The Normalized Difference Vegetation Index (NDVI) or 'greenness index', based on the Advanced Very High Resolution Radiometer (AVHRR) aboard the NOAA-7 satellite, has been widely interpreted as a measure of regional to global vegetation patterns. This study provides the first rigorous, quantitative evaluation of global relationships between the NDVI and geographically representative vegetation data-bases, including field metabolic measurements and carbon-balance results from global simulation models. Geographic reliability of the NDVIis judged by comparing NDVIvalues for different surface types with a general global trend and by statistical analysis of relationships to biomass amounts, net and gross primary productivity, and actual evapotranspiration. NDVIdata appear to be relatively reliable predictors of primary productivity except in areas of complex terrain, for seasonal values at high latitudes, and in extreme deserts. The strength of the NDVI-productivity relationship seems comparable to that of earlier climate-based productivity models. Little consistent relationship was found, across different vegetation types, between NDVI and biomass amounts or net biospheric CO 2 flux.
A real-time understanding of the distribution and duration of power outages after a major disaster is a precursor to minimizing their harmful consequences. Here, we develop an approach for using daily satellite nighttime lights data to create spatially disaggregated power outage estimates, tracking electricity restoration efforts after disasters strike. In contrast to existing utility data, these estimates are independent, open, and publicly-available, consistently measured across regions that may be serviced by several different power companies, and inclusive of distributed power supply (off-grid systems). We apply the methodology in Puerto Rico following Hurricane Maria, which caused the longest blackout in US history. Within all of the island’s settlements, we track outages and recovery times, and link these measures to census-based demographic characteristics of residents. Our results show an 80% decrease in lights, in total, immediately after Hurricane Maria. During the recovery, a disproportionate share of long-duration power failures (> 120 days) occurred in rural municipalities (41% of rural municipalities vs. 29% of urban municipalities), and in the northern and eastern districts. Unexpectedly, we also identify large disparities in electricity recovery between neighborhoods within the same urban area, based primarily on the density of housing. For many urban areas, poor residents, the most vulnerable to increased mortality and morbidity risks from power losses, shouldered the longest outages because they lived in less dense, detached housing where electricity restoration lagged. The approach developed in this study demonstrates the potential of satellite-based estimates of power recovery to improve the real-time monitoring of disaster impacts, globally, at a spatial resolution that is actionable for the disaster response community.
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