Machine Learning-Based Land Cover Classification and Impact Assessment in Pre-Wildfire and Post-Wildfire Areas
Author:
Affiliation:
1. Dept. of Civil, Environmental, and Construction Engineering, Univ. of Nevada, Las Vegas.
2. Professor, Dept. of Civil, Environmental, and Construction Engineering, Univ. of Nevada, Las Vegas.
Publisher
American Society of Civil Engineers
Link
https://ascelibrary.org/doi/pdf/10.1061/9780784485477.138
Reference18 articles.
1. Post-fire runoff and erosion from simulated rainfall on small plots, Colorado Front Range;Benavides-Solorio J.;Hydrological Processes,2001
2. Estimating impacts of wildfire and climate variability on streamflow in Victoria, Australia;Guo Y.;Hydrological Processes,2021
3. Implementation of machine-learning classification in remote sensing: An applied review;Maxwell A. E.;International Journal of Remote Sensing,2018
4. Rapid growth of the US wildland-urban interface raises wildfire risk
5. Richards, J. A. (2022). Supervised Classification Techniques. In J. A. Richards (Ed.), Remote Sensing Digital Image Analysis (pp. 263–367). Springer International Publishing. https://doi.org/10.1007/978-3-030-82327-6_8.
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