Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage
Abstract 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 algo...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-08-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-022-04886-6 |
| _version_ | 1811297490592333824 |
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| author | Chuangye Song Jiawen Sang Lin Zhang Huiming Liu Dongxiu Wu Weiying Yuan Chong Huang |
| author_facet | Chuangye Song Jiawen Sang Lin Zhang Huiming Liu Dongxiu Wu Weiying Yuan Chong Huang |
| author_sort | Chuangye Song |
| collection | DOAJ |
| description | Abstract 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 $$R/G$$ R / G , $$B/G$$ B / G , and $$ExG$$ ExG ( $$R$$ R , $$G$$ G , and $$B$$ B are the actual pixel digital numbers from the images based on each RGB channel, $$ExG$$ 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 $$ExG-ExR$$ E x G - E x R ( $$ExR$$ ExR is the abbreviation of the Excess Red index) to recognize plants in the image, and the fourth algorithm (Fun04) uses $$ExG$$ ExG and $$O{\text{tsu}}$$ O tsu to separate the plants from the background. $$Otsu$$ 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 $$R/G$$ R / G , $$B/G$$ B / G , and $$ExG$$ 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. |
| first_indexed | 2024-04-13T06:05:01Z |
| format | Article |
| id | doaj.art-512f7adb725c45faa27ae1cfb7b8fc89 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T06:05:01Z |
| publishDate | 2022-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-512f7adb725c45faa27ae1cfb7b8fc892022-12-22T02:59:17ZengBMCBMC Bioinformatics1471-21052022-08-0123111710.1186/s12859-022-04886-6Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverageChuangye Song0Jiawen Sang1Lin Zhang2Huiming Liu3Dongxiu Wu4Weiying Yuan5Chong Huang6State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of SciencesState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of SciencesState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of SciencesSatellite Application Centre for Ecology and Environment, Ministry of Ecology and EnvironmentState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of SciencesState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic and Natural Resources Research, Chinese Academy of SciencesAbstract 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 $$R/G$$ R / G , $$B/G$$ B / G , and $$ExG$$ ExG ( $$R$$ R , $$G$$ G , and $$B$$ B are the actual pixel digital numbers from the images based on each RGB channel, $$ExG$$ 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 $$ExG-ExR$$ E x G - E x R ( $$ExR$$ ExR is the abbreviation of the Excess Red index) to recognize plants in the image, and the fourth algorithm (Fun04) uses $$ExG$$ ExG and $$O{\text{tsu}}$$ O tsu to separate the plants from the background. $$Otsu$$ 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 $$R/G$$ R / G , $$B/G$$ B / G , and $$ExG$$ 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.https://doi.org/10.1186/s12859-022-04886-6Digital imageGrasslandField surveyMobile smart phoneCanopy density |
| spellingShingle | Chuangye Song Jiawen Sang Lin Zhang Huiming Liu Dongxiu Wu Weiying Yuan Chong Huang Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage BMC Bioinformatics Digital image Grassland Field survey Mobile smart phone Canopy density |
| title | Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage |
| title_full | Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage |
| title_fullStr | Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage |
| title_full_unstemmed | Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage |
| title_short | Adaptiveness of RGB-image derived algorithms in the measurement of fractional vegetation coverage |
| title_sort | adaptiveness of rgb image derived algorithms in the measurement of fractional vegetation coverage |
| topic | Digital image Grassland Field survey Mobile smart phone Canopy density |
| url | https://doi.org/10.1186/s12859-022-04886-6 |
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