A Comparative Analysis between GIMSS NDVIg and NDVI3g for Monitoring Vegetation Activity Change in the Northern Hemisphere during 1982–2008

Abstract

Abstract:The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group was released recently. The comparisons between the new and old versions should be conducted for linking existing studies with future applications of NDVI3g in monitoring vegetation activity change. Based on simple and piecewise linear regression methods, this study made a comparative analysis between NDVIg and NDVI3g for monitoring vegetation activity change and its responses to climate change in the middle and high latitudes of the Northern Hemisphere during 1982-2008. Our results indicated that there were large differences between NDVIg and NDVI3g in the spatial patterns for both the overall changing trends and the timing of Turning Points (TP) in NDVI time series, which spread over almost the entire study region. The average NDVI trend from NDVI3g was almost twice as great as that from NDVIg and the detected average timing of TP from NDVI3g was about one year later. Although the general spatial patterns were consistent between two data sets for detecting the responses of growing-season NDVI to temperature and precipitation changes, there were large differences in the response magnitude, with a OPEN ACCESSRemote Sens. 2013, 5 4032 higher response magnitude to temperature in NDVI3g and an opposite response to precipitation change for the two data sets. These results demonstrated that the NDVIg data set may underestimate the vegetation activity change trend and its response to climate change in the middle and high latitudes of the Northern Hemisphere during the past three decades.

Start of vegetation growing season on the Tibetan Plateau inferred from multiple methods based on GIMMS and SPOT NDVI data

“…Besides, we have also discussed the improvements necessary of the methods. However, previous studies demonstrated that the different sensor products can also lead to discrepancy in the SGS except for methods, because there exists inconsistency among them (Hogda et al, 2013;Jiang et al, 2013;Zhang et al, 2013), which can be seen from Figures 2, 3 and 6 in the study as well. Though we have analyzed the correlation between the SGS derived from the GIMMS and SPOT, the same study period is relatively short, which limited our understanding the discrepancy between the two types of remote sensing data.…”

Future Climate Impact on the Desertification in the Dry Land Asia Using AVHRR GIMMS NDVI3g Data

“…It is a proxy for vegetation productivity of the terrestrial ecosystem [15,16]. The GIMMS (Global Inventory Modeling and Mapping Studies) 15-day composite NDVI3g dataset applied here has been shown to be more accurate than the GIMMS NDVI for monitoring vegetation activity and phonological change [17][18][19]. Its spatial resolution is 8 km.…”

NDVI-Based Analysis on the Influence of Climate Change and Human Activities on Vegetation Restoration in the Shaanxi-Gansu-Ningxia Region, Central China

“…For example, although warming in the Northern Hemisphere is projected to increase in the future, the rate of global warming has slowed down over the past 15 years [23][24][25]. However, the response of vegetation to these trends remains uncertain, as the relationship between temperature and vegetation may change over time [26][27][28]. Additionally, in the specific context of the Loess Plateau, which is ecologically fragile and rich in energy resources, the local impacts of trends in the county's economic development may change [29,30].…”