Fireground Recognition and Spatio-Temporal Scalability Research Based on ICESat-2/ATLAS Vertical Structure Parameters
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. ICESat-2 Data Products
2.2.2. Sentinel-2 MSI
2.3. Determine the Burned in Woodland Based on ATLAS Photon Spots
2.4. ATLAS Fireground Recognition Classifier Construction
2.4.1. RF Algorithm Classifier
2.4.2. XGBoost Algorithm Classifier
2.4.3. Accuracy Assessment Criteria
3. Results
3.1. Analysis of the Contribution of ATLAS Parameters
3.2. Analysis of Recognition Capabilities of ATLAS
3.3. Analysis of the Spatial and Temporal Scalability of ATLAS Recognition of Firegrounds
4. Discussion
4.1. Effect of Different Burn Levels on Classification Accuracy
4.2. Limitations and Prospects
5. Conclusions
- (1)
- The parameter segment_landcover contributed the most to both the RF and XGBoost classifiers, with values of 7.8% and 21.2%, respectively, in the satellite-based photon-counting radar ATLAS data. The parameters associated with the canopy type, such as the canopy photon count, canopy openness, 95% quantile canopy height, etc., had relatively high contribution rates in both classifiers. The contributions of the parameters associated with the topography and overall type were relatively low. The use of ICESat-2/ATLAS vertical structure parameters to identify firegrounds has some feasibility.
- (2)
- The overall recognition accuracy of the XGBoost classifier, which is based on the vertical structure parameters of ATLAS in Shangri-La City via the 10-fold cross-validation method, is slightly better than that of the RF classifier. Both classifiers showed better potential with all evaluation metrics greater than 0.8 when tested with independent test samples, and both classifiers can be used for fire recognition with better results. The misclassified spots are mainly concentrated in gully areas with complex terrain, and the terrain may have some influence on the photon counting radar.
- (3)
- The RF classifier based on ATLAS vertical structure parameters is generally better than the XGBoost classifier for different spatial and temporal firegrounds, and the recognition effect is more stable and excellent. For areas with moderate fire severity, a combination of both classifiers can be used to complement each other for better recognition. The XGBoost classifier significantly outperforms the RF classifier for lighter areas.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Type | Name | Description | Path |
---|---|---|---|
Total | dem_h | Reference DEM elevation. | */dem_h |
h_dif_ref | Difference between h_te_median and dem_h. | */h_dif_ref | |
latitude | Latitude of each received photon. | */lat_ph | |
longitude | Longitude of each received photon. | */lon_ph | |
n_seg_ph | Number of photons within each land segment. | */n_seg_ph | |
segment_landcover | Reference landcover for segment derived from best global landcover product available. | */segment_landcover | |
asr | Apparent surface reflectance | */asr | |
Canopy | canopy_openness | STD of relative heights for all photons classified as canopy photons within the segment to provide inference of canopy openness. | */canopy/canopy_openness |
canopy_h_metrics | Relative canopy height metrics calculated at the following percentiles: 10 to 95% interval 5 stpe. | */canopy/canopy_h_metrics | |
centroid_height | Absolute height above reference ellipsoid associated with the centroid of all signal photons. | */canopy/centroid_height | |
h_canopy | 98% height of all the individual relative canopy heights. | */canopy/h_canopy | |
h_canopy_quad | Quadratic mean canopy height. | */canopy/h_canopy_quad | |
h_dif_canopy | Difference between h_canopy and canopy_h_metrics. | */canopy/h_dif_canopy | |
h_max_canopy | Maximum of individual relative canopy heights within segment. | */canopy/h_max_canopy | |
h_max_canopy_abs | Maximum of individual absolute canopy heights within segment. | */canopy/h_max_canopy_abs | |
h_mean_canopy | Mean of individual relative canopy heights within segment. | */canopy/h_mean_canopy | |
h_mean_canopy_abs | Mean of individual absolute canopy heights within segment. | */canopy/h_mean_canopy_abs | |
h_min_canopy | Minimum of individual relative canopy heights within segment. | */canopy/h_min_canopy | |
h_min_canopy_abs | Minimum of individual absolute canopy heights within segment. | */canopy/h_min_canopy_abs | |
n_ca_photons | Number of canopy photons within the segment. | */canopy/n_ca_photons | |
n_toc_photons | Number of top and canopy photons within segment. | */canopy/n_toc_photons | |
photon_rate_can | Photon rate of canopy photons within each segment. | */canopy/photon_rate_can | |
toc_roughness | STD of relative heights of all photons classified as top of canopy within the segment. | */canopy/toc_roughness | |
Terrain | n_te_photons | Number of classed terrain photons in the segment. | */terrain/n_te_photons |
photon_rate_te | Calculated photon rate for ground photons within each segment. | */terrain/photon_rate_te | |
terrain_slope | Slope of terrain within segment. | */terrain/terrain_slope |
Sentinel-2 MSI Bands | Resolution | Center Wavelength | Lower–Upper |
---|---|---|---|
B2 | 10 m | 496.6 nm (S2A)/492.1 nm (S2B) | 0.439–0.535 nm |
B3 | 10 m | 560.0 nm (S2A)/559.0 nm (S2B) | 0.537–0.582 nm |
B4 | 10 m | 664.5 nm (S2A)/665.0 nm (S2B) | 0.646–0.685 nm |
B8 | 10 m | 835.1 nm (S2A)/833.0 nm (S2B) | 0.767–0.908 nm |
B11 | 20 m | 1613.7 nm (S2A)/1610.4 nm (S2B) | 1.568–1.658 nm |
Model | Accuracy | Recall | Precision | F1-Measure | |
---|---|---|---|---|---|
RF | Training sets | 1.000 | 1.000 | 1.000 | 1.000 |
Cross-validation sets | 0.728 | 0.728 | 0.773 | 0.712 | |
Test sets | 0.833 | 0.833 | 0.844 | 0.821 | |
XGBoost | Training sets | 1.000 | 1.000 | 1.000 | 1.000 |
Cross-validation sets | 0.830 | 0.830 | 0.867 | 0.827 | |
Test sets | 0.812 | 0.812 | 0.832 | 0.819 |
Model and Area | Accuracy | Recall | Precision | F1-Measure | Error | |
---|---|---|---|---|---|---|
RF | Area2 | 0.719 | 0.719 | 1.000 | 0.836 | 0.281 |
Area3 | 0.313 | 0.313 | 1.000 | 0.476 | 0.688 | |
Area4 | 0.714 | 0.714 | 1.000 | 0.833 | 0.286 | |
Area5 | 0.400 | 0.400 | 1.000 | 0.571 | 0.600 | |
XGBoost | Area2 | 0.391 | 0.391 | 1.000 | 0.562 | 0.609 |
Area3 | 0.625 | 0.625 | 1.000 | 0.769 | 0.375 | |
Area4 | 0.143 | 0.143 | 1.000 | 0.250 | 0.857 | |
Area5 | 0.240 | 0.240 | 1.000 | 0.387 | 0.760 |
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Cao, G.; Wei, X.; Ye, J. Fireground Recognition and Spatio-Temporal Scalability Research Based on ICESat-2/ATLAS Vertical Structure Parameters. Forests 2024, 15, 1597. https://doi.org/10.3390/f15091597
Cao G, Wei X, Ye J. Fireground Recognition and Spatio-Temporal Scalability Research Based on ICESat-2/ATLAS Vertical Structure Parameters. Forests. 2024; 15(9):1597. https://doi.org/10.3390/f15091597
Chicago/Turabian StyleCao, Guojun, Xiaoyan Wei, and Jiangxia Ye. 2024. "Fireground Recognition and Spatio-Temporal Scalability Research Based on ICESat-2/ATLAS Vertical Structure Parameters" Forests 15, no. 9: 1597. https://doi.org/10.3390/f15091597