International Journal of Ecology and Environmental Sciences
Assessment of land surface temperature in relation to built-up index: A case study on Nagaon District, Assam, India
Abstract
When calculating the surface temperature from thermal data, the Land Surface Temperature is an essential statistic. The goal of this research is to use multi-temporal data to capture the time-space contrast of land surface temperature in the Nagaon district of Assam, as well as the relationship between land surface temperature and the built-up index. Ratio of Variation to the mean Normalized Difference Built-up Index (NDBI) employs the infrared spectrum to evaluate man-made urban environments. Three sets of satellite images spanning three decades (1992, 2004, and 2021) are imported into ArcGIS, where spatial-temporal LST data is extracted using the mono window algorithm approach, and the resulting dataset is examined statistically and cartographically. The findings denote that maximum and minimum temperatures have been rising during the period of 1992-2021. LST increases 2.77ºc in terms of minimum temperature from 1992 to 2004 and 1.93ºc rises from 2004 to 2021.The Pearson correlation approach has been applied to the question of how LST relates to NDBI. Based on the findings, we know that LST and NDBI have strong reciprocity (R2=0.867, 0.854, 0.947 for 1992, 2004 and 2021, respectively). Since the built-up area can be easily accessed from both NDBI and LST, this suggests that it is a large contributor to the urban heat island effect.
Keywords
References
Aires, F., Prigent, C., Rossow, W. B. and Rothstein, M. 2001. A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations. Journal of Geophysical Research: Atmospheres, 106(D14), 14887–14907. https://doi.org/10.1029/2001jd900085
Bakhit, S. and Abdelkader, S. 2019. Assessment of Urban Growth Patterns using Spatio-temporal Data and Analysis. Asian Journal of Applied Sciences.7 (5). doi:10.24203/ajas.v7i5.5975
Bastiaanssen, W., Menenti, M., Feddes, R. and Holtslag, A. 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212–213, 198–212. https://doi.org/10.1016/s0022-1694(98)00253-4
Das, G. and Dandapath ,P. 2016. A Spatio-temporal change analysis and assessment of the urban growth over Delhi National capital territory (NCT) during the period 1977-2014. International Journal of Experimental Research and Review (IJERR).Vol. 7:53-61.
Dong, J., Crow, W. and Bindlish, R. 2018. The Error Structure of the SMAP Single and Dual Channel Soil Moisture Retrievals. Geophysical Research Letters.45(2):758-765. doi: 10.1002/2017GL075656
Ferreira, L. and Duarte,D. 2019. Exploring the relationship between urban form, land surface temperature and vegetation indices in a subtropical megacity. Urban Climate.27:105-123. doi:10.1016/j.uclim.2018.11.002
Guha, S. and Govil, H. 2020. Land surface temperature and normalized difference vegetation index relationship: a seasonal study on a tropical city. SN Applied Sciences.2(10). doi:10.1007/s42452-020-03458-8
Hasnat, G. 2021. A Time Series Analysis of Forest Cover and Land Surface Temperature Change Over Dudpukuria-Dhopachari Wildlife Sanctuary Using Landsat Imagery. Frontiers In Forests And Global Change. doi:https://doi.org/10.3389/ffgc.2021.687988(2021)
Kaur, R. and Pandey, P. 2020. Monitoring and spatio-temporal analysis of UHI effect for Mansa district of Punjab, India. Advances in Environmental Research.Vol. 9, No. 1:19-39.
Khandelwal, S., Goyal, R., Kaul, N. and Mathew, A. (2018). Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 87–94. https://doi.org/10.1016/j.ejrs.2017.01.005
Kumari, B., Tayyab, M., Ahmed, I., Baig, M., Khan, M. and Rahman, A. 2022. Longitudinal study of land surface temperature (LST) using monoand split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arab Journal of Geoscience.13:1–19. doi:. https://doi.org/doi: 10.1007/s12517- 020-06068-1, (2020).
Lein, J. 2011. Environmental Sensing: Analytical Techniques For Earth Observation. Cham. Springer Science.
Macarof, P. and Statescu, F. 2017. Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi. Present Environment and Sustainable Development.(2):141-150. doi:10.1515/pesd-2017-0032
Malik, M, and Shukla, J. 2018. Retrieving of Land Surface Temperature Using Thermal Remote Sensing and GIS Techniques in Kandaihimmat Watershed, Hoshangabad, Madhya Pradesh. Journal of the Geological Society of India.92 (3):298-304. doi:10.1007/s12594-018-1010-y
Mallick, J., Kant, Y. and Bharath, B. 2008. Estimation of land surface temperature over Delhi using Landsat-7 ETM+. Journal of Indian Geophysics Union,12 (3),131–140.
Mustafa, E., Co, Y. and Liu, G.2020. Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms. Advances in Civil Engineering.2020:1-16. doi:10.1155/2020/7363546
Niclòs, R., Galve, J., Valiente, J., Estrela, M. and Coll, C. 2011. Accuracy assessment of land surface temperature retrievals from MSG2-SEVIRI data. Remote Sensing Environment.115(8):2126-2140. doi:10.1016/j.rse.2011.04.017
Orhan, O., Ekercin, S. and Dadaser-Celik F. 2014. Use of Landsat Land Surface Temperature and Vegetation Indices for Monitoring Drought in the Salt Lake Basin Area, Turkey. The Scientific World Journal.2014:1-11. doi:10.1155/2014/142939
Pal, S. and Ziaul, S. 2017. Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science.20(1):125-145. doi:10.1016/j.ejrs.2016.11.003
Pandey, A., Singh, S.,Berwal, S.,Kumar, Dinesh.,P, Puneeta.,P, Amit., L, Nilesh., Maithani, S.,Jain, V. and Kumar, K. 2014. Spatio – temporal variations of urban heat island over Delhi. Urban Climate.10:119-133. doi:10.1016/j.uclim.2014.10.005
Qin, Z., Karnieli, A. and Berliner, P. 2022. 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. doi:.https://doi.org/10.1080/01431160010006971(2001)
Rahaman, S. N. and Shermin, N. 2021. Identifying Built-up Area Expansion and Comparing Two Conventional Built-up Area Extraction Method from LANDSAT Imagery: A Case Study on Khulna City. Academia Letters. https://doi.org/10.20935/al758
Ranagalage, M. 2017. An Urban Heat Island Study of the Colombo Metropolitan Area, Sri Lanka, Based on Landsat Data (1997–2017). International Journal of Geo-Information. 6.189. doi:10.3390/ijgi6070189
Sun, D., Pinker, R. and Basara, J. 2004. Land Surface Temperature Estimation from the Next Generation of Geostationary Operational Environmental Satellites: GOES M–Q. Journal of Applied Meteorology.43(2):363-372.doi:https://doi.org/10.1175/1520-0450(2004)043<0363:LSTEFT>2.0.CO;2
Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), 335–344. https://doi.org/10.1016/j.isprsjprs.2009.03.007
Weng, Q., Lu, D. and Schubring, J. 2004. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing Environment.89(4):467-483. doi:10.1016/j.rse.2003.11.005
Xu, H. 2010.Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI). Photogrammetric Engineering & Remote Sensing.76(5):557-565. doi:10.14358/pers.76.5.557
Yadav, S., Hashia, H. and Perwaiz, S. (2019). Urban Built-Up and Leader in Energy and Environmental Design (LEED) Certification: A Case Study of National Capital Territory (NCT) of Delhi, India. Journal of Global Resources, 06(01), 81–88. https://doi.org/10.46587/jgr.2019.v06i01.013
Yuan, F. and Bauer, M. 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing Environment.106 (3):375-386. doi:10.1016/j.rse.2006.09.003
Zha, Y., Gao, J. and Ni, S. 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. https://doi.org/10.1080/01431160304987
Zheng, Y., Tang ,L. and Wang, H. 2021. An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2021.129488
Zhu, L., Cho, D., Jeon, C. and Lee, S. 2016. Evaluating Methods for Extracting Built-up Area Using NPP-VIIRS Night time Light Data and Local Spatial Statistics. Journal of the Korean Urban Geographical Society.19 (3):145-163. doi:10.21189/jkugs.19.3.11
Aires, F., Prigent, C., Rossow, W. B. and Rothstein, M. 2001. A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations. Journal of Geophysical Research: Atmospheres, 106(D14), 14887–14907. https://doi.org/10.1029/2001jd900085
Bakhit, S. and Abdelkader, S. 2019. Assessment of Urban Growth Patterns using Spatio-temporal Data and Analysis. Asian Journal of Applied Sciences.7 (5). doi:10.24203/ajas.v7i5.5975
Bastiaanssen, W., Menenti, M., Feddes, R. and Holtslag, A. 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212–213, 198–212. https://doi.org/10.1016/s0022-1694(98)00253-4
Das, G. and Dandapath ,P. 2016. A Spatio-temporal change analysis and assessment of the urban growth over Delhi National capital territory (NCT) during the period 1977-2014. International Journal of Experimental Research and Review (IJERR).Vol. 7:53-61.
Dong, J., Crow, W. and Bindlish, R. 2018. The Error Structure of the SMAP Single and Dual Channel Soil Moisture Retrievals. Geophysical Research Letters.45(2):758-765. doi: 10.1002/2017GL075656
Ferreira, L. and Duarte,D. 2019. Exploring the relationship between urban form, land surface temperature and vegetation indices in a subtropical megacity. Urban Climate.27:105-123. doi:10.1016/j.uclim.2018.11.002
Guha, S. and Govil, H. 2020. Land surface temperature and normalized difference vegetation index relationship: a seasonal study on a tropical city. SN Applied Sciences.2(10). doi:10.1007/s42452-020-03458-8
Hasnat, G. 2021. A Time Series Analysis of Forest Cover and Land Surface Temperature Change Over Dudpukuria-Dhopachari Wildlife Sanctuary Using Landsat Imagery. Frontiers In Forests And Global Change. doi:https://doi.org/10.3389/ffgc.2021.687988(2021)
Kaur, R. and Pandey, P. 2020. Monitoring and spatio-temporal analysis of UHI effect for Mansa district of Punjab, India. Advances in Environmental Research.Vol. 9, No. 1:19-39.
Khandelwal, S., Goyal, R., Kaul, N. and Mathew, A. (2018). Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 87–94. https://doi.org/10.1016/j.ejrs.2017.01.005
Kumari, B., Tayyab, M., Ahmed, I., Baig, M., Khan, M. and Rahman, A. 2022. Longitudinal study of land surface temperature (LST) using monoand split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arab Journal of Geoscience.13:1–19. doi:. https://doi.org/doi: 10.1007/s12517- 020-06068-1, (2020).
Lein, J. 2011. Environmental Sensing: Analytical Techniques For Earth Observation. Cham. Springer Science.
Macarof, P. and Statescu, F. 2017. Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi. Present Environment and Sustainable Development.(2):141-150. doi:10.1515/pesd-2017-0032
Malik, M, and Shukla, J. 2018. Retrieving of Land Surface Temperature Using Thermal Remote Sensing and GIS Techniques in Kandaihimmat Watershed, Hoshangabad, Madhya Pradesh. Journal of the Geological Society of India.92 (3):298-304. doi:10.1007/s12594-018-1010-y
Mallick, J., Kant, Y. and Bharath, B. 2008. Estimation of land surface temperature over Delhi using Landsat-7 ETM+. Journal of Indian Geophysics Union,12 (3),131–140.
Mustafa, E., Co, Y. and Liu, G.2020. Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms. Advances in Civil Engineering.2020:1-16. doi:10.1155/2020/7363546
Niclòs, R., Galve, J., Valiente, J., Estrela, M. and Coll, C. 2011. Accuracy assessment of land surface temperature retrievals from MSG2-SEVIRI data. Remote Sensing Environment.115(8):2126-2140. doi:10.1016/j.rse.2011.04.017
Orhan, O., Ekercin, S. and Dadaser-Celik F. 2014. Use of Landsat Land Surface Temperature and Vegetation Indices for Monitoring Drought in the Salt Lake Basin Area, Turkey. The Scientific World Journal.2014:1-11. doi:10.1155/2014/142939
Pal, S. and Ziaul, S. 2017. Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science.20(1):125-145. doi:10.1016/j.ejrs.2016.11.003
Pandey, A., Singh, S.,Berwal, S.,Kumar, Dinesh.,P, Puneeta.,P, Amit., L, Nilesh., Maithani, S.,Jain, V. and Kumar, K. 2014. Spatio – temporal variations of urban heat island over Delhi. Urban Climate.10:119-133. doi:10.1016/j.uclim.2014.10.005
Qin, Z., Karnieli, A. and Berliner, P. 2022. 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. doi:.https://doi.org/10.1080/01431160010006971(2001)
Rahaman, S. N. and Shermin, N. 2021. Identifying Built-up Area Expansion and Comparing Two Conventional Built-up Area Extraction Method from LANDSAT Imagery: A Case Study on Khulna City. Academia Letters. https://doi.org/10.20935/al758
Ranagalage, M. 2017. An Urban Heat Island Study of the Colombo Metropolitan Area, Sri Lanka, Based on Landsat Data (1997–2017). International Journal of Geo-Information. 6.189. doi:10.3390/ijgi6070189
Sun, D., Pinker, R. and Basara, J. 2004. Land Surface Temperature Estimation from the Next Generation of Geostationary Operational Environmental Satellites: GOES M–Q. Journal of Applied Meteorology.43(2):363-372.doi:https://doi.org/10.1175/1520-0450(2004)043<0363:LSTEFT>2.0.CO;2
Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), 335–344. https://doi.org/10.1016/j.isprsjprs.2009.03.007
Weng, Q., Lu, D. and Schubring, J. 2004. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing Environment.89(4):467-483. doi:10.1016/j.rse.2003.11.005
Xu, H. 2010.Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI). Photogrammetric Engineering & Remote Sensing.76(5):557-565. doi:10.14358/pers.76.5.557
Yadav, S., Hashia, H. and Perwaiz, S. (2019). Urban Built-Up and Leader in Energy and Environmental Design (LEED) Certification: A Case Study of National Capital Territory (NCT) of Delhi, India. Journal of Global Resources, 06(01), 81–88. https://doi.org/10.46587/jgr.2019.v06i01.013
Yuan, F. and Bauer, M. 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing Environment.106 (3):375-386. doi:10.1016/j.rse.2006.09.003
Zha, Y., Gao, J. and Ni, S. 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. https://doi.org/10.1080/01431160304987
Zheng, Y., Tang ,L. and Wang, H. 2021. An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2021.129488
Zhu, L., Cho, D., Jeon, C. and Lee, S. 2016. Evaluating Methods for Extracting Built-up Area Using NPP-VIIRS Night time Light Data and Local Spatial Statistics. Journal of the Korean Urban Geographical Society.19 (3):145-163. doi:10.21189/jkugs.19.3.11
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.
COPYRIGHT of this Journal vests fully with the National Instional Institute of Ecology. Any commercial use of the content on this site in any form is legally prohibited.