Abstract
Satellite remote sensing is an established geospatial technique that provides a precise method of land use and land cover evaluation at a global, regional, or local scale. The current study aims to assess the land cover and land use change of an arid region in Southern Punjab of Pakistan, i.e., Bahawalpur District. Landsat data is more consistent and pragmatic in providing historical imageries of the earth’s surface for conducting such studies for the last 50 years. Satellite data employed in current research includes Landsat 5 TM for historical years of 1987, 1998, and 2009 and Landsat 8 OLI for the t year of 2020. Maximum likelihood supervised classification and change detection techniques are applied to calculate area percent change, annual dynamic rate of change in specific time intervals. Remote sensing indices normalized difference vegetation index and normalized difference bareness index are also employed techniques for change detection procedures in this study. Results show that agriculture area expanded at the rate of 2.3% and barren land reduced at dynamic rate of − 0.5% from 1987 to 2020. Linear regression analysis reveals a strong correlation coefficient R2 of 0.99 between population growth rate and agriculture growth rate with 0.03 level of significance, 0.58 between agriculture growth rate and barren land reduction rate. Vegetation and bareness index-based change detection showed a 13.4% significant decline in bareness and 16.8% increase in vegetation mostly in southern side of the district towards lesser Cholistan. Linear regression between vegetation and bareness index also showed significant correlation R2 of 0.83 in 1987 and 0.91 in 2020. Significant land use change in this arid region is deteriorating the natural environment and arid ecosystem of the study area. Study provides a good example for local stakeholders to identify the more vulnerable parts of land use change in the district for better and sustainable land use planning.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdullah M (2021) Cholistan desert: a forgotten unique home of biodiversity. The Climax 6(1):2–4
Ahmed N, Mahmood A, Tahir SS, Bano A, Malik RN, Hassan S, Ashraf A (2014) Ethnomedicinal knowledge and relative importance of indigenous medicinal plants of Cholistan desert Punjab Province Pakistan. J Ethnopharmacol 155(2):1263–1275. https://doi.org/10.1016/j.jep.2014.07.007
Alam A, Bhat MS, Maheen M (2020) Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley. GeoJournal 85(6):1529–1543. https://doi.org/10.1007/s10708-019-10037-x
Alawamy JS, Balasundram SK, Hanif AHM, Sung CTB (2020) Detecting and analyzing land use and land cover changes in the region of Al-Jabal Al-Akhdar, Libya using time-series Landsat data from 1985 to 2017. Sustainability 12(11). https://www.mdpi.com/2071-1050/12/11/4490
Arshad A, Zhang W, Zaman MA, Dilawar A, Sajid Z (2019) Monitoring the impacts of spatio-temporal land-use changes on the regional climate of city Faisalabad. Pakistan Annals of GIS 25(1):57–70. https://doi.org/10.1080/19475683.2018.1543205
Arshad S, Khan AA, Butt I, Fatima M (2019) An appraisal of population growth rate through ESDA in Bahawalpur District Pakistan. Sindh Univ Res J (Science Series) 51(4):733–740
Ayele GT, Tebeje AK, Demissie SS, Belete MA, Jemberrie MA, Teshome WM, Mengistu DT, Teshale EZ (2018) Time series land cover mapping and change detection analysis using geographic information system and remote sensing, Northern Ethiopia. Air Soil Water Research 11:1–18. https://doi.org/10.1177/1178622117751603
Baqa MF, Chen F, Lu L, Qureshi S, Tariq A, Wang S, Jing L, Hamza S, Li Q (2021) Monitoring and modeling the patterns and trends of urban growth using urban sprawl matrix and CA-Markov model: a case study of Karachi, Pakistan. Land 10(7): 700. https://www.mdpi.com/2073-445X/10/7/700
Bhattacharjee S, Islam MT, Kabir ME, Kabir MM (2021) Land-use and land-cover change detection in a north-eastern wetland ecosystem of Bangladesh using remote sensing and GIS techniques. Earth Syst Environ 5(2):319–340. https://doi.org/10.1007/s41748-021-00228-3
Bruce CM, Hilbert DW (2006) Pre-processing methodology for application to Landsat TM/ETM+ imagery of the wet tropics. http://rainforest-crc.jcu.edu.au/publications/landsat_preprocessing.pdf. Accessed 10 Dec 2021
Bullock EL, Healey SP, Yang Z, Oduor P, Gorelick N, Omondi S, Ouko E, Cohen WB (2021) Three decades of land cover change in East Africa. Land 10(2):150. https://doi.org/10.3390/land10020150
Chughtai AH, Abbasi H, Karas IR (2021) A review on change detection method and accuracy assessment for land use land cover. Remote Sens Appl: Soc Environ 22:100482. https://doi.org/10.1016/j.rsase.2021.100482
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46. https://doi.org/10.1016/0034-4257(91)90048-B
Dangulla M, Manaf LA, Mohammad FR (2020) Spatio-temporal analysis of landuse/land cover dynamics in Sokoto metropolis using multi temporal satellite data and land change modeller. Indones J Geogr 52(3):306–316. https://doi.org/10.22146/ijg.46615
Du P, Liu P, Xia J, Feng L, Liu S, Tan K, Cheng L (2014) Remote sensing image interpretation for urban environment analysis: methods, system and examples. Remote Sensing 6(10):9458–9474. https://doi.org/10.3390/rs6109458
Duraisamy V, Bendapudi R, Jadhav A (2018) Identifying hotspots in land use land cover change and the drivers in a semi-arid region of India. Environ Monit Assess 190(9):535. https://doi.org/10.1007/s10661-018-6919-5
Fichera CR, Modica G, Pollino M (2012) Land cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics. Eur J Remote Sens 45(1):1–18. https://doi.org/10.5721/EuJRS20124501
Fonji SF, Taff GN (2010) Using satellite data to monitor land-use land-cover change in north-eastern Latvia. Springerplus 1:1–15. https://doi.org/10.1186/2193-1801-3-61
Tanveer A, Rizvi S, Ali A (2010) Bahawalpur district profile: pre investment study-2013 Government of Punjab, Pakistan. https://fdocuments.in/document/bahawalpur-district-profile-2013.html?page=1. Accessed 10 Dec 2021
Hadeel AS, Jabbar MT, Chen X (2010) Environmental change monitoring in the arid and semi-arid regions: a case study Al-Basrah Province, Iraq. Environ Monit Assess 167:371–385. https://doi.org/10.1007/s10661-009-1056-9
Haregeweyn N, Fikadub G, Tsunekawa A, Tsubo M (2012) The dynamics of urban expansion and its impacts on land use/land cover change and small-scale farmers living near the urban fringe: a case study of Bahir Dar, Ethiopia. Landsc Urban Plan 106:149–157. https://doi.org/10.1016/j.landurbplan.2012.02.016
Hassan Z, Shabbir R, Ahmad SS, Malik AH, Aziz N, Butt A, Erum S (2016) Dynamics of land use and land cover change (LULCC) using geospatial techniques: a case study of Islamabad Pakistan. Springerplus 5(1):812. https://doi.org/10.1186/s40064-016-2414-z
He C, Zhang J, Liu Z, Huang Q (2022) Characteristics and progress of land use/cover change research during 1990–2018. J Geog Sci 32(3):537–559. https://doi.org/10.1007/s11442-022-1960-2
How Jin Aik D, Ismail MH, Muharam FM (2020) Land use/land cover changes and the relationship with land surface temperature using Landsat and MODIS imageries in Cameron Highlands, Malaysia. Land 9(10):372. https://www.mdpi.com/2073-445X/9/10/372. Accessed 10 Dec 2021
Hussain S, Mubeen M, Akram W, Ahmad A, Habib-ur-Rahman M, Ghaffar A, Amin A, Awais M, Farid HU, Farooq A, Nasim W (2019) Study of land cover/land use changes using RS and GIS: a case study of Multan District Pakistan. Environ Monit Assess 192(1):2. https://doi.org/10.1007/s10661-019-7959-1
Hussain S, Mubeen M, Ahmad A, Akram W, Hammad HM, Ali M, Masood N, Amin A, Farid HU, Sultana SR, Fahad S, Wang D, Nasim W (2020) Using GIS tools to detect the land use/land cover changes during forty years in Lodhran District of Pakistan. Environ Sci Pollut Res 27(32):39676–39692. https://doi.org/10.1007/s11356-019-06072-3
Hussain S, Karuppannan S (2021) Land use/land cover changes and their impact on land surface temperature using remote sensing technique in district Khanewal, Punjab Pakistan. Geology, Ecology, and Landscapes: 1-13 https://doi.org/10.1080/24749508.2021.1923272
Imagine E (2008) Erdas Imagine. In: Version. https://www.hexagongeospatial.com/products/power-portfolio/erdas-imagine. Accessed 10 Dec 2021
Iqbal MF, Khan IA (2014) Spatiotemporal land use land cover change analysis and erosion risk mapping of Azad Jammu and Kashmir, Pakistan. Egypt J Remote Sens Space Sci 17:209–229
Jensen JR (1996) Introductory digital image processing: a remote sensing perspective. Prentice Hall
Jingan S, Jiupai N, Chaofu W, Deti X (2005) Land use change and its corresponding ecological responses: a review. J Geog Sci 15(2005):305–328. https://doi.org/10.1007/BF02837519
Josephson AL, Ricker-Gilbert J, Florax RJGM (2014) How does population density influence agricultural intensification and productivity? Evidence from Ethiopia. Food Policy 48:142–152. https://doi.org/10.1016/j.foodpol.2014.03.004
Kuma HG, Feyessa FF, Demissie TA (2022) Land-use/land-cover changes and implications in Southern Ethiopia: evidence from remote sensing and informants. Heliyon 8(3):e09071. https://doi.org/10.1016/j.heliyon.2022.e09071
Kumar S, Arya S, Jain K (2021) A SWIR-based vegetation index for change detection in land cover using multi-temporal Landsat satellite dataset. Int J Inf Technol. https://doi.org/10.1007/s41870-021-00797-6
Li H, Wang C, Zhong C, Su A, Xiong C, Wang J, Liu J (2017) Mapping urban bare land automatically from Landsat imagery with a simple index. Remote Sensing 9(3): 249. https://www.mdpi.com/2072-4292/9/3/249
Liang F, Zhang X, Li H, Yu H, Lin Q, Jiang M, Zhang J (2022) Land use classification based on maximum likelihood method. In: Peng JS(ed) Advances in Intelligent Data Analysis and Applications, Singapore. https://doi.org/10.1007/978-981-16-5036-9_15
Lin L, Hao Z, Post CJ, Mikhailova EA, Yu K, Yang L, Liu J (2020) Monitoring land cover change on a rapidly urbanizing island using Google Earth engine. Applied Sciences 10(20): 7336. https://www.mdpi.com/2076-3417/10/20/7336
Liu C, Li W, Zhu G, Zhou H, Yan H, Xue P (2020) Land use/land cover changes and their driving factors in the Northeastern Tibetan Plateau based on geographical detectors and Google Earth engine: a case study in Gannan Prefecture. Remote Sens 12:1–18. https://doi.org/10.3390/rs12193139
Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823–870. https://doi.org/10.1080/01431160600746456
Majeed M, Tariq A, Anwar MM, Khan AM, Arshad F, Mumtaz F, Farhan M, Zhang L, Zafar A, Aziz M, Abbasi S, Rahman G, Hussain S, Waheed M, Fatima K, Shaukat S (2021) Monitoring of land use–land cover change and potential causal factors of climate change in Jhelum District, Punjab, Pakistan, through GIS and multi-temporal satellite data. Land 10(10): 1026. https://www.mdpi.com/2073-445X/10/10/1026
Malik SM, Ali A (2017) Sustainability of subsistence livelihoods of agro-pastoralists in changing socioeconomic environment of Cholistan Desert-Pakistan. Pakistan J Commer Soc Sci 11(3):1100–1133. http://hdl.handle.net/10419/188331. Accessed 10 Dec 2021
Meyer WB, Turner BL (1994) Changes in land use and land cover: a global perspective. Cambridge Univ, Press
Mohajane M, Essahlaoui A, Oudija F, Hafyani ME, Hmaidi AE, Ouali AE, Randazzo G, Teodoro AC (2018) Land use/land cover (LULC) using Landsat data series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments 5(12):131. https://www.mdpi.com/2076-3298/5/12/131. Accessed 10 Dec 2021
Morawitz DF, Blewett TM, Cohen A, Alberti M (2006) Using NDVI to assess vegetative land cover change in Central Puget Sound. Environ Monit Assess 114(1):85–106. https://doi.org/10.1007/s10661-006-1679-z
Mumtaz F, Tao Y, de Leeuw G, Zhao L, Fan C, Elnashar A, Bashir B, Wang G, Li L, Naeem S, Arshad A, Wang D (2020) Modeling spatio-temporal land transformation and its associated impacts on land surface temperature (LST). Remote Sensing 12(18): 2987. https://www.mdpi.com/2072-4292/12/18/2987
Nguyen CT, Chidthaisong A, Kieu Diem P, Huo L-Z (2021) A modified bare soil index to identify bare land features during agricultural fallow-period in Southeast Asia using Landsat 8. Land 10(3): 231. https://www.mdpi.com/2073-445X/10/3/231
PCO (1998) Fifth population and housing census of 1998 in district census report: Bahawalpur. Pakistan Census Organization
PCRWR (2020) Pakistan Council of Research in Water Resources Annual Report 2019–20. http://pcrwr.gov.pk/wp-content/uploads/2020/Annual-Reports/Annual%20Report%202019-20.pdf. Accessed 10 Dec 2021
Phiri D, Morgenroth J (2017) Developments in Landsat land cover classification methods: a review. Remote Sensing 9(9): 967. https://www.mdpi.com/2072-4292/9/9/967
Rahman MTU, Tabassum F, Rasheduzzaman M, Saba H, Sarkar L, Ferdous J, Uddin SZ, Zahedul Islam AZM (2017) Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh. Environ Monit Assess 189(11):565. https://doi.org/10.1007/s10661-017-6272-0
Ricker-Gilbert J, Jumbe C, Chamberlin J (2014) How does population density influence agricultural intensification and productivity? Evidence from Malawi. Food Policy 48:114–128. https://doi.org/10.1016/j.foodpol.2014.02.006
Rimal B, Rijal S, Kunwar R (2020) Comparing support vector machines and maximum likelihood classifiers for mapping of urbanization. J Indian Soc Remote Sens 48(1):71–79. https://doi.org/10.1007/s12524-019-01056-9
Samie A, Abbas A, Azeem MM, Hamid S, Iqbal MA, Hasan SS, Deng X (2020) Examining the impacts of future land use/land cover changes on climate in Punjab Province, Pakistan: implications for environmental sustainability and economic growth. Environ Sci Pollut Res 27(20):25415–25433. https://doi.org/10.1007/s11356-020-08984-x
Sharma R, Nguyen TT, Grote U (2018) Changing consumption patterns—drivers and the environmental impact. Sustainability 10(11): 4190. https://www.mdpi.com/2071-1050/10/11/4190
Sisodia PS, Tiwari V, Kumar A (2014) Analysis of supervised maximum likelihood classification for remote sensing image. International conference on recent advances and innovations in engineering (ICRAIE-2014). https://doi.org/10.1109/ICRAIE.2014.6909319
Smits PC, Dellepiane SG, Schowengerdt RA (1999) Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach. Int J Remote Sens 20(8):1461–1486. https://doi.org/10.1080/014311699212560
Tewabe D, Fentahun T (2020) Assessing land use and land cover change detection using remote sensing in the Lake Tana Basin, Northwest Ethiopia. Cogent Environmental Science 6(1). https://doi.org/10.1080/23311843.2020.1778998
Tiamgne XT, Kalaba FK, Nyirenda VR (2021) Land use and cover change dynamics in Zambia’s Solwezi copper mining district. Scientific African 14. https://doi.org/10.1016/j.sciaf.2021.e01007
Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150. https://doi.org/10.1016/0034-4257(79)90013-0
Wulder MA, Loveland TR, Roy DP, Crawford CJ, Masek JG, Woodcock CE, Allen RG, Anderson MC, Belward AS, Cohen WB, Dwyer J, Erb A, Gao F, Griffiths P, Helder D, Hermosilla T, Hipple JD, Hostert P, Hughes MJ, Huntington J, Johnson DM, Kennedy R, Kilic A, Li Z, Lymburner L, McCorkel J, Pahlevan N, Scambos TA, Schaaf C, Schott JR, Sheng Y, Storey J, Vermote E, Vogelmann J, White JC, Wynne RH, Zhu Z (2019) Current status of Landsat program, science, and applications. Remote Sens Environ 225:127–147. https://doi.org/10.1016/j.rse.2019.02.015
Xu X, Min X (2013) Quantifying spatiotemporal patterns of urban expansion in China using remote sensing data. Cities 35:104–113. https://doi.org/10.1016/j.cities.2013.05.002
Zhao H, Chen X (2005) (2005) Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. Proceeding IEEE International Geoscience and Remote Sensing Symposium. 29:1666–1668. https://doi.org/10.1109/IGARSS.2005.1526319
Zhou Y, Yang G, Wang S, Wang L, Wang F, Liu X (2014) A new index for mapping built-up and bare land areas from Landsat-8 OLI data. Remote Sensing Letters 5(10):862–871. https://doi.org/10.1080/2150704X.2014.973996
Zimmerman O (2012) The hexagon advantage: the dynamic GIS-introducing the ERDAS IMAGINE spatial modeler. Proceedings GRSG AGM Nov 2012-Monitoring and Managing the Earth’s Resources. https://www.earthdoc.org/content/papers/10.3997/2214-4609-pdb.345.2. Accessed 24 Mar 2022
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Arshad, S., Hasan Kazmi, J., Fatima, M. et al. Change detection of land cover/land use dynamics in arid region of Bahawalpur District, Pakistan. Appl Geomat 14, 387–403 (2022). https://doi.org/10.1007/s12518-022-00441-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12518-022-00441-3