File Download

There are no files associated with this item.

  • Find it @ UNIST can give you direct access to the published full text of this article. (UNISTARs only)
Related Researcher

임정호

Im, Jungho
Intelligent Remote sensing and geospatial Information Science Lab.
Read More

Views & Downloads

Detailed Information

Cited time in webofscience Cited time in scopus
Metadata Downloads

Full metadata record

DC Field Value Language
dc.citation.endPage 1298 -
dc.citation.number 6 -
dc.citation.startPage 1285 -
dc.citation.title Korean Journal of Remote Sensing -
dc.citation.volume 35 -
dc.contributor.author Park, Sumin -
dc.contributor.author Son, Bokyung -
dc.contributor.author Im, Jungho -
dc.contributor.author Lee, Jaese -
dc.contributor.author Lee, Byungdoo -
dc.contributor.author Kwon, ChunGeun -
dc.date.accessioned 2023-12-21T18:13:50Z -
dc.date.available 2023-12-21T18:13:50Z -
dc.date.created 2020-01-03 -
dc.date.issued 2019-12 -
dc.description.abstract Drought is one of the factors that can cause wildfires. Drought is related to not only the occurrence of wildfires but also their frequency, extent and severity. In South Korea, most wildfires occur in dry seasons (i.e. spring and autumn), which are highly correlated to drought events. In this study, we examined the relationship between wildfire occurrence and drought factors, and developed satellite-based new drought indices for assessing wildfire risk over South Korea. Drought factors used in this study were high-resolution downscaled soil moisture, Normalized Different Water Index (NDWI), Normalized Multi-band Drought Index (NMDI), Normalized Different Drought Index (NDDI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI) and Vegetation Condition Index (VCI). Drought indices were then proposed through weighted linear combination and one-class support vector machine (One-class SVM) using the drought factors. We found that most drought factors, in particular, soil moisture, NDWI, and PCI were linked well to wildfire occurrence. The validation results using wildfire cases in 2018 showed that all five linear combinations produced consistently good performance (> 88% in occurrence match). In particular, the combination of soil moisture and NDWI, and the combination of soil moisture, NDWI, and precipitation were found to be appropriate for representing wildfire risk. -
dc.identifier.bibliographicCitation Korean Journal of Remote Sensing, v.35, no.6, pp.1285 - 1298 -
dc.identifier.doi 10.7780/kjrs.2019.35.6.3.11 -
dc.identifier.issn 1225-6161 -
dc.identifier.scopusid 2-s2.0-85119159903 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/30773 -
dc.language 한국어 -
dc.publisher 대한원격탐사학회 -
dc.title.alternative Development of Satellite-based Drought Indices for Assessing Wildfire Risk -
dc.title Development of Satellite-based Drought Indices for Assessing Wildfire Risk -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.identifier.kciid ART002538074 -
dc.type.docType Article -
dc.description.journalRegisteredClass scopus -
dc.description.journalRegisteredClass kci -
dc.subject.keywordAuthor Wildfire -
dc.subject.keywordAuthor drought index -
dc.subject.keywordAuthor soil moisture downscaling -
dc.subject.keywordAuthor Random forest -
dc.subject.keywordAuthor one-class SVM -

qrcode

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.