Can satellite-based data substitute for surveyed data to predict the spatial probability of forest fire? A geostatistical approach to forest fire in the Republic of Korea-Reference-Cited by-同舟云学术

Can satellite-based data substitute for surveyed data to predict the spatial probability of forest fire? A geostatistical approach to forest fire in the Republic of Korea

Published:2019-01-01 Issue:1 Volume:10 Page:719-739
ISSN:1947-5705
Container-title:Geomatics, Natural Hazards and Risk
language:en
Short-container-title:Geomatics, Natural Hazards and Risk

Author:

Lim Chul-Hee¹²^ORCID,Kim You Seung²³⁴,Won Myungsoo³,Kim Sea Jin²^ORCID,Lee Woo-Kyun²

Affiliation:

1. aInstitute of Life Science and Natural Resources, Korea University, Seoul, Republic of Korea;

2. bDepartment of Environmental Science and Ecological Engineering, Korea University, Seoul, Republic of Korea;

3. cForest Ecology and Climate Change Division, National Institute of Forest Science, Seoul, Republic of Korea;

4. dFINECOM Co., Ltd., Seoul, Republic of Korea

Funder

Korean Ministry of Environment as “Climate Change Correspondence Program”

Korea Forest Service

Korea University

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/19475705.2018.1543210

Reference55 articles.

1. Multiple remote sensing data sources to assess spatio-temporal patterns of fire incidence over Campos Amazônicos Savanna Vegetation Enclave (Brazilian Amazon)

2. Forest fire danger, life satisfaction and feelings of safety: evidence from Australia

3. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

4. Climate change presents increased potential for very large fires in the contiguous United States

5. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models

Cited by 35 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Early Wildfire Detection using Different Machine Learning Algorithms;Remote Sensing Applications: Society and Environment;2024-09

2. Doğal Afetlerin İklim Değişikliği Algısına Etkisi: Sosyal Medya Verileri ile Türkiye’de Durum;Süleyman Demirel Üniversitesi Vizyoner Dergisi;2024-05-29

3. Forest fire susceptibility assessment under small sample scenario: A semi-supervised learning approach using transductive support vector machine;Journal of Environmental Management;2024-05

4. Occurrence, probable causes, and management of forest wildfires in the Northern Highlands of Pakistan;Environmental Challenges;2024-04

5. An optimization-based approach for an integrated forest fire monitoring system with multiple technologies and surveillance drones;European Journal of Operational Research;2024-03