PreprintArticleVersion 1This version is not peer-reviewed
Localized Crop Classification by NDVI Time Series Analysis of Remote Sensing Satellite Data; Applications for Mechanization Strategy and Integrated Resources Management
Version 1
: Received: 17 July 2024 / Approved: 18 July 2024 / Online: 18 July 2024 (11:28:24 CEST)
How to cite:
Md-Tahir, H.; Mahmood, H. S.; Husain, M.; Khalil, A.; Shoaib, M.; Ali, M.; Ali, M. M.; Tasawar, M.; Khan, Y. A.; Awan, U. K.; Cheema, M. J. M. Localized Crop Classification by NDVI Time Series Analysis of Remote Sensing Satellite Data; Applications for Mechanization Strategy and Integrated Resources Management. Preprints2024, 2024071467. https://doi.org/10.20944/preprints202407.1467.v1
Md-Tahir, H.; Mahmood, H. S.; Husain, M.; Khalil, A.; Shoaib, M.; Ali, M.; Ali, M. M.; Tasawar, M.; Khan, Y. A.; Awan, U. K.; Cheema, M. J. M. Localized Crop Classification by NDVI Time Series Analysis of Remote Sensing Satellite Data; Applications for Mechanization Strategy and Integrated Resources Management. Preprints 2024, 2024071467. https://doi.org/10.20944/preprints202407.1467.v1
Md-Tahir, H.; Mahmood, H. S.; Husain, M.; Khalil, A.; Shoaib, M.; Ali, M.; Ali, M. M.; Tasawar, M.; Khan, Y. A.; Awan, U. K.; Cheema, M. J. M. Localized Crop Classification by NDVI Time Series Analysis of Remote Sensing Satellite Data; Applications for Mechanization Strategy and Integrated Resources Management. Preprints2024, 2024071467. https://doi.org/10.20944/preprints202407.1467.v1
APA Style
Md-Tahir, H., Mahmood, H. S., Husain, M., Khalil, A., Shoaib, M., Ali, M., Ali, M. M., Tasawar, M., Khan, Y. A., Awan, U. K., & Cheema, M. J. M. (2024). Localized Crop Classification by NDVI Time Series Analysis of Remote Sensing Satellite Data; Applications for Mechanization Strategy and Integrated Resources Management. Preprints. https://doi.org/10.20944/preprints202407.1467.v1
Chicago/Turabian Style
Md-Tahir, H., Usman Khalid Awan and Muhammad Jehanzeb Masud Cheema. 2024 "Localized Crop Classification by NDVI Time Series Analysis of Remote Sensing Satellite Data; Applications for Mechanization Strategy and Integrated Resources Management" Preprints. https://doi.org/10.20944/preprints202407.1467.v1
Abstract
In data-scarce regions, prudent planning and precise decision-making for sustainable development especially in agriculture remains a challenging task due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use 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 resources management. The (this)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 MODIS (Moderate Resolution Imaging Spectroradiometer) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with unsupervised classification technique to get LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as a key input for agricultural mechanization planning and resources management. The accuracy of the LULC map was 78%, 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 techniques of processing, which will make it more useful and applicable for regional agriculture and environmental management.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
