Ziyang Li , Huan Tao , Yongjian Huai , Xiaoying Nie
{"title":"基于自然图像的城市行道树树种识别和因子解释模型","authors":"Ziyang Li , Huan Tao , Yongjian Huai , Xiaoying Nie","doi":"10.1016/j.ufug.2024.128512","DOIUrl":null,"url":null,"abstract":"<div><div>Urban street trees bring the beautiful ecological environment for human beings, but also may harm human health. Tree pollen is an important allergen that causes people to suffer from asthma and rhinitis, causing a serious medical burden. In order to protect human health and reduce medical costs, urban street trees need to be accurately identified. However, the identification of the urban street tree is influenced by the natural image (images with light intensity, season and shooting conditions), tree characteristics and identification models. To solve the problem, we proposed an interpretation model for identifying tree from natural images, named Ev2S_SHAP. Then, we applied the method to explain the influence of environmental and leaf factors on tree recognition and the identification accuracy. The open-source natural image dataset of urban street tree with complex situations such as different angles, distances, light, and times was used as the research object to verify the proposed tree identification model. The results showed that the overall evaluation of the identification results for 50 tree species: recall, precision, accuracy, and F1 score were 98.12 %, 98.18 %, 98.11 %, and 98.13 %, respectively. The identification accuracy of deciduous shrubs, evergreen shrubs, deciduous <em>trees</em>, and evergreen <em>trees</em> was 98.85 %, 98.47 %, 98.76 %, and 96.38 %, respectively. Complex lights and angles, long-range shooting, and winter conditions weakened the extraction of leaf features and were not conducive to the identification of tree species. Leaf shape characteristics had important influence on tree species identification. The effects of circularity, minimum circumcircle, and perimeter on tree identification accounted for 94.36 %, 75.61 %, and 69.12 %, respectively. Circularity was positively correlated to the identification contribution of elliptic leaf species, but opposite to that of lanceolate leaf species. The perimeter contributed positively correlated to the identification of lanceolate leaf species, but the minimum circumcircle was negatively. The Ev2S-SHAP effectively improved the identification accuracy of tree species. The study provides an innovative method for identifying and interpreting tree species.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Urban street tree species identification and factor interpretation model based on natural images\",\"authors\":\"Ziyang Li , Huan Tao , Yongjian Huai , Xiaoying Nie\",\"doi\":\"10.1016/j.ufug.2024.128512\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Urban street trees bring the beautiful ecological environment for human beings, but also may harm human health. Tree pollen is an important allergen that causes people to suffer from asthma and rhinitis, causing a serious medical burden. In order to protect human health and reduce medical costs, urban street trees need to be accurately identified. However, the identification of the urban street tree is influenced by the natural image (images with light intensity, season and shooting conditions), tree characteristics and identification models. To solve the problem, we proposed an interpretation model for identifying tree from natural images, named Ev2S_SHAP. Then, we applied the method to explain the influence of environmental and leaf factors on tree recognition and the identification accuracy. The open-source natural image dataset of urban street tree with complex situations such as different angles, distances, light, and times was used as the research object to verify the proposed tree identification model. The results showed that the overall evaluation of the identification results for 50 tree species: recall, precision, accuracy, and F1 score were 98.12 %, 98.18 %, 98.11 %, and 98.13 %, respectively. The identification accuracy of deciduous shrubs, evergreen shrubs, deciduous <em>trees</em>, and evergreen <em>trees</em> was 98.85 %, 98.47 %, 98.76 %, and 96.38 %, respectively. Complex lights and angles, long-range shooting, and winter conditions weakened the extraction of leaf features and were not conducive to the identification of tree species. Leaf shape characteristics had important influence on tree species identification. The effects of circularity, minimum circumcircle, and perimeter on tree identification accounted for 94.36 %, 75.61 %, and 69.12 %, respectively. Circularity was positively correlated to the identification contribution of elliptic leaf species, but opposite to that of lanceolate leaf species. The perimeter contributed positively correlated to the identification of lanceolate leaf species, but the minimum circumcircle was negatively. The Ev2S-SHAP effectively improved the identification accuracy of tree species. The study provides an innovative method for identifying and interpreting tree species.</div></div>\",\"PeriodicalId\":49394,\"journal\":{\"name\":\"Urban Forestry & Urban Greening\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Urban Forestry & Urban Greening\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1618866724003108\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Urban Forestry & Urban Greening","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1618866724003108","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
Urban street tree species identification and factor interpretation model based on natural images
Urban street trees bring the beautiful ecological environment for human beings, but also may harm human health. Tree pollen is an important allergen that causes people to suffer from asthma and rhinitis, causing a serious medical burden. In order to protect human health and reduce medical costs, urban street trees need to be accurately identified. However, the identification of the urban street tree is influenced by the natural image (images with light intensity, season and shooting conditions), tree characteristics and identification models. To solve the problem, we proposed an interpretation model for identifying tree from natural images, named Ev2S_SHAP. Then, we applied the method to explain the influence of environmental and leaf factors on tree recognition and the identification accuracy. The open-source natural image dataset of urban street tree with complex situations such as different angles, distances, light, and times was used as the research object to verify the proposed tree identification model. The results showed that the overall evaluation of the identification results for 50 tree species: recall, precision, accuracy, and F1 score were 98.12 %, 98.18 %, 98.11 %, and 98.13 %, respectively. The identification accuracy of deciduous shrubs, evergreen shrubs, deciduous trees, and evergreen trees was 98.85 %, 98.47 %, 98.76 %, and 96.38 %, respectively. Complex lights and angles, long-range shooting, and winter conditions weakened the extraction of leaf features and were not conducive to the identification of tree species. Leaf shape characteristics had important influence on tree species identification. The effects of circularity, minimum circumcircle, and perimeter on tree identification accounted for 94.36 %, 75.61 %, and 69.12 %, respectively. Circularity was positively correlated to the identification contribution of elliptic leaf species, but opposite to that of lanceolate leaf species. The perimeter contributed positively correlated to the identification of lanceolate leaf species, but the minimum circumcircle was negatively. The Ev2S-SHAP effectively improved the identification accuracy of tree species. The study provides an innovative method for identifying and interpreting tree species.
期刊介绍:
Urban Forestry and Urban Greening is a refereed, international journal aimed at presenting high-quality research with urban and peri-urban woody and non-woody vegetation and its use, planning, design, establishment and management as its main topics. Urban Forestry and Urban Greening concentrates on all tree-dominated (as joint together in the urban forest) as well as other green resources in and around urban areas, such as woodlands, public and private urban parks and gardens, urban nature areas, street tree and square plantations, botanical gardens and cemeteries.
The journal welcomes basic and applied research papers, as well as review papers and short communications. Contributions should focus on one or more of the following aspects:
-Form and functions of urban forests and other vegetation, including aspects of urban ecology.
-Policy-making, planning and design related to urban forests and other vegetation.
-Selection and establishment of tree resources and other vegetation for urban environments.
-Management of urban forests and other vegetation.
Original contributions of a high academic standard are invited from a wide range of disciplines and fields, including forestry, biology, horticulture, arboriculture, landscape ecology, pathology, soil science, hydrology, landscape architecture, landscape planning, urban planning and design, economics, sociology, environmental psychology, public health, and education.