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

Remote Sensing

Shinya Tanaka⁴  Tomoaki Takahashi¹  Tomohiro Nishizono⁴  Fumiaki Kitahara³  Hideki Saito⁴  Toshiro Iehara¹  Eiji Kodani²  Yoshio Awaya⁵  Janet Nichol⁶ 

[1] Kyushu Research Center, Forestry and Forest Products Research Institute, 4-11-16 Kurokami, Kumamoto 860-0862, Japan; E-Mails:;Tohoku Research Center, Forestry and Forest Products Research Institute, 92-25 Nabeyashiki, Shimo-Kuriyagawa, Morioka, Iwate 020-0123 Japan; E-Mail:;Shikoku Research Center, Forestry and Forest Products Research Institute, 2-915 Asakuranishimachi, Kochi 780-8077, Japan; E-Mail:;Department of Forest Management, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan; E-Mails:;River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan; E-Mail:Department of Forest Management, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan;

关键词: environmental factors;    forest type;    k-nearest neighbor;    Landsat 7 ETM+;    stand volume;   

DOI  :  10.3390/rs70100378

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.