Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series-Reference-Cited by-同舟云学术

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Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series

Published:2021-04 Issue: Volume:764 Page:142844
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Michael Yaron,Helman David,Glickman Oren,Gabay David,Brenner Steve,Lensky Itamar M.

Funder

Ministry of Science and Technology, Israel

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference72 articles.

1. Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques;Adab;Nat. Hazards,2013

2. Big data and predictive analytics in fire risk using weather data;Agarwal;Risk Anal.,2020

3. Introducing a new remote sensing-based model for forecasting Forest fire danger conditions at a four-day scale;Ahmed;Remote Sens.,2019

4. Using multi variate data mining techniques for estimating fire susceptibility of Tyrolean forests;Arpaci;Appl. Geogr.,2014

5. Mapping Forest fuels through vegetation phenology: the role of coarse-resolution satellite time-series;Bajocco;PLoS One,2015

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2. A Novel Feature Extraction Method of Environmental Factors for Forest Fire Risk Modeling Based on Adaptive Time Window;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

3. Forest fire estimation and risk prediction using multispectral satellite images: Case study;Natural Hazards Research;2024-06

4. Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation;Science of The Total Environment;2024-05

5. AutoST-Net: A Spatiotemporal Feature-Driven Approach for Accurate Forest Fire Spread Prediction from Remote Sensing Data;Forests;2024-04-17

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