HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Agriculture / Faculty of Agriculture >
Peer-reviewed Journal Articles, etc >

Detection of ambrosia beetles using a pan-sharpened image generated from ALOS/AVNIR-2 and ALOS/PRISM imagery

Files in This Item:
Detection of ambrosia beetles using ALOS.pdf44.59 kBPDFView/Open
Please use this identifier to cite or link to this item:

Title: Detection of ambrosia beetles using a pan-sharpened image generated from ALOS/AVNIR-2 and ALOS/PRISM imagery
Authors: Sonobe, Rei Browse this author
Tani, HIroshi Browse this author
Wang, Xiufeng Browse this author
Keywords: ambrosia beetle
oak wilt
satellite imagery
visual detection
Issue Date: 2014
Publisher: Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria
Journal Title: Forest Systems
Volume: 23
Issue: 1
Start Page: 178
End Page: 182
Publisher DOI: 10.5424/fs/2014231-04572
Abstract: Aim of study: The ambrosia beetle, Platypus quercivorus, is a vector of Japanese oak wilt, which causes massive mortality of oak trees in Japan. ALOS/AVNIR-2 true color images can be used to help detect areas of oak wilt, although such detection by inventory surveys is not realistic. Applying pan-sharpening techniques, a higher spatial resolution multispectral image can be generated from lower-resolution multispectral images and higher-resolution panchromatic images. In this study, some pan-sharpening algorithms were considered and evaluated for the detection of damage points. Area of study: The oak forests in Kanazawa prefecture, Japan. Materials and methods: The ALOS/AVNIR-2 and ALOS/PRISM sensors were used. The pan-sharpening algorithms adopted were: Brovey transformation, Modified IHS transformation, Wavelet transformation, Ehlers fusion and High Pass Filter Resolution Merge. Four types of quantitative spectral analyses and visual detection were conducted to evaluate these algorithms. Main results: The Brovey transformation was the most useful algorithm to detect damage points, although it had an issue with the preservation of spectral characteristics.Research highlights: The detection rate of damage points was improved in 50% by applying the Brovey algorithm to a 10 m panchromatic image and 62.5 m multispectral image.
Type: article
Appears in Collections:農学院・農学研究院 (Graduate School of Agriculture / Faculty of Agriculture) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 薗部 礼

Export metadata:

OAI-PMH ( junii2 , jpcoar_1.0 )

MathJax is now OFF:


 - Hokkaido University