Wildfire susceptibility mapping by incorporating damage proxy maps, differenced normalized burn Ratio, and deep learning algorithms based on sentinel-1/2 data: a case study on Maui Island, Hawaii-Reference-Cited by-同舟云学术

Wildfire susceptibility mapping by incorporating damage proxy maps, differenced normalized burn Ratio, and deep learning algorithms based on sentinel-1/2 data: a case study on Maui Island, Hawaii

Published:2024-05-14 Issue:1 Volume:61 Page:
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Ramayanti Suci¹^ORCID,Kim BongChan¹^ORCID,Park Sungjae²^ORCID,Lee Chang-Wook¹²^ORCID

Affiliation:

1. Department of Science Education, Kangwon National University, Chuncheon-si, Gangwon-do, Republic of Korea

2. Department of Smart Regional Innovation, Kangwon National University, Chuncheon-si, Gangwon-do, Republic of Korea

Funder

Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation

National Research Foundation of Korea (NRF) grant funded by the Korea government

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2024.2353982

Reference77 articles.

1. Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

2. Spatial Mapping of Groundwater Potential Using Entropy Weighted Linear Aggregate Novel Approach and GIS

3. Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning

4. A deep learning ensemble model for wildfire susceptibility mapping

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