Satellite data is increasingly used to characterize green space for health outcome studies. Literature suggests that green space within 500 m of home is often used to represent neighborhood suitable for walking, air pollution and noise reduction, and natural healing. In this paper, we used satellite data of different spatial resolutions to derive normalized difference vegetation index (NDVI), an indicator of surface greenness, at buffer distances of 50, 100, 250 and 500 m. Data included those of ...
Satellite data is increasingly used to characterize green space for health outcome studies. Literature suggests that green space within 500 m of home is often used to represent neighborhood suitable for walking, air pollution and noise reduction, and natural healing. In this paper, we used satellite data of different spatial resolutions to derive normalized difference vegetation index (NDVI), an indicator of surface greenness, at buffer distances of 50, 100, 250 and 500 m. Data included those of 2 m spatial resolution from WorldView2, 5 m resolution from RapidEye and 30 m resolution from Landsat. We found that, after radiometric calibrations, the RapidEye and WorldView2 sensors had similar NDVI values, while Landsat imagery tended to have greater NDVI; however, these sensors showed similar vegetation distribution: locations high in vegetation cover being high in NDVI, and vice versa. We linked the green space estimates to a health survey, and identified that higher NDVI values were significantly associated with better health outcomes. We further investigated the impacts of buffer size and sensor spatial resolution on identified associations between NDVI and health outcomes. Overall, the identified health outcomes were similar across sensors of different spatial resolutions, but a mean trend was identified in bigger buffer size being associated with greater health outcome.
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