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A statistical framework for the analysis of long image time series: The effect of anthropogenic change on land surface phenology
Significant global changes affect the carbon and water cycles as well as the biodiversity on earth. Mapping and monitoring these changes can aid in the understanding and distinction between anthropogenic and biophysical impacts on the land surface. In the context of scientific and social debate on the pace and extent of global climate change, it is extremely important to have methods that are capable of distinguishing between expected variability and significant change. In this dissertation I have presented a statistical framework for the analysis of long image time series that consists of robust techniques for step change analysis, temporal trend analysis, and the modeling of land surface phenology (LSP) and analysis of LSP change. This framework helps to fill a gap in the remote sensing literature on appropriate approaches to quantitative change analysis. ^ I have described two main application areas for the statistical framework: (1) Quality analysis of NOAA AVHRR NDVI datasets. The analysis of more than 2 million km2 of desert and semi-desert ecoregions in Central Asia revealed significant sensor artifacts in the Pathfinder AVHRR Land (PAL) NDVI dataset. I have found that the comparison of data from any combination of NOAA-7, NOAA-9 and NOAA-14 can be used for land surface change analyses, but that the inclusion of NOAH-11 AVHRR NDVI data in trend analyses may result in the detection of spurious trends. Furthermore, I have shown that two versions of NOAA AVHRR NDVI datasets with similar characteristics can yield very different conclusions on land surface change. (2) Using the PAL NDVI data, I applied the framework to address the question of whether the institutional changes accompanying the collapse of the Soviet Union resulted in significant changes in land surface phenologies across Northern Eurasia and Kazakhstan in particular. Using multiple lines of evidence provided by the statistical framework, I was able to distinguish between anthropogenic impacts and interannual climatic fluctuations on the land surface phenology. ^
Environmental Sciences|Remote Sensing
de Beurs, Kirsten M, "A statistical framework for the analysis of long image time series: The effect of anthropogenic change on land surface phenology" (2005). ETD collection for University of Nebraska - Lincoln. AAI3199693.