Abstract
Mangroves are Earth's most active and diversified saline wetlands, which play an essential role in protecting coastal communities from storm surge, cyclonic winds, tsunami, and tidal waves. Space-borne satellite data provide vital information for monitoring mangrove and retrieving health-related parameters. The objective of this study is to map and model biophysical and biochemical parameters of mangrove forests over the Bhitarkanika reserve forest located on the eastern coast of Odisha. The present study has employed Sentinel–2A sensor's red-edge bands to derive both the aforementioned parameters. Furthermore, the near-proximal sensor (NPS) data were integrated with satellite data for mapping leaf chlorophyll and nitrogen contents with the help of an empirical model. The key findings indicate that EVI (Enhanced Vegetation Index) and measured leaf chlorophyll were significantly and positively correlated (R2 = 0.78). EVI showed a stronger relationship with foliage pigments, such as leaf chlorophyll and nitrogen. Leaf area index (LAI) of mangrove ranged between 1 and 4, with healthy dense mangrove depicted LAI more than 2.5. Leaf chlorophyll content for dense mangrove forests showed between 40 and 90 μg/cm2 as estimated from satellite-based (i.e. NAOC index) and empirical model. However, the NAOC (Normalized Area Over reflectance Curve) index has relatively overestimated the chlorophyll. A similar pattern was also obtained for leaf nitrogen. Nevertheless, integrating both satellite and handheld NPS instruments has provided a robust and dynamic way to monitor mangrove forests' health conditions. Satellite-derived biophysical and biochemical parameters offer vital information on mangrove, which could be crucial towards conservation, plantation, and mangrove management.
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References
Ahmad S, Chandra Pandey A, Kumar A et al (2020) Chlorophyll deficiency (chlorosis) detection based on spectral shift and yellowness index using hyperspectral AVIRIS-NG data in Sholayar reserve forest, Kerala. Remote Sens Appl Soc Environ 19:100369. https://doi.org/10.1016/j.rsase.2020.100369
Alchanatis V, Schmilovitch Z, Meron M (2005) In-field assessment of single leaf nitrogen status by spectral reflectance measurements. Precis Agric 6:25–39. https://doi.org/10.1007/s11119-005-0682-7
Anand A, Pandey PC, Petropoulos GP et al (2020) Use of hyperion for mangrove forest carbon stock assessment in Bhitarkanika forest reserve: a contribution towards blue carbon initiative. Remote Sens 12:597. https://doi.org/10.3390/rs12040597
Awange JL, Kyalo Kiema JB (2013) Marine and coastal resources. Environmental geoinformatics. Springer, Berlin Heidelberg, pp 397–413
Aye WN, Wen Y, Marin K et al (2019) Contribution of mangrove forest to the livelihood of local communities in Ayeyarwaddy region. Myanmar For 10:414. https://doi.org/10.3390/f10050414
Bar S, Parida BR, Pandey AC (2020) Landsat-8 and Sentinel-2 based Forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya. Remote Sens Appl Soc Environ 18:100324. https://doi.org/10.1016/j.rsase.2020.100324
Bonan G (1993) Importance of leaf area index and forest type when estimating photosynthesis in boreal forests. Remote Sens Environ 43:303–314. https://doi.org/10.1016/0034-4257(93)90072-6
Buermann W, Beaulieu C, Parida BR et al (2016) Climate-driven shifts in continental net primary production implicated as a driver of a recent abrupt increase in the land carbon sink. Biogeosciences 13:1597–1607. https://doi.org/10.5194/bg-13-1597-2016
Carmona F, Rivas R, Fonnegra DC (2015) Vegetation Index to estimate chlorophyll content from multispectral remote sensing data. Eur J Remote Sens 48:319–326. https://doi.org/10.5721/EuJRS20154818
Chaube NR, Lele N, Misra A et al (2019) Mangrove species discrimination and health assessment using AVIRIS-NG hyperspectral data. Curr Sci 116:1136. https://doi.org/10.18520/cs/v116/i7/1136-1142
Cheng Q, Wu X (2007) Correlation analysis of simulated MODIS vegetation indices and the red edge and rice agricultural parameter. In: Neale CMU, Owe M, D’Urso G (eds). Florence, Italy, p 67420U
Clevers JGPW, Gitelson AA (2013) Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. Int J Appl Earth Obs Geoinf 23:344–351. https://doi.org/10.1016/j.jag.2012.10.008
Curran PJ (1989) Remote sensing of foliar chemistry. Remote Sens Environ 30:271–278. https://doi.org/10.1016/0034-4257(89)90069-2
Darvishzadeh R, Skidmore A, Abdullah H et al (2019) Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model. Int J Appl Earth Obs Geoinf 79:58–70. https://doi.org/10.1016/j.jag.2019.03.003
DasGupta R, Shaw R (2013) Cumulative impacts of human interventions and climate change on mangrove ecosystems of South and Southeast Asia: an overview. J Ecosyst 2013:1–15. https://doi.org/10.1155/2013/379429
Dash J, Curran PJ (2004) The MERIS terrestrial chlorophyll index. Int J Remote Sens 25:5403–5413. https://doi.org/10.1080/0143116042000274015
ISFR (2019) India State of Forest Report 2019 by Forest Survey of India. Ministry of Environment and Forests & Climate Change, Government of India. https://fsi.nic.in/isfr19/vol1/chapter3.pdf. Accessed 10 July 2020. Dehradun, India
Delegido J, Fernández G, Gandía S, Moreno J (2008) Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques: application to PROBA/CHRIS data. Int J Remote Sens 29:7107–7127. https://doi.org/10.1080/01431160802238401
Delegido J, Verrelst J, Alonso L, Moreno J (2011) Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors 11:7063–7081. https://doi.org/10.3390/s110707063
Doughty CE, Goulden ML (2008) Seasonal patterns of tropical forest leaf area index and CO2 exchange. J Geophys Res. https://doi.org/10.1029/2007JG000590
Fei SX, Shan CH, Hua GZ (2011) Remote sensing of mangrove wetlands identification. Procedia Environ Sci 10:2287–2293. https://doi.org/10.1016/j.proenv.2011.09.357
Frampton WJ, Dash J, Watmough G, Milton EJ (2013) Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS J Photogramm Remote Sens 82:83–92. https://doi.org/10.1016/j.isprsjprs.2013.04.007
Ghosh SM, Behera MD, Paramanik S (2020) Canopy height estimation using sentinel series images through machine learning models in a mangrove forest. Remote Sens 12:1519. https://doi.org/10.3390/rs12091519
Gitelson AA, Gritz Y, Merzlyak MN (2003) Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol 160:271–282. https://doi.org/10.1078/0176-1617-00887
Gwal S, Singh S, Gupta S, Anand S (2020) Understanding forest biomass and net primary productivity in Himalayan ecosystem using geospatial approach. Model Earth Syst Environ 6:2517–2534. https://doi.org/10.1007/s40808-020-00844-4
Han S, Hendrickson LL, Ni B (2002) Comparison of satellite and aerial imagery for detecting leaf chlorophyll content in corn. Trans ASAE 45:011142. https://doi.org/10.13031/2013.9932
Hatfield JL, Gitelson AA, Schepers JS, Walthall CL (2008) Application of spectral remote sensing for agronomic decisions. Agron.j. https://doi.org/10.2134/agronj2006.0370c
Hati JP, Goswami S, Samanta S et al (2020) Estimation of vegetation stress in the mangrove forest using AVIRIS-NG airborne hyperspectral data. Model Earth Syst Environ. https://doi.org/10.1007/s40808-020-00916-5
Hussain N, Islam MdN (2020) Hot spot (G*) model for forest vulnerability assessment: a remote sensing-based geo-statistical investigation of the Sundarbans mangrove forest, Bangladesh. Model Earth Syst Environ 6:2141–2151. https://doi.org/10.1007/s40808-020-00828-4
Kathiresan K (2018) Mangrove forests of India. Curr Sci 114:976. https://doi.org/10.18520/cs/v114/i05/976-981
Kenduiywo BK, Mutua FN, Ngigi TG, Waithaka EH (2020) Mapping mangrove forest using Landsat 8 to support estimation of land-based emissions in Kenya. Model Earth Syst Environ 6:1619–1632. https://doi.org/10.1007/s40808-020-00778-x
Knipling EB (1970) Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens Environ 1:155–159. https://doi.org/10.1016/S0034-4257(70)80021-9
Lin C, Popescu SC, Huang SC et al (2015) A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves. Biogeosciences 12:49–66. https://doi.org/10.5194/bg-12-49-2015
Long J, Napton D, Giri C et al (2014) A mapping and monitoring assessment of the Philippines’ Mangrove forests from 1990 to 2010. J Coastal Res 30:260–271. https://doi.org/10.2112/JCOASTRES-D-13-00057.1
Mahadevan P, Wofsy SC, Matross DM et al (2008) A satellite-based biosphere parameterization for net ecosystem CO2 exchange: vegetation photosynthesis and respiration model (VPRM). Global Biogeochem Cycles. https://doi.org/10.1029/2006GB002735
Main R, Cho MA, Mathieu R et al (2011) An investigation into robust spectral indices for leaf chlorophyll estimation. ISPRS J Photogramm Remote Sens 66:751–761. https://doi.org/10.1016/j.isprsjprs.2011.08.001
Mandal RN, Naskar KR (2008) Diversity and classification of Indian mangroves: a review. Trop Ecol 49:131–146
Miao Y, Mulla DJ, Randall GW et al (2009) Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn. Precis Agric 10:45–62. https://doi.org/10.1007/s11119-008-9091-z
Mohanty PK, Panda US, Pal SR, Mishra P (2008) Monitoring and management of environmental changes along the Orissa Coast. J Coastal Res 24:13–27
Nagelkerken I, Blaber SJM, Bouillon S et al (2008) The habitat function of mangroves for terrestrial and marine fauna: a review. Aquat Bot 89:155–185. https://doi.org/10.1016/j.aquabot.2007.12.007
Parida BR, Kumar P (2020) Mapping and dynamic analysis of mangrove forest during 2009–2019 using landsat–5 and sentinel–2 satellite data along Odisha Coast. Trop Ecol. https://doi.org/10.1007/s42965-020-00112-7
Parida BR, Mandal SP (2020) Polarimetric decomposition methods for LULC mapping using ALOS L-band PolSAR data in Western parts of Mizoram. Northeast India SN Appl Sci 2:1049. https://doi.org/10.1007/s42452-020-2866-1
Parida BR, Pandey AC, Patel NR (2020) Greening and browning trends of vegetation in India and their responses to climatic and non-climatic drivers. Climate 8:92. https://doi.org/10.3390/cli8080092
Pattanaik C, Reddy CS, Dhal NK, Das S (2008) Utilization of mangrove forests in Bhitarkanika wildlife sanctuary, India. Indian. J Tradit Knowl Indian J Tradit Knowl 74:598–603
Rani M, Kumar P, Pandey PC et al (2018) Multi-temporal NDVI and surface temperature analysis for Urban Heat Island inbuilt surrounding of sub-humid region: a case study of two geographical regions. Remote Sens Appl Soc Environ 10:163–172. https://doi.org/10.1016/j.rsase.2018.03.007
Ranjan AK, Parida BR (2020) Estimating biochemical parameters of paddy using satellite and near-proximal sensor data in Sahibganj Province, Jharkhand (India). Remote Sens Appl Soc Environ 18:100293. https://doi.org/10.1016/j.rsase.2020.100293
Roy S, Mahapatra M, Chakraborty A (2019) Mapping and monitoring of mangrove along the Odisha coast based on remote sensing and GIS techniques. Model Earth Syst Environ 5:217–226. https://doi.org/10.1007/s40808-018-0529-7
Sahana M, Sajjad H, Ahmed R (2015) Assessing spatio-temporal health of forest cover using forest canopy density model and forest fragmentation approach in Sundarban reserve forest. India Model Earth Syst Environ 1:49. https://doi.org/10.1007/s40808-015-0043-0
Saigusa N, Yamamoto S, Murayama S et al (2002) Gross primary production and net ecosystem exchange of a cool-temperate deciduous forest estimated by the eddy covariance method. Agric For Meteorol 112:203–215. https://doi.org/10.1016/S0168-1923(02)00082-5
Schepers JS, Francis DD, Vigil M, Below FE (1992) Comparison of corn leaf nitrogen concentration and chlorophyll meter readings. Commun Soil Sci Plant Anal 23:2173–2187. https://doi.org/10.1080/00103629209368733
Selvam V (2003) Environmental classification of mangrove wetlands of India. Curr Sci 84:757–765
Singh S, Parida BR (2018) Satellite-based identification of aquaculture farming over Coastal Areas around Bhitarkanika, Odisha using a neural network method. Proceedings 2:331. https://doi.org/10.3390/ecrs-2-05144
Singh N, Parida BR (2019) Environmental factors associated with seasonal variations of night-time plant canopy and soil respiration fluxes in deciduous conifer forest, Western Himalaya, India. Trees 33:599–613. https://doi.org/10.1007/s00468-018-1804-y
Singh N, Patel NR, Bhattacharya BK et al (2014) Analyzing the dynamics and inter-linkages of carbon and water fluxes in subtropical pine (Pinus roxburghii) ecosystem. Agric For Meteorol 197:206–218. https://doi.org/10.1016/j.agrformet.2014.07.004
Singh N, Parida BR, Charakborty JS, Patel NR (2019) Net ecosystem exchange of CO2 in deciduous pine forest of lower Western Himalaya. India Resour 8:98. https://doi.org/10.3390/resources8020098
Tomlinson PB (2016) The botany of Mangroves, 2nd edn. Cambridge University Press, Cambridge
Twilley RR, Rovai AS, Riul P (2018) Coastal morphology explains global blue carbon distributions. Front Ecol Environ 16:503–508. https://doi.org/10.1002/fee.1937
Verrelst J, Alonso L, Camps-Valls G et al (2012) Retrieval of vegetation biophysical parameters using gaussian process techniques. IEEE Trans Geosci Remote Sens 50:1832–1843. https://doi.org/10.1109/TGRS.2011.2168962
Wang Y, Bonynge G, Nugranad J et al (2003) Remote sensing of Mangrove change along the Tanzania coast. Mar Geodesy 26:35–48. https://doi.org/10.1080/01490410306708
Wang R, Chen JM, Pavlic G, Arain A (2016) Improving winter leaf area index estimation in coniferous forests and its significance in estimating the land surface albedo. ISPRS J Photogramm Remote Sens 119:32–48. https://doi.org/10.1016/j.isprsjprs.2016.05.003
Waskom RM, Westfall DG, Spellman DE, Soltanpour PN (1996) Monitoring nitrogen status of corn with a portable chlorophyll meter. Commun Soil Sci Plant Anal 27:545–560. https://doi.org/10.1080/00103629609369576
Xiao X, Hollinger D, Aber J et al (2004) Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens Environ 89:519–534. https://doi.org/10.1016/j.rse.2003.11.008
Zhang C, Ren H, Dai X et al (2019) Spectral characteristics of copper-stressed vegetation leaves and further understanding of the copper stress vegetation index. Int J Remote Sens 40:4473–4488. https://doi.org/10.1080/01431161.2018.1563842
Acknowledgements
This research was supported by the University Grants Commission (UGC) under the start-up grant (F.4-5(209-FRP)/2015/BSR). Authors thanks to Alaska Satellite facility (ASF) team for providing the Sentinel–2 data.
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BRP: Conceptualization, methodology, software, analysis, visualization, validation, writing—original draft, review & editing. AK: methodology, software, analysis, visualization, validation, writing- original draft, review & editing.
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Parida, B.R., Kumari, A. Mapping and modeling mangrove biophysical and biochemical parameters using Sentinel-2A satellite data in Bhitarkanika National Park, Odisha. Model. Earth Syst. Environ. 7, 2463–2474 (2021). https://doi.org/10.1007/s40808-020-01005-3
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DOI: https://doi.org/10.1007/s40808-020-01005-3