Abstract
A 30-year series of global monthly Normalized Difference Vegetation Index (NDVI) imagery derived from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g archive was analyzed for the presence of trends in changing seasonality. Using the Seasonal Trend Analysis (STA) procedure, over half (56.30%) of land surfaces were found to exhibit significant trends. Almost half (46.10%) of the significant trends belonged to three classes of seasonal trends (or changes). Class 1 consisted of areas that experienced a uniform increase in NDVI throughout the year, and was primarily associated with forested areas, particularly broadleaf forests. Class 2 consisted of areas experiencing an increase in the amplitude of the annual seasonal signal whereby increases in NDVI in the green season were balanced by decreases in the brown season. These areas were found primarily in grassland and shrubland regions. Class 3 was found primarily in the Taiga and Tundra biomes and exhibited increases in the annual summer peak in NDVI. While no single attribution of cause could be determined for each of these classes, it was evident that they are primarily found in natural areas (as opposed to anthropogenic land cover conversions) and that they are consistent with climate-related ameliorations of growing conditions during the study period.
Highlights
In recent decades considerable interest has been focused on trends in vegetation phenology
In this paper we examine trends in the seasonality of Normalized Difference Vegetation Index (NDVI) using this archive and a recently-introduced analytical procedure known as Seasonal Trend Analysis (STA) [18]
Significant trends are primarily associated with three major classes of seasonal trends
Summary
In recent decades considerable interest has been focused on trends in vegetation phenology. The seasonal character of vegetation index measurements over time may exhibit trends that represent changes in land cover and viewing conditions (such as changes in the presence of water vapor and aerosols) as well as true phenological responses. NDVI has been shown to be very sensitive to ecosystem conditions [14,15,16], when paired with maximum value compositing to reduce the effects of atmospheric contaminants, such as clouds and aerosols [17]. It is of considerable value as an indicator of environmental change
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