Phenology-based classification of Sentinel-2 data to detect coastal mangroves

References

• Ahmed N, Cheung WWL, Thompson S, Glaser M. 2017. Solutions to blue carbon emissions: Shrimp cultivation, mangrove deforestation and climate change in coastal Bangladesh. Mar Policy. 82:68–75. http://doi.org/10.1016/j.marpol.2017.05.007.
   Web of Science ®Google Scholar
• Ahmed N, Glaser M. 2016. Coastal aquaculture, mangrove deforestation and blue carbon emissions: Is REDD + a solution? Mar Policy. 66:58–66. http://doi.org/10.1016/j.marpol.2016.01.011.
   Web of Science ®Google Scholar
• Alongi DM. 2002. Present state and future of the world’s mangrove forests. Envir Conserv. 29(3):331–349.
   Web of Science ®Google Scholar
• Anwar MS, Takewaka S. 2014. Analyses on phenological and morphological variations of mangrove forests along the southwest coast of Bangladesh. J Coast Conserv. 18(4):339–357.
   Web of Science ®Google Scholar
• Bajocco S, Ferrara C, Alivernini A, Bascietto M, Ricotta C. 2019. Remotely-sensed phenology of Italian forests: going beyond the species. Int J Appl Earth Obs Geoinf. 74:314–321..
   Web of Science ®Google Scholar
• Baloloy AB, Blanco AC, Raymund Rhommel RRC, Nadaoka K. 2020. Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS J Photogramm Remote Sens. 166:95–117..
   Web of Science ®Google Scholar
• Behera MD, Barnwal S, Paramanik S, Das P, Bhattyacharya BK, Jagadish B, Roy PS, Ghosh SM, Behera SK. 2021. Species-level classification and mapping of a mangrove forest using random forest—utilisation of aviris-ng and sentinel data. Remote Sens. 13(11).
   Google Scholar
• Belgiu M, Drăguţ L. 2016. Random forest in remote sensing: A review of applications and future directions. ISPRS J Photogramm Remote Sens. 114:24–31. https://linkinghub.elsevier.com/retrieve/pii/S0924271616000265.
   Web of Science ®Google Scholar
• Breiman L. 2001. Random forests. Mach Learn. 45(1):5–32..
   Web of Science ®Google Scholar
• Chen X, Wang D, Chen J, Wang C, Shen M. 2018. The mixed pixel effect in land surface phenology: A simulation study. Remote Sens Environ. 211:338–344.
   Web of Science ®Google Scholar
• Chen X, Yang Q, Deng B, Wang H. 2020. Mapping mangrove in Dongzhaigang, China using Sentinel-2 imagery. J Appl Rem Sens. 14(1):1.
   Web of Science ®Google Scholar
• Congalton RG, Green K. 2008. Assessing the accuracy of remotely sensed data. Cambridge: CRC Press. https://www.taylorfrancis.com/books/9781420055139.
   Google Scholar
• Das S, Siddiqi N. 1985. The mangroves and mangrove forests of Bangladesh. Mangrove silivculture Div Bull no. 2.
   Google Scholar
• Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, et al. 2012. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens Environ. 120:25–36. https://linkinghub.elsevier.com/retrieve/pii/S0034425712000636.
   Web of Science ®Google Scholar
• Dudley KL, Dennison PE, Roth KL, Roberts DA, Coates AR. 2015. A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients. Remote Sens Environ. 167:121–134. http://doi.org/10.1016/j.rse.2015.05.004.
   Web of Science ®Google Scholar
• European Space Agency. 2021. Sen2Cor. http://step.esa.int/main/snap-supported-plugins/sen2cor/.
   Google Scholar
• Fernandes ECM, Nair PKR. 1986. An evaluation of the structure and function of tropical homegardens. Agric Syst. 21(4):279–310. https://linkinghub.elsevier.com/retrieve/pii/0308521X86901046.
   Web of Science ®Google Scholar
• Flores-de-Santiago F, Kovacs JM, Flores-Verdugo F. 2013. The influence of seasonality in estimating mangrove leaf chlorophyll-a content from hyperspectral data. Wetlands Ecol Manage. 21(3):193–207. 10.1007/s11273-013-9290-x.
   Web of Science ®Google Scholar
• Flores-de-Santiago F, Kovacs J, Wang J, Flores-Verdugo F, Zhang C, González-Farías F. 2016. Examining the influence of seasonality, condition, and species composition on mangrove leaf pigment contents and laboratory based spectroscopy data. Remote Sens. 8(3):226. http://www.mdpi.com/2072-4292/8/3/226.
   Google Scholar
• Frantz D, Stellmes M, Roder A, Udelhoven T, Mader S, Hill J. 2016. Improving the spatial resolution of land surface phenology by fusing medium- and coarse-resolution inputs. IEEE Trans Geosci Remote Sens. 54(7):4153–4164. http://ieeexplore.ieee.org/document/7452606/.
   Web of Science ®Google Scholar
• Gao BC. 1996. NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ. 58(3):257–266.
   Web of Science ®Google Scholar
• 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(9):1519. https://www.mdpi.com/2072-4292/12/9/1519.
   Google Scholar
• Grabska E, Hostert P, Pflugmacher D, Ostapowicz K. 2019. Forest stand species mapping using the sentinel-2 time series. Remote Sens. 11(10):1–24.
   Google Scholar
• Gupta K, Mukhopadhyay A, Giri S, Chanda A, Datta Majumdar S, 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..
   PubMed Web of Science ®Google Scholar
• Hanes JM, Liang L, Morisette JT. 2014. Land surface phenology. In: Biophysical applications of satellite remote sensing [Internet]. [place unknown]; p. 99–125. 10.1007/978-3-642-25047-7_4.
   Google Scholar
• Hastie T, Tibshirani R, Friedman J. 2009. The elements of statistical learning. 2nd ed. New York (NY): Springer New York. 10.1007/b94608.
   Google Scholar
• Helman D, Lensky IM, Tessler N, Osem Y. 2015. A phenology-based method for monitoring woody and herbaceous vegetation in mediterranean forests from NDVI time series. Remote Sens. 7(9):12314–12335.
   Google Scholar
• Hościło A, Lewandowska A. 2019. Mapping forest type and tree species on a regional scale using multi-temporal sentinel-2 data. Remote Sens. 11(8):1–23.
   Google Scholar
• Hossain MS, Rahman MF, Thompson S, Nabi MR, Kibria MM. 2013. Climate change resilience assessment using livelihood assets of coastal fishing community in Nijhum Dwip. Bangladesh. Pertanika J Sci Technol. 21(2):397–422.
   Google Scholar
• Hossain KT, Tanim IA, Salauddin M. 2016. Change detection of forest cover: a study Nijhum Dwip National Park, Hatiya, Noakhali. Jagannath Univ J Life Earth Sci. 2(1):84–90.
   Google Scholar
• Huete A. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens Environ. 25(3):295–309. https://linkinghub.elsevier.com/retrieve/pii/003442578890106X.
   Web of Science ®Google Scholar
• Huete A. 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens Environ. 59(3):440–451. https://linkinghub.elsevier.com/retrieve/pii/S0034425796001125.
   Web of Science ®Google Scholar
• Huete AR, Liu HQ, van Leeuwen WJD. 1997. Use of vegetation indices in forested regions: issues of linearity and saturation. Int Geosci Remote Sens Symp. 4(1):1966–1968.
   Google Scholar
• Immitzer M, Vuolo F, Atzberger C. 2016. First experience with sentinel-2 data for crop and tree species classifications in Central Europe. Remote Sens. 8(3):166. http://www.mdpi.com/2072-4292/8/3/166.
   Google Scholar
• Islam SKA, Miah MAQ, Habib MA, Moula MG. 2014. Performance of some Mainland trees and palm species planted in the Coastal Islands of Bangladesh. J Asiat Soc Bangladesh. 40:9–15.
   Google Scholar
• Islam MS, Uddin MA, Hossain MA. 2021. Assessing the dynamics of land cover and shoreline changes of Nijhum Dwip (Island) of Bangladesh using remote sensing and GIS techniques. Reg Stud Mar Sci. 41:101578..
   Web of Science ®Google Scholar
• Jia M, Wang Z, Wang C, Mao D, Zhang Y. 2019. A new vegetation index to detect periodically submerged mangrove forest using single-tide Sentinel-2 imagery. Remote Sens. 11(17):1–17.
   Google Scholar
• Jiang Z, Huete AR, Chen J, Chen Y, Li J, Yan G, Zhang X. 2006. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sens Environ. 101(3):366–378.
   Web of Science ®Google Scholar
• Kovacs JM, King JML, Flores de Santiago F, Flores-Verdugo F. 2009. Evaluating the condition of a mangrove forest of the Mexican Pacific based on an estimated leaf area index mapping approach. Environ Monit Assess. 157(1–4):137–149. 10.1007/s10661-008-0523-z.
   PubMed Web of Science ®Google Scholar
• Kovacs JM, Wang J, Blanco-Correa M. 2001. Mapping disturbances in a mangrove forest using multi-date landsat TM imagery. Environ Manage. 27(5):763–776. 10.1007/s002670010186.
   PubMed Web of Science ®Google Scholar
• Kowalski K, Senf C, Hostert P, Pflugmacher D. 2020. Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series. Int J Appl Earth Obs Geoinf. 92:102172.
   Web of Science ®Google Scholar
• Kuhn M. 2009. The Caret package. 1–24.
   Google Scholar
• Kumar L, Ghosh MK. 2012. Land cover change detection of Hatiya Island, Bangladesh, using remote sensing techniques. J Appl Remote Sens. 6(1):063608.
   Google Scholar
• Lasaponara R. 2006. On the use of principal component analysis (PCA) for evaluating interannual vegetation anomalies from Spot/Vegetation NDVI temporal series. Ecol Modell. 194(4):429–434.
   Web of Science ®Google Scholar
• Lewis RR. 2005. Ecological engineering for successful management and restoration of mangrove forests. Ecol Eng. 24(4):403–418.
   Web of Science ®Google Scholar
• Liaw A, Wiener M. 2002. Classification and regression by randomForest. R News. 2(3):18–22.
   Google Scholar
• Li S, Ganguly S, Dungan JL, Wang W, Nemani RR. 2017. Sentinel-2 MSI radiometric characterization and cross-calibration with Landsat-8 OLI. ARS. 06(2):147–159. 10.4236/ars.2017.62011.
   Google Scholar
• Li H, Jia M, Zhang R, Ren Y, Wen X. 2019. Incorporating the plant phenological trajectory into mangrove species mapping with dense time series Sentinel-2 imagery and the Google Earth Engine platform. Remote Sens. 11(21):1–16.
   Google Scholar
• Liu HQ, Huete A. 1995. Feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans Geosci Remote Sens. 33(2):457–465.
   Web of Science ®Google Scholar
• Liu K, Li X, Shi X, Wang S. 2008. Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning. Wetlands. 28(2):336–346. 10.1672/06-91.1.
   Web of Science ®Google Scholar
• Lloyd D. 1990. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. Int J Remote Sens. 11(12):2269–2279.
   Web of Science ®Google Scholar
• Louis J, Debaecker V, Pflug B, Main-Knorn M, Bieniarz J, Mueller-Wilm U, Cadau E, Gascon F. 2016. Sentinel-2 SEN2COR: L2A processor for users. Eur Sp Agency. SP-740:1–8. https://esamultimedia.esa.int/multimedia/publications/SP-740/SP-740_toc.pdf.
   Google Scholar
• Madonsela S, Cho MA, Mathieu R, Mutanga O, Ramoelo A, Kaszta Ż, Kerchove RVD, Wolff E. 2017. Multi-phenology WorldView-2 imagery improves remote sensing of savannah tree species. Int J Appl Earth Obs Geoinf. 58:65–73. https://linkinghub.elsevier.com/retrieve/pii/S0303243417300181.
   Web of Science ®Google Scholar
• Mahmood R. 2017. Monitoring LULC dynamics using geospatial techniques: a case study of Coastal Hatiya Island of Bangladesh. Jagannath Univ J Life Earth Sci. 3(1):66–74.
   Google Scholar
• Mandal MSH, Kamruzzaman M, Hosaka T. 2020. Elucidating the phenology of the Sundarbans mangrove forest using 18-year time series of MODIS vegetation indices. Tropics. 29(2):41–55.
   Web of Science ®Google Scholar
• Manna S, Raychaudhuri B. 2020. Mapping distribution of Sundarban mangroves using Sentinel-2 data and new spectral metric for detecting their health condition. Geocarto Int. 35(4):434–452. http://doi.org/10.1080/10106049.2018.1520923.
   Web of Science ®Google Scholar
• Marzialetti F, Giulio S, Malavasi M, Sperandii MG, Acosta ATR, Carranza ML. 2019. Capturing coastal dune natural vegetation types using a phenology-based mapping approach: the potential of Sentinel-2. Remote Sens. 11(12):1–17.
   Google Scholar
• Maung WS, Sasaki J. 2021. Assessing the natural recovery of mangroves after human disturbance using neural network classification and sentinel-2 imagery in wunbaik mangrove forest, Myanmar. Remote Sens. 13(1):1–24.
   Google Scholar
• McFeeters SK. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int J Remote Sens. 17(7):1425–1432. 10.1080/01431169608948714.
   Web of Science ®Google Scholar
• Mitchell MW. 2011. Bias of the random forest out-of-bag (OOB) error for certain input parameters. OJS. 01(03):205–211.
   Google Scholar
• Murdiyarso D, Purbopuspito J, Kauffman JB, Warren MW, Sasmito SD, Donato DC, Manuri S, Krisnawati H, Taberima S, Kurnianto S. 2015. The potential of Indonesian mangrove forests for global climate change mitigation. Nature Clim Change. 5(12):1089–1092. http://www.nature.com/articles/nclimate2734.
   Web of Science ®Google Scholar
• Neri MP, Baloloy AB, Blanco AC. 2021. Limitation assessment and workflow refinement of the Mangrove Vegetation Index (MVI) – based mapping methodology using sentinel-2 imagery. In: Vol. XLVI. [place unknown]; p. 17–19.
   Google Scholar
• Parr T, Wilson JD, Hamrick J. 2020. Nonparametric feature impact and importance. 2. http://arxiv.org/abs/2006.04750.
   Google Scholar
• Pettorelli N, Vik JO, Mysterud A, Gaillard J-M, Tucker CJ, Stenseth NC. 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol. 20(9):503–510. https://linkinghub.elsevier.com/retrieve/pii/S016953470500162X.
   PubMed Web of Science ®Google Scholar
• Rahman MM, Ahiul S, Rahman IMM. 2015. Phenophases of five mangrove species of the sundarbans of Bangladesh. Int J Bus Soc Sci Res. 4(1):77–82. http://www.ijbssr.com/currentissueview/14013130.
   Google Scholar
• Rahman MM, Pramanik MAT. 2015. Monitoring mangrove plantation along the coastal belts of Bangladesh (1989-2010). J for Environ Sci. 31(3):225–234.
   Google Scholar
• Rahman M, Ullah R, Lan M, Sri Sumantyo JT, Kuze H, Tateishi R. 2013. Comparison of Landsat image classification methods for detecting mangrove forests in Sundarbans. Int J Remote Sens. 34(4):1041–1056.
   Web of Science ®Google Scholar
• Ramdani F, Rahman S, Giri C. 2019. Principal polar spectral indices for mapping mangroves forest in South East Asia: study case Indonesia. Int J Digit Earth. 12(10):1103–1117..
   Web of Science ®Google Scholar
• Razali SM, Nuruddin AA, Lion M. 2019. Mangrove vegetation health assessment based on remote sensing indices for Tanjung Piai, Malay Peninsular. J Landsc Ecol. 12(2):26–40. 10.2478/jlecol-2019-0008.
   Google Scholar
• Richards JA. 2013. Remote sensing digital image analysis. Berlin, Heidelberg: Springer Berlin Heidelberg. 10.1007/978-3-642-30062-2.
   Google Scholar
• Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP. 2012. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens. 67(1):93–104. http://doi.org/10.1016/j.isprsjprs.2011.11.002.
   Google Scholar
• Rosario E. 1997. Conservation management plans for protected areas other than the Sundarbans Reserved Forests, Dhaka.
   Google Scholar
• Rouse JW, Haas RH, Schell JA, Deeering D. 1973. Monitoring vegetation systems in the Great Plains with ERTS (Earth Resources Technology Satellite). In Third earth resources technology satellite-1 symposium, 351, p. 309–317.
   Google Scholar
• Saenger P, Siddiqi NA. 1993. Land from the sea: the mangrove afforestation program of Bangladesh. Ocean Coast Manag. 20(1):23–39.
   Web of Science ®Google Scholar
• Saha PK, Bodiuzzaman M, Uddin MN, Hossain MN, Shanta AS. 2016. A study on the management strategies of protected area in Bangladesh for bio diversity conservation on Nijhum Dwip, Noakhali, Bangladesh, March.
   Google Scholar
• Sajjaduzzaman M, Muhammed N, Koike M. 2005. Mangrove plantation destruction in Noakhali coastal forests of Bangladesh: a case study on causes, consequences and model prescription to halt deforestation. Int J Agric Biol. 7(5):732–734.
   Google Scholar
• Serajuddoula M, Khan M, Islam M, Shahjalal M. 1995. Introduction of non-mangrove[s] in raised land—a way to maintain sustainable forest in coastal belt of Bangladesh. Pakistan J For. 45(4):163–169.
   Google Scholar
• Sheeren D, Fauvel M, Josipovíc V, Lopes M, Planque C, Willm J, Dejoux JF. 2016. Tree species classification in temperate forests using Formosat-2 satellite image time series. Remote Sens. 8(9):1–29.
   Google Scholar
• Small C, Sousa D. 2019. Spatiotemporal characterization of mangrove phenology and disturbance response: the Bangladesh Sundarban. Remote Sens. 11(17):1–23.
   Google Scholar
• Sun C, Fagherazzi S, Liu Y. 2018. Classification mapping of salt marsh vegetation by flexible monthly NDVI time-series using Landsat imagery. Estuar Coast Shelf Sci. 213:61–80..
   Web of Science ®Google Scholar
• Suratman MN. 2008. Carbon sequestration potential of mangroves.
   Google Scholar
• Tian F, Wang Y, Fensholt R, Wang K, Zhang L, Huang Y. 2013. Mapping and Evaluation of NDVI Trends from Synthetic Time Series Obtained by Blending Landsat and MODIS Data around a Coalfield on the Loess Plateau. Remote Sens. 5(9):4255–4279. http://www.mdpi.com/2072-4292/5/9/4255.
   Google Scholar
• Tomaselli V, Adamo M, Veronico G, Sciandrello S, Tarantino C, Dimopoulos P, Medagli P, Nagendra H, Blonda P. 2017. Definition and application of expert knowledge on vegetation pattern, phenology, and seasonality for habitat mapping, as exemplified in a Mediterranean coastal site. Plant Biosyst – An Int J Deal with All Asp Plant Biol. 151(5):887–899. 10.1080/11263504.2016.1231143.
   Web of Science ®Google Scholar
• Tomlinson PB. 2016. The botany of mangroves. Cambridge: Cambridge University Press. http://ebooks.cambridge.org/ref/id/CBO9781139946575.
   Google Scholar
• Valderrama-Landeros L, Flores-Verdugo F, Rodríguez-Sobreyra R, Kovacs JM, Flores-de-Santiago F. 2021. Extrapolating canopy phenology information using Sentinel-2 data and the Google Earth Engine platform to identify the optimal dates for remotely sensed image acquisition of semiarid mangroves. J Environ Manage. 279:111617..
   PubMed Web of Science ®Google Scholar
• Vrieling A, Meroni M, Darvishzadeh R, Skidmore AK, Wang T, Zurita-Milla R, Oosterbeek K, O’Connor B, Paganini M. 2018. Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island. Remote Sens Environ. 215:517–529. https://linkinghub.elsevier.com/retrieve/pii/S003442571830107X.
   Web of Science ®Google Scholar
• Walker JJ, de Beurs KM, Wynne RH. 2014. Dryland vegetation phenology across an elevation gradient in Arizona, USA, investigated with fused MODIS and Landsat data. Remote Sens Environ. 144:85–97. https://linkinghub.elsevier.com/retrieve/pii/S0034425714000200.
   Web of Science ®Google Scholar
• Wang Q, Blackburn GA, Onojeghuo AO, Dash J, Zhou L, Zhang Y, Atkinson PM. 2017. Fusion of Landsat 8 OLI and Sentinel-2 MSI data. IEEE Trans Geosci Remote Sens. 55(7):3885–3899. http://ieeexplore.ieee.org/document/7894218/.
   Web of Science ®Google Scholar
• Wang D, Wan B, Qiu P, Su Y, Guo Q, Wang R, Sun F, Wu X. 2018. Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species. Remote Sens. 10(9):1–27.
   Google Scholar
• Wang T, Zhang H, Lin H, Fang C. 2016. Textural-spectral feature-based species classification of mangroves in Mai Po nature reserve from worldview-3 imagery. Remote Sens. 8(1):1–15.
   Google Scholar
• Winarso G, Purwanto AD, Yuwono D. 2014. New mangrove index as degradation/health indicator using remote sensing data: Segara Anakan and Alas Purwo case study. In: The proceedings of the 12th Biennial conference of the Pan Ocean Remote Sensing Conference. [place unknown]; p. 309–316.
   Google Scholar
• Xin Q, Olofsson P, Zhu Z, Tan B, Woodcock CE. 2013. Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data. Remote Sens Environ. 135:234–247. https://linkinghub.elsevier.com/retrieve/pii/S0034425713001235.
   Web of Science ®Google Scholar
• Zanaga D, Van De Kerchove R, De Keersmaecker W, Souverijns N, Brockmann C, Quast R, Wevers J, Grosu A, Paccini A, Vergnaud S. 2021. ESA WorldCover 10 m 2020 v100..
   Google Scholar
• Zhang X, Tian Q. 2013. A mangrove recognition index for remote sensing of mangrove forest from space. Curr Sci. 105(8):1149–1154.
   Web of Science ®Google Scholar
• Zhen J, Liao J, Shen G. 2018. Mapping mangrove forests of dongzhaigang nature reserve in China using Landsat 8 and Radarsat-2 polarimetric SAR data. Sensors. 18(11):4012. http://www.mdpi.com/1424-8220/18/11/4012.
   PubMed Web of Science ®Google Scholar