Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging
The rapid and accurate acquisition of vegetation information, particularly chlorophyll content, is essential for effective vegetation management and ensuring the safe operation of photovoltaic power plants. In this study, the vegetation within a photovoltaic power plant served as the research subjec...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1643945/full |
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| author | Ming Li Weiyi Wang Haoran Li Zekun Yang Jianjun Li |
| author_facet | Ming Li Weiyi Wang Haoran Li Zekun Yang Jianjun Li |
| author_sort | Ming Li |
| collection | DOAJ |
| description | The rapid and accurate acquisition of vegetation information, particularly chlorophyll content, is essential for effective vegetation management and ensuring the safe operation of photovoltaic power plants. In this study, the vegetation within a photovoltaic power plant served as the research subject, and multispectral images were acquired using unmanned aerial vehicles, while in situ chlorophyll measurements were obtained through ground-based sampling at multiple time points. From these images, twenty vegetation indices and thirty-two texture features were extracted. To reduce feature redundancy and enhance modeling efficiency, feature selection was performed using the minimum redundancy maximum relevance method and Pearson correlation analysis. The selected features were then used in three modeling strategies—vegetation index–based, texture feature–based, and fused index–texture–based—employing three conventional machine-learning regressors (partial least squares regression, random forest, support vector machine regression) and three deep-learning regressors (back propagation neural network, convolutional neural network, multilayer perceptron). Based on the optimal model, a spatiotemporal distribution map of chlorophyll content within the study area was generated. The results indicated that both vegetation indices and texture features exhibited significant correlations with chlorophyll content, with the strongest correlation observed between the green normalized difference vegetation index (GNDVI) and the NIR_Mean (Pearson coefficients of 0.82 and 0.65, respectively). Moreover, the fusion of vegetation indices and texture features effectively improved the accuracy of chlorophyll inversion models; among the six regression algorithms tested, the multilayer perceptron model achieved the highest performance (R² = 0.874, RMSE = 3.725, MAPE = 3.982%). This study provides a novel method for monitoring chlorophyll content in vegetation within photovoltaic power plant regions and offers informational support for refined regional vegetation management. |
| format | Article |
| id | doaj-art-342bd95cc57543d7b52b3c18a33f91b8 |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-342bd95cc57543d7b52b3c18a33f91b82025-08-20T03:05:31ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-08-011610.3389/fpls.2025.16439451643945Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imagingMing LiWeiyi WangHaoran LiZekun YangJianjun LiThe rapid and accurate acquisition of vegetation information, particularly chlorophyll content, is essential for effective vegetation management and ensuring the safe operation of photovoltaic power plants. In this study, the vegetation within a photovoltaic power plant served as the research subject, and multispectral images were acquired using unmanned aerial vehicles, while in situ chlorophyll measurements were obtained through ground-based sampling at multiple time points. From these images, twenty vegetation indices and thirty-two texture features were extracted. To reduce feature redundancy and enhance modeling efficiency, feature selection was performed using the minimum redundancy maximum relevance method and Pearson correlation analysis. The selected features were then used in three modeling strategies—vegetation index–based, texture feature–based, and fused index–texture–based—employing three conventional machine-learning regressors (partial least squares regression, random forest, support vector machine regression) and three deep-learning regressors (back propagation neural network, convolutional neural network, multilayer perceptron). Based on the optimal model, a spatiotemporal distribution map of chlorophyll content within the study area was generated. The results indicated that both vegetation indices and texture features exhibited significant correlations with chlorophyll content, with the strongest correlation observed between the green normalized difference vegetation index (GNDVI) and the NIR_Mean (Pearson coefficients of 0.82 and 0.65, respectively). Moreover, the fusion of vegetation indices and texture features effectively improved the accuracy of chlorophyll inversion models; among the six regression algorithms tested, the multilayer perceptron model achieved the highest performance (R² = 0.874, RMSE = 3.725, MAPE = 3.982%). This study provides a novel method for monitoring chlorophyll content in vegetation within photovoltaic power plant regions and offers informational support for refined regional vegetation management.https://www.frontiersin.org/articles/10.3389/fpls.2025.1643945/fullUAVchlorophyll contentvegetation indextexture featuredeep learning |
| spellingShingle | Ming Li Weiyi Wang Haoran Li Zekun Yang Jianjun Li Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging Frontiers in Plant Science UAV chlorophyll content vegetation index texture feature deep learning |
| title | Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging |
| title_full | Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging |
| title_fullStr | Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging |
| title_full_unstemmed | Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging |
| title_short | Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging |
| title_sort | monitoring of vegetation chlorophyll content in photovoltaic areas using uav mounted multispectral imaging |
| topic | UAV chlorophyll content vegetation index texture feature deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1643945/full |
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