Canopy Density Estimation Model in Peat Swamp Forest Using LiDAR Data and Landsat 8 OLI Satellite Imagery

Canopy density is one of the important parameters in measuring the forest conditions. Canopy density can be estimated by using a remote sensing technology system. Light Detection and Ranging (LiDAR) is an active remote sensing system which uses a laser that is emitted by a sensor to the objects on the earth surface. For a wide area, image utilization which solely relies on LiDAR is still relatively expensive, so it is necessary to develop a method that combine LiDAR data with other medium resolution images such as Landsat 8 OLI imagery. Therefore, this research was conducted to obtain the canopy density estimation model from LiDAR and Landsat 8 OLI data. The results showed that the best estimation model at the study site, PT Global Alam Lestari's peat swamp forest was FRCI = - 0.0171 + 8.691 GRVI. The equation model had coefficient of determination (R²) of 50.2%, standard deviation value (s) of 0.101, aggregate deviation (SA) value of 0.459, and correlation coefficient (r) between the actual FRCI and the estimation FRCI (best model) of 0.503.