Abstract
Changes in land use and land cover have severely influenced the sustainability of mangrove vegetation, especially in the hyper-arid, hyper-saline Red Sea coastal waters of Saudi Arabia. The present study investigates the effect of effluents released from an adjoining shrimp farm on the sustainability of a nearby mangrove woodland during operation and after closure of the farm. In addition, the consequences of dredging activities to fill coastal waters for land reclamation to develop a mega seaport at Jazan Economic City are explored. A band image-difference algorithm was applied to Landsat 5 Thematic Mapper and Landsat 08 Operational Land Imager satellite images obtained on different dates, which revealed a prominent vigour boom in the mangrove forest while the shrimp farm operated but a gradual decrease in vigour after its closure. During the investigation time frame of 2016 and 2017, spectral vegetation analysis of Sentinel-2A satellite images highlighted a strong negative correlation between dredging operations for seaport construction and the adjacent fragile mangrove forest. Dredging operations were responsible for a reduction of 19.30% in the Normalized Difference Vegetation Index, 27.5% in the Leaf Area Index, and 19.0% in the Optimized Soil Adjusted Vegetation Index. The results clearly show the potential application of spectral vegetation indices in the monitoring and analysis of anthropogenic impacts on coastal vegetation. We suggest strong management efforts for monitoring, assessing, and regulating measures to offset the negative trends in the sustainability of mangroves in Red Sea coastal regions.
Similar content being viewed by others
References
Adam, E., Mutanga, O., & Rugege, D. (2010). Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation. A review. Wetlands Ecology and Management, 18(3), 281–296.
Ahmed, N., & Thompson, S. (2019). The blue dimensions of aquaculture: A global synthesis. Science of the Total Environment, 652, 851–861.
Alatorre, L. C., Sánchez-Carrillo, S., Miramontes-Beltrán, S., Medina, R. J., Torres-Olave, M. E., Bravo, L. C., et al. (2016). Temporal changes of NDVI for qualitative environmental assessment of mangroves. Shrimp farming impacts the health decline of the arid mangroves in the Gulf of California (1990–2010). Journal of Arid Environments, 125, 98–109.
Almahasheer, H. (2018). Spatial coverage of mangrove communities in the Arabian Gulf. Environmental Monitoring and Assessment, 190(2), 85.
Almahasheer, H., Serrano, O., Duarte, C. M., Arias-Ortiz, A., Masque, P., & Irigoien, X. (2017). Low carbon sink capacity of Red Sea mangroves. Scientific Reports, 7(1), 1–10.
Anonymous. (1983). The causes and consequences of mangrove destruction. The Environmentalist, 3(Supplement 3), 33–49.
Arshad, M., Alrumman, S. A., & Eid, E. M. (2018). Evaluation of carbon sequestration in the sediment of polluted and non-polluted locations of mangroves. Fundamental and Applied Limnology, 192(1), 53–64.
Asrar, G. Q., Fuchs, M., Kanemasu, E. T., & Hatfield, J. L. (1984). Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agronomy Journal, 76(2), 300–306.
Baret, F., Jacquemoud, S., & Hanocq, J. F. (1993). The soil line concept in remote sensing. Remote Sensing Reviews, 7(1), 65–82.
Berni, J. A., Zarco-Tejada, P. J., Suárez Barranco, M. D., & Fereres Castiel, E. (2009). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 722–738.
Board, M. A. (2005). Millennium ecosystem assessment (p. 13). Washington: New Island.
Bowers, S. A., & Hanks, R. J. (1965). Reflection of radiant energy from the soil. Soil Science, 100, 130–138.
Breda, N. J. J. (2008). Leaf area index. Encyclopedia of Ecology (Second Edition), 2, 457–462.
Caballero, I., Navarro, G., & Ruiz, J. (2018). Multi-platform assessment of turbidity plumes during dredging operations in a major estuarine system. International Journal of Applied Earth Observation and Geoinformation, 68, 31–41.
Chaurasia, S., Bhattacharya, B. K., Dadhwal, V. K., & Parihar, J. S. (2006). Field-scale leaf area index estimation using IRS-1D LISS-III data. International Journal of Remote Sensing, 27(4), 637–644.
Chen, P. Y., Fedosejevs, G., Tiscareno-Lopez, M., & Arnold, J. G. (2006). Assessment of MODIS-EVI, MODIS-NDVI and VEGETATION-NDVI composite data using agricultural measurements: An example at cornfields in western Mexico. Environmental Monitoring and Assessment, 119(1–3), 69–82.
Conchedda, G., Durieux, L., & Mayaux, P. (2008). An object-based method for mapping and change analysis in mangrove ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing, 63(5), 578–589.
Davi, H., Soudani, K., Deckx, T., Dufrene, E., Le Dantec, V., & Francois, C. (2006). Estimation of forest leaf area index from SPOT imagery using NDVI distribution over forest stands. International Journal of Remote Sensing, 27(05), 885–902.
Delegido, J., Verrelst, J., Rivera, J. P., Ruiz-Verdú, A., & Moreno, J. (2015). Brown and green LAI mapping through spectral indices. International Journal of Applied Earth Observation and Geoinformation, 35, 350–358.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., & Meygret, A. (2012). Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25–36.
Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., & Li, X. (2016). Water bodies mapping from Sentinel-2 imagery with modified normalized difference water index at 10 m spatial resolution produced by sharpening the SWIR band. Remote Sensing, 8(4), 354.
Duke, N. C., Meinecke, J. O., Dittmann, S., Ellison, A. M., Anger, K., Berger, U., et al. (2007). A world without mangroves? Science, 317(5834), 41–42.
Eid, E. M., Keshta, A. E., Shaltout, K. H., Baldwin, A. H., & Sharaf El-Din, A. A. (2017). Carbon sequestration potential of the five Mediterranean lakes of Egypt. Fundamental & Applied Limnology, 190, 87–96.
Eitel, J. U., Vierling, L. A., Litvak, M. E., Long, D. S., Schulthess, U., Ager, A. A., & Stoscheck, L. (2011). Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland. Remote Sensing of Environment, 115(12), 3640–3646.
El Juhany, L. I. (2009). Present status and degradation trends of mangrove forests on the southern Red Sea coast of Saudi Arabia. American-Eurasian Journal of Agricultural and Environmental Science, 6(3), 328–340.
European Space Agency (ESA), (2015). Sentinel-2 user book. https://sentinels.copernicus.eu/documents/247904/685211/Sentinel-2_User_Handbook. Accessed 25 December 2018.
Fang, H., & Liang, S. (2008). Leaf area index models. Encyclopedia of Ecology, pp., 2139–2148.
Feola, A., Lisi, I., Salmeri, A., Venti, F., Pedroncini, A., Gabellini, M., & Romano, E. (2016). The platform of integrated tools to support environmental studies and management of dredging activities. Journal of Environmental Management, 166, 357–373.
Fernández-Manso, A., Fernández-Manso, O., & Quintano, C. (2016). SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. International Journal of Applied Earth Observation and Geoinformation, 50, 170–175.
Fisher, B., Turner, R. K., & Morling, P. (2009). Defining and classifying ecosystem services for decision making. Ecological Economics, 68(3), 643–653.
Gilman, E. L., Ellison, J., Duke, N. C., & Field, C. (2008). Threats to mangroves from climate change and adaptation options: A review. Aquatic Botany, 89(2), 237–250.
Gitelson, A. A., Vina, A., Arkebauer, T. J., Rundquist, D. C., Keydan, G., & Leavitt, B. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30(5), 1248.
Green, E. P., Mumby, P. J., Edwards, A. J., Clark, C. D., & Ellis, A. C. (1997). Estimating leaf area index of mangroves from satellite data. Aquatic Botany, 58(1), 11–19.
Gu, Y., Hunt, E., Wardlow, B., Basara, J. B., Brown, J. F., & Verdin, J. P. (2008). Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data. Geophysical Research Letters, 35(22), L22401.
Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2–3), 416–426.
Hawbaker, T. J., Vanderhoof, M. K., Beal, Y. J., Takacs, J. D., Schmidt, G. L., Falgout, J. T., et al. (2017). Mapping burned areas using dense time series of Landsat data. Remote Sensing of Environment, 198, 504–522.
Heiskanen, J. (2006). Estimating above-ground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data. International Journal of Remote Sensing, 27(6), 1135–1158.
Huemmrich, K. F., Black, T. A., Jarvis, P. G., McCaughey, J. H., & Hall, E. G. (1999). High temporal resolution NDVI phenology from micrometeorological radiation sensors. Journal of Geophysical Research: Atmospheres, 104(D22), 27935–27944.
Huete, A. R. (1988). A soil adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309.
Islam, M. R., & Tabeta, S. (2019). Shrimp vs prawn rice farming in Bangladesh. A comparative impact study on local environments and livelihoods. Ocean & Coastal Management, 168, 167–176.
Jaramillo, F., Licero, L., Åhlen, I., Manzoni, S., Rodríguez-Rodríguez, J. A., Guittard, A., & Martínez, O. (2018). Effects of hydro-climatic change and rehabilitation activities on salinity and mangroves in the Ciénaga Grande de Santa Marta, Colombia. Wetlands, 38(4), 755–767.
Jayanthi, M., Thirumurthy, S., Muralidhar, M., & Ravichandran, P. (2018). Impact of shrimp aquaculture development on important eco-systems in India. Global Environmental Change, 52, 10–21.
Jensen, J. R. (2007). Remote sensing of the environment. An earth resource perspective: Pearson prentice Hall. Upper Saddle River.
Jensen, J. R., Narumalani, S., Weatherbee, O., & Mackey, H. E. (1991). Remote sensing offers an alternative for mapping wetlands. Geo Info Systems, 1(8), 46–53.
Kamali, B., & Hashim, R. (2011). Mangrove restoration without planting. Ecological Engineering, 37(2), 387–391.
Khalil, A. (2004). Status of mangroves in the Red Sea Gulf of Aden. PERSGA Technical Series, 11.
Kinyanjui, M. J. (2011). NDVI-based vegetation monitoring in Mau forest complex, Kenya. African Journal of Ecology, 49(2), 165–174.
Kirwan, M. L., & Megonigal, J. P. (2013). Tidal wetland stability in the face of human impacts and sea-level rise. Nature, 504(7478), 53–60.
Lee, T. M., & Yeh, H. C. (2009). Applying remote sensing techniques to monitor shifting wetland vegetation. A case study of Danshui River estuary mangrove communities, Taiwan. Ecological Engineering, 35(4), 487–496.
Li, X., Bellerby, R., Craft, C., & Widney, S. E. (2018). Coastal wetland loss, consequences, and challenges for restoration. Anthropocene Coasts, 1(1), 1–15.
McCarthy, M. J., Radabaugh, K. R., Moyer, R. P., & Muller-Karger, F. E. (2018). Enabling efficient large-scale high-spatial-resolution wetland mapping using satellites. Remote Sensing of Environment, 208, 189–201.
Mutanga, O., & Skidmore, A. K. (2004). Narrowband vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 25(19), 3999–4014.
Myneni, R. B., Loss, S. O., & Tukker, C. J. (1996). Satellite-based identification of linked vegetation index and sea surface temperature. Anomaly areas from 1982–1990 for Africa, Australia, and South America. Geophysical Research Letters, 23(7), 729–732.
Otero, V., Van De Kerchove, R., Satyanarayana, B., Martínez-Espinosa, C., Fisol, M. A. B., Ibrahim, M. R. B., & Dahdouh-Guebas, F. (2018). Managing mangrove forests from the sky. Forest inventory using field data and unmanned aerial vehicle (UAV) imagery in the Matang Mangrove Forest Reserve, peninsular Malaysia. Forest Ecology and Management, 411, 35–45.
Pasqualini, V., Iltis, J., Dessay, N., Lointier, M., Guelorget, O., & Polidori, L. (1999). Mangrove mapping in North-Western Madagascar using SPOT-XS and SIR-C radar data. Hydrobiologia, 413, 127–133.
Pastor-Guzman, J., Dash, J., & Atkinson, P. M. (2018). Remote sensing of mangrove forest phenology and its environmental drivers. Remote Sensing of Environment, 205, 71–84.
Peterson, J. E. (2009). Life after oil: Economic alternatives for the Arab Gulf States. Mediterranean Quarterly, 20(3), 1–18.
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20(9), 503–510.
Pham, T. D., et al. (2019). Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges. Remote Sensing, 11(3), 230.
Plourde, L. C., Ollinger, S. V., Smith, M. L., & Martin, M. E. (2007). Estimating species abundance in a northern temperate forest using spectral mixture analysis. Photogrammetric Engineering & Remote Sensing, 73(7), 829–840.
Proisy, C., Viennois, G., Sidik, F., Andayani, A., Enright, J. A., Guitet, S., & Prosperi, J. (2018). Monitoring mangrove forests after aquaculture abandonment using time series of very high spatial resolution satellite images. A case study from the Perancak estuary, Bali, Indonesia. Marine Pollution Bulletin, 131, 61–71.
Ramsey, E.W., Jensen, J.R. (1996). Remote sensing of mangrove wetlands: Relating canopy spectra to site-specific data. Photogrammetric Engineering and Remote Sensing 62:939–948.
Rogers, K. L., Boon, P. I., Branigan, S., Duke, N. C., Field, C. D., Fitzsimons, J. A., Kirkman, H., Mackenzie, J. R., & Saintilan, N. (2016). The state of legislation and policy protecting Australia’s mangrove and salt marsh and their ecosystem services. Marine Policy, 72, 139–155.
Romañach, S. S., DeAngelis, D. L., Koh, H. L., Li, Y., Teh, S. Y., Barizan, R. S. R., & Zhai, L. (2018). Conservation and restoration of mangroves. Global status, perspectives, and prognosis. Ocean & Coastal Management, 154, 72–82.
Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95–107.
Suir, G. M., & Sasser, C. E. (2019a). Redistribution and impacts of nearshore berm sediments on the Chandeleur barrier islands, Louisiana. Ocean & Coastal Management, 168, 103–116.
Suir, G. M., & Sasser, C. E. (2019b). Redistribution and impact of nearshore berm sediments on the Chandeleur barrier islands, Louisiana. Ocean & Coastal Management, 168, 103–116.
Thenkabail, P. S., & Lyon, J. G. (2016). Hyperspectral remote sensing of vegetation. CRC Press.
Valderrama-Landeros, L., Flores-de-Santiago, F., Kovacs, J. M., & Flores-Verdugo, F. (2018). An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme. Environmental Monitoring and Assessment, 190(1), 23.
Viña, A., Gitelson, A. A., Nguy-Robertson, A. L., & Peng, Y. (2011). Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment, 115(12), 3468–3478.
Wang, Q., Adiku, S., Tenhunen, J., & Granier, A. (2005). On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sensing of Environment, 94(2), 244–255.
Wemple, B. C., Browning, T., Ziegler, A. D., Celi, J., Chun, K. P., Jaramillo, F., & Sawyer, D. (2018). Eco-hydrological disturbances associated with roads. Current knowledge, research needs, and management concerns with reference to the tropics. Ecohydrology, 11(3), e1881.
Yang, R. M., & Guo, W. W. (2019). Using time-series Sentinel-1 data for soil prediction on invaded coastal wetlands. Environmental Monitoring and Assessment, 191(7), 462.
Zhang, H. K., & Roy, D. P. (2016). Landsat 5 Thematic Mapper reflectance and NDVI 27-year time series inconsistencies due to satellite orbit change. Remote Sensing of Environment, 186, 217–233.
Funding
This work was funded by the Deanship of Scientific Research at King Khalid University through the General Research Project under grant number G.R.P. 52-40.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Arshad, M., Eid, E.M. & Hasan, M. Mangrove health along the hyper-arid southern Red Sea coast of Saudi Arabia. Environ Monit Assess 192, 189 (2020). https://doi.org/10.1007/s10661-020-8140-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-020-8140-6