Journal of Natural Environment

Spatial modeling of biomass of mangroves in the Hara protected area

Document Type : Research Paper

Authors

1 Department of Forest Sciences, Faculty of Natural Resources and Erath Sciences, Shahrekord University, Shahrekord, Iran

2 Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

3 Department of Environmental Science, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

10.22059/jne.2022.352079.2501

Abstract

The estimation of mangrove carbon stocks is crucial for providing vital information for the development of climate change adaptation programs and blue carbon strategies in coastal ecosystems. Therefore, the aim of this study was to estimate the carbon storage of mangroves in the Hara protected area of Hormozgan province. For this purpose, after field surveys and recording the diameter at the mangroves' collar, the above-ground and below-ground biomass was estimated using allometric equations. Then, a regression was fitted between the above-ground and below-ground biomass and the normalized vegetation index (NDVI) extracted from the satellite images to develop a map of the above-ground and below-ground biomass of mangroves in two coastal and island zones and tall and dwarf mangroves structures. The results showed that the average above-ground biomass in the coastal and island zones of the Hara protected area was 61.2 and 56.1 t/ha, respectively, and the average underground biomass was 15.6 and 12.5 t/ha, respectively. There was a significant difference between the values of these two biomasses in the two zones. The extent of tall mangroves in the coastal zone (59%) was greater than dwarf mangroves (41%), and in the island zone, the extent of tall mangroves (44%) was less than dwarf mangroves (56%). The amount of total biomass in tall mangroves in both zones was about 7.5 and 8 times greater than the value of this variable in dwarf mangroves, respectively. The results of this study can be used to prepare climate change adaptation plans for mangrove habitats.

Keywords

Alongi, D.M., 2021. Responses of mangrove ecosystems to climate change in the Anthropocene. In Mangroves: Ecology, biodiversity and management (pp. 201-224). Springer, Singapore.
Chmura, G.L., Anisfeld, S.C., Cahoon, D.R., Lynch, J.C., 2003. Global carbon sequestration in tidal, saline wetland soils. Global Biogeochemical Cycles 17(4), 111.
Clough, B.F., Dixon, P., Dalhaus, O., 1997. Allometric relationships for estimating biomass in multi-stemmed mangrove trees. Australian Journal of Botany 45(6), 1023-1031.
Cusack, M., Saderne, V., Arias-Ortiz, A., Masque, P., Krishnakumar, P.K., Rabaoui, L., Elyas, A.A., 2018. Organic carbon sequestration and storage in vegetated coastal habitats along the western coast of the Arabian Gulf. Environmental Research Letters 13(7), 074007.
Danehkar, A., Hasheni, A., Varasteh, R., Fadakar, S., Sharifipour, R., 2008. The spatial analysis of environmental sensitivity of coastal areas in Hormozgan province. The Department of the Environment, Hormozgan Province. 180 p. (In Persian)
Duke, N.C., Meynecke, J.O., Dittmann, S., Ellison, A.M., Anger, K., Berger, U., Koedam, N., 2007. A world without mangroves? Science 317(5834), 41-42.
Ellison, J.C., 2015. Vulnerability assessment of mangroves to climate change and sea-level rise impacts. Wetlands Ecology and Management 23(2), 115-137.
Eslami-Andargoli, L., Dale, P.E.R., Sipe, N., Chaseling, J., 2010. Local and landscape effects on spatial patterns of mangrove forest during wetter and drier periods: Moreton Bay, Southeast Queensland, Australia. Estuarine, Coastal and Shelf Science 89(1), 53-61.
FAO. 2020 Global Forest Resources Assessment 2020 (Rome: FAO) (available at: https://doi.org/10.4060/ca8753en)
Hutchison, J., Manica, A., Swetnam, R., Balmford, A., Spalding, M., 2014. Predicting global patterns in mangrove forest biomass. Conservation Letters 7(3), 233-240.
Kamruzzaman, M., Ahmed, S., Osawa, A., 2017. Biomass and net primary productivity of mangrove communities along the Oligohaline zone of Sundarbans, Bangladesh. Forest Ecosystems 4(1), 1-9.
Ke, L., Wong, T.W., Wong, Y.S., Tam, N.F., 2002. Fate of polycyclic aromatic hydrocarbon (PAH) contamination in a mangrove swamp in Hong Kong following an oil spill. Marine Pollution Bulletin 45(1-12), 339-347.
Khaleghi, M., Sawari, A., Safahiyeh, A., Hamidianpour, M., Ghaemmaghamo, S., 2021. Estimation of carbon production and storage of mangrove forests of Gwadar; Gulf of Oman using the PnET-CN model. Journal of Marine Science and Technology 23(3), 183-193. (In Persian)
Khodabakhshi Karlai, A., 2018. Evaluation of the temporal and spatial changes of Iran's mangroves and the estimation of aboveground biomass of mangroves of Nayband using Landsat images. Master's thesis, Persian Gulf University, Faculty of Marine Science and Technology, 110 p. (In Persian)
Kristensen, E., Bouillon, S., Dittmar, T., Marchand, C., 2008. Organic carbon dynamics in mangrove ecosystems: a review. Aquatic Botany 89(2), 201-219.
Lewis III, R.R., Milbrandt, E.C., Brown, B., Krauss, K.W., Rovai, A.S., Beever III, J.W., Flynn, L.L., 2016. Stress in mangrove forests: early detection and preemptive rehabilitation are essential for future successful worldwide mangrove forest management. Marine Pollution Bulletin 109(2), 764-771.
Mafi-Gholami, D., Baharlouii, M., Mahmoudi, B., 2016. Monitoring the rate of progression and regression in mangrove forests using remote sensing and the Digital Shoreline Analysis System (DSAS) (case study: Hara Biosphere Reserve). Environment Studies 43(4), 633-646. (In Persian)
Mafi-Gholami, D., Feghhi, J., Danehkar, A., Yarali, N., 2015a. Classification and Prioritization of Negative Factors Affecting on Mangrove Forests Using Delphi Method (a Case Study: Mangrove Forests of Hormozgan Province, Iran). Advances in Bioresearch 6(3), 121-135.
Mafi-Gholami, D., Feghhi, J., Danehkar, A., Yarali, N., 2015b. Prioritizing stresses and disturbances affecting mangrove forests using Fuzzy Analytic Hierarchy Process (FAHP). Case study: mangrove forests of Hormozgan Province, Iran. Advances in Environmental Sciences 7(3), 442-459.
Mafi-Gholami, D., Mahmoudi, B., Zenner, E.K., 2017. An analysis of the relationship between drought events and mangrove changes along the northern coasts of the Persian Gulf and Oman Sea. Estuarine, Coastal and Shelf Science 199, 141-151.
Mafi-Gholami, D., Pirasteh, S., Ellison, J.C., Jaafari, A., 2021. Fuzzy-based vulnerability assessment of coupled social-ecological systems to multiple environmental hazards and climate change. Journal of Environmental Management 299, 113573.
Mafi-Gholami, D., Zenner, E.K., Jaafari, A., 2020. Mangrove regional feedback to sea level rise and drought intensity at the end of the 21st century. Ecological Indicators 110, 105972.
Mafi-Gholami, D., Zenner, E.K., Jaafari, A., Ward, R.D., 2019. Modeling multi-decadal mangrove leaf area index in response to drought along the semi-arid southern coasts of Iran. Science of the Total Environment 656, 1326-1336.
Mcleod, E., Chmura, G.L., Bouillon, S., Salm, R., Björk, M., Duarte, C.M., Silliman, B.R., 2011. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Frontiers in Ecology and the Environment 9(10), 552-560.
Nguyen, H.H., McAlpine, C., Pullar, D., Johansen, K., Duke, N.C., 2013. The relationship of spatial-temporal changes in fringe mangrove extent and adjacent land-use: Case study of Kien Giang coast, Vietnam. Ocean & Coastal Management 76, 12-22.
Osland, M.J., Feher, L.C., Griffith, K.T., Cavanaugh, K.C., Enwright, N.M., Day, R.H., Rogers, K., 2017. Climatic controls on the global distribution, abundance, and species richness of mangrove forests. Ecological Monographs 87(2), 341-359.
Persian Gulf Studies Center (PGSC), 2017. Available online: http://www.persiangulfstudies.com/fa/index.asp?p=pages&id=205.
Powell, S.L., Cohen, W.B., Healey, S.P., Kennedy, R.E., Moisen, G.G., Pierce, K.B., Ohmann, J.L., 2010. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sensing of Environment 114(5), 1053-1068.
Ray, R., Ganguly, D., Chowdhury, C., Dey, M., Das, S., Dutta, M. K., & Jana, T.K., 2011. Carbon sequestration and annual increase of carbon stock in a mangrove forest. Atmospheric Environment 45(28), 5016-5024.
Rozainah, M.Z., Nazri, M.N., Sofawi, A.B., Hemati, Z., Juliana, W.A., 2018. Estimation of carbon pool in soil, above and below ground vegetation at different types of mangrove forests in Peninsular Malaysia. Marine Pollution Bulletin 137, 237-245.
Safari, J., 2018. Investigation and mapping of structural changes of mangroves in two coastal and island vegetation forms in the Hara Biosphere Reserve. Master's thesis, Faculty of Natural Resources and Earth Sciences, Shahrekord University. 85 p. (In Persian)
Safiari, S., 2016. Mangrove forests of Iran. Iranian Nature 2(2), 49-57. (In Persian)
Schile, L.M., Kauffman, J.B., Crooks, S., Fourqurean, J.W., Glavan, J., Megonigal, J.P., 2017. Limits on carbon sequestration in arid blue carbon ecosystems. Ecological Applications 27(3), 859-874.
Ward, R.D., Friess, D.A., Day, R.H., MacKenzie, R.A., 2016. Impacts of climate change on mangrove ecosystems: a region by region overview. Ecosystem Health and Sustainability 2(4), e01211.
 
Journal of Natural Environment
Volume 75, Special Issue Coastal and Marine Environment
February 2023
Pages 15-28