* Corresponding Author
Comparison of All Return Cover Index (ARCI) and First Return Cover Index (FRCI) Methods for Mapping Percentage of Mangrove Canopy Cover using LiDAR Data
https://doi.org/10.22146/ijg.86917 Mulyanto M(1), Muhammad Kamal(2*)
(1) Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Indonesia
(2) Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Indonesia
(*) Corresponding Author
Abstract
Indonesia has the largest mangrove forest in the world, around 3.3 million hectares or 19.5% of the entire mangrove’s world population. Mangroves have many ecological and economic benefits and are also threatened by several conditions, such as a decrease in area, land, degradation, and the health of mangrove vegetation. One of the methods in maintaining the sustainability of mangrove ecosystems is mapping the biophysical aspects of vegetation, namely mapping the percentage of mangrove canopy cover using field measurements or remote sensing. This study aims to compare the accuracy of Light Detection and Ranging (LiDAR) data based on All Return Cover Index (ARCI) and First Return Cover Index (FRCI) algorithms in mapping the percentage of mangrove canopy cover and analyzing its spatial distribution. The study area is a mangrove forest in Ratai Bay Pesawaran Lampung. This forest is dominated by a dense and evenly distributed canopy cover class with an average value of 78.24% which was acquired using the hemispherical photography method. ARCI and FRCI methods are dominated by the dense and evenly distributed cover class with an average percent cover value of 85.39% and 89.78%, respectively. The accuracy of mapping the percentage of mangrove canopy cover using FRCI is higher than ARCI, with a maximum accuracy value of 93.08% and a standard error of 5.95%. That value shows that using LiDAR data with the FRCI method for mapping the percentage of mangrove canopy cover produces a high accuracy value.
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DOI: https://doi.org/10.22146/ijg.86917
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