Canopy cover mapping in Ratai Bay mangrove forests using airborne LiDAR data

Mulyanto M.; Muhammad Kamal; Muhammad Sufwandika Wijaya

doi:10.1117/12.3009670

29 January 2024 Canopy cover mapping in Ratai Bay mangrove forests using airborne LiDAR data

Mulyanto M., Muhammad Kamal, Muhammad Sufwandika Wijaya

Proceedings Volume 12977, Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet; 129771R (2024) https://doi.org/10.1117/12.3009670
Event: 8th Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 2023, Yogyakarta, Indonesia

Abstract

Active system remote sensing technology is increasingly developing in extracting information on the biophysical aspects of mangrove vegetation, such as mapping the percentage of canopy cover. Mapping the percentage of mangrove canopy cover is essential to maintain the stability of coastal ecosystems. This study uses airborne LiDAR data based on the First Return Cover Index (FRCI) to map and analyze the variation and spatial distribution of the canopy cover percentage in the Ratai Bay mangrove forest, Pesawaran, Lampung, Indonesia. This study aims to (1) Analyze the variation and spatial distribution of the percentage of FRCI-based mangrove canopy cover using LiDAR data and (2) Calculate the accuracy level of the mapping results. FRCI is a LiDAR point cloud data rasterization algorithm that calculates pixel value information from canopy cover recorded by airborne LiDAR. The canopy cover value at each pixel and the regression function obtained from field measurements were integrated to build a model to obtain a map of the percentage of mangrove canopy cover. The resulting map identifies that the Ratai Bay mangrove forest is dominated by the dense and evenly distributed canopy cover class with a mean cover value of 89.78%, generally found in almost all study areas. This FRCI-based mangrove canopy cover percentage mapping has high mapping accuracy with minimum and maximum accuracy values of 92.31% and 93.09%, respectively. The results of this study indicate that the biophysical aspects of mangrove vegetation, especially canopy cover, can be mapped using LiDAR data with the FRCI algorithm.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mulyanto M., Muhammad Kamal, and Muhammad Sufwandika Wijaya "Canopy cover mapping in Ratai Bay mangrove forests using airborne LiDAR data", Proc. SPIE 12977, Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 129771R (29 January 2024); https://doi.org/10.1117/12.3009670

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

LIDAR

Data modeling

Associative arrays

Error analysis

Vegetation

Point clouds

Statistical analysis

Show All Keywords

Keywords/Phrases

Search In:

Publication Years