IJEP 45(4): 307-321 : Vol. 45 Issue. 4 (April 2025)
Sudhakar Pal, Arabinda Sharma* and Monashree Panigrahi
Gangadhar Meher University, School of Geography, Sambalpur – 768 001, Odisha, India
Abstract
In this modern era, rapid urbanization results in changes in landuse and land cover (LULC) patterns that have a significant impact on urban ecosystems and the loss of native diversity.This study aims to investigate the spatio-temporal variability in the quality of the urban ecosystem of Rourkela city over a decade. The analytic hierarchy process (AHP) is used to prepare urban ecosystem impact index (UEII) maps (2011 and 2021) by assigning the weights of various environmental parameters, namely air quality index (AQI), normalized difference vegetation index (NDVI), normalized difference building index (NDBI), normalized difference water index (NDWI) and land surface temperature (LST). The result revealed that the quality of the ecosystem in Rourkela, especially in the north-western (Kuarmunda region) and south-western (Kalunga region) parts of the city, has been deteriorating rapidly over the last decade. From 2011 to 2021, more than 5%, 6% and 2.5% of the area of least, low and moderate ecosystem impact zones decreased, respectively and on the other hand, almost 15% of the area of high ecosystem impact zone increased in Rourkela. Such conversion is the result of the large size alteration of open land (13%) and agricultural land (5%) into built-up areas (17%) because of rapid urbanization and industrial growth in Rourkela. The study’s findings may be helpful for policy and decision-makers in regard to ecosystem impact assessment, natural resource management in the area and sustainable development of urban areas. Furthermore, the result of this study will help to determine the degree of impact on the ecosystem due to growing anthropogenic activities.
Keywords
Urbanization, Land cover dynamics, Environmental indices, Analytic hierarchy process, Urban ecosystem impact index
References
- Erasu, D. 2017. Remote sensing-based urban landuse/land cover change detection and monitoring. J. Remote Sensing GIS. 6(2): 1000196.
- Bhat, P. A., A. A. Mir and P. Ahmed. 2017. Urban sprawl and its impact on landuse / land cover dynamics of Dehradun city, India. Int. J. Sustain. Built Env., 6(2): 513-521.
- Lai, A., et al. 2016. Impact of landuse change on atmospheric environment using refined land surface properties in the Pearl river delta, China. Adv. Meteorol., DOI: 10.1155/2016/3830592.
- Liu, C., et al. 2023. Assessment of occupation of natural habitat by urban expansion and its impact on crucial ecosystem services in China’s coastal zone. Ecol. Indicators. 154(7): 110682.
- Chen, W., G. Wang and J. Zeng. 2023. Impact of urbanization on ecosystem health in Chinese urban agglomerations. Env. Impact Assess. Review. 98 (68): 106964.
- Hu, T., et al. 2016. Mapping urban landuse by using landsat images and open social data. Remote Sensing. 8(2): 151.
- Aziz, J., et al. 2023. Heliyon exploring the nexus between landuse/ land cover (LULC) changes and population growth in a planned city of Islamabad and unplanned city of Rawalpindi , Pakistan. Heliyon. 9(2): e13297.
- Hyder, M. B. and T.Z. Haque. 2022. Understanding the linkages and importance of urban greenspaces for achieving sustainable development goals 2030. J. Sustain. Develop., 15(2): 144.
- Haldar, S., et al. 2023. Dynamicity of landuse/land cover (LULC): An analysis from peri-urban and rural neighbourhoods of Durgapur Municipal Corporation (DMC) in India. Regional Sustain., 4(2): 150-172.
- Pathirana, A., et al. 2014. Impact of urban growth-driven landuse change on microclimate and extreme precipitation – A sensitivity study. Atmos. Res., 138: 59-72.
- Liang, Y. and W. Song. 2022. Integrating potential ecosystem services losses into ecological risk assessment of landuse changes: A case study on the Qinghai-Tibet plateau. J. Env. Manage., 318(11): 115607.
- Li, X., et al. 2021. Contrasting effects of climate and LULC change on blue water resources at varying temporal and spatial scales. Sci. Total Env., 786: 147488.
- Guha, S., et al. 2020. A long-term seasonal analysis on the relationship between LST and NDBI using Landsat data. Quaternary Int., 575-576: 249-258.
- Ullah, W., et al. 2023. Analysis of the relationship among land surface temperature (LST), landuse land cover (LULC) and normalized difference vegetation index (NDVI) with topographic elements in the lower Himalayan region. Heliyon. 9(2): e13322.
- Acharki, S., 2022. Remote sensing applications: Society and environment planet scope contributions compared to Sentinel-2 and Landsat-8 for LULC mapping. Remote Sensing Applications Soc. Env., 27: 100774.
- Kumari, M., et al. 2022. Analysis of multi-temporal remotely sensed spectral indices influence on ecology of Singrauli sub-district, Madhya Pradesh using an ecological impact index. Egyptian J. Remote Sensing Space Sci., 25(3): 863-871
- Zhang, H., et al. 2022. Impacts of urbanization on ecosystem services in the Chengdu-Chongqing urban agglomeration: Changes and trade-offs. Ecol. Indicators. 139(4): 108920.
- Sangita, S. and R. Maity. 2023. Effect of climate change on soil erosion indicates a dominance of rainfall over LULC changes. J. Hydrol. Regional Studies. 47(3): 101373.
- Wikipedia. National Smart City Mission of India. Available at: https://en.wikipedia.org/wiki/Smart_ Cities_Mission. Accessed on 21 March 2023.
- Parsa, V., A. Yavari and A. Nejadi. 2016. Spatio-temporal analysis of landuse/land cover pattern changes in Arasbaran biosphere reserve, Iran. Modeling Earth Systems Env., 2(4): 1-13.
- Rashid, N., et al. 2022. Impact of landuse change and urbanization on urban heat island effect in Narayanganj city , Bangladesh: A remote sensing-based estimation. Env. Challenges. 8(6): 100571.
- Yang, X. and Z. Liu. 2005. Using satellite imagery and GIS for land-use and land-cover change mapping in an estuarine watershed. Int. J. Remote Sensing. 26(23): 5275–5296. DOI: 10.1080/014311 60500219224.
- Pal, S. and A. Sharma. 2023. How does the Covid- 19-related restriction affect the spatiotemporal variability of ambient air quality in a tropical city? Env. Monit. Assess., 195(7): 847.
- Ren, Y., et al. 2023. Attribution of climate change and human activities to vegetation NDVI in Jilin Province, China during 1998-2020. Ecol. Indicators. 153(4): 110415.
- Vadakkuveettil, A. and A. Grover. 2023. bi-temporal characterization of terrestrial temperature in relation to urban landuse land cover dynamics and policies in Kozhikode urban area, India. Land use Policy. 132(3): 106782.
- Essaadia, A., et al. 2022. The normalized difference vegetation index (NDVI) of the Zat valley, Marrakech: comparison and dynamics. Heliyon. 8(11): e12204.
- Roy, B. and E. Bari. 2022. Heliyon examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine. Heliyon. 8(8): e10668.
- Albraheem, L. and L. Al-Awlaqi. 2023. Geospatial analysis of wind energy plant in Saudi Arabia using a GIS-AHP technique. Energy Reports. 9: 5878-5898.
- Arshad, M., et al. 2023. Sustainable landfill sites selection using geospatial information and AHP-GDM approach: A case study of Abha-Khamis in Saudi Arabia. Heliyon. 9(6): e16432.
- Zangmene, F.L., et al. 2023. Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Bafoussam-Dschang region (West Cameroon). Adv. Space Res., 71: 5282-5301.
- Ozegin, K.O., S.O. Ilugbo and T. T. Ogunseye. 2023. Groundwater exploration in a landscape with heterogeneous geology: An application of geospatial and analytical hierarchical process (AHP) techniques in the Edo north region, in Nigeria. Groundwater Sustain. Develop., 20(11): 100871.
- Saaty, T. L. 1977. A scaling method for priorities in hierarchical structures. J. Mathematical Psychol., 15(3): 234-281.
- Panchal, S. and A.K. Shrivastava. 2022. Landslide hazard assessment using analytic hierarchy process (AHP): A case study of National Highway 5 in India. Ain Shams Eng. J., 13(3): 101626.
- Saaty, T.L. 1980. The analytic hierarchy process: Planning, priority setting, resources allocation. McGraw-Hill, New York.
- Wang, Z., et al. 2019. Impacts of landuse and land cover changes on ecosystem services in urban-rural transitional areas: A case study of the Yangtze river Delta, China. J. Env. Manage., 238: 490-501.
- Turner, B. L., E. F. Lambin and A. Reenberg. 2015. The emergence of land change science for global environmental change and sustainability. Proceedings National Academy Sci., 104(52): 20666-20671.
- Ayanlade, A. and M.T. Howard. 2017. Understanding changes in a tropical delta: A multi-method narrative of landuse / land cover change in the Niger Delta. Ecol. Modelling. 364: 53-65.
- Agrawal, M., et al. 2021. Ozone-induced stomatal closure, photosynthetic and oxidative stress in Arabidopsis thaliana and Medicago truncatula. Plant Sci., 303: 110726.
- Likens, G. E., C. T. Driscoll and D. C. Buso. 2020. Long-term effects of acid rain: response and recovery of a forest ecosystem. Sci., 368(6489): 786-791.
- Pettorelli, N., et al. 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evolution. 20(9): 503-510.
- Cheng, G., et al. 2019. Evaluating the relationships between NDVI and climate in the arid central Asia based on wavelet coherence. Remote Sensing. 11(9): 1093.
- Viana, C.M., et al. 2019. Landuse/land cover change detection and urban sprawl analysis (chapter 29). In Spatial modelling in GIS and R for earth and environmental sciences. Ed H.R. Pourghasemi and C. Gokceoglu. pp 621-651.
- Babazadeh, M. 2022. Estimation the influence of dynamics of land surface temperature on Delhi urban heat island. J. Earth Sci. Climatic Change. 13(11): 1000648.
- He, C., et al. 2010. Improving the normalized difference built- up index to map urban built-up areas using a semi-automatic segmentation approach. Remote Sensing Letters. 1(4): 213-221.
- Rasul, A., et al. 2018. Applying built-up and bare-soil indices from. Land. 7(3): 81.
- Li, J., X. Zhang and C. He. 2020. Urban heat island intensity analysis based on normalized difference built-up index (NDBI): A case study of Wuhan, China. Urban Climate. 31: 100553
- Lang, M., et al. 2017. Monitoring wetland changes and their impact on ecosystem services in China using multi-temporal Landsat data. Remote Sensing. 9(5): 438.
- Yu, K., et al. 2019. Monitoring vegetation water status using a hybrid vegetation water index based on visible and shortwave infrared bands. Remote Sensing. 11(2): 148.
- Wang, J., et al. 2020. Evaluation of vegetation water content variations and its response to meteorological factors based on MODIS NDWI in an arid region of northwest China. Remote Sensing. 12(15): 2423.
- Zhang, M., Y. Zhang and L. Zhang. 2018. Monitoring water dynamics and its relevance for water resource management using MODIS NDWI in the Yellow river basin, China.
- Guo, X., et al. 2017. The impact of land surface temperature on vegetation growth and health in urban areas: A case study of the Pearl river delta, China. Int. J. Appl. Earth Observation Geoinfor-mation. 61: 22-31.
- Hook, S. J., et al. 2019. Land surface temperature variation and monitoring implications for large lakes. Remote Sensing Env., 232: 111257.
