Improved Vegetation and Wildfire Fuel Type Mapping Using NASA AVIRIS-NG Hyperspectral Data, Interior AK

C. Smith, S. Panda, U. Bhatt, F. Meyer, Robert W. Haan
{"title":"Improved Vegetation and Wildfire Fuel Type Mapping Using NASA AVIRIS-NG Hyperspectral Data, Interior AK","authors":"C. Smith, S. Panda, U. Bhatt, F. Meyer, Robert W. Haan","doi":"10.1109/IGARSS39084.2020.9324136","DOIUrl":null,"url":null,"abstract":"In Alaska, wildfire map products have traditionally been generated from lower spatial and spectral resolution Landsat imagery such as LANDFIRE Program's Existing Vegetation Type (EVT) resulting in products that do not accurately assess fire fuel types for local sites. In this study we demonstrate the efficacy of AVIRIS-NG hyperspectral data for mapping Interior Alaska's vegetation and fuel type. Based on an evaluation of field plot data collected by the project team in 2019, the new vegetation map derived from AVIRIS-NG at Viereck IV level resulted in a 73% classification accuracy compared to the 32% accuracy of the LANDFIRE's product EVT derived from Landsat 8. Not only did our product more accurately classify fire fuels, it was also able to identify 20 dominant vegetation classes (percent cover > 1%) while the EVT product only identified eight dominant classes within the study area.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"37 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS39084.2020.9324136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In Alaska, wildfire map products have traditionally been generated from lower spatial and spectral resolution Landsat imagery such as LANDFIRE Program's Existing Vegetation Type (EVT) resulting in products that do not accurately assess fire fuel types for local sites. In this study we demonstrate the efficacy of AVIRIS-NG hyperspectral data for mapping Interior Alaska's vegetation and fuel type. Based on an evaluation of field plot data collected by the project team in 2019, the new vegetation map derived from AVIRIS-NG at Viereck IV level resulted in a 73% classification accuracy compared to the 32% accuracy of the LANDFIRE's product EVT derived from Landsat 8. Not only did our product more accurately classify fire fuels, it was also able to identify 20 dominant vegetation classes (percent cover > 1%) while the EVT product only identified eight dominant classes within the study area.
利用NASA AVIRIS-NG高光谱数据改进植被和野火燃料类型制图,Interior AK
在阿拉斯加,野火地图产品传统上是由较低空间和光谱分辨率的Landsat图像生成的,例如LANDFIRE计划的现有植被类型(EVT),导致产品不能准确评估当地的火灾燃料类型。在这项研究中,我们证明了AVIRIS-NG高光谱数据在绘制阿拉斯加内陆植被和燃料类型方面的功效。根据对项目团队在2019年收集的现场数据的评估,Viereck IV级别的AVIRIS-NG衍生的新植被图的分类精度为73%,而LANDFIRE衍生的Landsat 8产品EVT的分类精度为32%。我们的产品不仅能够更准确地对燃料进行分类,而且还能够识别出20种优势植被类别(覆盖率为1%),而EVT产品在研究区域内仅识别出8种优势植被类别。
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
自引率
0.00%
发文量
0
×
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