Land Use/Land Cover Change Detection and NDVI Estimation in Pakistan’s Southern Punjab Province

Yongguang Hu (), Ali Raza, Neyha Rubab Syed, Siham Acharki, Ram L. Ray, Sajjad Hussain, Hossein Dehghanisanij, Muhammad Zubair and Ahmed Elbeltagi
Additional contact information
Yongguang Hu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Ali Raza: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Neyha Rubab Syed: School of Energy & Environment, Power Engineering & Engineering Thermophysics, Southeast University, Nanjing 210009, China
Siham Acharki: Department of Earth Sciences, Faculty of Sciences and Techniques, Abdelmalek Essaadi University, Tangier 90000, Morocco
Ram L. Ray: Department of Agriculture, Nutrition and Human Ecology, College of Agriculture and Human Sciences, Prairie View A & M University, Prairie View, TX 77446, USA
Sajjad Hussain: Department of Environmental Sciences, COMSATS University Islamabad, Vehari Campus, Islamabad 61100, Pakistan
Hossein Dehghanisanij: Agricultural Research, Education and Extension Organization, Agricultural Engineering Research Institute, Karaj P.O. Box 31585-845, Alborz, Iran
Muhammad Zubair: School of Transportation, Southeast University, Nanjing 210009, China
Ahmed Elbeltagi: Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt

Sustainability, 2023, vol. 15, issue 4, 1-21

Abstract: Land use/land cover (LULC) changes are among the most significant human-caused global variations affecting the natural environment and ecosystems. Pakistan’s LULC patterns have undergone huge changes since the 1900s, with no clear mitigation plan. This paper aims to determine LULC and normalized difference vegetation index (NDVI) changes as well as their causes in Pakistan’s Southern Punjab province over four different periods (2000, 2007, 2014, and 2021). Landsat-based images of 30 m × 30 m spatial resolution were used to detect LULC changes, while NDVI dynamics were calculated using Modis Product MOD13Q1 (Tiles: h24 v5, h24 v6) at a resolution of 250 m. The iterative self-organizing (ISO) cluster method (object meta-clustering using the minimal distance center approach) was used to quantify the LULC changes in this research because of its straightforward approach that requires minimal human intervention. The accuracy assessment and the Kappa coefficient were calculated to assess the efficacy of results derived from LULC changes. Our findings revealed considerable changes in settlements, forests, and barren land in Southern Punjab. Compared to 2000, while forest cover had reduced by 31.03%, settlement had increased by 14.52% in 2021. Similarly, forest land had rapidly been converted into barren land. For example, barren land had increased by 12.87% in 2021 compared to 2000. The analysis showed that forests were reduced by 31.03%, while settlements and barren land increased by 14.52% and 12.87%, respectively, over the twenty year period in Southern Punjab. The forest area had decreased to 4.36% by 2021. It shows that 31.03% of forest land had been converted to urban land, barren ground, and farmland. Land that was formerly utilized for vegetation had been converted into urban land due to the expansion of infrastructure and the commercial sector in Southern Punjab. Consequently, proper monitoring of LULC changes is required. Furthermore, relevant agencies, governments, and policymakers must focus on land management development. Finally, the current study provides an overall scenario of how LULC trends are evolving over the study region, which aids in land use planning and management.

Keywords: land use/land cover; unsupervised classification; geographic information system; remote sensing; normalized difference vegetation index; Southern Punjab (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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