SPECTRAL CHARACTERIZATION OF A CLOSED CANOPY AND OPEN CANOPY FOREST IN NORTHERN SIERRA MADRE NATURAL PARK
- 1Training Centre for Applied Geodesy and Photogrammetry, National Engineering Centre, University of the Philippines, Diliman, Philippines
- 2Department of Geodetic Engineering, College of Engineering, University of the Philippines, Diliman, Philippines
- 3Philippine Council for Industry, Energy and Emerging Technology Research and Development, Department of Science and Technology, Philippines
Keywords: Spectral Mixture Analysis, Hyperspectral Imagery, Multiple Endmember Spectral Mixture Analysis
Abstract. Forest lands play crucial roles in nutrient recycling and climate regulation. The change of closed canopy forests to open canopy forests may indicate disturbance within the closed canopy forest. Within the local context of the Philippines, few studies have been conducted to monitor changes in closed canopy forest lands. Efforts to do so are limited by the spatial extent, remoteness and ruggedness of closed canopy forests. Satellite imagery can cover the spatial extent of forest lands as well as provide constant revisit periods for monitoring. However, while multispectral imaging can detect changes in land cover, it has limitations when detecting the subtler change from closed canopy to open canopy forest cover. This study aims to provide baseline spectral characterization of a closed canopy forest in the Philippines. For this study, a hyperspectral sensor (EO1-Hyperion) with 198 band channels ranging from 426.82 nm to 2395.50 nm and a pixel size of 30 m was used to characterize the spectral variations of closed canopy forest, open canopy forest, shrubs and cropland in Northern Sierra Madre, Philippines. Multiple endmember spectral mixture analysis (MESMA) was employed to sort the image into classes as well as to characterize intra-spectral variations among the identified classes. Spectral library endmembers were assembled, optimized and used to classify the image. The spectral libraries were optimized by using Endmember Average Root Mean Square Error (EAR) , Minimum Average Spectral Angle (MASA) and Iterative Endmember Selection (IES). Results overall agreement is 0.56 for EAR and IES and kappa coefficient is at 0.4.