Mapping Wildfire Burned Area Using GNSS-Reflectometry in Densely Vegetated Regions with Complex Topography: A Machine Learning Approach-Reference-Cited by-同舟云学术

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Mapping Wildfire Burned Area Using GNSS-Reflectometry in Densely Vegetated Regions with Complex Topography: A Machine Learning Approach

Published:2024-07-07 Issue: Volume:1 Page:2072-2076
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Kannan Archana¹,Melebari Amer¹,Tsagkatakis Grigorios²,Nelson Kurtis³,Ravindra Vinay⁴,Nag Sreeja⁴,Moghaddam Mahta¹

Affiliation:

1. University of Southern California,Ming Hsieh Department of Electrical and Computer Engineering,Los Angeles,CA,USA

2. Foundation for Research and Technology - Hellas (FORTH),Institute of Computer Science,Heraklion,Greece

3. EROS Center at United States Geological Survey,Sioux Falls,SD,United States

4. Bay Area Environmental Research Institute at NASA Ames Research Centre,Moffett Field,United States

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10641487.pdf?arnumber=10641487

Reference27 articles.

1. U.S. fires became larger, more frequent, and more widespread in the 2000s

2. Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring

3. Climate warming increases extreme daily wildfire growth risk in California

4. Trends in tree cover change over three decades related to interannual climate variability and wildfire in California

5. Application of remote sensing and geographic information systems to forest fire hazard mapping

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