Characterizing green and gray space exposure for epidemiological studies: Moving from 2D to 3D indicators
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 heal...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Giannico, Vincenzo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Heterologous expression of codon optimized Trichoderma reesei Cel6A in Pichia pastoris - Sun, Fubao Fuelbiol ELSEVIER, 2016, Jena |
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| Übergeordnetes Werk: |
volume:72 ; year:2022 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.ufug.2022.127567 |
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ELV057666857 |
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| 245 | 1 | 0 | |a Characterizing green and gray space exposure for epidemiological studies: Moving from 2D to 3D indicators |
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| 520 | |a 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 three-dimensional (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. | ||
| 520 | |a 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 three-dimensional (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. | ||
| 650 | 7 | |a Human health |2 Elsevier | |
| 650 | 7 | |a LiDAR |2 Elsevier | |
| 650 | 7 | |a Gray volume |2 Elsevier | |
| 650 | 7 | |a Urban forestry |2 Elsevier | |
| 650 | 7 | |a Green spaces |2 Elsevier | |
| 650 | 7 | |a 3D indicators |2 Elsevier | |
| 650 | 7 | |a Green volume |2 Elsevier | |
| 650 | 7 | |a Remote sensing |2 Elsevier | |
| 700 | 1 | |a Stafoggia, Massimo |4 oth | |
| 700 | 1 | |a Spano, Giuseppina |4 oth | |
| 700 | 1 | |a Elia, Mario |4 oth | |
| 700 | 1 | |a Dadvand, Payam |4 oth | |
| 700 | 1 | |a Sanesi, Giovanni |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Urban & Fischer |a Sun, Fubao Fuelbiol ELSEVIER |t Heterologous expression of codon optimized Trichoderma reesei Cel6A in Pichia pastoris |d 2016 |g Jena |w (DE-627)ELV024163988 |
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10.1016/j.ufug.2022.127567 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001908.pica (DE-627)ELV057666857 (ELSEVIER)S1618-8667(22)00110-8 DE-627 ger DE-627 rakwb eng 610 VZ 004 VZ 31.73 bkl 31.76 bkl 44.32 bkl Giannico, Vincenzo verfasserin aut Characterizing green and gray space exposure for epidemiological studies: Moving from 2D to 3D indicators 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 three-dimensional (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. 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 three-dimensional (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. Human health Elsevier LiDAR Elsevier Gray volume Elsevier Urban forestry Elsevier Green spaces Elsevier 3D indicators Elsevier Green volume Elsevier Remote sensing Elsevier Stafoggia, Massimo oth Spano, Giuseppina oth Elia, Mario oth Dadvand, Payam oth Sanesi, Giovanni oth Enthalten in Urban & Fischer Sun, Fubao Fuelbiol ELSEVIER Heterologous expression of codon optimized Trichoderma reesei Cel6A in Pichia pastoris 2016 Jena (DE-627)ELV024163988 volume:72 year:2022 pages:0 https://doi.org/10.1016/j.ufug.2022.127567 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_21 GBV_ILN_31 GBV_ILN_40 GBV_ILN_65 GBV_ILN_74 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2018 31.73 Mathematische Statistik VZ 31.76 Numerische Mathematik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 72 2022 0 |
| spelling |
10.1016/j.ufug.2022.127567 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001908.pica (DE-627)ELV057666857 (ELSEVIER)S1618-8667(22)00110-8 DE-627 ger DE-627 rakwb eng 610 VZ 004 VZ 31.73 bkl 31.76 bkl 44.32 bkl Giannico, Vincenzo verfasserin aut Characterizing green and gray space exposure for epidemiological studies: Moving from 2D to 3D indicators 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 three-dimensional (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. 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 three-dimensional (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. Human health Elsevier LiDAR Elsevier Gray volume Elsevier Urban forestry Elsevier Green spaces Elsevier 3D indicators Elsevier Green volume Elsevier Remote sensing Elsevier Stafoggia, Massimo oth Spano, Giuseppina oth Elia, Mario oth Dadvand, Payam oth Sanesi, Giovanni oth Enthalten in Urban & Fischer Sun, Fubao Fuelbiol ELSEVIER Heterologous expression of codon optimized Trichoderma reesei Cel6A in Pichia pastoris 2016 Jena (DE-627)ELV024163988 volume:72 year:2022 pages:0 https://doi.org/10.1016/j.ufug.2022.127567 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_21 GBV_ILN_31 GBV_ILN_40 GBV_ILN_65 GBV_ILN_74 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2018 31.73 Mathematische Statistik VZ 31.76 Numerische Mathematik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 72 2022 0 |
| allfields_unstemmed |
10.1016/j.ufug.2022.127567 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001908.pica (DE-627)ELV057666857 (ELSEVIER)S1618-8667(22)00110-8 DE-627 ger DE-627 rakwb eng 610 VZ 004 VZ 31.73 bkl 31.76 bkl 44.32 bkl Giannico, Vincenzo verfasserin aut Characterizing green and gray space exposure for epidemiological studies: Moving from 2D to 3D indicators 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 three-dimensional (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. 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 three-dimensional (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. Human health Elsevier LiDAR Elsevier Gray volume Elsevier Urban forestry Elsevier Green spaces Elsevier 3D indicators Elsevier Green volume Elsevier Remote sensing Elsevier Stafoggia, Massimo oth Spano, Giuseppina oth Elia, Mario oth Dadvand, Payam oth Sanesi, Giovanni oth Enthalten in Urban & Fischer Sun, Fubao Fuelbiol ELSEVIER Heterologous expression of codon optimized Trichoderma reesei Cel6A in Pichia pastoris 2016 Jena (DE-627)ELV024163988 volume:72 year:2022 pages:0 https://doi.org/10.1016/j.ufug.2022.127567 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_21 GBV_ILN_31 GBV_ILN_40 GBV_ILN_65 GBV_ILN_74 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2018 31.73 Mathematische Statistik VZ 31.76 Numerische Mathematik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 72 2022 0 |
| allfieldsGer |
10.1016/j.ufug.2022.127567 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001908.pica (DE-627)ELV057666857 (ELSEVIER)S1618-8667(22)00110-8 DE-627 ger DE-627 rakwb eng 610 VZ 004 VZ 31.73 bkl 31.76 bkl 44.32 bkl Giannico, Vincenzo verfasserin aut Characterizing green and gray space exposure for epidemiological studies: Moving from 2D to 3D indicators 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 three-dimensional (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. 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 three-dimensional (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. Human health Elsevier LiDAR Elsevier Gray volume Elsevier Urban forestry Elsevier Green spaces Elsevier 3D indicators Elsevier Green volume Elsevier Remote sensing Elsevier Stafoggia, Massimo oth Spano, Giuseppina oth Elia, Mario oth Dadvand, Payam oth Sanesi, Giovanni oth Enthalten in Urban & Fischer Sun, Fubao Fuelbiol ELSEVIER Heterologous expression of codon optimized Trichoderma reesei Cel6A in Pichia pastoris 2016 Jena (DE-627)ELV024163988 volume:72 year:2022 pages:0 https://doi.org/10.1016/j.ufug.2022.127567 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_21 GBV_ILN_31 GBV_ILN_40 GBV_ILN_65 GBV_ILN_74 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2018 31.73 Mathematische Statistik VZ 31.76 Numerische Mathematik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 72 2022 0 |
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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 three-dimensional (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. 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 three-dimensional (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. 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Characterizing green and gray space exposure for epidemiological studies: Moving from 2D to 3D indicators |
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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 three-dimensional (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. |
| abstractGer |
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 three-dimensional (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. |
| abstract_unstemmed |
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 three-dimensional (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. |
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| title_short |
Characterizing green and gray space exposure for epidemiological studies: Moving from 2D to 3D indicators |
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Stafoggia, Massimo Spano, Giuseppina Elia, Mario Dadvand, Payam Sanesi, Giovanni |
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10.1016/j.ufug.2022.127567 |
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