Land Use Land Cover Classification And Wheat Yield Prediction In The Lower Chenab Canal System Using Remote Sensing And Gis

Aftab Nazeer, Muhammad Mohsin Waqas (), Sikandar Ali, Usman Khalid Awan, MuhammadJehanzeb Masud Cheema and Allah Baksh
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Aftab Nazeer: Department of Agricultural Engineering, BZU, Multan, Pakistan
Muhammad Mohsin Waqas: Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan
Sikandar Ali: Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan
Usman Khalid Awan: International Center for Agricultural Research in the Dry Areas, Egypt
MuhammadJehanzeb Masud Cheema: Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan
Allah Baksh: Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan

Big Data In Agriculture (BDA), 2020, vol. 2, issue 2, 47-51

Abstract: Reliable and timely information regarding area under wheat and its yield prediction can help in better management of the commodity. The remotely sensed data especially in combination with Geographic Information System (GIS) can provide an important and powerful tool for both, land use land cover (LULC) classification and crop yield prediction. The study objectives include LULC classification and wheat yield prediction. The study was conducted for Rabi Season from Nov. 2011 to April 2012, in the command area of three distributaries i.e. Khurrian Wala, Killian Wala and Mungi of Lower Chennai Canal (LCC) system. The Landsat-7 imagery data with spatial resolution of 30 m was used for this study. Physical features were monitored and assessed using Normalized Difference Vegetative Index (NDVI). LULC classification was done for wheat and non-wheat area which shows wheat proportion and area 87.22% and 28867.95 Ha in Khurrian wala, 71.07% and 22423.20 Ha in Killian Wala and 79.18% and 17974.34 Ha in Mungi distributary, respectively. The correlation values between maximum NDVI value and yield data were 0.45, 0.36 and 0.39 for Khurrian Wala, Killian Wala and Mungi distributary, respectively. On the basis of this correlation, average wheat yield was estimated as 3.48 T/Ha, 3.83 T/Ha and 3.80 T/Ha for Khurrian Wala, Killian Wala and Mungi distributary, respectively.

Keywords: LULC; LandSat-7; NDVI; remote sensing and GIS; wheat yield prediction. (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:zib:zbnbda:v:2:y:2020:i:2:p:47-51

DOI: 10.26480/bda.02.2020.47.51

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