Name
Abstract | ||
|---|---|---|
Mediterranean pine forests in Spain experience wildland fire events with different frequencies, intensities, and severities which result in diverse socio-ecological consequences. In order to predict fire severity, spectral indices derived from remotely sensed images have been used extensively. Such spectral indices are usually used in combination with ground sampling to relate detected radiometric changes to actual fire effects. However, the potential of the tridimensional information captured by Airborne Laser Scanners (ALS) to severity mapping has been less explored. With the objective of addressing this question, in this paper, explanatory variables extracted from ALS point clouds are related to field estimations of the Composite Burn Index collected in four fires located in Aragon (Spain). Logistic regression models were developed and statistically tested and validated to map fire severity with up to 85.5% accuracy. The canopy relief ratio and the percentage of all returns above one meter height were the most significant variables and were therefore used to create a continuous map of severity levels. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3390/rs6054240 | REMOTE SENSING |
Keywords | Field | DocType |
fire severity,composite burn index,Airborne Laser Scanners (ALS),Mediterranean pine forest,logistic regression | Remote sensing,Relief ratio,Metre (music),Sampling (statistics),Geology,Point cloud,Logistic regression,Mediterranean climate,Canopy | Journal |
Volume | Issue | Citations |
6 | 5 | 4 |
PageRank | References | Authors |
0.70 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| a l montealegre | 1 | 14 | 1.86 |
| m t lamelas | 2 | 14 | 2.19 |
| Mihai A. Tanase | 3 | 54 | 7.66 |
| Juan de la Riva | 4 | 18 | 3.85 |