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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
| 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 | | crop | | Kauth-Thomas transform | | Landsat-8 | | 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: http://www.tandfonline.com/doi/abs/10.1080/01431161.2017.1323286. |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/70679 |
| 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)
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Submitter: 山谷 祐貴
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