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Assessment of changes in land use, land cover, and land surface temperature in the mangrove forest of Sundarbans, northeast coast of India

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Abstract

This paper investigates the impacts of changing land use–land cover on the land surface temperature (LST) and normalized differential vegetation index (NDVI) distribution in the Indian part of the Sundarbans Biosphere Reserve by utilizing remote sensing and geographical information system. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and Operational Land Imager images of the year 2000, 2010 and 2017, respectively, were used to assess the essential indicators for regional environmental health employing appropriate calibrations and corrections. It was observed that there has been a marked reduction in the areas of plantation, mangrove swamp, mangrove forests and agricultural land since 2000. In contrast, an increase in sand beach, waterlogged areas, mudflat, river, and agriculture area was observed. The mean NDVI values for mangrove forests and plantation have decreased from 0.441 to 0.229 and 0.266 to 0.195, respectively, while river, aquaculture, agricultural and open scrubs classes had higher values. The rate of increase in surface LST was highest over settlements, followed by sand beaches, mudflats, aquaculture, mangrove forest, river, plantations, waterlogged areas and agricultural field. LST showed a negative correlation with NDVI values probably due to the high rate of evapo-transpiration activities of the mangrove vegetations. All these above facts distinctly substantiates that there is an increase in open patches/non-vegetated cover and that the ecosystem is under constant stress.

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References

  • Ahmed, B., Kamruzzaman, M., Zhu, X., Rahman, M. S., & Choi, K. (2013). Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. Remote Sensing, 5, 5969–5998. https://doi.org/10.3390/rs5115969.

    Article  Google Scholar 

  • Bandyopadhyay, J., Mondal, I., & Roy, B. (2014). Change detection of land use and land cover and identification of interrelationship between geomorphology and land use land cover in and around Bakkhali–Fraserganj and Henry Island, South 24 parganas, West Bengal, India. International Journal of Remote Sensing and Geoscience, 3(2), 44–51.

    Google Scholar 

  • Banerjee, K., Chowdhury, M. R., Sengupta, K., Sett, S., & Mitra, A. (2012). Influence of anthropogenic and natural factors on the mangrove soil of Indian Sundarbans wetland. Archives of Environmental Science, 6, 80–91.

    Google Scholar 

  • Buyadi, S. N. A., Mohd, W. M. N. W., & Misni, A. (2013). Impact of land use changes on the surface temperature distribution of area surrounding the National Botanic Garden, Shah Alam. Procedia Social Behaviour Science, 101, 516–525.

    Article  Google Scholar 

  • Chander, G., & Markham, B. L. (2003). Revised Landsat-5 TM radiometric calibration procedures, and postcalibration dynamic ranges. IEEE Transactions on Geoscience and Remote Sensing, 41, 2674–2677.

    Article  Google Scholar 

  • Cristobal, J., Munoz, J. C. J., Sobrino, J. A., Ninyerola, M., & Pons, X. (2009). Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapour and air temperature. Journal of Geophysical Research, 114, 08–103. https://doi.org/10.1029/2008JD010616.

    Article  Google Scholar 

  • Dutta, D., Das, P. K., Paul, S., Sharma, J. R., & Dhadwal, V. K. (2014). Spatio-temporal assessment of ecological disturbance and its intensity in the mangrove forest using MODIS derived disturbance index. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences., XL, 8.

    Google Scholar 

  • Fahmy, T. Y. A., Fahmy, Y., Mobarak, F., El-Sakhawy, M., & Abou-Zeid, R. E. (2018). Biomass pyrolysis: Past, present, and future. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-0200-5.

    Article  Google Scholar 

  • Fahmy, Y., Fahmy, T. Y. A., Mobarak, F., El-Sakhawy, M., & Fadl, M. H. (2017). Agricultural residues (wastes) for manufacture of paper, board, and miscellaneous products: Background overview and future prospects. International Journal of ChemTech Research, 10(2), 424–448.

    Google Scholar 

  • Fahmy, Y., & Ibrahim, H. (1970). Rice straw for paper making. Cellulose Chemistry and Technology, 4(3), 339–348.

    CAS  Google Scholar 

  • Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., et al. (2005). Global consequences of land use. Science, 309, 570–574.

    Article  CAS  Google Scholar 

  • Ghosh, A., Schmidt, S., Fickert, T., & Nüsser, M. (2015). The Indian Sundarban mangrove forests: History, utilization, conservation strategies and local perception. Diversity, 7, 149–169. https://doi.org/10.3390/d7020149.

    Article  CAS  Google Scholar 

  • Giannini, M. B. (2015). Land surface temperature from landsat 5 TM images: comparison of different methods using airborne thermal data measurements and comparison with MODIS remote sensing estimates. Agricultural and Forest Meteorology, 129(2015), 151–173.

    Google Scholar 

  • Herold, M., Mayaux, P., Woodcock, C. E., Baccini, A., & Schmullius, C. C. (2008). Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sensing of Environment, 112(2008), 2538–2556. https://doi.org/10.1016/j.rse.2007.11.013.

    Article  Google Scholar 

  • Huan, P., Zhihao, Q. S., Xu, B., Zhang, C., Lu, L., & Gao, M. (2007). Impacts of land use/land cover change on the spatial variation of land surface temperature in Urumqi, China. Geoinformatics, Remotely Sensed Data and Information. Proceedings SPIE. https://doi.org/10.1117/12.761312.

  • Islam, M. S., & Islam, K. S. (2013). Application of thermal infrared remote sensing to explore the relationship between land use–land cover changes and urban heat Island effect: A case study of Khulna City. Journal of Bangladesh Institute of Planners, 6, 49–60.

    Google Scholar 

  • Jalili, S. Y. (2003). The effect of land use on land surface temperature in the Netherlands. Lund University, GEM Thesis Series nr, 1.

  • Julien, Y., Sobrino, J. A., Matter, C., Ruesca, A. B., Jimenezmuno, J. C., Soria, G., Hidalgo, V., Atitar, M., Franch, B., & Cuenca, J. (2011). Temporal analysis of normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters to detect changes in the Iberian land cover between 1981 and 2001. International Journal of Remote Sensing, 32(7), 2057–2068. https://doi.org/10.1080/01431161003762363.

    Article  Google Scholar 

  • Jung, M., Henkel, K., Herold, M., & Churkina, G. (2006). Exploiting synergies of global land cover products for carbon cycle modelling. Remote Sensing of Environment, 101, 534–553.

    Article  Google Scholar 

  • Kamble, B., Irmak, A., & Hubbard, K. (2013). Estimating crop coefficients using remote sensing-based vegetation Index. Remote Sensing, 2013(5), 1588–1602. https://doi.org/10.3390/rs5041588.

    Article  Google Scholar 

  • Karnieli, N., Agam, R. T., Pinker, M., Anderson, M. L., Imhoff, G., & Gutman, G. (2010). Use of NDVI and land surface temperature for drought assessment: Merits and limitations. Journal of Climate, 23, 618–633.

    Article  Google Scholar 

  • Katsiabani, K., Adaktilou, N., & Cartalis, C. (2009). A generalised methodology for estimating land surface temperature for non-urban areas of Greece through the combined use of NOAA–AVHRR data and ancillary information. Advances in Space Research, 43(2009), 930–940. https://doi.org/10.1016/j.asr.2008.09.002.

    Article  Google Scholar 

  • Kovacs, J. M., Wang, J., & Blanco-Correa, M. (2001). Mapping disturbances in a mangrove forest using multi-date LANDSAT TM imagery. Environmental Management, 27(5), 763–776.

    Article  CAS  Google Scholar 

  • Kumar, K. S., Bhaskar, P. U., & Padmakumari, K. (2012). Estimation of land surface temperature to study urban heat island effect using Landsat ETM+ image. International Journal of Engineering Science Technology, 4(2), 771–778.

    Google Scholar 

  • Kustas, W., & Anderson, M. (2009). Advances in thermal infrared remote sensing for land surface modelling. Agricultural and Forest Meteorology, 149, 2071–2081.

    Article  Google Scholar 

  • Lambin, F. E., & Ehrlich, D. (1997). Land-cover changes in Sub-Saharan Africa (1982–1991): Application of a change index based on remotely sensed surface temperature and vegetation indices at a continental scale. Remote Sensing of Environment, 61, 181–200.

    Article  Google Scholar 

  • Liu, L., & Zhang, Y. (2011). Urban heat Island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sensing, 3, 1535–1552. https://doi.org/10.3390/rs3071535.

    Article  Google Scholar 

  • Maiti, S. K., & Chowdhuri, A. (2013). Effects of anthropogenic pollution on mangrove biodiversity: A reiew. Journal of Environmental Protection, 4, 1428–1434.

    Article  Google Scholar 

  • Mallick, J., Kant, Y., & Bharath, B. D. (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM. Journal of Indian Geophysical Union, 12(3), 131–140.

    Google Scholar 

  • Marland, G., Roger, A., Pielke, S. R., Apps, M., Avissar, R., Betts, R. A., et al. (2003). The climatic impacts of land surface change and carbon management, and the implications for climate-change mitigation policy. Climate Policy, 3, 149–157.

    Article  Google Scholar 

  • Mcpherson, E. G., Nowak, D., Heisler, G., Grimmond, S., Souch, C., Grant, R., et al. (2003). Quantifying urban forest structure, function, and value: The Chicago Urban Forest Climate Project. Urban Ecosystems, 1, 49–61.

    Article  Google Scholar 

  • Misra, A., Murali, M. R., & Vethamony, P. (2015). Assessment of the land use/land cover (LULC) and mangrove changes along the Mandovi–Zuari Estuarine complex of Goa, India. Arabian Journal of Geoscience, 8, 267–279.

    Article  Google Scholar 

  • Mitra, A., Gangopadhyay, A., Banerjee, K., Dube, A., & Schmidt, A. (2009). Observed changes in water mass properties in the IndianSundarbans (northwestern Bay of Bengal) during 1980–2007. Current Science, 97(10), 1445–1452.

    CAS  Google Scholar 

  • Mondal, I., & Bandyopadhyay, J. (2014). Coastal zone mapping through geospatial technology for resource management of Indian sundarban, West Bengal, India. International Journal of Remote Sensing Applications (IJRSA), 4(2), 103–112. https://doi.org/10.14355/ijrsa.2014.0402.04.

    Article  Google Scholar 

  • Nagendra, H., Lucas, R., Pradinho, J., Jongman, R. H. G., Tarantino, C., Adamo, M., et al. (2013). Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity and threats. Ecological Indicators, 33, 45–59.

    Article  Google Scholar 

  • Omran, E. S. E. (2012). Detection of land-use and surface temperature change at different resolutions. Journal of Geographical Information System, 4, 189–203. https://doi.org/10.4236/jgis.2012.43024.

    Article  Google Scholar 

  • Qin, Z., Karnieli, A., & Berliner, P. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22(18), 3719–3746.

    Article  Google Scholar 

  • Rajeshwari, A., & Mani, N. D. (2014). Estimation of land surface temperature of Dindigul district using landsat 8 data. IJRET: International Journal of Research in Engineering and Technology. https://ijret.org/volumes/2014v03/i05/IJRET20140305025.pdf.

  • Sahana, M., Ahmed, R., & Sajja, H. (2016). Analyzing land surface temperature distribution in response to land use/land cover change using split window algorithm and spectral radiance model in Sundarban Biosphere Reserve, India. Model Earth System Environment, 2, 81. https://doi.org/10.1007/s40808-016-0135-5.

    Article  Google Scholar 

  • Thakur, S., Dharanirajan, K., Ghosh, P. B., Das, P., & De, T. K. (2017). Influence of anthropogenic activities on the landuse pattern of South Andaman Islands. Research Journal of Marine Sciences, 5(1), 1–10.

    Google Scholar 

  • Vicente-Serrano, S. M., Azorin-Molina, C., Sanchez-Lorenzo, A., Revuelto, J., Moran-Tejeda, E., Lopez-Moreno, J. I., et al. (2014). Sensitivity of reference evapotranspiration to changes in meteorological parameters in Spain (1961–2011). Water Resource Research, 50, 8458–8480.

    Article  Google Scholar 

  • Weng, Q., Liu, H., & Lu, D. (2007). Assessing the effects of land use and land cover patterns on thermal conditions using landscape metrics in city of Indianapolis, United States. Urban Ecosystem, 10, 203–219.

    Article  Google Scholar 

  • Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89, 467–483.

    Article  Google Scholar 

  • Yue, W., Xu, J., Tan, W., & Xu, L. (2007). The relationship between land surface temperature and NDVI with remote sensing: Application to Shanghai LANDSAT 7 ETM+ data. International Journal of Remote Sensing, 28(15), 3205–3226.

    Article  Google Scholar 

  • Zhang, Q., Xu, C. Y., Becker, S., Zhang, Z. X., Chen, Y. D., & Coulibaly, M. (2009). Trends and abrupt changes of precipitation maxima in the Pearl River basin, China. Atmospheric Science Letters, 10, 132–144.

    Article  CAS  Google Scholar 

  • Zhou, X., & Wang, Y. C. (2010). Dynamics of land surface temperature in response to land-use/cover change. Geographical Research, 49(1), 23–36. https://doi.org/10.1111/j.1745-5871.2010.00686.x.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Department of Science and Technology (DST), INSPIRE Division, Government of India, for the award of DST INSPIRE Fellowship (IF140969)] to Mr. Sandeep Thakur and he is grateful for it.

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Authors and Affiliations

  1. Department of Marine Science, University of Calcutta, 35 BC Road, Kolkata, 700019, India

    Sandeep Thakur, Debapriya Maity, Ismail Mondal, Ganesh Basumatary & Tarun Kumar De

  2. School of Oceanographic Studies, Jadavpur University, Kolkata, 700032, India

    Ismail Mondal

  3. Institute of Engineering and Management, Salt Lake, Sector-V, Kolkata, 700091, India

    Phani Bhushan Ghosh

  4. Department of Chemical Engineering, Jadavpur University, Kolkata, 700032, India

    Papita Das

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  1. Sandeep Thakur
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Thakur, S., Maity, D., Mondal, I. et al. Assessment of changes in land use, land cover, and land surface temperature in the mangrove forest of Sundarbans, northeast coast of India. Environ Dev Sustain 23, 1917–1943 (2021). https://doi.org/10.1007/s10668-020-00656-7

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