Classification and Distribution Analysis of Crop Types in Mauza Mustafabad Using GIS: Implications for Agricultural Policy and Land Use
GIS-Based Crop Classification in Mauza Mustafabad
Keywords:
Sentinel-2, Crop varieties, sustainable agricultural practices, Remote-sensingAbstract
The study investigates agricultural patterns in Mauza Mustafabad through detailed crop classifying and analyzing using Geographic Information System (GIS) and sentinel- 2 satellite imagery. The study accesses 22 different crop categories across 7433 Khasra employing a supervised classification approach in ArcGIS to generate high-resolution agriculture map, with the primer focus on dominant agricultural landscape of different crops such as wheat, fodder and orchards. The outcome of the present research indicates that 67% of the districts have been allocated to wheat production, which has a potential yield of 258,598 metric tons. Crop density and farm size variability are reflected by the extremely well-defined geographical distribution of crops, which is correlated with the land use pattern. Further, a growing pattern in fish farming has also been addressed, which reflects variations in farming methods that drive financial incentives. The integration of classified satellite imagery with real time Khasra border system provides a reliable spatial framework for assessing land utilization and improving administrative decision making. Overall, this research shows how the integration of GIS based crop mapping and land record system can support evidence based agricultural policy, resource management and food security planning in Pakistan’s precision agriculture context.
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Data Availability Statement
The data supporting the findings of this study are derived from Sentinel-2 satellite imagery (freely available via the Copernicus Open Access Hub) and cadastral Khasra land record data provided by the Punjab Land Records Authority (PLRA). Processed GIS layers, crop classification maps and analytical outputs are available from the corresponding author upon reasonable request, subject to data sharing and administrative permissions.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in AgriPaT are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
