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Spatiotemporal changes of vegetation and land surface temperature in the refugee camps and its surrounding areas of Bangladesh after the Rohingya influx from Myanmar

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Abstract

In August 24, 2017, a massive outbreak took place in the Rakhine state of Myanmar which triggered a huge refugee influx to the Teknaf Peninsula, Bangladesh. To settle the refugees, makeshift camps were built in large numbers destroying huge amount of forest areas near the existing Kutupalong and Nayapara camps. Refugees have been encroaching the nearby forest covers to collect fuelwood and other purposes. These forest destructions have put the wildlife and biodiversity of the system in a substantial pressure as well as altering the land surface temperature (LST). This paper has examined the extent of vegetation change and the changes of LST from 2017 to 2019 throughout Kutupalong and Balukhali camp and adjacent areas using Landsat 8 images. Random forest algorithm and Plank equation were applied on images to identify vegetation change and LST, respectively. The overall and kappa accuracies for the maps of 2017 are 96% and 92%, respectively, while it stands at 94% and 88% for the 2019 image. Results derived from the analysis suggest that an estimated 1876 hectares of forested lands have been decreased in the study area. LST of the study area increased spatially throughout the whole region with a maximum value of 34 °C which is significantly higher than the pre-influx period. If this trend of forest-clearing activities continues, the place will become barren land soon and the LST will also increase. All these factors will ultimately trigger the climate change impacts and biodiversity loss of the area.

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References

  • Adiguzel, F., Cetin, M., Kaya, E., Simsek, M., Gungor, S., & Sert, E. B. (2020). Defining suitable areas for bioclimatic comfort for landscape planning and landscape management in Hatay, Turkey. Theoretical and Applied Climatology, 139(3–4), 1493–1503.

    Article  Google Scholar 

  • Almutairi, A., & Warner, T. A. (2010). Change detection accuracy and image properties: A study using simulated data. Remote Sensing, 2(6), 1508–1529.

    Article  Google Scholar 

  • Amiri, R., Weng, Q., Alimohammadi, A., & Alavipanah, S. K. (2009). Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sensing of Environment, 113(12), 2606–2617.

    Article  Google Scholar 

  • Akbari, H., Pomerantz, M., & Taha, H. (2001). Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas. Solar Energy, 70(3), 295–310.

    Article  Google Scholar 

  • Avdan, U., & Jovanovska, G. (2016). Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Remote Sensing, 2016(3), 16. https://doi.org/10.1155/2016/1480307.

    Article  Google Scholar 

  • BBC News. (2018). Myanmar Rohingya: What you need to know about the crisis. Retrieved July 13, 2019, from https://www.bbc.com/news/world-asia-41566561.

  • BBS. (2011). Bangladesh population and housing census 2011.

  • Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31.

    Article  Google Scholar 

  • Benjamin, L., Ned, H., & Jakob, S.-W. (2019). RStoolbox: tools for remote sensing data analysis. R package version 0.2.6. Retrieved August 8, 2019 from https://CRAN.R-project.org/package=RStoolbox.

  • Berry, L. (2008). The impacts of environmental degradation on refugee—Host relationships. African Security Studies, 17(3), 125–131.

    Article  Google Scholar 

  • Bharath, S., Rajan, K., & Ramachandra, T. (2013). Geostatistics: An overview land surface temperature responses to land use land cover dynamics. A SciTechnol Journal, 1(4), 1–10. https://doi.org/10.4172/2327-4581.1000112.

    Article  Google Scholar 

  • Braun, A., Fakhri, F., & Hochschild, V. (2019). Refugee camp monitoring and environmental change assessment of Kutupalong, Bangladesh, based on radar imagery of sentinel-1 and ALOS-2. Remote Sensing, 11(17), 2047.

    Article  Google Scholar 

  • Braun, A., Lang, S., & Hochschild, V. (2016). Impact of refugee camps on their environment a case study using multi-temporal SAR data. Journal of Geography, Environment and Earth Science International, 4(2), 1–17.

    Article  Google Scholar 

  • Breiman, L. (2001). Random forests. Machine learning, 45(1), 5–32.

    Article  Google Scholar 

  • Bremner, J., López-Carr, D., Suter, L., & Davis, J. (2010). Population, poverty, environment, and climate dynamics in the developing world. Interdisciplinary Environmental Review, 11(2), 112–126.

    Article  Google Scholar 

  • Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3), 241–252.

    Article  Google Scholar 

  • Cetin, M. (2015). Evaluation of the sustainable tourism potential of a protected area for landscape planning: A case study of the ancient city of Pompeipolis in Kastamonu. International Journal of Sustainable Development & World Ecology, 22(6), 490–495.

    Article  Google Scholar 

  • Cetin, M. (2016). Sustainability of urban coastal area management: A case study on Cide. Journal of Sustainable Forestry, 35(7), 527–554. https://doi.org/10.1080/10549811.2016.1228072.

    Article  Google Scholar 

  • Cetin, M., Zeren, I., Sevik, H., Cakir, C., & Akpinar, H. (2018). A study on the determination of the natural park's sustainable tourism potential. Environmental Monitoring and Assessment, 190(3), 167.

    Article  Google Scholar 

  • Chowdhury, M. S. H., Nazia, N., Izumiyama, S., Muhammed, N., & Koike, M. (2014). Patterns and extent of threats to the protected areas of Bangladesh: The need for a relook at conservation strategies. Parks, 20(1), 91–104.

    Article  Google Scholar 

  • Dabelko, G. D., Lalasz, R., Thomas, R. E., Hildebrandt, T., Kaczor, J., Méndez, A., et al. (2002). Environmental change and security project report. Washington: The Woodrow Wilson Center.

    Google Scholar 

  • Ghimire, K. (1994). Refugees and deforestation1. International Migration, 32(4), 561–570.

    Article  Google Scholar 

  • Ghimire, S., Higaki, D., & Bhattarai, T. (2013). Estimation of soil erosion rates and eroded sediment in a degraded catchment of the Siwalik Hills, Nepal. Land, 2(3), 370–391.

    Article  Google Scholar 

  • Ghosh, A., Sharma, R., & Joshi, P. K. (2014). Random forest classification of urban landscape using Landsat archive and ancillary data: Combining seasonal maps with decision level fusion. Applied Geography, 48, 31–41.

    Article  Google Scholar 

  • Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern Recognition Letters, 27(4), 294–300. https://doi.org/10.1016/j.patrec.2005.08.011.

    Article  Google Scholar 

  • Glade, T. (2003). Landslide occurrence as a response to land use change: A review of evidence from New Zealand. CATENA, 51(3–4), 297–314.

    Article  Google Scholar 

  • Gondwe, S. V., Muchena, R., & Boys, J. (2018). Detecting land use and land cover and land surface temperature change in Lilongwe City, Malawi. Journal of Remote Sensing & GIS, 9(2), 17–26.

    Google Scholar 

  • Gounaridis, D., Apostolou, A., & Koukoulas, S. (2016). Land cover of Greece, 2010: A semi-automated classification using random forests. Journal of Maps, 12(5), 1055–1062. https://doi.org/10.1080/17445647.2015.1123656.

    Article  Google Scholar 

  • Hassan, M., Smith, A., Walker, K., Rahman, M., & Southworth, J. (2018). Rohingya refugee crisis and forest cover change in Teknaf, Bangladesh. Remote Sensing, 10(5), 689.

    Article  Google Scholar 

  • Haque, A. R. (2017). Influx of Rohingya refugees entails a serious burden on the economy. The Financial Express. Retrieved August 21, 2019, from https://thefinancialexpress.com.bd/views/views/influx-of-rohingya-refugees-entails-a-serious-burden-on-the-economy-1513519560.

  • Hijmans, R. J. (2019). raster: geographic data analysis and modeling. R package version 3.0–7. Retrieved August 8, 2019 from https://CRAN.R-project.org/package=raster.

  • Hussain, A. (2018). Rohingya influx, a threat to forest resources. Dhaka Tribune. Retrieved July 13, 2019, from https://www.dhakatribune.com/bangladesh/2018/03/20/rohingya-influx-a-threat-to-forest-resources.

  • Immitzer, M., Atzberger, C., & Koukal, T. (2012). Tree species classification with Random forest using very high spatial resolution 8-band worldview-2 satellite data. Remote Sensing, 4(9), 2661–2693. https://doi.org/10.3390/rs4092661.

    Article  Google Scholar 

  • Imtiaz, S. (2018). Ecological impact of Rohingya refugees on forest resources: Remote sensing analysis of vegetation cover change in Teknaf Peninsula in Bangladesh. Ecocycles, 4(1), 16–19.

    Article  Google Scholar 

  • International Organization for Migration (IOM). (2018). Rohingya refugee crisis response. Retrieved September 3, 2019 from https://www.iom.int/sitreps/bangladesh-iom-bangladesh-rohingya-refugee-crisis-response-external-update-december-2018.

  • IUCN Bangladesh. (2000). Red book of threatened mammals of Bangladesh. Dhaka: IUCN-The World Conservation Union.

    Google Scholar 

  • Jhonnerie, R., Siregar, V. P., Nababan, B., & Budi, L. (2015). Random forest classification for mangrove land cover mapping using Landsat 5 TM and ALOS PALSAR imageries. Procedia Environmental Sciences, 24, 215–221. https://doi.org/10.1016/j.proenv.2015.03.028.

    Article  Google Scholar 

  • Johnston, J., & Newton, J. (2004). Building green: A guide to using plants on roofs, walls and pavements (p. 95). London: Ecology Unit.

    Google Scholar 

  • Kachhwala, T. S. (1985). Temporal monitoring of forest land for change detection and forest cover mapping through satellite remote sensing. In Proceedings of the 6th Asian conference on remote sensing (pp. 77–83). Hyderabad: National Remote Sensing Agency.

  • Karlson, M., Ostwald, M., Reese, H., Sanou, J., Tankoano, B., & Mattsson, E. (2015). Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian woodlands using Landsat 8 and random forest. Remote Sensing, 7(8), 10017–10041. https://doi.org/10.3390/rs70810017.

    Article  Google Scholar 

  • Langer, S., Tiede, D., & Lüthje, F. (2015). Long-term monitoring of the environmental impact of a refugee camp based on landsat time series: The example of deforestation and reforestation during the whole lifespan of the camp Lukole, Tanzania. GI_Forum Journal Geographic Information Science, 1, 434–437. https://doi.org/10.1553/giscience2015s434.

    Article  Google Scholar 

  • Li, Y. Y., Zhang, H., & Kainz, W. (2012). Monitoring patterns of urban heat islands of the fast-growing Shanghai metropolis, China: Using time-series of Landsat TM/ETM+ data. International Journal of Applied Earth Observation and Geoinformation, 19, 127–138.

    Article  Google Scholar 

  • Li, Z. L., Tang, B. H., Wu, H., Ren, H., Yan, G., Wan, Z., et al. (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131, 14–37.

    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(7), 1535–1552. https://doi.org/10.3390/rs3071535.

    Article  Google Scholar 

  • Mahmud, F. (2017). For the Rohingya in Bangladesh’s refugee camps: Living is surviving. The Wire. Retrieved July 17, 2019 from https://thewire.in/189522/rohingya-bangladesh-refugee-camps/.

  • Mantey, S., Tagoe, N. D. & Abaidoo, C. A. (2014), Estimation of land surface temperature and vegetation abundance relationship – a case study, 3rd UMaT biennial international mining & mineral conference, 30 July–2 August, 2014.

  • Mohammed, E. A., Hani, Z. Y., & Kadhim, G. Q. (2018). Assessing land cover/use changes in Karbala city (Iraq) using GIS techniques and remote sensing data. Journal of Physics: Conference Series, 1032(1), 012047.

    Google Scholar 

  • Ndossi, M. I., & Avdan, U. (2016a). Application of open source coding technologies in the production of Land Surface Temperature (LST) maps from Landsat: A PyQGIS plugin. Remote Sensing. https://doi.org/10.3390/rs8050413.

    Article  Google Scholar 

  • Ndossi, M. I., & Avdan, U. (2016b). Inversion of land surface temperature (lst) using terra aster data: A comparison of three algorithms. Remote Sensing, 8(12), 1–19. https://doi.org/10.3390/rs8120993.

    Article  Google Scholar 

  • Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217–222. https://doi.org/10.1080/01431160412331269698.

    Article  Google Scholar 

  • Poumadere, M., Mays, C., Le Mer, S., & Blong, R. (2005). The 2003 heat wave in France: Dangerous climate change here and now. Risk Analysis: An International Journal, 25(6), 1483–1494.

    Article  Google Scholar 

  • Qin, Z., Dall’Olmo, G., Karnieli, A., & Berliner, P. (2001). Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data. Journal of Geophysical Research Atmospheres, 106(D19), 22655–22670. https://doi.org/10.1029/2000JD900452.

    Article  Google Scholar 

  • Raja, R. A., Anand, V., Kumar, A. S., Maithani, S., & Kumar, V. A. (2013). Wavelet based post classification change detection technique for urban growth monitoring. Journal of the Indian Society of Remote Sensing, 41(1), 35–43.

    Article  Google Scholar 

  • Rahman, M. H. (2019). Rohingya refugee crisis and human vs. elephant (Elephas maximus) conflicts in Cox’s Bazar district of Bangladesh. Journal of Wildlife and Biodiversity, 3(3), 10–21. https://doi.org/10.22120/jwb.2019.104762.1057.

    Article  Google Scholar 

  • Reuters. (2017). Life in the camps. Retrieved August 25, 2019 from https://fingfx.thomsonreuters.com/gfx/rngs/MYANMARROHINGYA/010051VB46G/index.html.

  • Rodriguez-Galiano, V., & Chica-Olmo, M. (2012). Land cover change analysis of a Mediterranean area in Spain using different sources of data: Multi-seasonal Landsat images, land surface temperature, digital terrain models and texture. Applied Geography, 35(1–2), 208–218. https://doi.org/10.1016/j.apgeog.2012.06.014.

    Article  Google Scholar 

  • Sahana, M., Ahmed, R., & Sajjad, 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. Modeling Earth Systems and Environment, 2(2), 81.

    Article  Google Scholar 

  • Sailor, D. J. (1998). Simulations of annual degree day impacts of urban vegetative augmentation. Atmospheric Environment, 32(1), 43–52.

    Article  CAS  Google Scholar 

  • Sesnie, S., Gessler, P., Finegan, B., & Thessler, S. (2008). Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments. Remote Sensing of Environment, 112(5), 2145–2159.

    Article  Google Scholar 

  • Sinha, S., Pandey, P. C., Sharma, L. K., Nathawat, M. S., Kumar, P., & Kanga, S. (2014). Remote estimation of land surface temperature for different LULC features of a moist deciduous tropical forest region. In Remote sensing applications in environmental research (pp. 57–68).

  • Skole, D., & Tucker, C. (1993). Tropical deforestation and habitat fragmentation in the Amazon: Satellite data from 1978 to 1988. Science, 260(5116), 1905–1910.

    Article  CAS  Google Scholar 

  • Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of environment, 90(4), 434–440.

    Article  Google Scholar 

  • Sud, Y. C., Lau, W. K., Walker, G. K., Kim, J. H., Liston, G. E., & Sellers, P. J. (1996). Biogeophysical consequences of a tropical deforestation scenario: A GCM simulation study. Journal of Climate, 9(12), 3225–3247.

    Article  Google Scholar 

  • Tani, M., & Rahman, M. A. (2018). Deforestation in the Teknaf Peninsula of Bangladesh. Berlin: Springer.

    Book  Google Scholar 

  • Thanh Noi, P., & Kappas, M. (2017). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors (Basel, Switzerland). https://doi.org/10.3390/s18010018.

    Article  Google Scholar 

  • Uddin, M. B., & Mukul, S. A. (2007). Improving forest dependent livelihoods through NTFPs and home gardens: a case study from Satchari National Park. In Making conservation work: Lining rural livelihoods and protected areas in Bangladesh (pp. 13–35). East-West Center, Honolulu, and Nishorgo Program of the Bangladesh Forest Department Dhaka, Bangladesh.

  • UNDP Bangladesh and UN WOMEN Bangladesh. (2018). Report on environmental impact of Rohingya influx. Dhaka, Bangladesh.

  • UNHCR. (2019). Rohingya emergency. Retrieved July 17, 2019 from https://www.unhcr.org/en-us/rohingya-emergency.html?query=rohingya%20crisis.

  • USGS. (2016). Landsat collections—2016.

  • Wang, C., & Myint, S. (2016). Environmental concerns of deforestation in Myanmar 2001–2010. Remote Sensing, 8(9), 728.

    Article  Google Scholar 

  • Wang, M., He, G., Zhang, Z., Wang, G., Wang, Z., Yin, R., et al. (2019). A radiance-based split-window algorithm for land surface temperature retrieval: Theory and application to MODIS data. International Journal of Applied Earth Observation and Geoinformation, 76, 204–217.

    Article  Google Scholar 

  • Weng, Q. (2001). A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. International Journal of Remote Sensing, 22(10), 1999–2014.

    Google Scholar 

  • Weng, Q., & Yang, S. (2004). Managing the adverse thermal effects of urban development in a densely populated Chinese city. Journal of Environmental Management, 70(2), 145–156.

    Article  Google Scholar 

  • White, M. A., Nemani, R. R., Thornton, P. E., & Running, S. W. (2002). Satellite evidence of phenological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest. Ecosystems, 5(3), 260–273.

    Article  Google Scholar 

  • Yankovich, K. S., Yankovich, E. P., & Baranovskiy, N. V. (2019). Classification of vegetation to estimate forest fire danger using landsat 8 images: Case study. Mathematical Problems in Engineering, 2019, 1–14. https://doi.org/10.1155/2019/6296417.

    Article  Google Scholar 

  • Zhang, J., Wang, Y., & Li, Y. (2006). A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6. Computers & Geosciences, 32(10), 1796–1805.

    Article  CAS  Google Scholar 

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The authors are thankful to the Department of Geography and Environmental Studies, University of Chittagong, Bangladesh, for different forms of supports during the study.

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Rashid, K.J., Hoque, M.A., Esha, T.A. et al. Spatiotemporal changes of vegetation and land surface temperature in the refugee camps and its surrounding areas of Bangladesh after the Rohingya influx from Myanmar. Environ Dev Sustain 23, 3562–3577 (2021). https://doi.org/10.1007/s10668-020-00733-x

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