Seminar III Spring Semester (8 May 2015) Chuphan Chompuchan ( 蘇潘 ) Dept. of Soil and Water Conservation National Chung Hsing University

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1 Seminar III Spring Semester (8 May 2015) Chuphan Chompuchan ( 蘇潘 ) Dept. of Soil and Water Conservation National Chung Hsing University

2 Introduction Normalized Burn Ratio (NBR) Ground measurement: Composite Burn Index (CBI) Assessing the performance of NBR Modified CBI & NBR Relative dnbr (RdNBR) Relativized Burn Ratio (RBR) Geometrically structured Composite Burn Index (GeoCBI) Discussion and conclusion 2

3 Keeley J. E. (2009) Fire intensity Energy released Fire severity or Burn severity Organic matter loss Ecosystem response - Erosion - Vegetation recovery Societal impacts - Loss of life or property - Suppression costs 3

4 Indices from remote sensing Ecological effects P. Morgan et al. (2014) Severity 4

5 P. Morgan et al. (2014) (1 st order effects) (2 rd order effects) 5

6 NBR requires SWIR in the TM/ETM sensors (band 7) NBR is sensitive to the changes in live green vegetation, moisture content, and some soil conditions which may occur after fire NBR takes values ranging between -1 and 1 NBR Band4 Band4 Band7 Band7 NIR NIR SWIR2 SWIR2 Band 4 = near-infrared ( μm) Band 7 = shortwave infrared ( μm) 6

7 dnbr NBR pre NBR fire post fire (x 1,000) Severity levels and example range of dnbr (scaled by 10 3 ) Note: dnbr less than 550, or greater than +1,350 may also occur, but are not considered burned. It maybe caused by misregistration, clouds, or other factors not related to real land cover differences. 7

8 Case study: Camp Gurnsey, Wyoming (USA) in 2006 Pre-fire NBR Pre-fire Post-fire NBR Post-fire 8

9 Extended Assessment (EA) Severity based on post-fire assessment at peak of green of next growing season Forests/shrublands Initial Assessment (IA) Severity based on immediate post-fire assessment Grasslands/shrublands Initial and extended assessments require imagery from different periods 9

Composite Burn Index (CBI) Ground measure of burn severity An index to represent the magnitude of fire effects (magnitude of change from pre-fire conditions) A comparable values regardless of

10 Composite Burn Index (CBI) Ground measure of burn severity An index to represent the magnitude of fire effects (magnitude of change from pre-fire conditions) A comparable values regardless of community type/location/time CBI ranges from 0.0 to 3.0 (unburned to highest severity) Designed for moderateresolution remote sensing applications (i.e. LANDSAT) The vegetation is considered to be composed of 5 strata 10

11 Burn severity High Moderate Low Unburnt CBI Example De Santis A. and E. Chuvieco (2009) B+C - - Brown Brown Green LAI D+E Brown Brown Green Green Green LAI Moderatehigh Sub- Stratum DCH DCH DCH 50% Soil 50% DCH DCH = Dark Charcoal B = Low shrubs C = Tall shrubs D = Intermediate trees E = Big trees Soil

12 C.H. Key (2005) CBI - Spatial Resolution Plot level (30m) Aerial photo (resolution m) LANDSAT NBR (resolution 30 m)

13 13 A.E. Cocke, P.Z.Fule and J. E. Crouse (2005) Case study: the south side of San Francisco Peak (a) Year 2001, (b) Year 2002

14 14 D. P. Roy, L. Boschetti, and S. N. Trigg (2006) Isolines of the NBR The ideal trajectory of changes in NIR & MIR The actual trajectory of changes in NIR & MIR

15 D. P. Roy, L. Boschetti, and S. N. Trigg (2006) 15

16 J. D. Miller and A. E. Thode (2007) Moderate % Canopy Cover NBR = 0.4 NBR = dnbr = dnbr = The absolute change index RdNBR = The relative change index High % Canopy Cover NBR = 0.8 NBR = 0.05 dnbr = 0.75 High % Canopy Cover NBR = 0.8 NBR = 0.4 dnbr =

17 J. D. Miller and A. E. Thode (2007) 17

18 18 S. A. Parks, G. K. Dillon and C. Miller (2014) Relativized Burn Ratio Adding to ensures that the denominator will never be zero

$Chuvieco (2009) 10 spectra were generated the severity scenarios by varying the FCOV (fraction of$ vegetation cover) from 0.

19 19 De Santis A. and E. Chuvieco (2009) 10 spectra were generated the severity scenarios by varying the FCOV (fraction of vegetation cover) from 0.1 to 1 Simulation results show that changes in FCOV have a higher effect as the burn severity level decreases Geometrically structured Composite Burn Index (GeoCBI) was proposed. GeoCBI m n m 1 CBI m n m 1 m FCOV FCOV where m = each vegetation stratum n = the number of strata m m

20 L. Schepers et al. (2014) 20

21 Coniferous forest Shrub land S. Veraverbeke et al. (2011) 21

22 22 De Santis A. and E. Chuvieco (2009) S. Veraverbeke et al. (2011)

23 For case B, GeoCBI would underestimate severity, similarly to the CBI It is

from the upper canopy) From the point of view of short-term post-fire

Case A Case B Case C Green canopies Low FCOV Dark charcoal substratum Green

23 23 For case B, GeoCBI would underestimate severity, similarly to the CBI It is common to most optical remote sensing techniques (observed the reflectance from the upper canopy) From the point of view of short-term post-fire management, case B do not require a rapid intervention (low value of GeoCBI) Case A Case B Case C Green canopies Low FCOV Dark charcoal substratum Green canopies High FCOV Dark charcoal substratum Green canopies High FCOV Unburnt soil substratum

24 C. A. Cansler and D. McKenzie (2014) Case study: the northern Cascade Range of Washington, USA 24

25 C. A. Cansler and D. McKenzie (2014) 25

26 Burn severity assessment Soil burn severity / vegetation severity What imagery? : Moderate resolution remotely sensed imagery What index? : dnbr / RdNBR / RBR / etc. Timing of imagery? : Initial assessment / Extend assessment Field measures : CBI / GeoCBI Opinions Severity level Subjective? Validate process Human error RS imagery conditions Cloud cover/ cloud shadow 26

27 Cansler C. A. and D. McKenzie (2014) How Robust Are Burn Severity Indices When Applied in a New Region? Evaluation of Alternate Field-Based and Remote-Sensing Methods. Remote Sensing, 2: Cocke A.E., P.Z.Fule and J. E. Crouse (2005) Comparison of burn severity assessment using Differenced Normalized Burn Ratio and ground data. International Journal of Wildland Fire, 14: De Santis A. and E. Chuvieco (2009) GeoCBI: A modified version of the Composite Burn Index for the initial assessment of the short-term burn severity from remotely sensed data. Remote Sensing of Environment, 113: Jon E. Keeley (2009) Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire, 18, Key C.H. (2005) Remote sensing sensitivity to fire severity and fire recovery. Proceedings of the 5th International Workshop on Remote Sensing and GIS Applications to Forest Fire Management: Fire Effects Assessment: Miller J. D. and A. E. Thode (2007) Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dnbr). Remote Sensing of Environment, 109: Morgan P., R.E. Keane, G.K. Dillon, T.B. Jain, A.T. Hudak, E.C. Karau, P.G. Sikkink, Z.A. Holden and E.K. Strand (2014) Challenges of assessing fire and burn severity using field measures, remote sensing and modelling. International Journal of Wildland Fire, 23: Parks S. A., G. K. Dillon and C. Miller (2014) A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio. Remote Sensing, 6: Roy D. P., L. Boschetti, and S.N. Trigg (2006) Remote sensing of fire severity: assessing the performance of the Normalized Burn Ratio. IEEE Geoscience and Remote Sensing Letters, 3(1): Schepers L., B. Haest, S. Veraverbeke, T.Spanhove, J.V.Borre and R.Goossens (2014) Burned Area Detection and Burn Severity Assessment of a Heathland Fire in Belgium Using Airborne Imaging Spectroscopy (APEX). Remote Sensing, 6: Veraverbeke S., S. Lhermitte, W.W. Verstraeten and R. Goossens (2011) Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat Thematic Mapper, International Journal of Remote Sensing, 32(12):

28 28

Comparison of maximum likelihood estimators and regression models in Mediterranean forests fires for severity mapping using Landsat TM and ETM+ Data

1 2 3 4 5 6 7 8 9 Type of the Paper (Article) Comparison of maximum likelihood estimators and regression models in Mediterranean forests fires for severity mapping using Landsat TM and ETM+ Data Alexander