Quantifying Spatio-Temporal Land Surface Temperature and Biophysical Indices for Sustainable Management of Watershed: A Study of Vishwamitri Watershed of Gujarat
DOI:
https://doi.org/10.58825/jog.2023.17.1.82Keywords:
LST, Biophysical Indices, Watershed, Vishwamitri WatershedAbstract
Spatio-Temporal Analysis of the nexus between vegetation dynamics and climatic parameters like surface temperature is essential in environmental and biophysical studies and for monitoring and management of watersheds. This study explored the spatio-temporal distribution of land surface temperature (LST), Normalised Difference Water Index (NDWI), and Normalized Difference Vegetation Index (NDVI) and the relationship between them in the Vishwamitri watershed of Gujarat for the Pre Monsoon and Post Monsoon of the Year 2001 and 2016 using Landsat dataset. The findings of the study showed that the LST of the Vishwamitri watershed. The mean LST value of the year 2001 was 46.19°C in the pre-monsoon season and 39.27°C in the post-monsoon season. Mean LST values for the year 2016 were 49.34°C in pre-monsoon and 35.21°C in the post-monsoon season as observed. The spatial distribution of NDVI and LST reflects an inverse relationship. A strong positive correlation between LST with NDVI is observed over highly dense built-up areas. In summary, the LST is greatly controlled by surface characteristics. The results of this study illustrate there has been a dynamic change in vegetation cover of the watershed in all seasons. There was also a negative correlation between LST and NDVI in the studied years. The study concludes that there has been a degradation of vegetation and intensification of LST in the year 2016 as compared with the year 2001. This study can be used as a reference for land use and environmental planning in a tropical city.
References
Alemu, M.M. (2019). Analysis of Spatio-temporal Land Surface Temperature and Normalized Difference Vegetation Index Changes in the Andassa Watershed, Blue Nile Basin, Ethiopia[J]. Journal of Resources and Ecology, 2019, 10(1): 77-85.
Arnfield, A.J. (2003): “Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island”, International Journal of Climatology, Vol. 23, pp.1-26.
Arnold, C. L., Jr., and C.J. Gibbons (2016) ‘‘Impervious surface coverage: The emergence of a key environmental indicator,’’ J. Amer. planning Assoc., vol. 62, no. 2, pp. 243–258, https://doi.org/10.1080/01944369608975688.
Buyantuyev, A. and Wu, J. (2010). Urban heat islands and landscape heterogeneity: Linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landscape Ecology, 25, 17–33. https://doi.org/10.1007/s10980-009-9402-4.
Chen X.L., H.M. Zhao, P.X. Li and Z.Y. Yin (2006), Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens of Environment 104(2): p. 133-146. https://doi.org/10.1016/j.rse.2005.11.016.
Dousset B. and F. Gourmelon (2003). Satellite multi-sensor data analysis of urban surface temperatures and land cover, Photogrammetry and Remote Sensing 58 pp. 43–54 Environment, 106, 375–386. doi:10.1016/S0924-2716(03)00016-9.
Fathian F, Prasad A D, Dehghan Z, et al. 2015. Influence of land use/land cover change on land surface temperature using RS and GIS techniques. International Journal of Hydrology Science and Technology, 5(3):195-207. https://doi.org/10.1504/IJHST.2015.071348.
Foley J.A., R. DeFries, G. P. Asner, C. Barford, G. Bonan, S.R. Carpenter, F.S. Chapin, M.T. Coe, G. C Daily and H. K. Gibbs (2005) Global consequences of land use. Science 309:570–574.
Fu P. and Q. Weng (2016) A time series analysis of urbanization-induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sens Environ 175:205–214
Gallo, K.P., A.L. McNab, T.R. Karl, J.F. Brown, J.J. Hood and J.D. Tarpley (1993). The use of NOAA AVHRR data for assessment of urban heat island effect. Journal of Applied Meteorology. 32, pp. 899-908.
Gallo, K.P. and T.W. Owen ( 1998), Assessment of urban heat island: A multi-sensor perspective for the Dallas-Ft. Worth, USA region. Geocarto International, 13, 35– 41.
Gao, B. (1996), “NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space Remote Sensing of Environment Volume 58, Issue 3, December Pages 257-266 https://doi.org/10.1016/S0034-4257(96)00067-3.
Grimm N.B., S.H. Faeth, N.E. Golubiewski, C.L. Redman, J. Wu, X. Bai, J.M. Briggs and Grimm N (2008) Global change and the ecology of cities. Science 319:756–760.
Guha, S., Govil, H. (2020). Land surface temperature and normalized difference vegetation index relationship: a seasonal study on a tropical city. SN Applied Sciences (2020) 2:1661, https://doi.org/10.1007/s42452-020-03458-8.
Joshi J. P and B. Bhatt. 2012. “Estimating Temporal Land Surface Temperature Using Remote Sensing: A Study Of Vadodara Urban Area, Gujarat.” International Journal of Geology, Earth and Environmental Sciences 2 (1): 123–30. https://www.cibtech.org/J-GEOLOGY-EARTH,
Liu H, Q. Zhan, C. Yang and J. Wang (2018) Characterizing the spatio-temporal pattern of land surface temperature through time series clustering: based on the latent pattern and morphology. Remote Sens 10:654.
Liu Y, J. Peng and Y. Wang (2018) Efficiency of landscape metrics characterizing urban land surface temperature. Landsc Urban Plan 180:36–53.
Liu H. and Q. H. Weng (2013) Landscape metrics for analysing urbanization-induced land use and land cover changes. Geocarto Int., 28, 582–593.
Lo C.P., D.A. Quattrochi and J.C. Luvall, (1997, Application of high resolution thermal infrared remote sensing and GIS to assess the urban heat island effect, International Journal of Remote Sensing 18 pp. 287–304. https://doi.org/10.1080/014311697219079.
Lu, D. and Q. Weng (2009), Extraction of urban impervious surfaces from an IKONOS image. International Journal of Remote Sensing. 30, 5pp. 1297-1311. https://doi.org/10.1080/01431160802508985.
Mallik J., Y. Kant and B. D. Bharath (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J. Ind. Geophysics Union, 12(3), 131-140.
McFeetemrs S.K., (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Rem. Sens. 17, 1425–1432. https://doi.org/10.1080/01431169608948714.
Patz J.A., D. Campbell-Lendrum, T. Holloway and J.A. Foley (2005). Impact of regional climate change on human health. Nat Cell Biol 438:310–317.
Peng J, J. Ma, Q. Liu, Y. Liu, Y. Hu, Y. Li and Y. Yue (2018) Spatial-temporal change of land surface temperature across 285 cities in China: an urban-rural contrast perspective. Sci Total Environ 635:487–497.
Sobrino, J. A., J. C. Jiménez-Muoz and L. Paolini. (2004), Land surface temperature retrieval from LANDSAT TM 5. // Remote Sensing Environment. 90, pp. 434-446. https://doi.org/10.1016/j.rse.2004.02.003.
Sundara K., P. B. and K. Padmakumari. Estimation of Land Surface Temperature to study Urban Heat Island effect using Landsat ETM+ Image. International Journal of Engineering Science and Technology, 4(2), 771-778.
Tooke, T. R.; Coops, N. C.; Goodwina, N. R.; Voogt, J. A. .(2008)Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications. 10.1016/j.rse.2008.10.005.
Walawender, J.P., M. Szymanowski M. and M.J. Hajto (2014). Land Surface Temperature Patterns in the Urban Agglomeration of Krakow (Poland) Derived from Landsat-7/ETM+ Data. Pure Appl. Geophys., 171, 913–940.
Wang F, Z. Qin and C. Song (2015). An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data. Remote Sensing, 7(4): 4268–4289. https://doi.org/10.3390/rs70404268.
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, Vol. 22, pp.1999-2014.
Weng, Q., Lu, D. and B. Liang (2006): “Urban surface biophysical descriptors and land surface temperature variations”, Photogrammetric Engineering & Remote Sensing, Vol. 72, pp.1275-1286.
Zhang X.F., C.H. Liao (2014). Remote Sensing Collaborative Inversion and Assimilation Simulation of Ecological Environment Parameters; Science Press: Beijing, China, 2014.
Zhang, Y., I.O.A. Odeh.; C.F. Han (2009), Bi-temporal characterization of land surface temperature in relation to the impervious surface area, NDVI, and NDBI, using sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation. 11, (2009), pp. 256-264. https://doi.org/10.1016/j.jag.2009.03.001.
Zhang Z. and G. He. (2013) Generation of Landsat surface temperature product for China, 2000–2010. Int. J. Remote Sens. 34, 7369–7375.
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