Abstract
Mangrove species are shrubs and trees that are ecologically efficient and salt tolerant, predominantly grow in the intertidal region where land and ocean meet. These protects the shoreline from erosion, natural calamities, and climate change. Map**, monitoring, and assessment of mangrove species are important for the conservation, restoration, and protection purposes. Earth observation using remote sensing satellite data plays a vital role in map** and monitoring land-surface features. Studying mangroves with remote sensing also helps to track seasonal changes and understand how these ecosystems work. In the present study Mangrove species in and around Eastern Lagoon National Park of Abu Dhabi, United Arab Emirates, is mapped using the Multispectral Landsat 8 OLI data. It has been found that a large part of the area is covered by grey mangroves (i.e., Avicennia marina), including salt marshes. Vegetation indices like RVI, NDVI, SAVI, AVI, and CMRI are derived for map** and health assessment to categorize mangrove species in high, medium, and low phenology activity, and found the maximum productivity was attained during March 2020 (0 to 366 days). Multispectral reflectance spectra of homogenous units were also collected and classified. For the first time, the satellite-based phenological information has been derived in the region using Landsat 8 OLI data, which will help in understanding biodiversity, climate change impacts, and human activities, and enabling decision-makers for the protection and sustainable use of these valuable coastal ecosystems.
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References
Adole T, Dash J, Atkinson PM (2016) A systematic review of vegetation phenology in Africa. Ecol Inform 34:117–128
Akbar MR, Arisanto PAA, Sukirno BA, Merdeka PH, Priadhi MM, Zallesa S (2020) Mangrove vegetation health index analysis by implementing NDVI (normalized difference vegetation index) classification method on sentinel-2 image data case study: Segara Anakan, Kabupaten Cilacap. IOP Confer Ser Earth Environ Sci 584(1):012069. https://doi.org/10.1088/1755-1315/584/1/012069
Almahasheer H (2018) Spatial coverage of mangrove communities in the Arabian Gulf. Environ Monit Assess 190(2):85
Alongi DM (2018) Mangrove Forests. In: Blue Carbon. Springer Briefs in Climate Studies. Springer, Cham, pp 23–36. https://doi.org/10.1007/978-3-319-91698-9_3
Alsumaiti TS (2014) An assessment of Avicennia marina forest structure and aboveground biomass in Eastern Mangrove Lagoon National Park, Abu Dhabi. Arab World Geogr 17:166–185
Asrar GQ, Fuchs M, Kanemasu ET, Hatfield JL (1984) Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat 1. Agron J 76:300–306. https://doi.org/10.2134/agronj1984.00021962007600020029x
Bannari A, Morin D, Bonn F, Huete A (1995) A review of vegetation indices. Remote Sens Rev 1(13):95–120. https://doi.org/10.1080/02757259509532298
Baret F, Guyot G (1991) Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens Envirosn 35:161–173. https://doi.org/10.1016/0034-4257(91)90009-U
Campbell JB (1987) Introduction to remote sensing. The Guilford Press
Cardenas NY, Joyce KE, Maier SW (2017) Monitoring mangrove forests: are we taking full advantage of technology? Int J Appl Earth Obs Geoinf 63:1–14
De Jong SM, Shen Y, de Vries J, Bijnaar G, van Maanen B, Augustinus P, Verweij P (2021) Map** mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm. Int J Appl Obser Geoinfo 97:102293. https://doi.org/10.1016/j.jag.2020.102293
Deblonde G, Cihlar J (1993) A multiyear analysis of the relationship between surface environmental variables and NDVI over the Canadian landmass. Remote Sens Rev 7(2):151–177. https://doi.org/10.1080/02757259309532172
Duke N, Nagelkerken I, Agardy T, Wells S, Van Lavieren H (2014) The importance of mangroves to people: a call to action. United Nations Environment Programme World Conservation Monitoring Centre, Cambridge, UK ISBN 92-807-3397-4
Duke NC (1990) Phenological trends with latitude in the mangrove tree Avicennia marina. J Ecol 78(1):113–133. https://doi.org/10.2307/2261040
Elmahdy SI, Mohamed MM (2013) Change detection and map** of mangrove using multi-temporal remote sensing data: a case study of Abu Dhabi, UAE. J Geom 7(1):41–45
Embabi NS (1993) Environmental aspects of geographical distribution of mangrove in the United Arab Emirates. In: Lieth H, Masoom AA (eds) Towards the rational use of high salinity tolerant plants. Tasks for vegetation science, vol 27. Springer. https://doi.org/10.1007/978-94-011-1858-3_5
Fraser RS, Kaufman YJ (1985) The relative importance of aerosol scattering and absorption in remote sensing. IEEE Trans Geosc Remote Sens 5:625–633. https://doi.org/10.1109/TGRS.1985.289380
Friedl M, Henebry G, Reed B, Huete A (2006) Land surface phenology: a community white paper requested by NASA. Tech Rep, Chicago. https://cce.nasa.gov/mtg2008_ab_presentations/Phenology_Friedl_whitepaper.pdf
Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J, Duke N (2011) Status and distribution of mangrove forests of the world using earth observation satellite data. Glob Ecol Biogeogr 20:154–159
Gruninger JH, Ratkowski AJ, Hoke ML (2004) The sequential maximum angle convex cone (SMACC) endmember model. In algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery. Int Soc Opt Photon 5425:1–14. https://doi.org/10.1117/12.543794
Gupta K, Mukhopadhyay A, Giri S, Chanda A, Majumdar SD, Samanta S, Mitra D, Samal RN, Pattnaik AK, Hazra S (2018) An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery. MethodsX 5:1129–1139. https://doi.org/10.1016/j.mex.2018.09.011
Habshi AA, Youssef T, Aizpuru M, Blasco F (2007) New mangrove ecosystem data along the UAE coast using remote sensing. Aquat Ecosyst Health Manag 10(3):309–319. https://doi.org/10.1080/14634980701512525
Hegazy AK (1998) Perspectives on survival, phenology, litter fall and decomposition, and caloric content of Avicennia marina in the Arabian Gulf region. J Arid Environ 40:417–429. https://doi.org/10.1006/jare.1998.0457
Henebry GM, de Beurs KM, Gitelson AA (2005) Land surface phenologies of Uzbekistan and Turkmenistan between 1982 and 1999. Arid Ecosyst 11:25–32
Himayah S, Danoedoro P (2016) The utilization of Landsat 8 multitemporal imagery and forest canopy density (FCD) model for forest reclamation priority of natural disaster areas at Kelud Mountain, East Java. IOP Conf Ser Earth Environ Sci 47(1):012043. https://doi.org/10.1088/1755-1315/47/1/012043
Holben BN, Kaufman YJ, Kendall JD (1990) NOAA-11 AVHRR visible and near-IR inflight calibration. Int J Remote Sens 11(8):1511–1519. https://doi.org/10.1080/01431169008955109
Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25(3):295–309. https://doi.org/10.1016/0034-4257(88)90106-X
Ihlen V (2019) Landsat 8 Data Users Handbook. US Geological Survey, Sioux Falls, SD, USA
Issa S, Dahy B, Saleous N, Ksiksi T (2019) Carbon stock assessment of date palm using remote sensing coupled with field-based measurements in Abu Dhabi (United Arab Emirates). Int J Remote Sens 40:7561–7580. https://doi.org/10.1080/01431161.2019.1602795
Jordan CF (1969) Derivation of leaf-area index from quality of light on the forest floor. Ecology 4:663–666. https://doi.org/10.2307/1936256
Kauffman JB, Adame M, Arifanti VB, Schile-Beers LM, Bernardino AF, Bhomia RK, Donato DC, Feller IC, Ferreira TO, del Carmen Jesus Garcia M (2020) Total ecosystem carbon stocks of mangroves across broad global environmental and physical gradients. Ecol Monogr 90:e01405
Kaufman YJ (1984) Atmospheric effects on remote sensing of surface reflectance. In Remote sensing: critical review of technology. Int Soc Opt Photon 475:20–33. https://doi.org/10.1117/12.966238
Kimball J (2014) Vegetation Phenology. In: Njoku EG (ed) Encyclopedia of remote sensing. Encyclopedia of Earth Sciences Series. https://doi.org/10.1007/978-0-387-36699-9_188
Klostermanm ST, Hufkens K, Gray JM, Melaas E, Sonnentag O, Lavine I, Mitchell L, Norman R, Friedl MA, Richardson AD (2014) Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 11(16):4305–4320. https://doi.org/10.5194/bg-11-4305-2014
Kruse FA, Lefkoff AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz AF (1993) The spectrimage processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote Sens Environ 44:145–163. https://doi.org/10.1016/0034-4257(93)90013-N
Lopez-Portillo J, Ezcurra E (1985) Litter fall of Avicennia germinans L. in a one-year cycle in a mudflat at the Laguna de Mecoacan, Tabasco, Mexico. Biotropica 1:186–190. https://doi.org/10.2307/2388215
Mateos-Molina D, Antonopoulou M, Baldwin R, Bejarano I, Burt JA, García-Charton JA, Al-Ghais SM, Walgamage J, Taylor OJ (2020) Applying an integrated approach to coastal marine habitat map** in the north-western United Arab Emirates. Mar Environ Res 161:105095. https://doi.org/10.1016/j.marenvres.2020.105095
Maurya K, Mahajan S, Chaube N (2021) Remote sensing techniques: map** and monitoring of mangrove ecosystem—a review Complex Intell. Syst 7:2797–2818
Moore GE, Grizzle RE, Ward KM, Alshihi RM (2015) Distribution, pore-water chemistry, and stand characteristics of the mangroves of the United Arab Emirates. J Coas Res 31(4):957–963. https://doi.org/10.2112/JCOASTRES-D-14-00142.1
Niranjan Kumar K, Ouarda TB (2014) Precipitation variability over UAE and global SST teleconnections. J Geop Res Atmospheres 119(17):10–313. https://doi.org/10.1002/2014JD021724
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66. https://doi.org/10.1109/TSMC.1979.4310076
Ouarda TB, Charron C, Kumar KN, Marpu PR, Ghedira H, Molini A, Khayal I (2014) Evolution of the rainfall regime in the United Arab Emirates. J Hydrol 514:258–270. https://doi.org/10.1016/j.jhydrol.2014.04.032
Pastor-Guzman J, Dash J, Atkinson PM (2018) Remote sensing of mangrove forest phenology and its environmental drivers. Remote Sens Environ 205:71–84. https://doi.org/10.1016/j.rse.2017.11.009
Pham TD, Yokoya N, Bui DT, Yoshino K, Friess DA (2019) Remote sensing approaches for monitoring mangrove species, structure, and biomass: opportunities and challenges. Remote Sens 11:230
Prince SD, Tucker CJ (1986) Satellite remote sensing of rangelands in Botswana II. NOAA AVHRR and herbaceous vegetation. Int J Remote Sens 7(11):1555–1570. https://doi.org/10.1080/01431168608948953
Qi X, Wang K, Zhang C (2013) Effectiveness of ecological restoration projects in a karst region of southwest China assessed using vegetation succession map**. Ecol Eng 54:245–253. https://doi.org/10.1016/j.ecoleng.2013.01.002
Rani V, Sreelekshmi S, Preethy CM, BijoyNandan S (2016) Phenology and litterfall dynamics structuring Ecosystem productivity in a tropical mangrove stand on South West coast of India. Reg Stud Mar Sci 8:400–407. https://doi.org/10.1016/j.rsma.2016.02.008
Reed DN, Anderson TM, Dempewolf J, Metzger K, Serneels S (2009) The spatial distribution of vegetation types in the Serengeti ecosystem: the influence of rainfall and topographic relief on vegetation patch characteristics. J Biogeogr 36(4):770–782. https://doi.org/10.1111/j.1365-2699.2008.02017.x
Rhyma PP, Norizah K, Hamdan O, Faridah-Hanum I, Zulfa AW (2020) Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation. Remote Sens Appl: Soc Environ 1(17):100280. https://doi.org/10.1016/j.rsase.2019.100280
Rouse JW, Hass RH, Schell JA, Freden SC, Mercanti EP, Becker MA (1974) Monitoring vegetation system in the great palins with ERTS. Third Earth Resource Technology Satellite-1. Symposium Technical presentation, 309-317.
Roy PS, Sharma KP, Jain A (1996) Stratification of density in dry deciduous forest using satellite remote sensing digital data—an approach based on spectral indices. J Biosci 21(5):723–734. https://doi.org/10.1007/BF02703148
Saito H, Bellan MF, Al-Habshi A, Aizpuru M, Blasco F (2003) Mangrove research and coastal ecosystem studies with SPOT-4 HRVIR and TERRA ASTER in the Arabian Gulf. Int J Remote Sens 24(21):4073–4092. https://doi.org/10.1080/0143116021000035030
Samara F, Solovieva N, Ghalayini T, Nasrallah ZA, Saburova M (2020) Assessment of the environmental status of the mangrove ecosystem in the United Arab Emirates. Water. 12(6):1623. https://doi.org/10.3390/w12061623
Schwartz MD (2003) Phenology: an integrative environmental science. Kluwer Academic Publishers, Dordrecht
Simard M, Fatoyinbo L, Smetanka C, Rivera-Monroy VH, Castañeda-Moya E, Thomas N, Van der Stocken T (2019) Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nat Geosci 12:40–45
Son NT, Thanh BX, Da CT (2016) Monitoring mangrove forest changes from multi-temporal Landsat data in Can Gio Biosphere Reserve, Vietnam. Wetlands 36(3):565–576. https://doi.org/10.1007/s13157-016-0767-2
Srikanth S, Lum SK, Chen Z (2016) Mangrove root: adaptations and ecological importance. Trees. 30:451–465
Su Mon M, Mizoue N, Htun NZ, Kajisa T, Yoshida S (2012) Estimating forest canopy density of tropical mixed deciduous vegetation using Landsat data: a comparison of three classification approaches. Int J Remote Sens 33(4):1042–1057. https://doi.org/10.1080/01431161.2010.549851
Switzer AD, Yu F, Gouramanis C, Soria JLA, Pham DT (2014) Integrating different records to assess coastal hazards at multi-century timescales. J Coas Res 70:723–729. https://doi.org/10.2112/SI70-122.1
Teixeira Pinto C, **g X, Leigh L (2020) Evaluation analysis of Landsat level-1 and level-2 data products using in situ measurements. Remote Sens 12(16):2597
Thakur S, Mondal I, Ghosh PB, Das P, De TK (2020) A review of the application of multispectral remote sensing in the study of mangrove ecosystems with special emphasis on image processing techniques. Spat Inf Res 28:39–51
Townshend JR, Justice CO (1986) Analysis of the dynamics of African vegetation using the normalized difference vegetation index. Int J Remote Sens 7(11):1435–1445. https://doi.org/10.1080/01431168608948946
Tran TV, Reef R, Zhu X (2022) A review of spectral indices for mangrove remote sensing. Remote Sens 14:4868. https://doi.org/10.3390/rs14194868
Wang L, Jia M, Yin D, Tian J (2019) A review of remote sensing for mangrove forests: 1956-2018. Remote Sens Environ 231:111223. https://doi.org/10.1016/j.rse.2019.111223
Zhang X, Treitz PM, Chen D, Quan C, Shi L, Li X (2017) Map** mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure. Int J Appl Earth Obs Geoinf 62:201–214. https://doi.org/10.1016/j.jag.2017.06.010
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This project was funded by the Research Office, Zayed University, United Arab Emirates (Project No. R 21005).
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Conceptualization: I.B.S., M.S., and P.R.K.; methodology: I.B.S., M.S., and P.R.K.; software: M.S., P.R.K., and A.K.; validation: I.B.S., M.S., and F.H.; formal analysis: I.B.S., M.S., and P.R.K.; investigation: M.S., P.R.K., and A.K.; resources: M.S., K.A., P.R.K., and Y.N.; data curation: M.S., P.R.K., A.K., and C.M.X.; writing—original draft preparation: I.B.S., M.S., and P.R.K.; writing—review and editing: M.S., I.B.S., and A.K.; visualization: I.B.S., M.S., P.R.K., A.A.T., L.M., and C.M.X.; supervision: I.B.S.; funding acquisition: I.B.S.
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Salem, I.B., Nazzal, Y., Sharma, M. et al. Map** and monitoring of mangrove species in Eastern Lagoon National Park, Abu Dhabi, using Landsat 8 OLI data. Arab J Geosci 17, 118 (2024). https://doi.org/10.1007/s12517-024-11925-4
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DOI: https://doi.org/10.1007/s12517-024-11925-4