Monitoring the dynamic changes in vegetation cover using spatio-temporal remote sensing data from 1984 to 2020

Sajjad Hussain, Shujing Qin*, Wajid Nasim, Muhammad Adnan Bukhari, Muhammad Mubeen, Shah Fahad, Ali Raza, Hazem Ghassan Abdo, Aqil Tariq*, B. G. Mousa, Faisal Mumtaz, Muhammad Aslam

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
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Abstract

Anthropogenic activities and natural climate changes are the central driving forces of global ecosystems and agriculture changes. Climate changes, such as rainfall and temperature changes, have had the greatest impact on different types of plant production around the world. In the present study, we investigated the spatiotemporal variation of major crops (cotton, rice, wheat, and sugarcane) in the District Vehari, Pakistan, from 1984 to 2020 using remote sensing (RS) technology. The crop identification was pre-processed in ArcGIS software based on Landsat images. After pre-processing, supervised classification was used, which explains the maximum likelihood classification (MLC) to identify the vegetation changes. Our results showed that in the study area cultivated areas under wheat and cotton decreased by almost 5.4% and 9.1% from 1984 to 2020, respectively. Vegetated areas have maximum values of NDVI (>0.4), and built-up areas showed fewer NDVI values (0 to 0.2) in the District Vehari. During the Rabi season, the temperature was increased from 19.93 °C to 21.17 °C. The average temperature was calculated at 34.28 °C to 35.54 °C during the Kharif season in the District Vehari. Our results showed that temperature negatively affects sugarcane, rice, and cotton crops during the Rabi season, and precipitation positively affects sugarcane, rice, and cotton crops during the Kharif season in the study area. Accurate and timely assessment of crop estimation and relation to climate change can give very useful information for decision-makers, governments, and planners in formulating policies regarding crop management and improving agriculture yields.

Original languageEnglish
Article number1609
Number of pages18
JournalAtmosphere
Volume13
Issue number10
DOIs
Publication statusPublished - 30 Sep 2022

Keywords

  • climate change
  • GIS
  • normalized difference vegetation index
  • remote sensing
  • Southern Punjab

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