in stratospheric aerosol optical depth and precipitation have a smaller effect, while artifacts associated with variations in solar zenith angle are negligible. These results indicate that temperature changes between the early 1980s and the late 1990s are responsible for much of the observed increase in satellite measures of northern forest greenness.

Driving force and changing trends of vegetation phenology in the Loess Plateau of China from 2000 to 2010

“…Its spatial resolution is 250 m and the temporal resolution is 10 days. To reduce the effect of bare soils and sparsely vegetated grids on the NDVI trends, grid cells with an annual mean NDVI smaller than 0.1 during the 11 years were excluded from the analysis, as in Zhou et al (2001Zhou et al ( , 2003. Many studies have validated these kinds of data for vegetation growing conditions, biomass estimation, environment monitoring, and global change (Li et al 2011;Jeganathan et al 2014;de Jong et al 2013;Chen et al 2006;Fang et al 2007;Xiao et al 2002).…”

Detection and attribution of anthropogenic forcing to diurnal temperature range changes from 1950 to 1999: comparing multi-model simulations with observations

“…The linear trends are listed and those marked with ''*'' are statistically significant (p \ 0.05). A 5-point (i.e., 5-year) running averaging was applied for visualization purpose only, with the first and last 2 year values applied using a recycling boundary condition (e.g., Zhou et al 2001Zhou et al , 2003Zhou et al , 2007Zhou et al , 2008. Furthermore, given the non-stationary properties of the data involved and the pitfalls associated with use of standard statistical techniques, applications of other advanced statistical techniques are not warranted.…”

Effects of precipitation, temperature and topographic parameters on evergreen vegetation greenery in the Western Ghats, India

“…These studies and several others, conclude that there is a strong relationship between climate variability and fluctuations in satellite-derived vegetation indices at local, regional and continental scales Tateishi and Kajiwara, 1992;Myneni et al, 1997;Paruelo and Lauenroth, 1998;Schwartz and Reed, 1999;Ichii et al, 2002;Nemani et al, 2003;Jolly and Running, 2004;Tateishi and Ebata, 2004;Zhou et al, 2003;Karlsen et al, 2006;Suzuki et al, 2006).…”