Seasonal variation in classification accuracy of forest-cover types examined by a single band or band combinations

Abstract

In vegetation remote sensing, classification accuracy cannot be fixed, due to seasonal variations in spectral reflectance characteristics. This study aims to clarify the seasonal variability of classification accuracy by forest-cover type. In particular, this paper describes seasonal variability by each band or band combinations. The study area is located in the vicinities of Hisayama and Sasaguri in Fukuoka Prefecture, Japan. Natural broadleaved, coniferous plantation, and bamboo forests were studied. Supervised classification was applied to six SPOT/HRV images taken in 1997. Kappa analysis was applied to assess the classification accuracy and compare any two error matrices. The results revealed that some single band or two-band combinations were as accurate as, or more accurate than, the full band (all three bands). The disadvantages of using a full band were especially apparent in the season with high classification accuracy. This study indicates that using all given bands does not necessarily result in the highest classification accuracy. This study also suggests that band selection within the scope of forest type and seasonal variability can contribute to better forest-cover-type classifications.

Key words:

• Classification accuracy
• SPOT/HRV
• Seasonal variation
• Forest-cover type
• Kappa coefficient