Improved Forest Cover Classification in an Industrialized Mountain Area in Japan

Abstract

Space-borne satellite imagery is increasingly used for classifying and characterizing forest cover in mountain environments. Using medium resolution satellite imagery, acquired over an industrial mountain area near the city of Naeba, in the central part of Honshu, Japan, this study attempts to characterize forest cover types situated in an area affected by prolonged anthropogenic land use and land cover change (LUCC) processes. The image was topographically corrected, and training sites selected and assessed for their spectral separability between forest classes. Using the ground truthed training sites and a supervised spectral angle mapper (SAM) classifier, dominant forest cover types were classified. Post-classification forest cover classification accuracies range between 77–89%. Results highlight how an assessment of the spectral separability of forest cover types prior to image classification, combined with ground validation that focuses on documenting and noting areas affected by human modifications to the forest, can aid in refining the forest classification training areas, which in turn can lead to improved image classification accuracies. Through refinement of the training areas used in the classification via ground truthing, it is possible to account for localized land use and land cover disturbances (ie forest harvesting, thinning) that create non-representative training areas. It is then possible to select additional training areas that are more representative of a forest spectral class and not a localized anomaly created via human disturbance.