Abstract
Canopy cover is one of the most important variables in ecology, hydrology, and forest management, and useful as a basis for defining forests. LiDAR is an active remote sensing method that provides the height information of an object in three-dimensional space. The method allows for the mapping of terrain, canopy height and cover. Its only setback is that it has to be integrated with Landsat to cover a large area. The main objective of this study is to generate the canopy cover estimation model using Landsat 8 OLI and LiDAR. Landsat 8 OLI vegetation indices and LiDAR-derived canopy cover estimation, through First Return Canopy Index (FRCI) method, were used to obtain a regression model. The performance of this model was then assessed using correlation, aggregate deviation, and raster display. Lastly, the best canopy cover estimation was obtained using equation, FRCI = 2.22 + 5.63Ln(NDVI), with R2 at 0.663, standard deviation at 0.161, correlation between actual and predicted value at 0.663, aggregate deviation at -0.182 and error at 56.10%.
Highlights
Canopy hydrology cover is one of the most important parameters used in ecology and forest management (Nakamura et al, 2017)
LiDAR is well-suited for canopy cover estimation since it can penetrate through the canopy (Korhonen & Morsdorf, 2014)
Plot sample identification The sample point was selected using purposive sampling by considering the presence of clouds, stand density based on canopy cover value, and canopy height
Summary
Canopy hydrology cover is one of the most important parameters used in ecology and forest management (Nakamura et al, 2017) It is defined as an area of land covered by vertical projections of canopies, which describes the structural conditions of a forest (Jennings et al, 1999). Other benefits of using this system are: can be used during day and night, more effective and efficient in operational cost compared to terrestrial surveys, can provide high precision and accurate elevation data, and can process a large amount of data in a short period of time (Jakubowski et al, 2013) This technology has promising benefits, it is often limited in spatial coverage, and is relatively expensive due to its acquisition cost. It can be integrated with another remote sensing technology
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