| Autor: |
Saket Gowravaram, Haiyang Chao, Zhenghao Lin, Sheena Parsons, Tiebiao Zhao, Ming Xin, Xiaolin Hu, Pengzhi Tian, Harold Patrick Flanagan, Guanghui Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 3519-3530 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2151-1535 |
| DOI: |
10.1109/JSTARS.2023.3255230 |
| Popis: |
This article focuses on the mapping and rate of spread (ROS) measurement of grass fires using near infrared (NIR) images acquired by a small fixed-wing unmanned aircraft system (UAS) operating at low altitudes. A new method is proposed for spatiotemporal representation of grass fire evolution using time labeled UAS NIR orthomosaics stitched from aerial images collected at varying time stamps over different regions of fire. Furthermore, a novel NIR intensity variance thresholding method is proposed for accurate identification and delineation of grass fire fronts based on the obtained NIR mosaics in digital numbers. The proposed methods are demonstrated and validated using UAS NIR imagery acquired over a prescribed tallgrass fire in Kansas (around 13 ha.). Three NIR short time-series orthomosaics are generated at a time interval of about 2 min with a spatial registration accuracy of 1.45 m (RMSE). The mean ROS for head, flank, and back tallgrass fires are measured to be 0.28, 0.1, and 0.025 m/s. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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