Full TGIF Record # 315289
Item 1 of 1
DOI:10.1016/j.ufug.2020.126661
Web URL(s):https://www.sciencedirect.com/science/article/pii/S1618866719305254
    Last checked: 01/22/2021
https://www.sciencedirect.com/science/article/pii/S1618866719305254/pdfft?md5=c360336ce168e6721d1dd38788e56396&pid=1-s2.0-S1618866719305254-main.pdf
    Last checked: 04/09/2021
    Requires: PDF Reader
    Access conditions: Item is within a limited-access website
Publication Type:
i
Refereed
Author(s):Qian, Yuguo; Zhou, Weiqi; Nytch, Christopher J.; Han, Lijian; Li, Zhiqiang
Author Affiliation:Qian and Han: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China; Zhou and Li: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Sciences, Beijing, China; Nytch: Department of Environmental Sciences, University of Puerto Rico, Río Piedras Campus, San Juan, Puerto Rico
Title:A new index to differentiate tree and grass based on high resolution image and object-based methods
Source:Urban Forestry & Urban Greening. Vol. 53, August 2020, p. 1-8.
# of Pages:8
Publishing Information:Jena, Germany: Urban & Fischer
Related Web URL:https://www.sciencedirect.com/science/article/pii/S1618866719305254#abs0015
    Last checked: 04/09/2021
    Notes: Abstract only
Abstract/Contents:"Urban trees and grass have different ecological functions and services. Remote sensing provides a feasible way of quantifying urban vegetative cover and distribution at large scale. Most previous studies have used supervised classification based on high resolution images to map urban trees and grass. However, due to the lack of specialized features for distinguishing coarse and fine vegetation, the classification accuracy of urban trees and grass is consistently low. Although adding 3D topographical information can improve accuracy, such data has limited availability. This paper developed a tree-grass differentiation index (TGDI) to facilitate the fast and effective classification of urban trees and grass. We examined the performance of the new index by applying it to different classification methods. We compared the classification of Method 1: supervised classification without TGDI; Method 2: supervised classification with TGDI; and Method 3: rule-based classification with TGDI. The results showed that the overall accuracy of Method 1, Method 2, and Method 3 were, 84 %, 88 %, and 90.5 %, respectively. Using the new index can improve the classification of urban trees and grass regardless if TGDI is used alone for rule-based classification or added as a feature for supervised classification. The main advantage of using TGDI is to reduce the misclassification of sunlit portions of trees into grass. The producer accuracy of tree and the user accuracy of grass can be improved by more than 10 % when TGDI is applied to supervised classification. This study synthesized texture and spectral features, which enhances the traditional approach of index construction based on spectral features alone, and without the requirement of detailed 3D surface data. The results suggest a novel way forward for developing indexes that can yield improved accuracies and expand the utility of remote sensing for illuminating patterns of ecological structure and function in urban environments."
Language:English
References:53
Note:"Article 126661"
Satellite images
Maps
Flowcharts
Pictures, color & b/w
Graphs
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Qian, Y., W. Zhou, C. J. Nytch, L. Han, and Z. Li. 2020. A new index to differentiate tree and grass based on high resolution image and object-based methods. Urban Forestry & Urban Greening. 53:p. 1-8.
Fastlink to access this record outside TGIF: https://tic.msu.edu/tgif/flink?recno=315289
If there are problems with this record, send us feedback about record 315289.
Choices for finding the above item:
DOI: 10.1016/j.ufug.2020.126661
Web URL(s):
https://www.sciencedirect.com/science/article/pii/S1618866719305254
    Last checked: 01/22/2021
https://www.sciencedirect.com/science/article/pii/S1618866719305254/pdfft?md5=c360336ce168e6721d1dd38788e56396&pid=1-s2.0-S1618866719305254-main.pdf
    Last checked: 04/09/2021
    Requires: PDF Reader
    Access conditions: Item is within a limited-access website
Find Item @ MSU
MSU catalog number: b5268048~S1a
Request through your local library's inter-library loan service (bring or send a copy of this TGIF record)