A new index to differentiate tree and grass based on high resolution image and object-based methods
文献类型：期刊论文
| 作者 | Qian, Yuguo; Zhou, Weiqi; Nytch, Christopher J.; Han, Lijian; Li, Zhiqiang |
| 刊名 | URBAN FORESTRY & URBAN GREENING
 |
| 出版日期 | 2020-08 |
| 卷号 | 53页码:1-22 |
| 关键词 | Index Land cover classification OBIA High resolution image Tree and grass |
| ISSN号 | 1618-8667 |
| 英文摘要 | 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. |
| 源URL | [http://ir.rcees.ac.cn/handle/311016/44194]  |
| 专题 | 生态环境研究中心_城市与区域生态国家重点实验室 |
| 推荐引用方式 GB/T 7714 | Qian, Yuguo,Zhou, Weiqi,Nytch, Christopher J.,et al. A new index to differentiate tree and grass based on high resolution image and object-based methods[J]. URBAN FORESTRY & URBAN GREENING,2020,53:1-22. |
| APA | Qian, Yuguo,Zhou, Weiqi,Nytch, Christopher J.,Han, Lijian,&Li, Zhiqiang.(2020).A new index to differentiate tree and grass based on high resolution image and object-based methods.URBAN FORESTRY & URBAN GREENING,53,1-22. |
| MLA | Qian, Yuguo,et al."A new index to differentiate tree and grass based on high resolution image and object-based methods".URBAN FORESTRY & URBAN GREENING 53(2020):1-22. |
入库方式： OAI收割
来源：生态环境研究中心
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