Characterizing Topographic Influences of Bushfire Severity Using Machine Learning Models: A Case Study in a Hilly Terrain of Victoria, Australia
Author:
Affiliation:
1. Department of Infrastructure Engineering, The University of Melbourne Faculty of Engineering and Information Technology, Melbourne, VIC, Australia
2. Data61, Commonwealth Scientific and Industrial Research Organisation, Kensington, NSW, Australia
Funder
University of Melbourne
Australian Commonwealth Government
Commonwealth Scientific and Industrial Research Organisation
Research Training Program Scholarships and CSIRO
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
https://ieeexplore.ieee.org/ielam/4609443/9973430/10059148-aam.pdf
Reference103 articles.
1. Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland
2. A comparison of bushfire fuel hazard assessors and assessment methods in dry sclerophyll forest near Sydney, Australia
3. Leveraging Google Earth Engine (GEE) and Landsat Images to Assess Bushfire Severity and Postfire Short‐Term Vegetation Recovery
4. Climate statistics for Australian locations,0
5. Use of Vegetation Change Tracker and Support Vector Machine to Map Disturbance Types in Greater Yellowstone Ecosystems in a 1984–2010 Landsat Time Series
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