A Burned Area Mapping Algorithm for Sentinel-2 Data Based on Approximate Reasoning and Region Growing
Abstract
:1. Introduction
2. Study Areas and Datasets
2.1. Training and Exportability Sites
2.2. Sentinel-2 Dataset
2.3. Reference Fire Perimeters
3. Methods
- Selection of the input features;
- Definition of the soft constraints (membership function, MF) for each input feature from training data and application to derive partial evidence of burn (MD);
- Selection of OWAs, according to their semantic, for the soft integration;
- Computation of the global degree of evidence of burn for generating seed and growing layers;
- Implementation of the RG algorithm;
- Segmentation of the RG output score to derive burned area maps.
3.1. Separability Analysis
3.2. Definition of the Membership Functions
3.3. OWA Operators for Computing Global Evidence
3.4. Region Growing
3.5. Validation
4. Results
4.1. Separability and Membership Functions
4.2. Partial and Global Evidence of Burn
4.3. RG Burn Score and Validation
4.4. Exportability Results
- Seed layer: OWAAND;
- Seed selection: OWAAND > 0.9;
- Growing layers: OWAAverage, OWAAlmostOR and OWAOR;
- RG algorithm: OWAgrow > 0;
- Burned area mapping: RGscore > 0.
5. Discussion
6. Conclusions
- Customization to S2 imagery for implementing a convergence of evidence approach;
- Exploitation of additional spectral bands available from the S2 MSI instrument;
- Automatic interpretation of input features (e.g., post-fire and Δpost-pre reflectance) through membership functions (MFs) defined from training statistics (partial evidence of burn);
- Tests of OWA operators from AND-like (for seed selection) to OR-like (for growing layer) integration criteria;
- Implementation of OWA global evidence in a region growing (RG) algorithm;
- Accuracy assessment over a wide range of conditions/locations in Southern Europe for the 2017 summer fire season.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
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CLC2012 Class (%) | EMS Fire Damage | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Bare | Crops | Forest | Shrub | Urban | CD | HD | MD | ND | ||
Vesuvius Italy | Site | 6.6 | 38.7 | 36.5 | 10.4 | 7.78 | 0.0 | 12.8 | 3.7 | 2.3 |
BA | 3.4 | 4.8 | 47.2 | 34.4 | 10.26 | 0.0 | 68.2 | 19.7 | 12.0 | |
Leiria Portugal | Site | 0.2 | 15.4 | 43.1 | 40.2 | 1.10 | 0.0 | 8.9 | 5.6 | 2.3 |
BA | - | 5.6 | 66.7 | 27.5 | 0.17 | 0.0 | 53.0 | 33.1 | 13.8 | |
Calar Spain | Site | 13.6 | 8.2 | 61.2 | 17.0 | - | 18.9 | 16.5 | 3.6 | 2.0 |
BA | 0.3 | 2.4 | 76.4 | 20.9 | - | 46.2 | 40.2 | 8.7 | 4.9 | |
Huelva Spain | Site | 0.8 | 11.9 | 39.7 | 46.6 | 0.89 | 0.0 | 15.4 | 1.1 | 0.0 |
BA | 0.2 | 0.1 | 61.2 | 37.7 | 0.71 | 0.0 | 93.1 | 6.7 | 0.2 | |
Zakynthos Greece | Site | 5.1 | 52.5 | 32.5 | 3.7 | 6.17 | - | 3.2 | - | - |
BA | 2.3 | 14.6 | 81.8 | 1.3 | 0.02 | - | 100 | - | - | |
Kalamos Greece | Site | 2.0 | 40.6 | 24.2 | 24.7 | 8.36 | - | 20.0 | - | - |
BA | 3.3 | 25.1 | 33.9 | 36.3 | 1.37 | 100 | - | - |
Study Site | Pre-Fire S2 | Post-Fire S2 | EMS Date | EMS Source (https://emergency.copernicus.eu/mapping/list-of-activations-rapid, access 1 May 2021) |
---|---|---|---|---|
Vesuvius—Italy | 08/04 | 22/07 | 16/07 | EMSR213 |
Leiria—Portugal | 04/06 | 04/07 | 20/06 | EMSR207 |
Calar—Spain | 15/07 | 04/08 | 04/08 | EMSR216 |
Huelva—Spain | 11/06 | 01/07 | 27/06 | EMSR209 |
Zakynthos—Greece | 25/07 | 03/09 | 18/08 | EMSR224 |
Kalamos—Greece | 28/07 | 17/08 | 18/08 | EMSR224 |
S2 Band | M Post fFire | M ΔPost Fire-Pre Fire |
---|---|---|
Green (b3) | 0.577 | 0.027 |
Red (b4) | 0.321 | 0.454 |
RE1 (b5) | 0.879 | 0.214 |
RE2 (b6) | 2.091 | 1.571 |
RE3 (b7) | 1.917 | 1.561 |
NIR (b8) | 1.812 | 1.530 |
SWIR1 (b11) | 0.873 | 0.099 |
SWIR2 (b12) | 0.029 | 1.100 |
S2 Band | Burned | Unburned | MF Parameters | |||||
---|---|---|---|---|---|---|---|---|
10% | 50% | 90% | 10% | 50% | 90% | k | x0 | |
PostRE2 | 0.058 | 0.074 | 0.102 | 0.147 | 0.220 | 0.286 | −125.89 | 0.111 |
PostRE3 | 0.061 | 0.077 | 0.112 | 0.156 | 0.249 | 0.339 | −115.77 | 0.116 |
PostNIR | 0.054 | 0.073 | 0.115 | 0.147 | 0.264 | 0.370 | −123.66 | 0.109 |
ΔRE2 | −0.126 | −0.098 | −0.063 | −0.021 | 0.012 | 0.088 | −120.29 | −0.06 |
ΔRE3 | −0.158 | −0.124 | −0.075 | −0.026 | 0.012 | 0.108 | −93.721 | −0.075 |
ΔNIR | −0.180 | −0.139 | −0.085 | −0.034 | 0.011 | 0.111 | −87.14 | −0.086 |
ΔSWIR2 | 0.025 | 0.063 | 0.114 | −0.030 | 0.0084 | 0.024 | 236.98 | 0.044 |
OWAgrow | oe | ce | dc | RelB (%) | Tot BA RG (ha) | Tot BA REF (ha) |
---|---|---|---|---|---|---|
Average | 0.15 | 0.12 | 0.87 | +1.82 | 1676.39 | 1744.07 |
AlmostOR | 0.10 | 0.20 | 0.85 | −5.81 | 1959.69 | |
OR | 0.09 | 0.22 | 0.84 | −7.70 | 2029.95 |
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Sali, M.; Piaser, E.; Boschetti, M.; Brivio, P.A.; Sona, G.; Bordogna, G.; Stroppiana, D. A Burned Area Mapping Algorithm for Sentinel-2 Data Based on Approximate Reasoning and Region Growing. Remote Sens. 2021, 13, 2214. https://doi.org/10.3390/rs13112214
Sali M, Piaser E, Boschetti M, Brivio PA, Sona G, Bordogna G, Stroppiana D. A Burned Area Mapping Algorithm for Sentinel-2 Data Based on Approximate Reasoning and Region Growing. Remote Sensing. 2021; 13(11):2214. https://doi.org/10.3390/rs13112214
Chicago/Turabian StyleSali, Matteo, Erika Piaser, Mirco Boschetti, Pietro Alessandro Brivio, Giovanna Sona, Gloria Bordogna, and Daniela Stroppiana. 2021. "A Burned Area Mapping Algorithm for Sentinel-2 Data Based on Approximate Reasoning and Region Growing" Remote Sensing 13, no. 11: 2214. https://doi.org/10.3390/rs13112214