An Updated Results of Five Tropical Mangrove Species Classification Using Recent PRISMA Hyperspectral Imagery : A Case Study in Talumpuk Cape, Thailand
Abstract
Monitoring coastal ecosystem regarding the IUCN Red List of Threatened Species are essential. It was reported that 11 of 70 mangrove species are at risk of extinction. Hyperspectral imagery, hundreds of narrow spectral bands, has been effectively utilized to discriminate mangrove species. It is capable of delivering detailed spectral reflectance of mangrove varieties—however, some confusion between Rhizophora mangroves (Rhizophora mucronata and Rhizophora apiculata) still remains. This study aimed to classify mangrove species by utilizing a clearer PRISMA hyperspectral image of the Talumpuk cape with less than 0.03% cloud coverage and a spectral angle mapper (SAM) algorithm. Two sets of data were subsequently prepared 1) all spectral bands and 2) seven spectral bands selected by a genetic algorithm. Overall accuracy and dependent sample t-tests were then employed to evaluate the classification results of the two datasets, gaining 81.3% and 76.0%, respectively. Statistical testing revealed that null hypothesis H0: (0.7 – 0.6) = 0.1 was significantly rejected at 99.0% confident level (p-value < 0.001). Despite comparable overall accuracies between the two datasets, it was evident that the genetic algorithm helped reduce the confusion between the two Rhizophora species. This was supported by improved producer and user accuracies. It is anticipated that the capabilities of PRISMA satellite images have many advantages in mangrove ecosystem monitoring.References
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Gerald, B. (2018). A brief review of independent, dependent and one sample t-test. International Journal of Applied Mathematics and Theoretical Physics, 4(2), 50-54.
Hati, J. P., Samanta, S., Chaube, N. R., Misra, A., Giri, S., Pramanick, N., Gupta, K., Majumdar, S. D., Chanda, A., & Mukhopadhyay, A. (2021). Mangrove classification using airborne hyperspectral AVIRIS-NG and comparing with other spaceborne hyperspectral and multispectral data. The Egyptian Journal of Remote Sensing and Space Science, 24(2), 273-281.
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Pelozo, A., T Boeger, M. R., Sereneski-de-Lima, C., & Soffiatti, P. (2016). Leaf morphological strategies of seedlings and saplings of Rhizophora mangle (Rhizophoraceae), Laguncularia racemosa (Combretaceae) and Avicennia schaueriana (Acanthaceae) from Southern Brazil. Revista de Biología Tropical, 64(1), 305-317.
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Suyadi, Nurdiansah, D., Renyaan, J., Hapsari, B. W., Rahayu, E. M. D., Sugiharto, A., & Ulumuddin, Y. I. (2023). Better Approaches are Required for Successful Mangrove Restoration and Rehabilitation Program. In AIP Conference Proceedings. (pp. 050001). Indonesia: AIP Publishing LLC.
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Ullah, S., Groen, T. A., Schlerf, M., Skidmore, A. K., Nieuwenhuis, W., & Vaiphasa, C. (2012). Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5–14 µm) to discriminate vegetation species. Sensors, 12(7), 8755-8769.
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Wang, X., Tan, L., & Fan, J. (2023). Performance Evaluation of Mangrove Species Classification Based on Multi-Source Remote Sensing Data Using Extremely Randomized Trees in Fucheng Town, Leizhou City, Guangdong Province. Remote Sensing, 15(5), 1386.
Watanakij, N., & Vaiphasa, C. (2016). Improving the accuracy of mangrove species discrimination using object based and high spatial resolution imagery: A case study in Pak Phanang, Thailand. International Journal of Geoinformatics, 12(3), 41-49.
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Bandyopadhyay, S., & Pal, S. K. (2001). Pixel classification using variable string genetic algorithms with chromosome differentiation. IEEE Transactions on Geoscience and Remote Sensing, 39(2), 303-308.
Cardenas, S. M. M., Cohen, M. C. L., Ruiz, D. P. C., Souza, A. V., Gomez-Neita, J. S., Pessenda, L. C. R., & Culligan, N. (2022). Death and Regeneration of an Amazonian Mangrove Forest by Anthropic and Natural Forces. Remote Sensing, 14(24), 6197.
Carfora, M. F., Casa, R., Laneve, G., Mzid, N., Pascucci, S., & Pignatti, S. (2022). Prisma Noise Coefficients Estimation. In International Geoscience and Remote Sensing Symposium (IGARSS). (pp. 7531-7534). Malaysia: IEEE.
Chen, G., Zhong, C., Li, M., Yu, Z., Liu, X., & Jia, M. (2022). Disturbance of mangrove forests in Guangxi Beilun Estuary during 1990-2020. National Remote Sensing Bulletin, 26(6), 1112-1120.
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.
Flores-de-Santiago, F., Kovacs, J. M., Wang, J., Flores-Verdugo, F., Zhang, C., & González-Farías, F. (2016). Examining the influence of seasonality, condition, and species composition on mangrove leaf pigment contents and laboratory based spectroscopy data. Remote Sensing, 8(3), 226.
Gerald, B. (2018). A brief review of independent, dependent and one sample t-test. International Journal of Applied Mathematics and Theoretical Physics, 4(2), 50-54.
Hati, J. P., Samanta, S., Chaube, N. R., Misra, A., Giri, S., Pramanick, N., Gupta, K., Majumdar, S. D., Chanda, A., & Mukhopadhyay, A. (2021). Mangrove classification using airborne hyperspectral AVIRIS-NG and comparing with other spaceborne hyperspectral and multispectral data. The Egyptian Journal of Remote Sensing and Space Science, 24(2), 273-281.
Hayati, A. N., Afiati, N., & Helmi, M. (2023). Carbon Sequestration of Above Ground Biomass Approach in the Rehabilitated Mangrove Stand at Jepara Regency, Central Java, Indonesia. Jurnal Ilmiah Perikanan dan Kelautan, 15(1), 224-235.
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., & Sillaparat, S. (2019). Tropical Mangrove Species Classification Using Random Forest Algorithm and Very High-Resolution Satellite Imagery. BURAPHA SCIENCE JOURNAL, 24(2), 742-753. (in Thai)
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, 34(4), 415-442.
L3HARRIS. (2021). ENVI 5.6. 1025 W. NASA Boulevard Melbourne, FL 32919. Licence No. E21-0076
Lassalle, G., Ferreira, M. P., Cué La Rosa, L. E., Del'Papa Moreira Scafutto, R., & de Souza Filho, C. R. (2023). Advances in multi- and hyperspectral remote sensing of mangrove species: A synthesis and study case on airborne and multisource spaceborne imagery. Isprs Journal of Photogrammetry and Remote Sensing, 195, 298-312.
Li, Q., Wong, F. K. K., & Fung, T. (2021). Mapping multi-layered mangroves from multispectral, hyperspectral, and LiDAR data. Remote Sensing of Environment, 258, 112403.
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.
Mahmood, H., 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, 5, 100094.
Manjunath, K., Kumar, T., Kundu, N., & Panigrahy, S. (2013). Discrimination of mangrove species and mudflat classes using in situ hyperspectral data: a case study of Indian Sundarbans. GIScience & remote sensing, 50(4), 400-417.
McLeod, E., & Salm, R. V. (2006). Managing mangroves for resilience to climate change (Vol.64). Gland, Switzerland: World Conservation Union (IUCN).
Mitchell, M. (1998). An Introduction to Genetic Algorithms. USA: The MIT Press, Cambridge, Massachusetts.
Morrissette, H. K., Baez, S. K., Beers, L., Bood, N., Martinez, N. D., Novelo, K., Andrews, G., Balan, L., Beers, C. S., Betancourt, S. A., Blanco, R., Bowden, E., Burns-Perez, V., Carcamo, M., Chevez, L., Crooks, S., Feller, I. C., Galvez, G., Garbutt, K., Gongora, R., Grijalva, E., Lefcheck, J., Mahung, A., Mattis, C., McKoy, T., McLaughlin, D., Meza, J., Pott, E., Ramirez, G., Ramnarace, V., Rash, A., Rosado, S., Santos, H., Santoya, L., Sosa, W., Ugarte, G., Viamil, J., Young, A., Young, J., & Canty, S. W. J. (2023). Belize Blue Carbon: Establishing a national carbon stock estimate for mangrove ecosystems. Science of The Total Environment, 870, 161829.
Muangkasem, S., Vaiphasa, C., & Intarat, K. (2022). Tropical Mangrove Species Classification Using PRISMA Hyperspectral Data: A Case Study in Talumpuk Cape, Thailand. BURAPHA SCIENCE JOURNAL, 27(3), 2017-2042. (in Thai)
Panapitukkul, N., Duarte, C., Thampanya, U., Kheowvongsri, P., Srichai, N., Geertz-Hansen, O., Terrados, J., & Boromthanarath, S. (1998). Mangrove colonization: mangrove progression over the growing Pak Phanang (SE Thailand) mud flat. Estuarine, Coastal and Shelf Science, 47(1), 51-61.
Pelozo, A., T Boeger, M. R., Sereneski-de-Lima, C., & Soffiatti, P. (2016). Leaf morphological strategies of seedlings and saplings of Rhizophora mangle (Rhizophoraceae), Laguncularia racemosa (Combretaceae) and Avicennia schaueriana (Acanthaceae) from Southern Brazil. Revista de Biología Tropical, 64(1), 305-317.
Polidoro, B. A., Carpenter, K. E., Collins, L., Duke, N. C., Ellison, A. M., Ellison, J. C., Farnsworth, E. J., Fernando, E. S., Kathiresan, K., & Koedam, N. E. (2010). The loss of species: mangrove extinction risk and geographic areas of global concern. PloS one, 5(4), e10095.
Prasad, K.A., & Gnanappazham, L. (2014). Discrimination of mangrove species of Rhizophoraceae using laboratory spectral signatures. In 2014 IEEE Geoscience and Remote Sensing Symposium. (pp. 2906-2909). IEEE.
Ramassamy, V., & Kannabira, B. (1996). Leaf epidermis and taxonomy in Rhizophoraceae. Indian Forester, 122, 1049-1061.
Rodda, S. R., Thumaty, K. C., Fararoda, R., Jha, C. S., & Dadhwal, V. K. (2022). Unique characteristics of ecosystem CO2 exchange in Sundarban mangrove forest and their relationship with environmental factors. Estuarine, Coastal and Shelf Science, 267, 107764.
Selamat, M., Mashoreng, S., Amri, K., & Rappe, R. (2020). The use of sentinel 2A imageries to improve mangrove inventarization at coremap CTI monitoring areas. In IOP Conference Series: Earth and Environmental Science. (pp. 012065). Indonesia: IOP Publishing.
Shen, H. K., Zhao, B. Y., Chen, M. Y., Huang, R. Y., Yu, K. F., & Liang, W. (2022). Changes of the area of Spartina alterniflora and mangroves in Guangxi Shankou Mangrove National Nature Reserve from 1995 to 2019. Chinese Journal of Applied Ecology, 33(2), 397-404.
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.
Soeprobowati, T. R., Anggoro, S., Puryono, S., Purnaweni, H., Sularto, R. B., & Mersyah, R. (2022). Species Composition and Distribution in the Mangrove Ecosystem in the City of Bengkulu, Indonesia. Water (Switzerland), 14(21).
Suyadi, Nurdiansah, D., Renyaan, J., Hapsari, B. W., Rahayu, E. M. D., Sugiharto, A., & Ulumuddin, Y. I. (2023). Better Approaches are Required for Successful Mangrove Restoration and Rehabilitation Program. In AIP Conference Proceedings. (pp. 050001). Indonesia: AIP Publishing LLC.
Teeratanatorn, W. (2000). Mangroves of Pak Phanang Bay. Bangkok: Royal Forest Department. (in Thai)
Titisari, P. W., Elfis, E., Chahyana, I., Janna, N., Nurdila, H., & Widari, R. S. (2022). Management Strategies of Mangrove Biodiversity and the Role of Sustainable Ecotourism in Achieving Development Goals. Journal of Tropical Biodiversity and Biotechnology, 7(3), 72243.
Ullah, S., Groen, T. A., Schlerf, M., Skidmore, A. K., Nieuwenhuis, W., & Vaiphasa, C. (2012). Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5–14 µm) to discriminate vegetation species. Sensors, 12(7), 8755-8769.
Vaiphasa, C. (2006). Remote sensing techniques for mangrove mapping. Wageningen University and Research.
Vaiphasa, C., De Boer, W., Skidmore, A., Panitchart, S., Vaiphasa, T., Bamrongrugsa, N., & Santitamnont, P. (2007a). 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. (2007b). 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, 21(4), 1182.
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.
Wan, L., Zhang, H., Wang, T., Li, G., & Lin, H. (2018). Mangrove species discrimination from very high resolution imagery using gaussian markov random field model. Wetlands, 38(5), 861-874.
Wang, X., Tan, L., & Fan, J. (2023). Performance Evaluation of Mangrove Species Classification Based on Multi-Source Remote Sensing Data Using Extremely Randomized Trees in Fucheng Town, Leizhou City, Guangdong Province. Remote Sensing, 15(5), 1386.
Watanakij, N., & Vaiphasa, C. (2016). Improving the accuracy of mangrove species discrimination using object based and high spatial resolution imagery: A case study in Pak Phanang, Thailand. International Journal of Geoinformatics, 12(3), 41-49.
Wilkerson, S. (2008). Application of the Paired t-test. XULAneXUS, 5(1), 7.
Wirsansky, E. (2020). Hands-on genetic algorithms with Python: applying genetic algorithms to solve real-world deep learning and artificial intelligence problems. Packt Publishing Ltd.
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Published
2023-09-29
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