An Examination of the Relation between Burn Severity and Forest Height Change in the Taylor Complex Fire using LIDAR data from ICESat/GLAS

Andrew Maher

Qingyuan Zhanga,b, Bobby H. Braswellc,

Elizabeth M. Middletonb, and Andrew T. Hudakd

aGoddard Earth Sciences & Technology Center, University of Maryland, Baltimore County, Baltimore, MD 21228bBiospheric Sciences Branch, Code 614.4, NASA/Goddard Space Flight Center, Greenbelt, MD 20771

cComplex Systems Research Center, University of New Hampshire, Durham, NH, 03824dRocky Mountain Research Station, USDA Forest Service, Moscow, ID 83843

Motivation

• Wildfires consume above ground biomass, releasing carbon into the atmosphere.

• Standard remote sensing methods for analyzing post-fire burn areas can estimate total carbon release, biomass loss, and smoke production, but cannot directly observe the change in vegetation structure (Miller et al, 2006).

• Better understanding of change in vegetation structure would allow for a more detailed ecological analysis of fire disturbance areas, validation of current change detection algorithms, and other scientific analyses.

Landsat TM Burn Severity Classification

• Reflectance of TM band 4 (near infrared; NIR) and band 7 (short wave infrared; SWIR)

• Burn severity levels (high, moderate, low, unburned) derived from dNBR thresholds• Charred area; no direct observation of vegetation structure

74

74

NBR

firepostfirepre NBRNBRdNBR

ICESat/GLAS• 70m diameter footprints spaced

every 175m• Distribution of vegetation height

within footprint• Non-continuous swaths (time series

analysis becomes tricky)

Products UsedGLA01

–Raw waveform (digital bins)–Background noise

GLA14–Signal beginning, signal end, etc.–Gaussian fits (up to 6)

Typical GLAS shot waveform (Harding et al. 2005)

Objectives

1. Assess fire disturbance using burn severity classes derived dNBR.2. Investigate the fire effect on canopy heights using GLAS data.3. Compare two approaches and their relation.

• Use Taylor Complex fire in Alaska, 2004 as case study.– Largest US fire of 2004 (478,274 ha)– Landsat TM images on 9/15/2003 and 9/8/2004 were acquired.

Low, Moderate, and High Burn Severity Areas in Alaskan Taylor Complex Fire (Lentile et al. 2006)

Methods1. Location Matching Approach

• Find location with an overlapping pre-fire and post-fire shot. • Characterize change in waveform in relation to burn severity.

2. Statistical Approach• Apply filters to GLA14 data and GLA01 waveforms to reduce bad

interpretations of waveforms and/or noisy waveforms. • Calculate means and confidence intervals of various height

metrics (i.e. extent, height of mean energy) for each burn severity, pre-fire and post-fire.

Burn Severity Map of Taylor Complex Fire with GLAS shots from 2003-2006 (~57,000 shots)

Location Matching ApproachBurn severity class: Low

Location Matching ApproachBurn severity class: Moderate

Location Matching ApproachBurn severity class: High

Comparison between fire effect assessed by GLAS/ICESat based height difference and Landsat TM based burn severity

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Severity Level

Diff

eren

ce o

f pr

e- a

nd p

ost-

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max

imum

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heig

ht (

m)

low moderate high

Location Matching Approach

Statistical Approach - Filtering

• Numerical filters on GLA01 and GLA14 data– Max lidar return > 70– Signal to noise ratio (snr) > mean(snr of all

shots)– Extent (signal beginning – ground peak offset)

< 80m– Common area

• Visual Filter on waveform– Verify GLA14 data matches the

features of the waveform.

Conclusions

• GLAS data shows that change in canopy height correlates with burn severity.

• Only pre-fire heights are significantly different for different burn severities.– Differencing indices, such as dNBR, are

known to be correlated with pre-change image (Miller et al. 2006).

Future Considerations• Statistically quantify correlation of

dNBR values to change in canopy heights.

• Analyze height change in unburned areas.

– Check for spatial and/or temporal patterns in unburned height change to identify cause (i.e. incorrect burn classification and/or noise)

– Search for other causes of disturbance.

• Test alternative waveform processing methods.

– More advanced numerical analysis (i.e. Fourier analysis, wavelets)

– Web-based machine learning application

Thank You

Qingyuan ZhangQingyuan Zhang

Bobby H. BraswellBobby H. Braswell

Elizabeth M. MiddletonElizabeth M. Middleton

Andrew T. HudakAndrew T. Hudak

George HurttGeorge Hurtt

Research and Discover ProgramResearch and Discover Program

NASA Goddard Space Flight CenterNASA Goddard Space Flight Center