Vol. 116 No. 2 (2022)
Research Papers

Agricultural crop pattern mapping and change analysis at a sub-district level in South-eastern region, Bangladesh using Landsat satellite data from 2010 to 2019

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  • Biswajit Nath,
  • Monir Hossain,
  • Sahadeb Chandra Majumder
Biswajit Nath
Department of Geography and Environmental Studies, Faculty of Biological Sciences, University of Chittagong, Chittagong-4331, Bangladesh
Monir Hossain
Department of Geography and Environmental Studies, Faculty of Biological Sciences, University of Chittagong, Chittagong-4331, Bangladesh
Sahadeb Chandra Majumder
Community Partnerships to Strengthen Sustainable Development Program (Compass), U.S. Forest Service, International Programs, Banani, Dhaka-1213, Bangladesh
DOI: https://doi.org/10.36253/jaeid-11961

Published 2022-12-26

Keywords

  • agricultural crop mapping,
  • winter season,
  • remote sensing,
  • Landsat satellite,
  • South-eastern region,
  • Bangladesh
    • ...More
    Less

How to Cite

Nath, B., Hossain, M. ., & Majumder, S. C. . (2022). Agricultural crop pattern mapping and change analysis at a sub-district level in South-eastern region, Bangladesh using Landsat satellite data from 2010 to 2019. Journal of Agriculture and Environment for International Development (JAEID), 116(2), 5–38. https://doi.org/10.36253/jaeid-11961
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Abstract

The study first time identified and analyzed winter season agricultural crop patterns (ACP) derived from Land use (LU) maps in between 2010 to 2019 of south-eastern regions of Chittagong, Bangladesh. ACP identification was a challenging task in the worldwide research relevant to crop-related studies. To overcome this, we have considered frequently used traditional unsupervised classifier, such as K-means clustering algorithm technique. This has been applied on 30m pixel Landsat satellite reflectance images to identify crop pattern of the study area using the ENVI 5.3 and ArcGIS 10.8 software’s, respectively. Multiple crops with seven classes were identified with the validation of in-situ ground-truth data and Google Earth (GE) images. The overall accuracy and kappa coefficient values were found at 81.96% and 0.79, respectively. The results suggest a significant variation of crop patterns in the study area and in recent time, the area largely dependent on mixed irrigation approach. Moreover, the crop pattern change was observed in the studied period as mixed crop 19% (9282.17 ha), Lentils (Pelon) 24.80% (11594.38 ha), Melon (Bangi) 22.37% (10461.08 ha), Chilis 17.90% (8367.48 ha), Paddy rice, unused land, and other crops, respectively. Among them, Lentils (Pelon) and Melon (Bangi) are identified as two common crops followed by mixed crops category, cultivated in the winter season as it required less irrigation compared to paddy rice area

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