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Remote Sens. 2015, 7(1), 378-394; doi:10.3390/rs70100378

Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

1
Department of Forest Management, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan
2
Kyushu Research Center, Forestry and Forest Products Research Institute, 4-11-16 Kurokami, Kumamoto 860-0862, Japan
3
Shikoku Research Center, Forestry and Forest Products Research Institute, 2-915 Asakuranishimachi, Kochi 780-8077, Japan
4
Tohoku Research Center, Forestry and Forest Products Research Institute, 92-25 Nabeyashiki, Shimo-Kuriyagawa, Morioka, Iwate 020-0123 Japan
5
River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Janet Nichol and Prasad S. Thenkabail
Received: 18 July 2014 / Accepted: 16 December 2014 / Published: 31 December 2014
View Full-Text   |   Download PDF [2000 KB, uploaded 31 December 2014]   |  

Abstract

The main objective of this study was to evaluate the effectiveness of adding feature variables, such as forest type information and topographic- and climatic-environmental factors to satellite image data, on the accuracy of stand volume estimates made with the k-nearest neighbor (k-NN) technique in southwestern Japan. Data from the Forest Resources Monitoring Survey—a national plot sampling survey in Japan—was used as in situ data in this study. The estimates obtained from three Landsat Enhanced Thematic Mapper Plus (ETM+) datasets acquired in different seasons with various combinations of additional feature variables were compared. The results showed that although the addition of environmental factors to satellite image data did not always help improve estimation accuracy, the use of summer rainfall (SRF) data had a consistent positive effect on accuracy improvement. Therefore, SRF may be a useful feature variable to consider in stand volume estimation in this study area. Moreover, the use of forest type information is very effective at reducing k-NN estimation errors when using an optimum combination of satellite image data and environmental factors. All of the results indicated that the k-NN technique combined with appropriate feature variables is applicable to nationwide stand volume estimation in Japan. View Full-Text
Keywords: environmental factors; forest type; k-nearest neighbor; Landsat 7 ETM+; stand volume environmental factors; forest type; k-nearest neighbor; Landsat 7 ETM+; stand volume
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Tanaka, S.; Takahashi, T.; Nishizono, T.; Kitahara, F.; Saito, H.; Iehara, T.; Kodani, E.; Awaya, Y. Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables. Remote Sens. 2015, 7, 378-394.

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