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 >

Mapping crop cover using multi-temporal Landsat 8 OLI imagery

Files in This Item:
Manuscript_TRES-PAP-2016-1123.docx.pdf760.89 kBPDFView/Open
Please use this identifier to cite or link to this item:

Title: Mapping crop cover using multi-temporal Landsat 8 OLI imagery
Authors: Sonobe, Rei Browse this author →KAKEN DB
Yamaya, Yuki Browse this author
Tani, Hiroshi Browse this author →KAKEN DB
Wang, Xiufeng Browse this author →KAKEN DB
Kobayashi, Nobuyuki Browse this author
Mochizuki, Kan-ichiro Browse this author
Keywords: Bayesian optimisation
Kauth-Thomas transform
random forests
vegetation indices
Issue Date: 18-May-2017
Publisher: Taylor & Francis
Journal Title: International Journal of Remote Sensing
Volume: 38
Issue: 15
Start Page: 4348
End Page: 4361
Publisher DOI: 10.1080/01431161.2017.1323286
Abstract: ABSTRACT: Crop classification maps are useful for estimating amounts of crops harvested, which could help address challenges in food security. Remote-sensing techniques are useful tools for generating crop maps. Optical remote sensing is one of the most attractive options because it offers vegetation indices (VIs) with frequent revisits and has adequate spatial and spectral resolution and some data has been distributed free of charge. However, sufficient consideration has not been given to the potential of VIs calculated from Landsat 8 Operational Land Imager (OLI) data. This article describes the use of Landsat 8 OLI data for the classification of crops in Hokkaido, Japan. In addition to reflectance, VIs calculated from simple formulas that consisted of combinations of two or more reflectance wavebands were evaluated, as well as the six components of the Kauth–Thomas transform. The VIs based on shortwave infrared bands (bands 6 or 7) improved classification accuracy, and using a combination of all derived data from Landsat 8 OLI data resulted in an overall accuracy of 94.5% (allocation disagreement = 4.492 and quantity disagreement = 1.017).
Rights: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 18 May 2017, available online:
Type: article (author version)
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: 山谷 祐貴

Export metadata:

OAI-PMH ( junii2 , jpcoar )

MathJax is now OFF:


 - Hokkaido University