Changes in Landscape Greenness and Climatic Factors over 25 Years (1989–2013) in the USA
Nash, M., J. Wickham, J. Christensen, AND T. Wade. Changes in Landscape Greenness and Climatic Factors over 25 Years (1989–2013) in the USA. Remote Sensing. MDPI AG, Basel, Switzerland, 9(3):295, (2017).
Long-term monitoring using simple and inexpensive methods can potentially detect broad-scale, slow changes, such as those caused by climate change over decades, as well as more local and rapid changes such as those caused by drought or flooding of agriculture and tree mortality associated with insect infestation over years. Such monitoring can provide environmental decision-makers with early warning signals for widespread general trends as well as a means to identify specific areas where land conditions are degrading or improving.
Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can be achieved using the Normalized Difference Vegetation Index (NDVI), an indicator of greenness. However, distinguishing gradual shifts in NDVI (e.g. climate change) versus direct and rapid changes (e.g., fire, land development) is challenging as changes can be confounded by time-dependent patterns, and variation associated with climatic factors. In the present study we leveraged a method, that we previously developed for a pilot study, to address these confounding factors by evaluating NDVI change using autoregression techniques that compare results from univariate (NDVI vs. time) and multivariate analyses (NDVI vs. time and climatic factors) for ~7,660,636 1-km2 pixels comprising the 48 contiguous states of the USA, over a 25-year period (1989−2013). NDVI changed significantly for 48% of the nation over the 25-year in the univariate analyses where most significant trends (85%) indicated an increase in greenness over time. By including climatic factors in the multivariate analyses of NDVI over time, the detection of significant NDVI trends increased to 53% (an increase of 5%). Comparisons of univariate and multivariate analyses for each pixel showed that less than 4% of the pixels had a significant NDVI trend attributable to gradual climatic changes while the remainder of pixels with a significant NDVI trend indicated that changes were due to direct factors. While most NDVI changes were attributable to direct factors like wildfires, drought or flooding of agriculture, and tree mortality associated with insect infestation, these conditions may be indirectly influenced by changes in climatic factors