Tropical Mangrove Species Classification Using PRISMA Hyperspectral Data: A Case Study in Talumpuk Cape, Thailand
Abstract
Endangered mangroves under the IUCN Red List of Ecosystems are one of the most severe issues of the world's coastal ecosystems. This concern required the necessary monitoring for mangrove ecosystems and their diversity. Nowadays, hyperspectral satellite imagery is associated with hundreds of wavelengths to categorize mangrove species. Therefore, this is an excellent opportunity for a new earth observation hyperspectral satellite, PRISMA, delivered by the Italian Space Agency. Currently, PRISMA information has not been previously used for mangrove species classification. This experiment launched the first-time examination of applying PRISMA hyperspectral on mangroves species categorization in Talumpuk cape, Pak Phanang District, Nakhon Si Thammarat Province. In the classification, two spectral band selectors, genetic algorithm (GA) and sequential maximum angle convex cone (SMACC), were associated with the spectral angle mapper (SAM) classifier to determine the most satisfactory hyperspectral band set. Classifications from those two selectors and entire bands were compared using overall accuracy and dependent sample t-test. The result revealed that the GA band selection could improve the classification accuracy from 81% to 82% compared to the entire band combination. This outcome undoubtedly proves the performance of PRISMA imagery's application on mangrove species classification. Keywords : PRISMA hyperspectral data ; remote sensing ; classification ; mangrove ; species compositionReferences
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Salghuna, N., & Pillutla, R. (2017). Mapping Mangrove Species Using Hyperspectral Data: A Case Study of Pichavaram Mangrove Ecosystem, Tamil Nadu. Earth Systems and Environment, 1(2), 1-12.
Sievers, M., Chowdhury, M. R., Adame, M. F., Bhadury, P., Bhargava, R., Buelow, C., Friess, D. A., Ghosh, A., Hayes, M. A., & McClure, E. C. (2020). Indian Sundarbans mangrove forest considered endangered under Red List of Ecosystems, but there is cause for optimism. Biological Conservation, 251, 108751.
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Vaiphasa, C. (2006). Remote sensing techniques for mangrove mapping.
Vaiphasa, C., De Boer, W., Skidmore, A., Panitchart, S., Vaiphasa, T., Bamrongrugsa, N., & Santitamnont, P. (2007). Impact of solid shrimp pond waste materials on mangrove growth and mortality: a case study from Pak Phanang, Thailand. Hydrobiologia, 591(1), 47-57.
Vaiphasa, C., Ongsomwang, S., Vaiphasa, T., & Skidmore, A. K. (2005). Tropical mangrove species discrimination using hyperspectral data: A laboratory study. Estuarine Coastal and Shelf Science, 65(1-2), 371-379.
Vaiphasa, C., Skidmore, A. K., & de Boer, W. F. (2006). A post-classifier for mangrove mapping using ecological data. Isprs Journal of Photogrammetry and Remote Sensing, 61(1), 1-10.
Vaiphasa, C., Skidmore, A. K., de Boer, W. F., & Vaiphasa, T. (2007). A hyperspectral band selector for plant species discrimination. Isprs Journal of Photogrammetry and Remote Sensing, 62(3), 225-235.
Vangi, E., D'Amico, G., Francini, S., Giannetti, F., Lasserre, B., Marchetti, M., & Chirici, G. (2021). The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination. Sensors (Basel), 21(4).
Wan, L., Lin, Y., Zhang, H., Wang, F., Liu, M., & Lin, H. (2020). GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong. Remote Sensing, 12(4), 656.
Wang, Y., Chao, B., Dong, P., Zhang, D., Yu, W., Hu, W., Ma, Z., Chen, G., Liu, Z., & Chen, B. (2021). Simulating spatial change of mangrove habitat under the impact of coastal land use: Coupling MaxEnt and Dyna-CLUE models. Science of The Total Environment, 147914.
Wong, F. K., & Fung, T. (2014). Combining eo-1 hyperion and envisat asar data for mangrove species classification in Mai Po Ramsar Site, Hong Kong. International Journal of Remote Sensing, 35(23), 7828-7856.
Xia, J., Yokoya, N., & Pham, T. D. (2020). Probabilistic mangrove species mapping with multiple-source remote-sensing datasets using label distribution learning in Xuan Thuy National Park, Vietnam. Remote Sensing, 12(22), 3834.
Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office.
Bai, J., Meng, Y., Gou, R., Lyu, J., Dai, Z., Diao, X., Zhang, H., Luo, Y., Zhu, X., & Lin, G. (2021). Mangrove diversity enhances plant biomass production and carbon storage in Hainan island, China. Functional Ecology, 35(3), 774-786.
Basyuni, M., Nainggolan, S. S., Qurrahman, T., Hasibuan, P. A. Z., Sumaiyah, S., Sumardi, S., Siregar, E. S., & Nuryawan, A. (2019). Effect of Salt and Fresh Water Concentration on Polyisoprenoid Content in Bruguiera cylindrica Seedlings. Open access Macedonian journal of medical sciences, 7(22), 3803.
Chakraborty, S. K. (2019). Bioinvasion and Environmental Perturbation: Synergistic Impact on Coastal–Mangrove Ecosystems of West Bengal, India. In Impacts of Invasive Species on Coastal Environments (pp. 171-245). Springer.
Cochard, R. (2017). Coastal water pollution and its potential mitigation by vegetated wetlands: An overview of issues in Southeast Asia. Redefining Diversity & Dynamics of Natural Resources Management in Asia, Volume 1, 189-230.
Constance, A., Haverkamp, P. J., Bunbury, N., & Schaepman-Strub, G. (2021). Extent change of protected mangrove forest and its relation to wave power exposure on Aldabra Atoll. Global Ecology and Conservation, 27, e01564.
Da Silva, M. F., Cruz, M. V., Vidal Júnior, J. D. D., Zucchi, M. I., Mori, G. M., & De Souza, A. P. (2021). Geographical and environmental contributions to genomic divergence in mangrove forests. Biological Journal of the Linnean Society, 132(3), 573-589.
Fauzi, N. F. M., Min, T. H., & Hashim, A. M. (2020). Assessment of Mangrove Replanting Site at Kg Tanjung Kepah, Lekir, Perak. IOP Conference Series: Earth and Environmental Science.
Friis, G., & Burt, J. A. (2020). Evolution of mangrove research in an extreme environment: Historical trends and future opportunities in Arabia. Ocean & Coastal Management, 195, 105288.
Ghosh, M. K., Kumar, L., & Roy, C. (2016). Mapping long-term changes in mangrove species composition and distribution in the Sundarbans. Forests, 7(12), 305.
Halder, N. K., Merchant, A., Misbahuzzaman, K., Wagner, S., & Mukul, S. A. (2021). Why some trees are more vulnerable during catastrophic cyclone events in the Sundarbans mangrove forest of Bangladesh? Forest Ecology and Management, 490, 119117.
Halder, S., Samanta, K., Das, S., & Pathak, D. (2021). Monitoring the inter-decade spatial–temporal dynamics of the Sundarban mangrove forest of India from 1990 to 2019. Regional Studies in Marine Science, 44, 101718.
He, Z., Shi, Q., Liu, K., Cao, J., Zhan, W., & Cao, B. (2020). Object-oriented mangrove species classification using hyperspectral data and 3-D siamese residual network. IEEE Geoscience and Remote Sensing Letters, 17(12), 2150-2154.
Hickey, S., Radford, B., Callow, J., Phinn, S., Duarte, C. M., & Lovelock, C. (2021). ENSO feedback drives variations in dieback at a marginal mangrove site. Scientific reports, 11(1), 1-9.
Hossain, M., Ahmed, M., Islam, T., Uddin, M. Z., Ahmed, Z. U., & Saha, C. (2021). Paradigm shift in the management of the Sundarbans mangrove forest of Bangladesh: issues and challenges. Trees, Forests and People, 100094.
Intarat, K. (2018). Remote sensing technique for mangrove studies: Tropical mangrove species classification with convolutional neural network and tropical mangrove tree biomass modelling with terrestrial laser scanner [Thesis, Chulalongkorn ]. Chulalongkorn University.
Intarat, K., & Vaiphasa, C. (2020). Modeling Mangrove Above-Ground Biomass Using Terrestrial Laser Scanning Techniques: A Case Study of the Avicennia Marina Species in the Bang Pu District, Thailand. International Journal of Geoinformatics, 16.
Jing, X., Leigh, L., Helder, D., Pinto, C. T., & Aaron, D. (2019). Lifetime absolute calibration of the EO-1 Hyperion sensor and its validation. IEEE Transactions on Geoscience and Remote Sensing, 57(11), 9466-9475.
Koedsin, W., & Vaiphasa, C. (2013). Discrimination of Tropical Mangroves at the Species Level with EO-1 Hyperion Data. Remote Sensing, 5(7), 3562-3582.
Kumar, T., Mandal, A., Dutta, D., Nagaraja, R., & Dadhwal, V. K. (2019). Discrimination and classification of mangrove forests using EO-1 Hyperion data: a case study of Indian Sundarbans. Geocarto International, 415-442.
Loizzo, R., Daraio, M., Guarini, R., Longo, F., Lorusso, R., Dini, L., & Lopinto, E. (2019). Prisma Mission Status and Perspective. International Geoscience and Remote Sensing Symposium IGARSS, 4503-4506.
Loizzo, R., Guarini, R., Longo, F., Scopa, T., Formaro, R., Facchinetti, C., & Varacalli, G. (2018). Prisma: The Italian hyperspectral mission. International Geoscience and Remote Sensing Symposium IGARSS 175-178.
McLeod, E., & Salm, R. V. (2006). Managing mangroves for resilience to climate change. World Conservation Union (IUCN) Gland.
Murugan, S., & Anandhi, D. U. (2016). An Overview of Crustacean Diversity in Mangrove Ecosystem. Arthropod Diversity and Conservation in the Tropics and Sub-tropics, 81-99.
Ramsar. (2018). Laem Talumphuk Non-hunting Area (Lists of Ramsar Sites, Ramsar Sites in Thailand, Thailand Geography-Related Lists, Issue. W. H. Encyclopedia.
Salghuna, N., & Pillutla, R. (2017). Mapping Mangrove Species Using Hyperspectral Data: A Case Study of Pichavaram Mangrove Ecosystem, Tamil Nadu. Earth Systems and Environment, 1(2), 1-12.
Sievers, M., Chowdhury, M. R., Adame, M. F., Bhadury, P., Bhargava, R., Buelow, C., Friess, D. A., Ghosh, A., Hayes, M. A., & McClure, E. C. (2020). Indian Sundarbans mangrove forest considered endangered under Red List of Ecosystems, but there is cause for optimism. Biological Conservation, 251, 108751.
Singh, M., Griaud, C., & Collins, C. M. (2021). An evaluation of the effectiveness of protected areas in Thailand. Ecological Indicators, 125, 107536.
Thuy, T. D., Tuan, V. Q., & Nam, P. K. (2021). Does the devolution of forest management help conserve mangrove in the Mekong Delta of Viet Nam? Land Use Policy, 106, 105440.
Vaiphasa, C. (2006). Remote sensing techniques for mangrove mapping.
Vaiphasa, C., De Boer, W., Skidmore, A., Panitchart, S., Vaiphasa, T., Bamrongrugsa, N., & Santitamnont, P. (2007). Impact of solid shrimp pond waste materials on mangrove growth and mortality: a case study from Pak Phanang, Thailand. Hydrobiologia, 591(1), 47-57.
Vaiphasa, C., Ongsomwang, S., Vaiphasa, T., & Skidmore, A. K. (2005). Tropical mangrove species discrimination using hyperspectral data: A laboratory study. Estuarine Coastal and Shelf Science, 65(1-2), 371-379.
Vaiphasa, C., Skidmore, A. K., & de Boer, W. F. (2006). A post-classifier for mangrove mapping using ecological data. Isprs Journal of Photogrammetry and Remote Sensing, 61(1), 1-10.
Vaiphasa, C., Skidmore, A. K., de Boer, W. F., & Vaiphasa, T. (2007). A hyperspectral band selector for plant species discrimination. Isprs Journal of Photogrammetry and Remote Sensing, 62(3), 225-235.
Vangi, E., D'Amico, G., Francini, S., Giannetti, F., Lasserre, B., Marchetti, M., & Chirici, G. (2021). The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination. Sensors (Basel), 21(4).
Wan, L., Lin, Y., Zhang, H., Wang, F., Liu, M., & Lin, H. (2020). GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong. Remote Sensing, 12(4), 656.
Wang, Y., Chao, B., Dong, P., Zhang, D., Yu, W., Hu, W., Ma, Z., Chen, G., Liu, Z., & Chen, B. (2021). Simulating spatial change of mangrove habitat under the impact of coastal land use: Coupling MaxEnt and Dyna-CLUE models. Science of The Total Environment, 147914.
Wong, F. K., & Fung, T. (2014). Combining eo-1 hyperion and envisat asar data for mangrove species classification in Mai Po Ramsar Site, Hong Kong. International Journal of Remote Sensing, 35(23), 7828-7856.
Xia, J., Yokoya, N., & Pham, T. D. (2020). Probabilistic mangrove species mapping with multiple-source remote-sensing datasets using label distribution learning in Xuan Thuy National Park, Vietnam. Remote Sensing, 12(22), 3834.
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2022-09-08
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