UAV Multispectral Imagery Predicts Dead Fuel Moisture Content
Forest floor dead fuel moisture content (DFMC) is an important factor in the occurrence of forest fires, and predicting DFMC is important for accurate fire risk forecasting. Large areas of forest surface DFMC are difficult to predict via manual methods. In this paper, we propose an unmanned aerial v...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/14/9/1724 |
| _version_ | 1797580039214071808 |
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| author | Jian Xing Chaoyong Wang Ying Liu Zibo Chao Jiabo Guo Haitao Wang Xinfang Chang |
| author_facet | Jian Xing Chaoyong Wang Ying Liu Zibo Chao Jiabo Guo Haitao Wang Xinfang Chang |
| author_sort | Jian Xing |
| collection | DOAJ |
| description | Forest floor dead fuel moisture content (DFMC) is an important factor in the occurrence of forest fires, and predicting DFMC is important for accurate fire risk forecasting. Large areas of forest surface DFMC are difficult to predict via manual methods. In this paper, we propose an unmanned aerial vehicle (UAV)-based forest surface DFMC prediction method, in which a UAV is equipped with a multispectral camera to collect multispectral images of dead combustible material on the forest surface over a large area, combined with a deep-learning algorithm to achieve the large-scale prediction of DFMC on the forest surface. From 9 March to 23 March 2023, 5945 multispectral images and 480 sets of dead combustible samples were collected from an urban forestry demonstration site in Harbin, China, using an M300 RTK UAV with an MS600Pro multispectral camera. The multispectral images were segmented by a K-means clustering algorithm to obtain multispectral images containing only dead combustibles on the ground surface. The segmented multispectral images were then trained with the actual moisture content measured by the weighing method through the ConvNeXt deep-learning model, with 3985 images as the training set, 504 images as the validation set, and 498 images as the test set. The results showed that the MAE and RMSE of the test set are 1.54% and 5.45%, respectively, and the accuracy is 92.26% with high precision, achieving the accurate prediction of DFMC over a large range. The proposed new method for predicting DFMC via UAV multispectral cameras is expected to solve the real-time large-range accurate prediction of the moisture content of dead combustible material on the forest surface during the spring fire-prevention period in northeast China, thus providing technical support for improving the accuracy of forest fire risk-level forecasting and forest fire spread trend prediction. |
| first_indexed | 2024-03-10T22:45:37Z |
| format | Article |
| id | doaj.art-ae32f29106cb44ed8797c7fa0365d2f7 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T22:45:37Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-ae32f29106cb44ed8797c7fa0365d2f72023-11-19T10:45:02ZengMDPI AGForests1999-49072023-08-01149172410.3390/f14091724UAV Multispectral Imagery Predicts Dead Fuel Moisture ContentJian Xing0Chaoyong Wang1Ying Liu2Zibo Chao3Jiabo Guo4Haitao Wang5Xinfang Chang6College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaForest floor dead fuel moisture content (DFMC) is an important factor in the occurrence of forest fires, and predicting DFMC is important for accurate fire risk forecasting. Large areas of forest surface DFMC are difficult to predict via manual methods. In this paper, we propose an unmanned aerial vehicle (UAV)-based forest surface DFMC prediction method, in which a UAV is equipped with a multispectral camera to collect multispectral images of dead combustible material on the forest surface over a large area, combined with a deep-learning algorithm to achieve the large-scale prediction of DFMC on the forest surface. From 9 March to 23 March 2023, 5945 multispectral images and 480 sets of dead combustible samples were collected from an urban forestry demonstration site in Harbin, China, using an M300 RTK UAV with an MS600Pro multispectral camera. The multispectral images were segmented by a K-means clustering algorithm to obtain multispectral images containing only dead combustibles on the ground surface. The segmented multispectral images were then trained with the actual moisture content measured by the weighing method through the ConvNeXt deep-learning model, with 3985 images as the training set, 504 images as the validation set, and 498 images as the test set. The results showed that the MAE and RMSE of the test set are 1.54% and 5.45%, respectively, and the accuracy is 92.26% with high precision, achieving the accurate prediction of DFMC over a large range. The proposed new method for predicting DFMC via UAV multispectral cameras is expected to solve the real-time large-range accurate prediction of the moisture content of dead combustible material on the forest surface during the spring fire-prevention period in northeast China, thus providing technical support for improving the accuracy of forest fire risk-level forecasting and forest fire spread trend prediction.https://www.mdpi.com/1999-4907/14/9/1724unmanned aerial vehicle multispectralforest surface dead fuel moisture contentimage segmentationdeep learning |
| spellingShingle | Jian Xing Chaoyong Wang Ying Liu Zibo Chao Jiabo Guo Haitao Wang Xinfang Chang UAV Multispectral Imagery Predicts Dead Fuel Moisture Content Forests unmanned aerial vehicle multispectral forest surface dead fuel moisture content image segmentation deep learning |
| title | UAV Multispectral Imagery Predicts Dead Fuel Moisture Content |
| title_full | UAV Multispectral Imagery Predicts Dead Fuel Moisture Content |
| title_fullStr | UAV Multispectral Imagery Predicts Dead Fuel Moisture Content |
| title_full_unstemmed | UAV Multispectral Imagery Predicts Dead Fuel Moisture Content |
| title_short | UAV Multispectral Imagery Predicts Dead Fuel Moisture Content |
| title_sort | uav multispectral imagery predicts dead fuel moisture content |
| topic | unmanned aerial vehicle multispectral forest surface dead fuel moisture content image segmentation deep learning |
| url | https://www.mdpi.com/1999-4907/14/9/1724 |
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