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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  1. Ahmed, S., Sarker, S. K., Friess, D. A., Kamruzzaman, M., Jacobs, M., Islam, M. A., Alam, M. A., Suvo, M. J., Sani, M. N. H., Dey, T., Naabeh, C. S. S., & Pretzsch, H. (2022). Salinity reduces site quality and mangrove forest functions. From monitoring to understanding. Science of the Total Environment, 853(July), 158662. https://doi.org/10.1016/j.scitotenv.2022.158662">[Crossref]

  2. Almeida, D. R. ., Stark, S. C., Chazdon, R., Nelson, B. W., Cesar, R. G., Meli, P., Gorgens, E. B., Duarte, M. M., Valbuena, R., Moreno, V. S., Mendes, A. F., Amazonas, N., Gonçalves, N. B., Silva, C. A., Schietti, J., & Brancalion, P. H. S. (2019). The effectiveness of lidar remote sensing for monitoring forest cover attributes and landscape restoration. Forest Ecology and Management, 438(February), 34–43. https://doi.org/10.1016/j.foreco.2019.02.002">[Crossref] 

  3.  Almeida, P. M. M. de., Madureira Cruz, C. B., Amaral, F. G., Almeida Furtado, L. F., dos Santos Duarte, G., da Silva, G. F., Silva de Barros, R., Pereira Abrantes Marques, J. V. F., Cupertino Bastos, R. M., dos Santos Rosario, E., Santos, V. F., Alves, A., de Oliveira Chaves, F., & Gomes Soares, M. L. (2020). Mangrove Typology: A Proposal for Mapping based on High Spatial Resolution Orbital Remote Sensing. Journal of Coastal Research, 95(sp1), 1. https://doi.org/10.2112/SI95-001.1">[Crossref] 

  4. Alongi, D. M. (2009). The Energetics of Mangrove Forests. Dordrecht: Springer.

  5. Arjasakusuma, S., Swahyu Kusuma, S., & Phinn, S. (2020). Evaluating variable selection and machine learning algorithms for estimating forest heights by combining lidar and hyperspectral data. ISPRS International Journal of Geo-Information, 9(9), 507.

  6. Armston, J. D. (2009). Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery. Journal of Applied Remote Sensing, 3(1), 033540. https://doi.org/10.1117/1.3216031">[Crossref] 

  7. Aslan, A., & Aljahdali, M. O. (2022). Characterizing Global Patterns of Mangrove Canopy Height and Aboveground Biomass Derived from SRTM Data. Forests, 13(10). https://doi.org/10.3390/f13101545">[Crossref] 

  8. Bakx, T. R. M., Koma, Z., Seijmonsbergen, A. C., & Kissling, W. D. (2019). Use and categorization of light detection and ranging vegetation metrics in avian diversity and species distribution research. Diversity and Distributions, 25(7), 1045–1059. https://doi.org/10.1111/ddi.12915">[Crossref] 

  9. Barenblitt, A., Fatoyinbo, L., Thomas, N., Stovall, A., de Sousa, C., Nwobi, C., & Duncanson, L. (2023). Invasion in the Niger Delta: remote sensing of mangrove conversion to invasive Nypa fruticans from 2015 to 2020. Remote Sensing in Ecology and Conservation, Sdg 15, 1–19. https://doi.org/10.1002/rse2.353">[Crossref] 

  10. Chianucci, F. (2019). An overview of in situ digital canopy photography in forestry. Canadian Journal of Forest Research, 227–242. https://doi.org/10.1139/cjfr-2019-0055">[Crossref] 

  11. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034-4257(91)90048-B">[Crossref] 

  12. Coops, N. C., Tompalski, P., Goodbody, T. R. H., Queinnec, M., Luther, J. E., Bolton, D. K., White, J. C., Wulder, M. A., van Lier, O. R., & Hermosilla, T. (2021). Modelling lidar-derived estimates of forest attributes over space and time: A review of approaches and future trends. Remote Sensing of Environment, 260(April), 112477. https://doi.org/10.1016/j.rse.2021.112477">[Crossref] 

  13. Duke, N. C., Ball, M. C., & Ellison, J. C. (1998). Factors influencing biodiversity and distributional gradients in mangroves. Global Ecology and Biogeography Letters, 7(1), 27–47. https://doi.org/10.2307/2997695">[Crossref] 

  14. Dwiputra, M. A., & Mustofa, A. (2021). The Comparison of RGB 564 and RGB 573 Band Composite of Landsat 8 for Mangrove Vegetation Distribution Identification on Pahawang Island, Lampung. IOP Conference Series: Earth and Environmental Science, 830(1). https://doi.org/10.1088/1755-1315/830/1/012017">[Crossref] 

  15. Ehbrecht, M., Seidel, D., Annighöfer, P., Kreft, H., Köhler, M., Zemp, D. C., Puettmann, K., Nilus, R., Babweteera, F., Willim, K., Stiers, M., Soto, D., Boehmer, H. J., Fisichelli, N., Burnett, M., Juday, G., Stephens, S. L., & Ammer, C. (2021). Global patterns and climatic controls of forest structural complexity. Nature Communications, 12(1), 1–12. https://doi.org/10.1038/s41467-020-20767-z">[Crossref] 

  16. Guo, X., Coops, N. C., Tompalski, P., Nielsen, S. E., Bater, C. W., & John Stadt, J. (2017). Regional mapping of vegetation structure for biodiversity monitoring using airborne lidar data. Ecological Informatics, 38, 50–61. https://doi.org/10.1016/j.ecoinf.2017.01.005">[Crossref] 

  17. FAO. (2007). The world’s mangroves 1980-2005. FAO Forestry Paper, 153, 89.

  18. 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.

  19. Hopkinson, C., & Chasmer, L. (2009). Testing LiDAR models of fractional cover across multiple forest ecozones. Remote Sensing of Environment, 113(1), 275–288. https://doi.org/10.1016/j.rse.2008.09.012">[Crossref] 

  20. Jennings, S. B., Brown, N. D., & Sheil, D. (1999). Assessing forest canopies and understorey illumination: Canopy closure, canopy cover and other measures. Forestry, 72(1), 59–73. https://doi.org/10.1093/forestry/72.1.59">[Crossref] 

  21. Juniansah, A., Tama, G. C., Febriani, K. R., Baharain, M. N., Kanekaputra, T., Wulandari, Y. S., & Kamal, M. (2018). Mangrove Leaf Area Index Estimation Using Sentinel 2A Imagery in Teluk Ratai, Pesawaran Lampung. IOP Conference Series: Earth and Environmental Science, 165(1). https://doi.org/10.1088/1755-1315/165/1/012004">[Crossref] 

  22. Kamal, M., Hartono, H., Wicaksono, P., Adi, N. S., & Arjasakusuma, S. (2016). Assessment of Mangrove Forest Degradation Through Canopy Fractional Cover in Karimunjawa Island, Central Java, Indonesia. Geoplanning: Journal of Geomatics and Planning, 3(2), 107. https://doi.org/10.14710/geoplanning.3.2.107-116">[Crossref] 

  23. Kamal, M., Phinn, S., & Johansen, K. (2015). Object-based approach for multi-scale mangrove composition mapping using multiresolution image datasets. In Remote Sensing (Vol. 7, Issue 4). https://doi.org/10.3390/rs70404753">[Crossref] 

  24. Kamal, M., Phinn, S., & Johansen, K. (2016). Assessment of multiresolution image data for mangrove leaf area index mapping. Remote Sensing of Environment, 176, 242–254. https://doi.org/10.1016/j.rse.2016.02.013">[Crossref] 

  25. Kamal, M., Sidik, F., Prananda, A. R. A., & Mahardhika, S. A. (2021). Mapping Leaf Area Index of restored mangroves using WorldView-2 imagery in Perancak Estuary, Bali, Indonesia. Remote Sensing Applications: Society and Environment, 23(November 2020), 100567. https://doi.org/10.1016/j.rsase.2021.100567">[Crossref] 

  26. Khosravipour, A., Skidmore, A. K., Isenburg, M., Wang, T., & Hussin, Y. A. (2014). Generating pit-free canopy height models from airborne lidar. Photogrammetric Engineering and Remote Sensing, 80(9), 863–872. https://doi.org/10.14358/PERS.80.9.863">[Crossref] 

  27. Kodikara, K. A. S., Jayatissa, L. P., Huxham, M., Dahdouh-Guebas, F., & Koedam, N. (2018). The effects of salinity on growth and survival of mangrove seedlings changes with age. Acta Botanica Brasilica, 32(1), 37–46. https://doi.org/10.1590/0102-33062017abb0100">[Crossref] 

  28. 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. https://doi.org/10.14214/sf.315">[Crossref] 

  29. Lefsky, M. A., Cohen, W. B., Parker, G. G., & Harding, D. J. (2002). Lidar remote sensing for ecosystem studies. BioScience, 52(1), 19–30. https://doi.org/10.1641/0006-3568(2002)052%5b0019:LRSFES%5d2.0.CO;2">[Crossref] 

  30. Li, Q., Wong, F. K. K., & Fung, T. (2019). Classification of mangrove species using combined WordView-3 and LiDAR data in Mai Po Nature Reserve, Hong Kong. Remote Sensing, 11(18), 1–17. https://doi.org/10.3390/rs11182114">[Crossref] 

  31. Li, Q., Wong, F. K. K., & Fung, T. (2021). Mapping multi-layered mangroves from multispectral, hyperspectral, and LiDAR data. Remote Sensing of Environment, 258(September 2020), 112403. https://doi.org/10.1016/j.rse.2021.112403">[Crossref] 

  32. Lucas, R., Lule, A. V., Rodríguez, M. T., Kamal, M., Thomas, N., Asbridge, E., & Kuenzer, C. (2017). Spatial Ecology of Mangrove Forests : A Remote Sensing Perspective. 87–112.

  33. Luther, J. E., Fournier, R. A., van Lier, O. R., & Bujold, M. (2019). Extending ALS-based mapping of forest attributes with medium resolution satellite and environmental data. Remote Sensing, 11(9). https://doi.org/10.3390/rs11091092">[Crossref] 

  34. Mahoney, C., Hall, R. J., Hopkinson, C., Filiatrault, M., Beaudoin, A., & Chen, Q. (2018). A forest attribute mapping framework: A pilot study in a Northern boreal forest, Northwest Territories, Canada. Remote Sensing, 10(9). https://doi.org/10.3390/rs10091338">[Crossref] 

  35. Ministry of Environment and Forestry. (2021). Peta Mangrove Nasional.

  36. Murray, N. J., Phinn, S. R., DeWitt, M., Ferrari, R., Johnston, R., Lyons, M. B., Clinton, N., Thau, D., & Fuller, R. A. (2019). The global distribution and trajectory of tidal flats. Nature, 565(7738), 222–225. https://doi.org/10.1038/s41586-018-0805-8">[Crossref] 

  37. Nabilah, R., Sitanggang, F. I., & Rahayu, Y. (2021). Mangrove Species Diversity, Stand Structure, and Zonation - A Case Study at Pahawang Kecil Island. IOP Conference Series: Earth and Environmental Science, 830(1). https://doi.org/10.1088/1755-1315/830/1/012004">[Crossref] 

  38. National Standardization Agency. (2020). SNI 7717:2020 Spesifikasi Informasi Geospasial Mangrove.

  39. Nicolas, J., Schaduw, W., Bachmid, F., Reinhart, G., Lengkong, E. M., Maleke, D. C., Upara, U., Lasut, H. E., Mamesah, J., Azis, A., Tamarol, Y. L., Sulastri, H., Puteri, S. M. A., & Saladi, J. D. (2004). Mangrove Health Index and Carbon Potential of Mangrove Vegetation in Marine Tourism Area of Nusantara Dian Center, Molas Village, Bunaken District, North Sulawesi Province. 201.

  40. Nurdiansah, D., & Dharmawan, I. W. E. (2021). Struktur Dan Kondisi Kesehatan Komunitas Mangrove Di Pulau Middleburg-Miossu, Papua Barat. Jurnal Ilmu Dan Teknologi Kelautan Tropis, 13(1), 81–96. https://doi.org/10.29244/jitkt.v13i1.34484">[Crossref] 

  41. Pasaribu, R. A., Aditama, F. A., & Setyabudi, P. (2021). Object-based image analysis (OBIA) for mapping mangrove using Unmanned Aerial Vehicle (UAV) on Tidung Kecil Island, Kepulauan Seribu, DKI Jakarta Province. IOP Conference Series: Earth and Environmental Science, 944(1). https://doi.org/10.1088/1755-1315/944/1/012037">[Crossref] 

  42. Poorazimy, M., Ronoud, G., Yu, X., Luoma, V., Hyyppä, J., Saarinen, N., Kankare, V., & Vastaranta, M. (2022). Feasibility of Bi-Temporal Airborne Laser Scanning Data in Detecting Species-Specific Individual Tree Crown Growth of Boreal Forests. Remote Sensing, 14(19). https://doi.org/10.3390/rs14194845">[Crossref] 

  43. Ou, J., Tian, Y., Zhang, Q., Xie, X., Zhang, Y., Tao, J., & Lin, J. (2023). Coupling UAV Hyperspectral and LiDAR Data for Mangrove Classification Using XGBoost in China’s Pinglu Canal Estuary. Forests, 14(9). https://doi.org/10.3390/f14091838">[Crossref] 

  44. Saenger, P. (2002). Mangrove Ecology, Silviculture and Conservation. In Mangrove Ecology, Silviculture and Conservation. https://doi.org/10.1007/978-94-015-9962-7">[Crossref] 

  45. Simard, M., Zhang, K., Rivera-Monroy, V. H., Ross, M. S., Ruiz, P. L., Castañeda-Moya, E., Twilley, R. R., & Rodriguez, E. (2006). Mapping height and biomass of mangrove forests in Everglades National Park with SRTM elevation data. Photogrammetric Engineering and Remote Sensing, 72(3), 299–311. https://doi.org/10.14358/PERS.72.3.299">[Crossref] 

  46. Spalding, M., Blasco, F., & Field, C. (1997). World Mangrove Atlas. https://archive.org/details/worldmangroveatl97spal">  

  47. Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., & Wu, X. (2018). Artificial mangrove species mapping using Pléiades-1: An evaluation of pixel-based and object-based classifications with selected machine learning algorithms. Remote Sensing, 10(2). https://doi.org/10.3390/rs10020294">[Crossref] 

  48. Wang, D., Wan, B., Qiu, P., Tan, X., & Zhang, Q. (2022). Mapping mangrove species using combined UAV-LiDAR and Sentinel-2 data: Feature selection and point density effects. Advances in Space Research, 69(3), 1494–1512. https://doi.org/10.1016/j.asr.2021.11.020">[Crossref] 

  49. Wicaksono, P., Danoedoro, P., Hartono, H., Nehren, U., & Ribbe, L. (2011). Preliminary work of mangrove ecosystem carbon stock mapping in small island using remote sensing: above and below ground carbon stock mapping on medium resolution satellite image. Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 8174, 81741B. https://doi.org/10.1117/12.897926">[Crossref] 

  50. Wijaya, M. S., Kamal, M., & Widayani, P. (2023). Mapping of Mangrove Composition in Ratai Bay , Lampung Province Using Pleiades-1 Satellite Imagery. God of Earth, 23(2), 107–122. https://doi.org/https:/doi.org/10.17509/gea.v23i2.59612">[Crossref] 

  51. Xie, Y., Sha, Z., & Yu, M. (2008). Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology, 1(1), 9–23. https://doi.org/10.1093/jpe/rtm005">[Crossref] 

  52. Yin, D., & Wang, L. (2019). Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges. Remote Sensing of Environment, 223(December 2018), 34–49. https://doi.org/10.1016/j.rse.2018.12.034">[Crossref]

  53. Zhang, K., Houle, P. A., Ross, M. S., Ruiz, P. L., & Simard, M. (2006). Airborne laser mapping of mangroves on the biscayne bay coast, Miami, Florida. International Geoscience and Remote Sensing Symposium (IGARSS), 3733–3737. https://doi.org/10.1109/IGARSS.2006.961">[Crossref] 


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