TY - JOUR
T1 - Remotely-sensed imagery vs. eye-level photography
T2 - Evaluating associations among measurements of tree cover density
AU - Jiang, Bin
AU - Deal, Brian
AU - Pan, Hao Zhi
AU - Larsen, Linda
AU - Hsieh, Chung Heng
AU - Chang, Chun Yen
AU - Sullivan, William C.
N1 - Publisher Copyright:
© 2016 The Author(s)
PY - 2017/1/1
Y1 - 2017/1/1
N2 - The easy availability and widespread use of remotely-sensed imagery, especially Google Earth satellite imagery, makes it simple for urban forestry professionals to assess a site and measure tree cover density without visiting the site. Remotely-sensed tree cover density has become the dominant criterion for urban forestry regulations in many countries, but it is unclear how much such measures match the eye-level tree cover density that people experience; or the information gained through site visits, eye-level photography, or from consulting with citizens. To address this uncertainty, we assessed associations among two remotely-sensed and three eye-level tree cover density measures for 140 community street sites across the Midwestern United States with low, medium, or high tree cover coverage by using linear regression analysis. We found significant associations among the two remotely-sensed measures and the three eye-level measures across the three levels of tree cover. The associations between any pair of remotely-sensed and eye-level measures, however, diminish dramatically as canopy cover increased. At high levels of canopy cover, all associations between the remotely-sensed measures and the eye-level measures became statistically insignificant. These findings suggest that measures from remotely-sensed imagery fail to represent the amount of tree cover people perceive at eye-level when canopy cover is medium or high at the site scale. Therefore, the current urban forestry planning regulations, which rely heavily on remotely-sensed tree cover density measurements, need to be revised. We suggest strategic spots where eye-level measures of tree cover density should be emphasized.
AB - The easy availability and widespread use of remotely-sensed imagery, especially Google Earth satellite imagery, makes it simple for urban forestry professionals to assess a site and measure tree cover density without visiting the site. Remotely-sensed tree cover density has become the dominant criterion for urban forestry regulations in many countries, but it is unclear how much such measures match the eye-level tree cover density that people experience; or the information gained through site visits, eye-level photography, or from consulting with citizens. To address this uncertainty, we assessed associations among two remotely-sensed and three eye-level tree cover density measures for 140 community street sites across the Midwestern United States with low, medium, or high tree cover coverage by using linear regression analysis. We found significant associations among the two remotely-sensed measures and the three eye-level measures across the three levels of tree cover. The associations between any pair of remotely-sensed and eye-level measures, however, diminish dramatically as canopy cover increased. At high levels of canopy cover, all associations between the remotely-sensed measures and the eye-level measures became statistically insignificant. These findings suggest that measures from remotely-sensed imagery fail to represent the amount of tree cover people perceive at eye-level when canopy cover is medium or high at the site scale. Therefore, the current urban forestry planning regulations, which rely heavily on remotely-sensed tree cover density measurements, need to be revised. We suggest strategic spots where eye-level measures of tree cover density should be emphasized.
KW - Association
KW - Eye-level photography
KW - Remotely-sensed imagery
KW - Tree cover density
KW - Urban forestry
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U2 - 10.1016/j.landurbplan.2016.07.010
DO - 10.1016/j.landurbplan.2016.07.010
M3 - Article
AN - SCOPUS:84979620561
SN - 0169-2046
VL - 157
SP - 270
EP - 281
JO - Landscape and Urban Planning
JF - Landscape and Urban Planning
ER -