* 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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Azis, M. A. (2019). Pendugaan Tutupan Tajuk pada Hutan Rawa Gambut Menggunakan Data LiDAR. Studi Kasus: PT. Rimba Makmur Utama, Kalimantan Tengah. (Bachelor thesis, IPB University, Bogor, Indonesia). Retrieved from http://repository.ipb.ac.id.
Arkes, J. (2023). Regression Analysis: A Practical Introduction. Second Edition, Routledge, London and New York.
Bunting, P., Rosenqvist, A., Lucas R. M., Rebelo, L. M., Hilarides, L., Thomas, N., Hardy, A., Itoh, T., Shimada, M. & Finlayson, C. M. (2018). The Global Mangrove Watch—A New 2010 Global Baseline of Mangrove Extent. Remote Sensing. 10 (10), pp. 1—19. doi: https://doi.org/10.3390/rs10101669.
Dharmawan, I. W. E. (2020). Hemispherical Photography: analisis tutupan kanopi komunitas mangrove. Makassar: Nas Media Pustaka.
Drezner, Z., & Turel, O. (2011). Normalizing variables with too-frequent values using a Kolmogorov–Smirnov test: A practical approach. Computers & Industrial Engineering, 61(4), 1240-1244. doi: https://doi.org/10.1016/j.cie.2011.07.015.
Frazer, G. W., Canham, C. D., & Lertzman, K. P. (1999). Gap Light Analyzer (GLA), Version 2.0: Imaging software to extract canopy structure and gap light transmission indices from true-colour fisheye photographs, users manual and program documentation. Millbrook, New York: Simon Fraser University, Burnaby, British Columbia, and the Institute of Ecosystem Studies,
Ghozali, I. (2011). Aplikasi multivariate dengan program IBM SPSS 19. Semarang: Badan Penerbit Universitas Diponegoro, 68.
Hamilton, S. E., & Casey, D. (2016). Creation of a high spatio‐temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC‐21). Global Ecology and Biogeography, 25(6), 729-738. doi: https://doi.org/10.1111/geb.12449.
Hopkinson, C., & Chasmer, L. (2009). Testing LiDAR models of fractional cover across multiple forest ecozones. Remote Sensing of Environment, 113(1), 275-288. doi: https://doi.org/10.1016/j.rse.2008.09.012.
Kamal, M., Farda, N. M., Jamaluddin, I., Parela, A., Wikantika, K., Prasetyo, L. B., & Irawan, B. (2020). A preliminary study on machine learning and google earth engine for mangrove mapping. In IOP Conference Series: Earth and Environmental Science, 500(1), 012038. doi: https://doi.org/10.1088/1755-1315/500/1/012038.
Korhonen, L., Korhonen, K. T., Rautiainen, M., & Stenberg, P. (2006). Estimation of forest canopy cover: a comparison of field measurement techniques. Silva Fennica, 40(4), 577—588. doi: https://doi.org/10.14214/sf.315.
Ma, Q., Su, Y., & Guo, Q. (2017). Comparison of canopy cover estimations from airborne LiDAR, aerial imagery, and satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9), 4225-4236. doi: https://doi.org/10.1109/JSTARS.2017.2711482.
M, M. (2023) Perbandingan Metode All Return Cover Index (ARCI) dan First Return Cover Index (FRCI) untuk Pemetaan Persentase Tutupan Kanopi Mangrove Menggunakan Data Lidar. (Bachelor thesis, UGM, Yogyakarta, Indonesia).
Prasetyo, L. B., Nursal, W. I., Setiawan, Y., Rudianto, Y., Wikantika, K., & Irawan, B. (2019, October). Canopy cover of mangrove estimation based on airborne LIDAR & Landsat 8 OLI. In IOP Conference Series: Earth and Environmental Science (Vol. 335, No. 1, p. 012029). IOP Publishing.
Rahardian, A., Prasetyo, L. B., Setiawan, Y., & Wikantika, K. (2019). Tinjauan historis data dan informasi luas mangrove Indonesia. Media Konservasi, 24(2), 163-178. doi: https://doi.org/10.29243/medkon.24.2.163-178.
Robertson, A. I., & Alongi, D. M. (Eds.). (1992). Tropical Mangrove Ecosystems. Washington, DC: American Geophysical Union.
Smith, A. M., Falkowski, M. J., Hudak, A. T., Evans, J. S., Robinson, A. P., & Steele, C. M. (2009). A cross-comparison of field, spectral, and lidar estimates of forest canopy cover. Canadian Journal of Remote Sensing, 35(5), 447-459. doi: https://doi.org/10.5589/m09-038.
Verhevden, A., Kairo, J. G., Beeckman, H., & Koedam, N. (2004). Growth rings, growth ring formation and age determination in the mangrove Rhizophora mucronata. Annals of Botany, 94(1), 59. doi: https://doi.org/10.1093/aob/mch115.
Wijaya, M, S., Kamal, M., Widayani, P., & Arjasakusuma, S. (2023). Classification of Mangrove Structure using Airborne LiDAR in Ratai Bay, Lampung Province, Indonesia. Journal of Geomatics and Planning, 10(2), 123—134. https://doi.org/10.14710/geoplanning.10.2.123-134.
DOI: https://doi.org/10.22146/ijg.86917
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Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)
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