Characterizing land condition variability in Northern China from 1982 to 2011

Abstract

For the last three decades, Northern China has been considered as one of the most sensitive areas regarding global environmental change. The integration of AVHRR GIMMS and MODIS NDVI data (1982-2011), of which for the overlapping period of 2000-2006 show good consistency, were used for characterizing land condition variability. The trends of standardized annually RNDVI, temperature, precipitation and PDSI were obtained using a linear regression model. The results showed that Northern China has a general increase in greenness for the period 1982-2011 (a = 0.05). Also, annually RNDVI is significantly correlated with temperature and precipitation data at the regional scale (p \ 0.05), implying that temperature and precipitation are the dominant limiting factors for vegetation growth. Since the greening is not uniform, factors other than temperature and precipitation may contribute to greening in some areas, while the grassland and cropland ecosystem are becoming increasingly vulnerable to drought. The results of trend analysis indicate that greenness seems to be evident in most of the study areas.

Spatio-Temporal Changes in Vegetation Activity and Its Driving Factors during the Growing Season in China from 1982 to 2011

“…In China, the growing season for natural vegetation is mainly between March and November [16,58]. For crops, the growing season is very complicated due to various cropping systems and frequent crop rotations.…”

“…There are several NDVI datasets derived from different satellites, but the dataset derived from National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) has been widely used to monitor vegetation activities on regional [7,[11][12][13]] to global scales [5,6,14]. For example, in China, many studies had been conducted to analyze the interannual trends in vegetation based on the NOAA-AVHRR NDVI [15][16][17][18]. However, these studies were mainly focused on either annual or seasonal NDVI variations.…”

Several challenges in monitoring and assessing desertification

“…For eliminating the effects of rainfall or precipitation on vegetation fluctuation, rain use efficiency (RUE) was used for detecting desertification (Prince et al 1998), and another research work showed that functional modifications associated with vegetation structure caused by desertification can be captured with precipitation use efficiency (PUE) and precipitation marginal response (PMS) (Veron and Jose 2010). All these indicators such as vegetation cover, NDVI, NPP, RUE or PUE, and PMS only can be used as indicating vegetation status, but they do not directly denote whether desertification occurs or not Prince et al 2007;An et al 2014). Seasonal sums of multi-temporal NDVI are strongly correlated with vegetation production (Prince 1991;Nicholson et al 1998), and it also be used to analyze degraded land (Wessels et al 2004).…”

Global‐scale mapping of changes in ecosystem functioning from earth observation‐based trends in total and recurrent vegetation

“…The normalized difference vegetation index (NDVI) (Tucker, ) has been found to be related to vegetation greenness or vigour (Myneni et al ., ) and is widely used as a proxy for the distribution of net primary production (NPP) due to the availability of data covering more than three decades from the early 1980s until the present. Analysis of trends in vegetation productivity and their drivers at global and regional scales is done in different ways depending on the biome studied: EO‐based vegetation sums/averages over the full year (Fensholt et al ., ; van Leeuwen et al ., ; An et al ., ) or seasonal integrals (NDVI SIN ) based on specific months covering the growing season (Piao et al ., ; Fensholt et al ., ) are widely used. However, the choice of vegetation integration method and period (annual or seasonal) has implications for the analysis of long‐term NDVI trends.…”