Author:
Saleh Muhammad Buce,Malta Daerangga ,Prasetyo Lilik Budi,Yudi Setiawan ,Sahid Hudjimartsu ,Wijayanto Arif Kurnia
Abstract
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.
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