Geography and Human Relationships

Geography and Human Relationships

Investigating the impact of land use changes on the biophysical environment of Sefidroud river bank in the area of Rudbar city using random forest model (RTC) and spectral indices and its effect on land surface thermal conditions (LST)

Document Type : Original Article

Author
mehdi feyzolahpour
university of zanjan
10.22034/gahr.2024.459099.2143
Abstract
In this research, the rapid change of land use and its impact on different biophysical environments along the Sefidroud River coast were investigated. For this purpose, the data of 1991, 2014 and 2023 were downloaded from Landsat 5 and 8 satellites and by means of random forest model and spectral indices of normalized difference of vegetation cover (NDVI), normalized difference of water (NDWI), index Modified normalized water difference (MNDWI), normalized barren land difference index (NDBaI), adjusted soil vegetation index (SAVI) and land surface temperature (LST) were processed. The relationship between these indicators and the surface temperature of the earth was also determined using the Pearson correlation coefficient and R2 determination coefficient, and the correctness of land use in the random forest model and each of the indicators was also estimated using the Kappa coefficient. The results showed that the decreasing trend of NDVI and SAVI index was accompanied by the increasing trend of MNDWI, NDWI and NDBaI indexes and this had an effect on the LST index. Among the spectral indices, the MNDWI index with values of -0.45 in 2014 had the highest negative correlation and the NDBaI index with values of 0.54 in 2014 had the highest positive correlation. A decreasing trend in correlations was observed among all indicators from 1991 to 2023. The values obtained from the random forest model showed that the extent of barren lands was accompanied by a significant decrease and decreased from 8.52 square kilometers in 1991 to 4.82 square kilometers in 2023, and by this amount, it was reduced to covered land. A thin herb has been added.
Keywords
Biophysical index
land surface temperature
Pearson correlation
land use
Sefidroud river bank

Subjects

Anderson, J.R. 1976. Report- USGS Numbered Series-964. In: A Land Use and Land Cover Classification System for Use with Remote Sensor Data. US Government Printing Office.
Ballanti, L., Byrd, K.B., Woo, I., Ellings, C. 2017. Remote sensing for wetland mapping and historical change detection at the Nisqually River Delta. Sustainability 9 (11), pp. 1919-1935.
Bijeesh, T. V., & Narasimhamurthy, K. N. 2019, March. A Comparative Study of Spectral Indices for Surface Water Delineation Using Landsat 8 Images. In 2019 IEEE International Conference on Data Science and Communication (IconDSC), pp. 1-5.
Breiman, Leo. 2001. Random Forests. Machine Learning. 45, pp. 5-32.
Denil, M., Matheson, D.,   Freitas, N. 2014. Narrowing the Gap: Random Forests in Theory and in Practice. Proceedings of the 31st International Conference on Machine Learning, Beijing, China. JMLR: W and P. Vol.32. 9 pages.
Dópido, I., Villa, A., Plaza, A., Gamba, P. 2012. A quantitative and comparative assessment of unmixing-based feature extraction techniques for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5 (2), pp. 421– 435.
Ghosh, S., Chatterjee, N.D., Dinda, S. 2018. Relation between urban biophysical composition and dynamics of land surface temperature in the Kolkata metropolitan area: a GIS and statistical based analysis for sustainable planning. Modeling Earth Systems Environ. https://doi.org/10.1007/s40808-018-0535-9.
Gong, Y., Duan, B., Fang, S., Zhu, R., Wu, X., Ma, Y., Peng, Y. 2018. Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis. Plant Methods 14 (1), 1–14.
Huete, A.R.. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Env. 25 (3), 295–309.
Jat, M.K., Garg, P.K., Khare, D. 2008. Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. Int. J. Appl. Earth Observ. Geoinformation 10 (1), 26–43.
Lary, D.J., Alavi, A.H., Gandomi, A.H., Walker, A.L. 2016. Machine learning in geosciences and remote sensing. Geosci. Front. 7 (1), pp. 3–10.
Li, J. J., Wang, X. R., Wang, X. J., Ma, W. C., Zhang, H. 2009. Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China. Ecol. Complex. https://doi.org/10.1016/j. ecocom.2009.02.002.
Liu, Y., Jiang, Y. 2020. Urban growth sustainability of Islamabad, Pakistan, over the last 3 decades: a perspective based on object-based backdating change detection. GeoJournal, pp. 1–21.
Louppe, G. 2014. Understanding Random Forests, From Theory to Practice. University of Liège. Faculty of Applied Sciences. Department of Electrical Engineering and Computer Science. 223 pages.
Mahato, S., Pal, S. 2021. Land surface thermal alteration and pattern simulation based on influencing factors of rural landscape. Geocarto Int. https://doi.org/ 10.1080/10106049.2021.1920634.
Maity, B., Mallick, S.K., Rudra, S. 2020. Spatiotemporal dynamics of urban landscape in Asansol municipal corporation, West Bengal, India: a geospatial analysis. GeoJournal. https://doi.org/10.1007/s10708-020-10315-z.
Mallick, S.K., Maity, B., Rudra, S. 2021. Aquaculture-based Water Quality Assessment and Risk Remediation along the Rasulpur River Belt, West Bengal. In: Shit, P.K. et al. (Eds.), Spatial Modeling and Assessment of EnvironmentalContaminants, Environmental Challenges and Solutions. Springer, pp. 213–231.
Mallick, S.K., Rudra, S. 2021. Livelihood and environmental sustainability analysis using aquaculture-based indicators: a study on selected CD blocks of Purba Medinipur District, West Bengal. Indian J. Geogr. Environ. 17–18, 14–24.
Mas, J.F., Flores, J.J. 2008. The application of artificial neural networks to the analysis of remotely sensed data. Int. J. Remote Sens. 29 (3), pp. 617–663.
Maxwell, A.E., Warner, T.A., Fang, F. 2018. Implementation of machine-learning classification in remote sensing: An applied review. Int. J. Remote Sens. 39 (9), pp. 2784–2817.
McFeeters, S.K. 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.
Mundia, C.N., Aniya, M. 2006. Dynamics of land use/cover changes and degradation of Nairobi city, Kenya. Land Degradation Develop. 17 (1), 97–108.
Oommen, T., Misra, D., Twarakavi, N.K., Prakash, A., Sahoo, B., Bandopadhyay, S. 2008. An objective analysis of support vector machine-based classification for remote sensing. Math. Geosci. 40 (4), pp. 409–424.
Pattanayak, S.P., Diwakar, S.K. 2018. Seasonal Comparative Study of NDVI, NDBI, and NDWI of Hyderabad City (Telangana) Based on LISS-III Image Using Remote Sensing and DIP. Khoj: An International Peer Reviewed. J. Geogr. 5 (1), pp. 78-92.
Plaza, A., Du, Q., Bioucas-Dias, J.M., Jia, X., Kruse, F.A. 2011. Foreword to the special issue on spectral unmixing of remotely sensed data. IEEE Trans. Geosci. Remote Sens. 49 (11), pp. 4103–4110.
Polykretis, C., Grillakis, M.G., Alexakis, D.D. 2020. Exploring the impact of various spectral indices on land cover change detection using change vector analysis: A case study of Crete Island, Greece. Remote Sens. 12 (2), pp. 319-334.
Rawat, J.S., Biswas, V., Kumar, M. 2013. Changes in land use/cover using geospatial techniques: A case study of Ramnagar town area, district Nainital, Uttarakhand, India. Egypt. J. Remote Sensing Space Sci. 16 (1), 111–117. https://doi.org/ 10.1016/j.ejrs.2013. 04.002.
Rokni, K., Ahmad, A., Selamat, A., Hazini, S. 2014. Water feature extraction and change detection using multitemporal Landsat imagery. Remote Sensing 6 (5), pp. 4173–4189.
Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. 1973. Monitoring vegetation systems in the great plains with ERTS (Earth Resources Technology Satellite). Proceedings of 3rd Earth Resources Technology Satellite Symposium.
Senkal, O. 2010. Modeling of solar radiation using remote sensing and artificial neural network in Turkey. Energy 35 (12), pp. 4795–4801.
Sharma, R., Ghosh, A., Joshi, P.K. 2013. Analyzing spatiotemporal footprints of urbanization on environment of Surat city using satellite-derived bio-physical parameters. Geocarto Int. 28 (5), 420–438.
Sinha, P., Verma, N.K., Ayele, E. 2016. Urban built-up area extraction and change detection of Adama municipal area using time-series Landsat images. Int. J. Adv. Remote Sens. GIS 5 (8), pp. 1886–1895.
Singha, S., Bellerby, T.J., Trieschmann, O. 2013. Satellite oil spill detection using artificial neural networks. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6 (6), pp. 2355–2363.
Sobrino, J.A., Jiménez-Muñoz, J.C., Paolini, L. 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sens. Env. 90 (4), 434–440. https://doi.org/10.1016/j.rse.2004.02.003.
Valenti, W.C., Kimpara, J.M., Preto, B.d.L., Moraes-Valenti, P. 2018. Indicators of sustainability to assess aquaculture systems. Ecol. Ind. 88, 402–413.
Weng, Q., Liu, H., Liang, B., Lu, D. 2008. The spatial variations of urban land surface temperatures: pertinent factors, zoning effect, and seasonal variability. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 1 (2), 154–166. https://doi.org/10.1109/ JSTARS.2008.917869.
Wu, C. 2009. Quantifying high-resolution impervious surfaces using spectral mixture analysis. Int. J. Remote Sens. 30 (11), pp. 2915–2932.
Wu, C., Murray, A.T. 2003. Estimating impervious surface distribution by spectral mixture analysis. Remote Sens. Environ. 84 (4), pp. 493–505.
Xu, H. 2006. Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27 (14), 3025–3033.
Xu, H.Q., Chen, B.Q. 2004. Remote sensing of the urban heat island and its changes in Xiamen City of SE China. J. Environ. Sci. (China) 16 (2), 276–281.
Zhao, H., Chen, X. 2005. Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS’05, 3, 1666–1668.
Zhong, Y., Wang, X., Zhao, L., Feng, R., Zhang, L., Xu, Y. 2016. Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery. ISPRS J. Photogramm. Remote Sens. 119, PP. 49–63.
Zhao, S., Song, K., Gui, F., Cai, H., Jin, W., Wu, C. 2013. The energy ecological footprint for small fish farm in China. Ecol. Ind. 29, 62–67.
Ziaul, S., Pal, S. 2016. Image based surface temperature extraction and trend detection in an urban area of West Bengal, India. J. Environ. Geogr. 9 (3–4), 13–25.

  • Receive Date 23 May 2024
  • Revise Date 02 July 2024
  • Accept Date 30 July 2024