Vegetation Phenology in Global Change Studies
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Changes in the character of the vegetated land surface are frequently expressed in terms of temporal trends in the Normalized Difference Vegetation Index (NDVI) retrieved from spaceborne sensors. In the past these change studies were typically based upon AVHRR data. By the end of 2011, we acquired 11 full years of NASA MODIS data which is a greatly improved dataset compared to extant AVHRR datasets. In this chapter, we present a change analysis based on a global NASA MODIS product (MCD43C4) at a 0.05° (~5.6 km) spatial resolution and a 16-day temporal resolution from 2001 through 2011. This new change map based on 11 years of data presents statistically significant positive and negative changes resulting from both direct and indirect impacts of climatic variability and change, disturbances, and human activity. We found significant negative changes in 8.7 % of the global land area (or 11.8 × 106 km2), with hotspots in Canada, southeastern USA, Kazakhstan, and Argentina. Significant positive changes appeared in 6.0 % of the global land area (8.0 × 106 km2) with hotspots in Turkey, China and Western Africa. Attribution is the key challenge in any change analysis. We provide several examples attributable to major modes of change, focusing both on natural disturbances arising from climatic variability and change, and also on changes arising directly from human actions.
KeywordsNormalize Difference Vegetation Index Land Surface Temperature Advance Very High Resolution Radiometer Advance Very High Resolution Radiometer Bidirectional Reflectance Distribution Function
This research was supported in part by the NEESPI and NASA LCLUC projects entitled Evaluating the effects of institutional changes on regional hydrometeorology: Assessing the vulnerability of the Eurasian semi-arid grain belt (NNG06GC22G) to GMH and Land abandonment in Russia: Understanding recent trends and assessing future vulnerability and adaptation to changing climate and population dynamics (NNX09AI29G) to KMdB. We would like to thank P. de Beurs for the application development that allowed us to estimate the trend statistics efficiently.
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