Spatio-temporal monitoring of urban street-side vegetation greenery using Baidu Street View images |
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| Affiliation: | 1. College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China;2. Agricultural and Biological Engineering Department / Tropical Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, Homestead, FL 33031, USA;3. General Education Department, Taishan College of Science and Technology, Tai’an 271000, China;4. Dezhou Natural Resources Bureau & Forestry Bureau, Dezhou 25300, China;1. Davis College, Division of Forestry and Natural Resources, West Virginia University, Morgantown, WV, USA;2. Bartlett Tree Research Laboratories, 13768 Hamilton Rd., Charlotte, NC, USA;3. InnoRenew CoE, Izola, Slovenia and University of Primorska, Koper, Slovenia;4. North Elementary School, Morgantown, WV, USA;1. Department of Systems Engineering, Czech University of Life Sciences Prague, Kamýcká 129, 165 21 Prague 6-Suchdol, Czech Republic;2. Nature Conservation Agency of the Czech Republic, Kaplanova 1931/1, 148 00 Prague 11-Chodov, Czech Republic;1. Faculty of Forestry, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada;2. Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97331, USA;3. Department of Forest Resources & Environmental Conservation, Virginia Tech, Blacksburg, VA 24061, USA;4. Planning Department, Union County, Monroe, NC, USA;5. North Carolina State Extension, USA;1. Department of Landscape Architecture, College of Horticulture, Post-Doctoral Research Station in Public Administration, Nanjing Agricultural University, Nanjing, China;2. Department of Landscape Architecture, College of Horticulture, Nanjing Agricultural University, Nanjing, China;3. Department of Architecture and Urban Planning, Tongji University, Shanghai, China, |
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| Abstract: | Street-side vegetation greenery contributes substantial health benefits for pedestrians. Multi-year street view images are expected to enable the monitoring of dynamic street-side vegetation greenery changes and the development of targeted urban landscape plans. However, the potential of multi-year street view images used for the assessment of street-side vegetation greenery has not been evaluated yet. Besides, complicated urban landscapes may make it difficult to accurately quantify vegetation greenery. This study developed a framework to assess the spatio-temporal variation of street-side vegetation greenery using the Baidu Street View images and a new Vegetation Greenery Index (VGI). The proposed analytical framework was applied to Tai’an city, a highly populated city where urbanization has been rapid in China. The level of vegetation greenery estimated using the proposed framework was compared with ground truths randomly collected at sampling sites along the road networks in 2014 and 2019 to assess the applicability. Results demonstrated that the proposed VGI method could accurately quantify street-side vegetation greenery. The comparison of multi-year VGI layers could identify locations where vegetation greenery substantially changed and quantify the overall change in urban greenery. Vegetation greenery estimates were well agreed with the ground truths. Spatio-temporal variations in the urban vegetation greenery were attributed to trees that were newly planted or removed, the natural growth of the existing vegetation, and new building construction. The proposed framework is expected to be a useful tool to evaluate urban vegetation greenery and help urban landscape planning. |
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| Keywords: | Vegetation Greenery Index Vegetation greenery coverage change index Baidu street view Urban vegetation greenery monitoring Street-side vegetation greenery |
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