Localized Crop Classification by NDVI Time Series Analysis of Remote Sensing Satellite Data; Applications for Mechanization Strategy and Integrated Resource Management

• Published in: AgriEngineering 2024-09, Vol.6 (3), p.2429-2444
• Main Authors: Md-Tahir, Hafiz, Mahmood, Hafiz Sultan, Husain, Muzammil, Khalil, Ayesha, Shoaib, Muhammad, Ali, Mahmood, Ali, Muhammad Mohsin, Tasawar, Muhammad, Khan, Yasir Ali, Awan, Usman Khalid, Cheema, Muhammad Jehanzeb Masud
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural commodities; agricultural mechanization; Agricultural practices; Agricultural production; Agriculture; Algorithms; Artificial intelligence; Availability; Classification; Climate change; Cotton; Crop identification; Crop rotation; Cropping systems; Crops; Data analysis; Data collection; Decision making; Deep learning; Environmental management; Error analysis; Farmers; Farms; Geographic information systems; geographical information system; Land cover; Land use; Land use planning; Landsat; Landsat satellites; LULC classification; Machine learning; Mechanization; Natural resources; Normalized difference vegetative index; Pattern analysis; Phenology; Productivity; Regional planning; Remote sensing; Resource management; Satellite imagery; Spectroradiometers; Strategic planning; Sustainable development; Thematic Mappers (LANDSAT); Time series; Vegetation; vegetation index; Water resources; Wheat
• ISSN: 2624-7402; 2624-7402
• DOI: 10.3390/agriengineering6030142

In data-scarce regions, prudent planning and precise decision-making for sustainable development, especially in agriculture, remain challenging due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use and land cover (LULC) classes and crop identification. Applying remote sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resource management. The study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with an unsupervised classification technique to obtain LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as key inputs for agricultural mechanization planning and resource management. The accuracy of the LULC map was 78%, as assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced processing techniques, that would make it more useful and applicable for regional agriculture and environmental management.