Mangrove Mapping and Above-Ground Biomass Change Detection using Satellite Images in Coastal Areas of Thai Binh Province, Vietnam
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- 2019-10-23 (2)
- 2019-10-23 (1)
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Mangroves are recognized as a highly valuable resource due to their provision of multiple ecosystem services. Therefore, mangrove ecosystems mapping and monitoring is a crucial objective, especially for tropical regions. Thai Binh province is one of the most important mangrove ecosystems in Vietnam. The mangrove ecosystem in this province has faced threats of deforestation from urban development, land reclamation, tourism activities, and natural disasters. Recently, to maintain the fundamental functions of the ecosystems, a large mangrove area was planted in Thai Binh. The aim of this research is to detect the change in the mangrove areas and to create an aboveground biomass map for mangrove forests in Thai Binh province. Landsat and Sentinel-2 satellite images from 1998 to 2018 were analysed using the supervised classification method to detect mangrove area change. Mangrove Above-ground Biomass (AGB) was estimated using linear regression between vegetation indices and field AGB survey. The accuracy assessment for the classified images of 1998, 2003 and 2007, 2013 and 2018 are 93%, 86%, 96%, 94% and 91% respectively with kappa of 0.8881, 0.7953, 0.9357, 0.9114 and 0.8761. The mangrove cover in the study area was estimated at 5874.93 ha in 1998. This figure decreased significantly to 4433.85 ha in 2007, before recovery began to take place in the study area, which was estimated at 6587.88 ha in 2018. In 1998, the average AGB in this study area was 22.57 ton/ha, and in 2018 it was 37.74 ton/ha with a standard error of 12.41 ton/ha and the root mean square error (RMSE) was ±12.08 ton/ha.
Abburu, S., & Golla, S. B. (2015). Satellite image classification methods and techniques: A review. International journal of computer applications, 119(8). doi:https://doi.org/10.5120/21088-3779
Alongi, D. M. (2002). Present state and future of the world's mangrove forests. Environmental conservation, 29(3), 331-349. doi:https://doi.org/10.1017/S0376892902000231
Amy E., Z., G., L.-G., David, A. C., Jugo, I., Steven, J., Simon, L. L., . . . Jerome, C. (2016). Global Wood Density Database (Publication no. https://doi.org/10.5061/dryad.234/1). https://dryad.figshare.com/articles/Global_Wood_Density_Database/4172847
Anaya, J. A., Chuvieco, E., & Palacios-Orueta, A. (2009). Aboveground biomass assessment in Colombia: A remote sensing approach. Forest Ecology and Management, 257(4), 1237-1246. doi:https://doi.org/10.1016/j.foreco.2008.11.016
Anderson, G., Hanson, J., & Haas, R. (1993). Evaluating Landsat Thematic Mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands. Remote Sensing of Environment, 45(2), 165-175. doi:https://doi.org/10.1016/0034-4257(93)90040-5
Banko, G. (1998). A review of assessing the accuracy of classifications of remotely sensed data and of methods including remote sensing data in forest inventory. IIASA Interim Report(IIASA, Laxenburg, Austria: IR-98-081). doi:http://pure.iiasa.ac.at/5570
Chai, T., & Draxler, R. R. J. G. m. d. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. 7(3), 1247-1250.
Cúc, N. T. K. (2013). Nghien cuu kha nang hap thu nang luong song cua rung ngap man trong tai Nam Dinh va Thai Binh.
Darmawan, S., Takeuchi, W., Nakazono, E., Vetrita, Y., Winarso, G., Dien, V. T., . . . Sari, D. K. (2015). Characterization of mangrove forest types based on ALOS-PALSAR mosaic for estimating above ground biomass in Southeast Asia. Paper presented at the ACRS 2015-36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings.
Dat, P. T., & Yoshino, K. (2011). Monitoring mangrove forest using multi-temporal satellite data in the Northern Coast of Vietnam. Paper presented at the the 32nd Asian Conf. on Remote Sensing.
FAO. (2007). The world's mangroves 1980–2005. Rome: FAO, FAO Forestry Paper, 153, 77p. doi:http://www.fao.org/3/a1427e/a1427e00.htm
Foody, G. M., Cutler, M. E., Mcmorrow, J., Pelz, D., Tangki, H., Boyd, D. S., & Douglas, I. (2001). Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Global Ecology and Biogeography, 10(4), 379-387. doi: https://doi.org/10.1046/j.1466-822X.2001.00248.x
Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T., . . . Duke, N. (2011). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1), 154-159. doi: https://doi.org/10.1111/j.1466-8238.2010.00584.x
Giri, C., Pengra, B., Zhu, Z., Singh, A., & Tieszen, L. L. (2007). Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuarine, coastal and shelf science, 73(1-2), 91-100. doi:https://doi.org/10.1016/j.ecss.2006.12.019
Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289-298. doi:https://doi.org/10.1016/S0034-4257(96)00072-7
Green, E. P., Clark, C. D., Mumby, P. J., Edwards, A. J., & Ellis, A. (1998). Remote sensing techniques for mangrove mapping. International journal of remote sensing, 19(5), 935-956. doi:https://doi.org/10.1080/014311698215801
Hamdan, O., Aziz, H. K., & Hasmadi, I. M. (2014). L-band ALOS PALSAR for biomass estimation of Matang Mangroves, Malaysia. Remote Sensing of Environment, 155, 69-78. doi:https://doi.org/10.1016/j.rse.2014.04.029
Hamdan, O., Khairunnisa, M., Ammar, A., Hasmadi, I. M., & Aziz, H. K. (2013). Mangrove carbon stock assessment by optical satellite imagery. Journal of Tropical Forest Science, 554-565.
Hanh, N. T. H. (2017). Studying and Evaluating the Ability to form Carbon Sinks in Biomass of the Pure Sonneratia caseolaris Plantation in the Coastal Area of Tien Lang district, Hai Phong city. 33(1).
Heumann, B. W. (2011). Satellite remote sensing of mangrove forests: Recent advances and future opportunities. Progress in Physical Geography, 35(1), 87-108. doi:https://doi.org/10.1177%2F0309133310385371
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309. doi:https://doi.org/10.1016/0034-4257(88)90106-X
Kamal, M., & Phinn, S. (2011). Hyperspectral data for mangrove species mapping: A comparison of pixel-based and object-based approach. Remote Sensing, 3(10), 2222-2242. doi:https://doi.org/10.3390/rs3102222
Khan, M. N. I., Suwa, R., & Hagihara, A. (2005). Allometric relationships for estimating the aboveground phytomass and leaf area of mangrove Kandelia candel (L.) Druce trees in the Manko Wetland, Okinawa Island, Japan. Trees, 19(3), 266-272.
Komiyama, A., Poungparn, S., & Kato, S. (2005). Common allometric equations for estimating the tree weight of mangroves. Journal of Tropical Ecology, 21(4), 471-477. doi:https://doi.org/10.1017/S0266467405002476
Kumar, L., & Mutanga, O. (2017). Remote sensing of above-ground biomass. In: Multidisciplinary Digital Publishing Institute.
Landis, J. R., & Koch, G. G. J. b. (1977). The measurement of observer agreement for categorical data. 159-174. doi:https://doi.org/10.2307/2529310
Li, X., Gar‐On Yeh, A., Wang, S., Liu, K., Liu, X., Qian, J., & Chen, X. (2007). Regression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images. International journal of remote sensing, 28(24), 5567-5582. doi:https://doi.org/10.1080/01431160701227638
Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., & Moran, E. (2016). A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 9(1), 63-105. doi:https://doi.org/10.1080/17538947.2014.990526
Muhd-Ekhzarizal, M., Mohd-Hasmadi, I., Hamdan, O., Mohamad-Roslan, M., & Noor-Shaila, S. (2018). Estimation of aboveground biomass in mangrove forests using vegetation indices from SPOT-5 Image. Journal of Tropical Forest Science, 30(2), 224-233. doi:https://doi.org/10.26525/jtfs2018.30.2.224233
Mutanga, O., Adam, E., & Cho, M. A. (2012). High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18, 399-406. doi:https://doi.org/10.1016/j.jag.2012.03.012
Polidoro, B. A., Carpenter, K. E., Collins, L., Duke, N. C., Ellison, A. M., Ellison, J. C., . . . Koedam, N. E. (2010). The loss of species: mangrove extinction risk and geographic areas of global concern. PloS one, 5(4), e10095. doi:https://doi.org/10.1371/journal.pone.0010095
Regression, G. W. (2016). Help| ArcGIS for Desktop [Internet]. Desktop. arcgis. com. 2016 [cited 9 March 2016]. In.
Syed, M. A., Hussin, Y. A., & Weir, M. (2001). Detecting fragmented mangroves in the Sundarbans, Bangladesh using optical and radar satellite images. Paper presented at the Paper presented at the 22nd Asian Conference on Remote Sensing.
Trần, V. T., Phan, T. T., Đoàn, H. G., Phạm, M. D., Nguyễn, T. H., & Nguyễn, M. Q. (2016). Nghien cuu anh huong cua bien doi khi hau den mot so he sinh thai ven bien tinh Thai Binh va kha nang ung pho. Vietnam National University, 392 - 399.
Tuan, L., Yukihiro, M., Dao, Q., Tho, N., & Dao, P. (2003). Environmental management in mangrove areas. Environmental Informatics Archives, 1, 38-52.
Wicaksono, P., Danoedoro, P., Hartono, & Nehren, U. (2016). Mangrove biomass carbon stock mapping of the Karimunjawa Islands using multispectral remote sensing. International journal of remote sensing, 37(1), 26-52. doi:https://doi.org/10.1080/01431161.2015.1117679
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. Paper presented at the Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII.
Winarso, G., Vetrita, Y., Purwanto, A. D., Anggraini, N., Darmawan, S., & Yuwono, D. M. (2017). Mangrove above ground biomass estimation using combination of Landsat 8 and Alos Palsar data. International Journal of Remote Sensing and Earth Sciences (IJReSES), 12(2), 85-96. doi:https://doi.org/10.30536/j.ijreses.2015.v12.a2687
Zaitunah, A., Ahmad, A., & Safitri, R. (2018). Normalized difference vegetation index (ndvi) analysis for land cover types using landsat 8 oli in besitang watershed, Indonesia. Paper presented at the IOP Conference Series: Earth and Environmental Science.
Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J., & Ryu, S.-R. (2004). Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, 93(3), 402-411. doi:https://doi.org/10.1016/j.rse.2004.08.008
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