Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV
Fire severity is a key factor for management of post-fire vegetation regeneration strategies because it quantifies the impact of fire, describing the amount of damage. Several indices have been developed for estimation of fire severity based on terrestrial observation by satellite imagery. In order...
| 主要な著者: | , , , , , |
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| フォーマット: | info:eu-repo/semantics/article |
| 言語: | English |
| 出版事項: |
MDPI
2020
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| 主題: | |
| オンライン･アクセス: | http://hdl.handle.net/10835/7572 |
| _version_ | 1789406392777441280 |
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| author | Carvajal Ramírez, Fernando Marques da Silva, José Rafael Agüera Vega, Francisco Martínez Carricondo, Patricio Jesús Serrano, João Moral, Francisco Jesús |
| author_facet | Carvajal Ramírez, Fernando Marques da Silva, José Rafael Agüera Vega, Francisco Martínez Carricondo, Patricio Jesús Serrano, João Moral, Francisco Jesús |
| author_sort | Carvajal Ramírez, Fernando |
| collection | DSpace |
| description | Fire severity is a key factor for management of post-fire vegetation regeneration strategies because it quantifies the impact of fire, describing the amount of damage. Several indices have been developed for estimation of fire severity based on terrestrial observation by satellite imagery. In order to avoid the implicit limitations of this kind of data, this work employed an Unmanned Aerial Vehicle (UAV) carrying a high-resolution multispectral sensor including green, red, near-infrared, and red edge bands. Flights were carried out pre- and post-controlled fire in a Mediterranean forest. The products obtained from the UAV-photogrammetric projects based on the Structure from Motion (SfM) algorithm were a Digital Surface Model (DSM) and multispectral images orthorectified in both periods and co-registered in the same absolute coordinate system to find the temporal differences (d) between pre- and post-fire values of the Excess Green Index (EGI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Red Edge (NDRE) index. The differences of indices (dEGI, dNDVI, and dNDRE) were reclassified into fire severity classes, which were compared with the reference data identified through the in situ fire damage location and Artificial Neural Network classification. Applying an error matrix analysis to the three difference of indices, the overall Kappa accuracies of the severity maps were 0.411, 0.563, and 0.211 and the Cramer’s Value statistics were 0.411, 0.582, and 0.269 for dEGI, dNDVI, and dNDRE, respectively. The chi-square test, used to compare the average of each severity class, determined that there were no significant differences between the three severity maps, with a 95% confidence level. It was concluded that dNDVI was the index that best estimated the fire severity according to the UAV flight conditions and sensor specifications. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-7572 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-75722023-04-12T19:29:40Z Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV Carvajal Ramírez, Fernando Marques da Silva, José Rafael Agüera Vega, Francisco Martínez Carricondo, Patricio Jesús Serrano, João Moral, Francisco Jesús Fire Severity UAV Multispectral Imagery Fire severity is a key factor for management of post-fire vegetation regeneration strategies because it quantifies the impact of fire, describing the amount of damage. Several indices have been developed for estimation of fire severity based on terrestrial observation by satellite imagery. In order to avoid the implicit limitations of this kind of data, this work employed an Unmanned Aerial Vehicle (UAV) carrying a high-resolution multispectral sensor including green, red, near-infrared, and red edge bands. Flights were carried out pre- and post-controlled fire in a Mediterranean forest. The products obtained from the UAV-photogrammetric projects based on the Structure from Motion (SfM) algorithm were a Digital Surface Model (DSM) and multispectral images orthorectified in both periods and co-registered in the same absolute coordinate system to find the temporal differences (d) between pre- and post-fire values of the Excess Green Index (EGI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Red Edge (NDRE) index. The differences of indices (dEGI, dNDVI, and dNDRE) were reclassified into fire severity classes, which were compared with the reference data identified through the in situ fire damage location and Artificial Neural Network classification. Applying an error matrix analysis to the three difference of indices, the overall Kappa accuracies of the severity maps were 0.411, 0.563, and 0.211 and the Cramer’s Value statistics were 0.411, 0.582, and 0.269 for dEGI, dNDVI, and dNDRE, respectively. The chi-square test, used to compare the average of each severity class, determined that there were no significant differences between the three severity maps, with a 95% confidence level. It was concluded that dNDVI was the index that best estimated the fire severity according to the UAV flight conditions and sensor specifications. 2020-01-17T13:03:21Z 2020-01-17T13:03:21Z 2019-04-26 info:eu-repo/semantics/article 2072-4292 http://hdl.handle.net/10835/7572 en https://www.mdpi.com/2072-4292/11/9/993 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | Fire Severity UAV Multispectral Imagery Carvajal Ramírez, Fernando Marques da Silva, José Rafael Agüera Vega, Francisco Martínez Carricondo, Patricio Jesús Serrano, João Moral, Francisco Jesús Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV |
| title | Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV |
| title_full | Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV |
| title_fullStr | Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV |
| title_full_unstemmed | Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV |
| title_short | Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV |
| title_sort | evaluation of fire severity indices based on pre- and post-fire multispectral imagery sensed from uav |
| topic | Fire Severity UAV Multispectral Imagery |
| url | http://hdl.handle.net/10835/7572 |
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