The presence of green spaces has been associated with improved physical health and better mental health and wellbeing. In contrast, the presence of gray features including build-up areas might have a negative impact on the health and wellbeing of citizens. To date, the available evidence on the health effects of green and gray spaces have mainly relied on 2-dimensional (2D) indicators of these spaces such as land use maps or, more recently, satellite derived indices (e.g., green space indices such as normalized difference vegetation index (NDVI) or gray space indices such as imperviousness). Although they are acceptable proxies of these exposures, 2D indicators could have inaccuracies when characterizing diverse set of vegetation types in combination with different types of gray spaces, which is typical of urban environments. To overcome this gap, we developed a set of threedimensional (3D) indicators derived mainly from airborne LiDAR (Light Detection and Ranging) acquired in 2008 and 2010 over the metropolitan area of Rome (Italy). In particular, we extracted volume of green features such as shrubs and trees (Green volume [m3/ha]), volume of buildings (Gray volume[m3/ha]), a novel index called Normalized Difference Green-Gray Volume index (NDGG) as well as indicators of the tree count. We compared the 3D indicators with two widely used 2D indicators for characterizing green and gray spaces (i.e., NDVI and imperviousness) in different buffers around 79140 address points in the city. For the green indicators, we found that the Pearson correlations between NDVI and Green Volume were 0.47 (50 m buffer) and 0.33 (300 m buffer) while the correlations between NDVI and number of trees were 0.56 (50 m buffer) and 0.58 (300 m buffer). For gray indicators, the correlations between imperviousness and gray volume were 0.62 (50 m buffer) and 0.79 (300 m buffer). For NDGG, the correlations were higher with both NDVI (0.76 and 0.83 for 50 m and 300 m buffers) and imperviousness (-0.75 and-0.83 for 50 m and 300 m buffers). Our results showed that the use of 3D indicators can have potential benefits, especially regarding green features which can be highly heterogeneous in complex urban landscapes such as the city of Rome.
Characterizing green and gray space exposure for epidemiological studies: moving from 2D to 3D indicators
Vincenzo Giannico;Giuseppina Spano;Mario Elia;Giovanni Sanesi
2022-01-01
Abstract
The presence of green spaces has been associated with improved physical health and better mental health and wellbeing. In contrast, the presence of gray features including build-up areas might have a negative impact on the health and wellbeing of citizens. To date, the available evidence on the health effects of green and gray spaces have mainly relied on 2-dimensional (2D) indicators of these spaces such as land use maps or, more recently, satellite derived indices (e.g., green space indices such as normalized difference vegetation index (NDVI) or gray space indices such as imperviousness). Although they are acceptable proxies of these exposures, 2D indicators could have inaccuracies when characterizing diverse set of vegetation types in combination with different types of gray spaces, which is typical of urban environments. To overcome this gap, we developed a set of threedimensional (3D) indicators derived mainly from airborne LiDAR (Light Detection and Ranging) acquired in 2008 and 2010 over the metropolitan area of Rome (Italy). In particular, we extracted volume of green features such as shrubs and trees (Green volume [m3/ha]), volume of buildings (Gray volume[m3/ha]), a novel index called Normalized Difference Green-Gray Volume index (NDGG) as well as indicators of the tree count. We compared the 3D indicators with two widely used 2D indicators for characterizing green and gray spaces (i.e., NDVI and imperviousness) in different buffers around 79140 address points in the city. For the green indicators, we found that the Pearson correlations between NDVI and Green Volume were 0.47 (50 m buffer) and 0.33 (300 m buffer) while the correlations between NDVI and number of trees were 0.56 (50 m buffer) and 0.58 (300 m buffer). For gray indicators, the correlations between imperviousness and gray volume were 0.62 (50 m buffer) and 0.79 (300 m buffer). For NDGG, the correlations were higher with both NDVI (0.76 and 0.83 for 50 m and 300 m buffers) and imperviousness (-0.75 and-0.83 for 50 m and 300 m buffers). Our results showed that the use of 3D indicators can have potential benefits, especially regarding green features which can be highly heterogeneous in complex urban landscapes such as the city of Rome.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
