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Detecting Change in Landscape Greenness over Large Areas: An Example for New Mexico, USA
Citation:
Nash, M., D. Bradford, J. Wickham, AND T. Wade. Detecting Change in Landscape Greenness over Large Areas: An Example for New Mexico, USA. REMOTE SENSING OF ENVIRONMENT. Elsevier Science Ltd, New York, NY, 150(0):152-162, (2014).
Impact/Purpose:
The ability to detect NDVI trend was greatly improved by including climate variables in the multivariate analyses of NDVI over time. The comparisons of univariate and multivariate analyses revealed that for most of the pixels with a significant NDVI trend in either analysis, the trend was consistent with changes in local factors rather than climate change; only 0.8% of the pixels had a significant NDVI trend associated with change in the climate variables.
Description:
Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can potentially detect large-scale, slow changes (e.g., climate change), as well as more local and rapid changes (e.g., fire, land development). A useful indicator for detecting change in land cover is a measure of greenness, the Normalized Difference Vegetation Index (NDVI). Detecting change in NDVI, however, can be confounded by time-dependent patterns (e.g., seasonal effects) and variation associated with climate factors. In the present study we provide a method to address these confounding factors by evaluating NDVI change using autoregression techniques that compare results from univariate (i.e., NDVI vs. time) and multivariate analyses (NDVI vs. time and climate variables) for ~314,000 1-km2 pixels comprising the state of New Mexico over an 18-year period (1989−2006).