Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1207-1211, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-1207-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
24 Jun 2016
A STUDY ON PRODUCING HIGHLY RELIABILE REFERENCE DATA SETS FOR GLOBAL LAND COVER VALIDATION
N. Soyama1, K. Muramatsu2, M. Daigo3, F. Ochiai4, and N. Fujiwara4 1Department of Faculty of Human Studies, Tenri University, 1050 Somanouchi, Tenri-shi, Nara, Japan
2Department of Environmental Science, Nara Women's University, Kitauoya Nishimachi, Nara-shi, Nara, Japan
3Faculty of Economics, Doshisha University, Karasuma-higashi-iru, Imadegawa-dori, Kamigyo-ku, Kyoto-shi, Kyoto, Japan
4KYOSEI Science Centre for Life and Nature, Nara Women's University, Kitauoya Nishimachi, Nara-shi, Nara, Japan
Keywords: Global land cove, Validation, Reference data, FLUXNET, spatial resolution, IGBP class Abstract. Validating the accuracy of land cover products using a reliable reference dataset is an important task. A reliable reference dataset is produced with information derived from ground truth data. Recently, the amount of ground truth data derived from information collected by volunteers has been increasing globally. The acquisition of volunteer-based reference data demonstrates great potential. However information given by volunteers is limited useful vegetation information to produce a complete reference dataset based on the plant functional type (PFT) with five specialized forest classes. In this study, we examined the availability and applicability of FLUXNET information to produce reference data with higher levels of reliability. FLUXNET information was useful especially for forest classes for interpretation in comparison with the reference dataset using information given by volunteers.
Conference paper (PDF, 2252 KB)


Citation: Soyama, N., Muramatsu, K., Daigo, M., Ochiai, F., and Fujiwara, N.: A STUDY ON PRODUCING HIGHLY RELIABILE REFERENCE DATA SETS FOR GLOBAL LAND COVER VALIDATION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1207-1211, https://doi.org/10.5194/isprs-archives-XLI-B8-1207-2016, 2016.

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