DOI: https://doi.org/10.14710/geoplanning.10.2.123-134

Classification of Mangrove Vegetation Structure using Airborne LiDAR in Ratai Bay, Lampung Province, Indonesia

Muhammad Sufwandika Wijaya orcid  -  Badan Informasi Geospasial, Bogor, Indonesia, Indonesia
*Muhammad Kamal orcid scopus publons  -  Faculty of Geography, Universitas Gadjah Mada, Indonesia
Prima Widayani orcid scopus  -  Faculty of Geography, Universitas Gadjah Mada, Indonesia
Sanjiwana Arjasakusuma orcid scopus  -  Faculty of Geography, Universitas Gadjah Mada, Indonesia

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Abstract

Mapping and inventory of the distribution and composition of mangrove vegetation structures are crucial in managing mangrove ecosystems. The availability of airborne LiDAR remote sensing technology provides capability of mapping vegetation structures in three dimensions. It provides an alternative data source for mapping and inventory of the distribution of mangrove ecosystems. This study aims to test the ability of airborne LiDAR data to classify mangrove vegetation structures conducted in Ratai Bay, Pesawaran District, Lampung Province. The classification system applied integrates structure attributes of lifeforms, canopy height, and canopy cover percentage. Airborne LiDAR data are derived into canopy height models (CHM) and canopy cover percentage models, then grouped by examining statistics and the zonation distribution of mangroves in the study area. The results of this study show that airborne LiDAR data are able to map vegetation structures accurately. The canopy height model derived using a pit-free algorithm can represent the maximum tree height with an error range of 3.17 meters and 82.3-88.6% accuracy. On the other hand, the canopy cover percentage model using LiDAR Fraction Cover (LFC) tends to be overestimate, with an error range of 16.6% and an accuracy of 79.6-94.7%. Meanwhile, the classification results of vegetation structures show an overall accuracy of 77%.

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Keywords: LIDAR; mangroves; vegetation structure
Funding: The research was funded by Rekognisi Tugas Akhir (RTA – Final Project Recognition Grant) 2023 from Universitas Gadjah Mada, Indonesia, under Grant number 5075/UN1.P.II/Dit-Lit/PT.01.01/2023.

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Section: Original Research
Language : EN
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