Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage

doi:10.1186/s12859-022-04886-6

	Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage
	Song, Chuangye ; Sang, Jiawen 1; Zhang, Lin ; Liu, Huiming 2; Wu, Dongxiu ; Yuan, Weiying ; Huang, Chong 3
	2022
发表期刊	BMC BIOINFORMATICS
ISSN	1471-2105
卷号	23 期号:1
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.
关键词	Digital image Grassland Field survey Mobile smart phone Canopy density
学科领域	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
DOI	10.1186/s12859-022-04886-6
收录类别	SCI
语种	英语
WOS关键词	DIGITAL IMAGES ; TECHNICAL NOTE ; PLANT ; INDEXES ; BIOMASS ; AREA
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000847685400001
出版者	BMC
文献子类	Article
出版地	LONDON
资助机构	Chinese Academy of Sciences [KFJ-SW-YW043-4, KFJ-SW-YW037-01, XDA19050402, XDA26010101-4, XDA19020301, XDA28060400] ; Ministry of Sciences and Technology the People's Republic of China [2019QZKK0301, 2021FY10070503]
作者邮箱	huangch@lreis.ac.cn
作品OA属性	gold, Green Published
引用统计
文献类型	期刊论文
条目标识符	http://ir.ibcas.ac.cn/handle/2S10CLM1/28775
专题	植被与环境变化国家重点实验室
作者单位	1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Minist Ecol & Environm, Satellite Applicat Ctr Ecol & Environm, Beijing 100094, Peoples R China 4.Chinese Acad Sci, Inst Geog & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, 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).
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).
MLA	Song, Chuangye,et al."Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage".BMC BIOINFORMATICS 23.1(2022).

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