Geospatial technologies for estimating post-wildfire severity through satellite imagery and vegetation types: a case study of the Gangneung Wildfire, South Korea

IF 1 4区 地球科学 Q4 GEOSCIENCES, MULTIDISCIPLINARY
Liadira K. Widya, Chang-Wook Lee
{"title":"Geospatial technologies for estimating post-wildfire severity through satellite imagery and vegetation types: a case study of the Gangneung Wildfire, South Korea","authors":"Liadira K. Widya, Chang-Wook Lee","doi":"10.1007/s12303-023-0045-2","DOIUrl":null,"url":null,"abstract":"<p>Wildfires have caused natural environmental damage that has contributed to deforestation, consequently demonstrating a significant influence on atmospheric emissions. Wildfires occur frequently in South Korea, especially during the spring season. This study assessed post-wildfires areas in Gangneung, South Korea, on April 11, 2023, which were generated by implementing remote sensing technology and statistical analysis. Remote sensing and classification techniques, including PlanetScope, have been developed for identifying wildfire-damaged areas. The method for classifying post-wildfire mapping estimation includes the utilization of deep learning approaches, especially using the U-Net architecture. Therefore, the assessment of wildfire severity can be conducted using Sentinel-2 and Sentinel-5P imagery in addition to an analysis of the vegetation type and air pollutant within the affected region. In the present study, Sentinel-2 imagery was to generate spectral indices, including the differenced normalized burn ratio (dNBR), differenced normalized difference moisture index (dNDMI), differenced soil adjusted vegetation index (dSAVI), and differenced normalized vegetation index (dNDVI). Sentinel-5P imagery was utilized to produce carbon monoxide (CO) column number densities. The estimation of wildfire areas was conducted using a PlanetScope classified image with the U-Net classifier, which was evaluated based on the overall accuracy value of 95% and kappa accuracy of 0.901. The wildfire severity level was shown by dNBR, which was correlated with the parameters, including RBR, dNDMI, dSAVI, dNDVI, and CO. The statistical analysis demonstrated a significant and positive correlation between the wildfire severity and the parameters. Moreover, the average of vegetation indices (NDMI, SAVI, and NDVI) before and after a wildfire were found to decrease by vegetation type, including 17.55% in mixed barren land areas, 17.49% in other grasses, 24.71% in mixed forest land, 22.48% in coniferous land, 13.48% in fields, and 4.29% in paddy fields. On the basis of the results, these estimates can be employed to identify the level of damage caused by wildfires to vegetation and air quality.</p>","PeriodicalId":12659,"journal":{"name":"Geosciences Journal","volume":"3 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geosciences Journal","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s12303-023-0045-2","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Wildfires have caused natural environmental damage that has contributed to deforestation, consequently demonstrating a significant influence on atmospheric emissions. Wildfires occur frequently in South Korea, especially during the spring season. This study assessed post-wildfires areas in Gangneung, South Korea, on April 11, 2023, which were generated by implementing remote sensing technology and statistical analysis. Remote sensing and classification techniques, including PlanetScope, have been developed for identifying wildfire-damaged areas. The method for classifying post-wildfire mapping estimation includes the utilization of deep learning approaches, especially using the U-Net architecture. Therefore, the assessment of wildfire severity can be conducted using Sentinel-2 and Sentinel-5P imagery in addition to an analysis of the vegetation type and air pollutant within the affected region. In the present study, Sentinel-2 imagery was to generate spectral indices, including the differenced normalized burn ratio (dNBR), differenced normalized difference moisture index (dNDMI), differenced soil adjusted vegetation index (dSAVI), and differenced normalized vegetation index (dNDVI). Sentinel-5P imagery was utilized to produce carbon monoxide (CO) column number densities. The estimation of wildfire areas was conducted using a PlanetScope classified image with the U-Net classifier, which was evaluated based on the overall accuracy value of 95% and kappa accuracy of 0.901. The wildfire severity level was shown by dNBR, which was correlated with the parameters, including RBR, dNDMI, dSAVI, dNDVI, and CO. The statistical analysis demonstrated a significant and positive correlation between the wildfire severity and the parameters. Moreover, the average of vegetation indices (NDMI, SAVI, and NDVI) before and after a wildfire were found to decrease by vegetation type, including 17.55% in mixed barren land areas, 17.49% in other grasses, 24.71% in mixed forest land, 22.48% in coniferous land, 13.48% in fields, and 4.29% in paddy fields. On the basis of the results, these estimates can be employed to identify the level of damage caused by wildfires to vegetation and air quality.

通过卫星图像和植被类型估算火灾后严重程度的地理空间技术:韩国江陵野火案例研究
野火对自然环境造成破坏,导致森林砍伐,从而对大气排放产生重大影响。韩国经常发生野火,尤其是在春季。本研究通过遥感技术和统计分析,对 2023 年 4 月 11 日韩国江陵的野火后地区进行了评估。包括 PlanetScope 在内的遥感和分类技术已被开发用于识别野火灾区。对野火后绘图估算进行分类的方法包括利用深度学习方法,特别是使用 U-Net 架构。因此,除了对受影响区域内的植被类型和空气污染物进行分析外,还可利用哨兵-2 和哨兵-5P 图像对野火严重程度进行评估。在本研究中,哨兵-2 图像用于生成光谱指数,包括差分归一化燃烧比(dNBR)、差分归一化差异水分指数(dNDMI)、差分土壤调整植被指数(dSAVI)和差分归一化植被指数(dNDVI)。哨兵-5P 图像用于生成一氧化碳 (CO) 柱数密度。利用 PlanetScope 分类图像和 U-Net 分类器对野火区域进行了估算,根据 95% 的总体准确率和 0.901 的 kappa 准确率进行了评估。野火严重程度由 dNBR 表示,dNBR 与 RBR、dNDMI、dSAVI、dNDVI 和 CO 等参数相关。统计分析表明,野火严重程度与参数之间存在显著的正相关。此外,不同植被类型在野火前后的植被指数(NDMI、SAVI 和 NDVI)平均值均有所下降,其中混交荒地下降 17.55%,其他草地下降 17.49%,混交林地下降 24.71%,针叶林地下降 22.48%,田地下降 13.48%,水田下降 4.29%。根据这些估算结果,可以确定野火对植被和空气质量的破坏程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Geosciences Journal
Geosciences Journal 地学-地球科学综合
CiteScore
2.70
自引率
8.30%
发文量
33
审稿时长
6 months
期刊介绍: Geosciences Journal opens a new era for the publication of geoscientific research articles in English, covering geology, geophysics, geochemistry, paleontology, structural geology, mineralogy, petrology, stratigraphy, sedimentology, environmental geology, economic geology, petroleum geology, hydrogeology, remote sensing and planetary geology.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信