Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage
文献类型：期刊论文
| 作者 | Song, Chuangye1; Sang, Jiawen1,4; Zhang, Lin1; Liu, Huiming2; Wu, Dongxiu1; Yuan, Weiying1; Huang, Chong3 |
| 刊名 | BMC BIOINFORMATICS
 |
| 出版日期 | 2022-08-30 |
| 卷号 | 23期号:1页码:17 |
| 关键词 | Digital image Grassland Field survey Mobile smart phone Canopy density |
| ISSN号 | 1471-2105 |
| DOI | 10.1186/s12859-022-04886-6 |
| 通讯作者 | Huang, Chong(huangch@lreis.ac.cn) |
| 英文摘要 | Background: Fractional vegetation coverage (FVC) is a crucial parameter in determining vegetation structure. Automatic measurement of FVC using digital images captured by mobile smart devices is a potential direction for future research on field survey methods in plant ecology, and this algorithm is crucial for accurate FVC measurement. However, there is a lack of insight into the influence of illumination on the accuracy of FVC measurements. Therefore, the main objective of this research is to assess the adaptiveness and performance of different algorithms under varying light conditions for FVC measurements and to deepen our understanding of the influence of illumination on FVC measurement. Methods and results: Based on a literature survey, we selected four algorithms that have been reported to have high accuracy in automatic FVC measurements. The first algorithm (Fun01) identifies green plants based on the combination of R/G, B/G, and ExG (R, G, and B are the actual pixel digital numbers from the images based on each RGB channel, ExG is the abbreviation of the Excess Green index), the second algorithm (Fun02) is a decision tree that uses color properties to discriminate plants from the background, the third algorithm (Fun03) uses ExG - ExR (ExR is the abbreviation of the Excess Red index) to recognize plants in the image, and the fourth algorithm (Fun04) uses ExG and Otsu to separate the plants from the background. Otsu is an algorithm used to determine a threshold to transform the image into a binary image for the vegetation and background. We measured the FVC of several surveyed quadrats using these four algorithms under three scenarios, namely overcast sky, solar forenoon, and solar noon. FVC values obtained using the Photoshop-assisted manual identification method were used as a reference to assess the accuracy of the four algorithms selected. Results indicate that under the overcast sky scenario, Fun01 was more accurate than the other algorithms and the MAPE (mean absolute percentage error), BIAS, relBIAS (relative BIAS), RMSE (root mean square error), and relRMSE (relative RMSE) are 8.68%, 1.3, 3.97, 3.13, and 12.33%, respectively. Under the scenario of the solar forenoon, Fun02 (decision tree) was more accurate than other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 22.70%, - 2.86, - 7.70, 5.00, and 41.23%. Under the solar noon scenario, Fun02 was also more accurate than the other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 20.60%, - 6.39, - 20.67, 7.30, and 24.49%, respectively. Conclusions: Given that each algorithm has its own optimal application scenario, among the four algorithms selected, Fun01 (the combination of R/G, B/G, and ExG) can be recommended for measuring FVC on cloudy days. Fun02 (decision tree) is more suitable for measuring the FVC on sunny days. However, it considerably underestimates the FVC in most cases. We expect the findings of this study to serve as a useful reference for automatic vegetation cover measurements. |
| WOS关键词 | DIGITAL IMAGES ; TECHNICAL NOTE ; PLANT ; INDEXES ; BIOMASS ; AREA |
| 资助项目 | Chinese Academy of Sciences[KFJ-SW-YW043-4] ; Chinese Academy of Sciences[KFJ-SW-YW037-01] ; Chinese Academy of Sciences[XDA19050402] ; Chinese Academy of Sciences[XDA26010101-4] ; Chinese Academy of Sciences[XDA19020301] ; Chinese Academy of Sciences[XDA28060400] ; Ministry of Sciences and Technology the People's Republic of China[2019QZKK0301] ; Ministry of Sciences and Technology the People's Republic of China[2021FY10070503] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology |
| 语种 | 英语 |
| WOS记录号 | WOS:000847685400001 |
| 出版者 | BMC |
| 资助机构 | Chinese Academy of Sciences ; Ministry of Sciences and Technology the People's Republic of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/182100]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Huang, Chong |
| 作者单位 | 1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.Minist Ecol & Environm, Satellite Applicat Ctr Ecol & Environm, Beijing 100094, Peoples R China 3.Chinese Acad Sci, Inst Geog & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Song, Chuangye,Sang, Jiawen,Zhang, Lin,et al. Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage[J]. BMC BIOINFORMATICS,2022,23(1):17. |
| APA | Song, Chuangye.,Sang, Jiawen.,Zhang, Lin.,Liu, Huiming.,Wu, Dongxiu.,...&Huang, Chong.(2022).Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage.BMC BIOINFORMATICS,23(1),17. |
| MLA | Song, Chuangye,et al."Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage".BMC BIOINFORMATICS 23.1(2022):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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