High-Resolution Mapping of Seasonal Crop Pattern Using Sentinel Imagery in Mountainous Region of Nepal: A Semi-Automatic Approach
Sustainable agricultural management requires knowledge of where and when crops are grown, what they are, and for how long. However, such information is not yet available in Nepal. Remote sensing coupled with farmers’ knowledge offers a solution to fill this gap. In this study, we created a high-reso...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Geomatics |
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| Online Access: | https://www.mdpi.com/2673-7418/3/2/17 |
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| author | Bhogendra Mishra Rupesh Bhandari Krishna Prasad Bhandari Dinesh Mani Bhandari Nirajan Luintel Ashok Dahal Shobha Poudel |
| author_facet | Bhogendra Mishra Rupesh Bhandari Krishna Prasad Bhandari Dinesh Mani Bhandari Nirajan Luintel Ashok Dahal Shobha Poudel |
| author_sort | Bhogendra Mishra |
| collection | DOAJ |
| description | Sustainable agricultural management requires knowledge of where and when crops are grown, what they are, and for how long. However, such information is not yet available in Nepal. Remote sensing coupled with farmers’ knowledge offers a solution to fill this gap. In this study, we created a high-resolution (10 m) seasonal crop map and cropping pattern in a mountainous area of Nepal through a semi-automatic workflow using Sentinel-2 A/B time-series images coupled with farmer knowledge. We identified agricultural areas through iterative self-organizing data clustering of Sentinel imagery and topographic information using a digital elevation model automatically. This agricultural area was analyzed to develop crop calendars and to track seasonal crop dynamics using rule-based methods. Finally, we computed a pixel-level crop-intensity map. In the end our results were compared to ground-truth data collected in the field and published crop calendars, with an overall accuracy of 88% and kappa coefficient of 0.83. We found variations in crop intensity and seasonal crop extension across the study area, with higher intensity in plain areas with irrigation facilities and longer fallow cycles in dry and hilly regions. The semi-automatic workflow was successfully implemented in the heterogeneous topography and is applicable to the diverse topography of the entire country, providing crucial information for mapping and monitoring crops that is very useful for the formulation of strategic agricultural plans and food security in Nepal. |
| first_indexed | 2024-03-11T02:25:19Z |
| format | Article |
| id | doaj.art-5ab9e705b72e45dba0d5fe9020884573 |
| institution | Directory Open Access Journal |
| issn | 2673-7418 |
| language | English |
| last_indexed | 2024-03-11T02:25:19Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geomatics |
| spelling | doaj.art-5ab9e705b72e45dba0d5fe90208845732023-11-18T10:36:14ZengMDPI AGGeomatics2673-74182023-04-013231232710.3390/geomatics3020017High-Resolution Mapping of Seasonal Crop Pattern Using Sentinel Imagery in Mountainous Region of Nepal: A Semi-Automatic ApproachBhogendra Mishra0Rupesh Bhandari1Krishna Prasad Bhandari2Dinesh Mani Bhandari3Nirajan Luintel4Ashok Dahal5Shobha Poudel6Center for Space Science and Geomatics Studies, Institute of Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, NepalCenter for Space Science and Geomatics Studies, Institute of Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, NepalCenter for Space Science and Geomatics Studies, Institute of Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, NepalCenter for Space Science and Geomatics Studies, Institute of Engineering, Pashchimanchal Campus, Tribhuvan University, Pokhara, NepalScience Hub, Kathmandu, NepalITC, the University of Twente, 7522 Enschede, The NetherlandsPolicy Research Institute, Narayanhiti, Kathmandu, NepalSustainable agricultural management requires knowledge of where and when crops are grown, what they are, and for how long. However, such information is not yet available in Nepal. Remote sensing coupled with farmers’ knowledge offers a solution to fill this gap. In this study, we created a high-resolution (10 m) seasonal crop map and cropping pattern in a mountainous area of Nepal through a semi-automatic workflow using Sentinel-2 A/B time-series images coupled with farmer knowledge. We identified agricultural areas through iterative self-organizing data clustering of Sentinel imagery and topographic information using a digital elevation model automatically. This agricultural area was analyzed to develop crop calendars and to track seasonal crop dynamics using rule-based methods. Finally, we computed a pixel-level crop-intensity map. In the end our results were compared to ground-truth data collected in the field and published crop calendars, with an overall accuracy of 88% and kappa coefficient of 0.83. We found variations in crop intensity and seasonal crop extension across the study area, with higher intensity in plain areas with irrigation facilities and longer fallow cycles in dry and hilly regions. The semi-automatic workflow was successfully implemented in the heterogeneous topography and is applicable to the diverse topography of the entire country, providing crucial information for mapping and monitoring crops that is very useful for the formulation of strategic agricultural plans and food security in Nepal.https://www.mdpi.com/2673-7418/3/2/17Sentinel 2crop patterncrop calendarNDVINepal |
| spellingShingle | Bhogendra Mishra Rupesh Bhandari Krishna Prasad Bhandari Dinesh Mani Bhandari Nirajan Luintel Ashok Dahal Shobha Poudel High-Resolution Mapping of Seasonal Crop Pattern Using Sentinel Imagery in Mountainous Region of Nepal: A Semi-Automatic Approach Geomatics Sentinel 2 crop pattern crop calendar NDVI Nepal |
| title | High-Resolution Mapping of Seasonal Crop Pattern Using Sentinel Imagery in Mountainous Region of Nepal: A Semi-Automatic Approach |
| title_full | High-Resolution Mapping of Seasonal Crop Pattern Using Sentinel Imagery in Mountainous Region of Nepal: A Semi-Automatic Approach |
| title_fullStr | High-Resolution Mapping of Seasonal Crop Pattern Using Sentinel Imagery in Mountainous Region of Nepal: A Semi-Automatic Approach |
| title_full_unstemmed | High-Resolution Mapping of Seasonal Crop Pattern Using Sentinel Imagery in Mountainous Region of Nepal: A Semi-Automatic Approach |
| title_short | High-Resolution Mapping of Seasonal Crop Pattern Using Sentinel Imagery in Mountainous Region of Nepal: A Semi-Automatic Approach |
| title_sort | high resolution mapping of seasonal crop pattern using sentinel imagery in mountainous region of nepal a semi automatic approach |
| topic | Sentinel 2 crop pattern crop calendar NDVI Nepal |
| url | https://www.mdpi.com/2673-7418/3/2/17 |
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