Exploring Burnt Area Delineation with Cross-Resolution Mapping: A Case Study of Very High and Medium-Resolution Data
Remote sensing is essential for mapping and monitoring burnt areas. Integrating Very High-Resolution (VHR) data with medium-resolution datasets like Landsat and deep learning algorithms can enhance mapping accuracy. This study employs two deep learning algorithms, UNET and Gated Recurrent Unit (GRU)...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/3009 |
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| author | Sai Balakavi Vineet Vadrevu Kristofer Lasko |
| author_facet | Sai Balakavi Vineet Vadrevu Kristofer Lasko |
| author_sort | Sai Balakavi |
| collection | DOAJ |
| description | Remote sensing is essential for mapping and monitoring burnt areas. Integrating Very High-Resolution (VHR) data with medium-resolution datasets like Landsat and deep learning algorithms can enhance mapping accuracy. This study employs two deep learning algorithms, UNET and Gated Recurrent Unit (GRU), to classify burnt areas in the Bandipur Forest, Karnataka, India. We explore using VHR imagery with limited samples to train models on Landsat imagery for burnt area delineation. Four models were analyzed: (a) custom UNET with Landsat labels, (b) custom UNET with PlanetScope-labeled data on Landsat, (c) custom UNET-GRU with Landsat labels, and (d) custom UNET-GRU with PlanetScope-labeled data on Landsat. Custom UNET with Landsat labels achieved the best performance, excelling in precision (0.89), accuracy (0.98), and segmentation quality (Mean IOU: 0.65, Dice Coefficient: 0.78). Using PlanetScope labels resulted in slightly lower performance, but its high recall (0.87 for UNET-GRU) demonstrating its potential for identifying positive instances. In the study, we highlight the potential and limitations of integrating VHR with medium-resolution satellite data for burnt area delineation using deep learning. |
| format | Article |
| id | doaj-art-0dd4e64b658a4db4ae95877fde493979 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-0dd4e64b658a4db4ae95877fde4939792025-08-20T03:12:15ZengMDPI AGSensors1424-82202025-05-012510300910.3390/s25103009Exploring Burnt Area Delineation with Cross-Resolution Mapping: A Case Study of Very High and Medium-Resolution DataSai Balakavi0Vineet Vadrevu1Kristofer Lasko2Universities Space Research Association (USRA) Science and Technology Institute, Huntsville, AL 35805, USAJames Clemens High School, Madison, AL 35756, USAGeospatial Research Laboratory, Engineer Research and Development Center, US Army Corp of Engineers, Alexandria, VA 22315, USARemote sensing is essential for mapping and monitoring burnt areas. Integrating Very High-Resolution (VHR) data with medium-resolution datasets like Landsat and deep learning algorithms can enhance mapping accuracy. This study employs two deep learning algorithms, UNET and Gated Recurrent Unit (GRU), to classify burnt areas in the Bandipur Forest, Karnataka, India. We explore using VHR imagery with limited samples to train models on Landsat imagery for burnt area delineation. Four models were analyzed: (a) custom UNET with Landsat labels, (b) custom UNET with PlanetScope-labeled data on Landsat, (c) custom UNET-GRU with Landsat labels, and (d) custom UNET-GRU with PlanetScope-labeled data on Landsat. Custom UNET with Landsat labels achieved the best performance, excelling in precision (0.89), accuracy (0.98), and segmentation quality (Mean IOU: 0.65, Dice Coefficient: 0.78). Using PlanetScope labels resulted in slightly lower performance, but its high recall (0.87 for UNET-GRU) demonstrating its potential for identifying positive instances. In the study, we highlight the potential and limitations of integrating VHR with medium-resolution satellite data for burnt area delineation using deep learning.https://www.mdpi.com/1424-8220/25/10/3009satellite remote sensingvery high resolution (VHR)PlanetScopedeep learningUNETGRU |
| spellingShingle | Sai Balakavi Vineet Vadrevu Kristofer Lasko Exploring Burnt Area Delineation with Cross-Resolution Mapping: A Case Study of Very High and Medium-Resolution Data Sensors satellite remote sensing very high resolution (VHR) PlanetScope deep learning UNET GRU |
| title | Exploring Burnt Area Delineation with Cross-Resolution Mapping: A Case Study of Very High and Medium-Resolution Data |
| title_full | Exploring Burnt Area Delineation with Cross-Resolution Mapping: A Case Study of Very High and Medium-Resolution Data |
| title_fullStr | Exploring Burnt Area Delineation with Cross-Resolution Mapping: A Case Study of Very High and Medium-Resolution Data |
| title_full_unstemmed | Exploring Burnt Area Delineation with Cross-Resolution Mapping: A Case Study of Very High and Medium-Resolution Data |
| title_short | Exploring Burnt Area Delineation with Cross-Resolution Mapping: A Case Study of Very High and Medium-Resolution Data |
| title_sort | exploring burnt area delineation with cross resolution mapping a case study of very high and medium resolution data |
| topic | satellite remote sensing very high resolution (VHR) PlanetScope deep learning UNET GRU |
| url | https://www.mdpi.com/1424-8220/25/10/3009 |
| work_keys_str_mv | AT saibalakavi exploringburntareadelineationwithcrossresolutionmappingacasestudyofveryhighandmediumresolutiondata AT vineetvadrevu exploringburntareadelineationwithcrossresolutionmappingacasestudyofveryhighandmediumresolutiondata AT kristoferlasko exploringburntareadelineationwithcrossresolutionmappingacasestudyofveryhighandmediumresolutiondata |