Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite
This study presents a methodology that focuses on detecting agricultural burned areas using Sentinel-2 multispectral data at 10 m. We developed a simple, locally adapted, straightforward approach of multi-index threshold to extract post-winter agricultural burned areas at high resolution for 2019-21...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016122001224 |
| _version_ | 1811294072881545216 |
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| author | Monish Vijay Deshpande Dhanyalekshmi Pillai Meha Jain |
| author_facet | Monish Vijay Deshpande Dhanyalekshmi Pillai Meha Jain |
| author_sort | Monish Vijay Deshpande |
| collection | DOAJ |
| description | This study presents a methodology that focuses on detecting agricultural burned areas using Sentinel-2 multispectral data at 10 m. We developed a simple, locally adapted, straightforward approach of multi-index threshold to extract post-winter agricultural burned areas at high resolution for 2019-21. Further, we design a new method for virtual sample collection using already validated fire location data and visual interpretation conditioned using strict selection criteria to improve sample accuracy. Sampling accuracy showed near-perfect agreement with an average Cohen's Kappa value of 0.98. We retrieved monthly ABAs at a resolution of 10 m, and these products were validated against reference burned sample plots identified using visual interpretation of Planet (3m) satellite data. Overall, we found that our method performed well, with an F1 score of 83.63% and low commission (20%) and omission (7%) errors. When compared to global burnt area products, validation accuracy demonstrated an exceptional subpixel scale detecting capability. The study also addresses the complexity of residue burnings and burn signatures’ volatile nature by performing multilevel masking and temporal corrections. • A novel remotely sensed data aided virtual sampling approach to acquire burned and unburned samples. • An integrated method to extract smallholder agricultural burned area using Sentinel-2 multispectral data at a high resolution of 10 m |
| first_indexed | 2024-04-13T05:10:59Z |
| format | Article |
| id | doaj.art-8280665f2bd5441fb1ee047bc9ce76da |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-04-13T05:10:59Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-8280665f2bd5441fb1ee047bc9ce76da2022-12-22T03:01:01ZengElsevierMethodsX2215-01612022-01-019101741Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satelliteMonish Vijay Deshpande0Dhanyalekshmi Pillai1Meha Jain2Indian Institute of Science Education and Research Bhopal (IISERB), Bhopal, India; Max Planck Partner Group (IISERB), Max Planck Society, Munich, GermanyIndian Institute of Science Education and Research Bhopal (IISERB), Bhopal, India; Max Planck Partner Group (IISERB), Max Planck Society, Munich, Germany; Corresponding author.School for Environment and Sustainability, University of Michigan, Ann Arbor USAThis study presents a methodology that focuses on detecting agricultural burned areas using Sentinel-2 multispectral data at 10 m. We developed a simple, locally adapted, straightforward approach of multi-index threshold to extract post-winter agricultural burned areas at high resolution for 2019-21. Further, we design a new method for virtual sample collection using already validated fire location data and visual interpretation conditioned using strict selection criteria to improve sample accuracy. Sampling accuracy showed near-perfect agreement with an average Cohen's Kappa value of 0.98. We retrieved monthly ABAs at a resolution of 10 m, and these products were validated against reference burned sample plots identified using visual interpretation of Planet (3m) satellite data. Overall, we found that our method performed well, with an F1 score of 83.63% and low commission (20%) and omission (7%) errors. When compared to global burnt area products, validation accuracy demonstrated an exceptional subpixel scale detecting capability. The study also addresses the complexity of residue burnings and burn signatures’ volatile nature by performing multilevel masking and temporal corrections. • A novel remotely sensed data aided virtual sampling approach to acquire burned and unburned samples. • An integrated method to extract smallholder agricultural burned area using Sentinel-2 multispectral data at a high resolution of 10 mhttp://www.sciencedirect.com/science/article/pii/S2215016122001224Agricultural burned area mappingSentinel-2Planet dataGoogle Earth engine |
| spellingShingle | Monish Vijay Deshpande Dhanyalekshmi Pillai Meha Jain Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite MethodsX Agricultural burned area mapping Sentinel-2 Planet data Google Earth engine |
| title | Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite |
| title_full | Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite |
| title_fullStr | Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite |
| title_full_unstemmed | Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite |
| title_short | Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite |
| title_sort | agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from sentinel 2 satellite |
| topic | Agricultural burned area mapping Sentinel-2 Planet data Google Earth engine |
| url | http://www.sciencedirect.com/science/article/pii/S2215016122001224 |
| work_keys_str_mv | AT monishvijaydeshpande agriculturalburnedareadetectionusinganintegratedapproachutilizingmultispectralinstrumentbasedfireandvegetationindicesfromsentinel2satellite AT dhanyalekshmipillai agriculturalburnedareadetectionusinganintegratedapproachutilizingmultispectralinstrumentbasedfireandvegetationindicesfromsentinel2satellite AT mehajain agriculturalburnedareadetectionusinganintegratedapproachutilizingmultispectralinstrumentbasedfireandvegetationindicesfromsentinel2satellite |