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Fire Measurements Introduction Pre- and Active Fire Measures Ryan and Noste CBI Spatial Severity Assessments. FOR 274: Forest Measurements. Shifting Earth Science Priorities.

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For 274 forest measurements

  • Fire Measurements

  • Introduction

  • Pre- and Active Fire Measures

  • Ryan and Noste

  • CBI

  • Spatial Severity Assessments

FOR 274: Forest Measurements


Shifting Earth Science Priorities

Research is needed to understand the complex inter-connected roles of biological and climate systems, while understanding the consequences for society, mitigation, and feedbacks in a changing climate.

Reid et al. (Science, 2009)


Shifting Earth Science Priorities

To make meaningful management decisions in the face of uncertainty, physical drivers of climate and their biological response need to be mechanistically connected.

However, understanding of the impacts of climate change is lacking at regional and local scales, where on-the-ground management activities are implemented.

Evaluating Progress of the U.S. Climate Change Science Program: Methods and Preliminary Results (NRC, 2007))


Importance of Wildland Fires

  • Key element in the Earth system.

  • Disturbance agent that rapidly transfers biogeochemical and hydrological stocks stored in terrestrial vegetation to the atmosphere.

  • Affects vegetation, soils, and airflow with substantial effects on the terrestrial, subterranean, and atmospheric cycles within regional water- and air-sheds.

  • Considerable ecological, economic, and social impacts, prompting policy changes and other societal responses to land management.


Wildland Fire Challenges

  • The principal challenges are to quantify the:

  • Structure and heterogeneity of pre-fire fuels

  • Energy released during combustion

  • Landscape-scale impacts on soils and vegetation

The Grand Challenge is how to integrate the pre-, active-, and post-fire measurements and physical process models into a robust and well documented framework

Kremens, Smith and Dickinson (2010)


The Next Generation of Fire Spread Models

The WFDS model has been developed by the National institute of Standards and Technology (NIST)

) http://www2.bfrl.nist.gov/userpages/wmell/public.html#ICFEM_wfds_sim


These physics-based fire spread models require parameterization and validation with real-world examples.

This highlights the need to measure the pre-fire fuels, active fire properties, and post-fire effects in a coincident manner.

The data inputs to these models have to be of an adequate spatial and temporal scale to capture natural variations

The research outputs need to sync with those predicted by the models!


Characterization of the Pre-Fire Fuels parameterization and validation with real-world examples.

Ground based LiDAR enable fuel voxels to be characterized.

Hiers et al (2009)

Image Source: MJ Falkowski


Introducing the Fire Energy Field parameterization and validation with real-world examples.

This describes the radiant, conductive, and convective energy flow produced by a wildland fire.

To fully characterize the energy field the directions and magnitude of all the component would be known – this would enable reliable predictions of fire effects


Fire remote sensing essentials emittance

Energy emitted ( parameterization and validation with real-world examples. q ) at a given wavelength and temperature is given by the Stefan-Boltzmann law:

q  = ε T4 [ = 5.67 x 10-8 watts/m2/K4]

ε = emissivity, 0 <= ε <= 1, and is the efficiency that surface emits energy when compared to a black body

Fire-Remote Sensing Essentials: Emittance


Fires follow the curve

Fires follow the curve parameterization and validation with real-world examples.

Wooster et al 2005


How Much Fuel (Carbon) is Combusted? parameterization and validation with real-world examples.

Fire Line Intensity: I = HWR

H is known

Need Measurement of:

W – Fuel Combusted

R – Rate of Spread

In Crown Fires W can be ‘very Difficult’


Also many large fires occur in remote areas

Also Many Large Fires Occur in Remote Areas parameterization and validation with real-world examples.

NASA 2000


Energy= εσT parameterization and validation with real-world examples. 4

Andrews and Rothermel 1982 – Heat Per Unit Area:


This equation can be applied to satellite data

This Equation can be Applied to Satellite Data parameterization and validation with real-world examples.

Wooster, M.J., et al. (2005) Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release, JGR, 110, D24311, doi:10.1029/2005JD006318,


0 3 6 9 11 parameterization and validation with real-world examples.

Day of Burn


A Week of W: Southern Africa parameterization and validation with real-world examples.

Roberts, G., et al. (2005) Retrieval of biomass combustion rates and totals from fire radiative power observations: Application to southern Africa using geostationary SEVIRI Imagery, JGR, 110, D21111, doi: 10.1029/2005JD006018


A Week of W: Southern Africa parameterization and validation with real-world examples.

Biomass

Combusted

= 3.2 million tonnes (1.5 Mtonnes C)

(4.3-5.1 million tonnes adj. for cloud)

Cloud effect

Roberts, G., et al. (2005) Retrieval of biomass combustion rates and totals from fire radiative power observations: Application to southern Africa using geostationary SEVIRI Imagery, JGR, 110, D21111, doi: 10.1029/2005JD006018


Using heat flux vs smoke emission relations improving regional emissions air quality modeling

Using Heat Flux vs. Smoke Emission Relations: Improving Regional Emissions/Air Quality Modeling

Ichoku and Kaufman (2005)


Provide inputs into regional smoke transport models

Measure Heat Flux & Emissions over Regional Emissions/Air Quality ModelingSpace and Time

Provide Inputs into Regional Smoke Transport Models


Improving measures of the wildfire background

Evaluate Contributions Relative to Agriculture/Industry Regional Emissions/Air Quality Modeling

Improving Measures of the Wildfire Background

With Fire Inputs Without Fire Inputs Difference

Smith, Lamb (WSU), and Potter (PNW) – JFSP (in review)


Remote Sensing of Fires: Regional Emissions/Air Quality ModelingSurface Changes

Pre-fire surfaces: The fuels or green and yellow (senesced) vegetation

Post-fire surfaces: charred vegetation, mineral (white) ash, exposed soils


Remote Sensing of Fires: Regional Emissions/Air Quality ModelingSurface Changes

Visible (TM bands 1-3): sharp drop that generally recovers with re-growth

Near infrared (TM band 4): very sharp drop that slowly recovers with re-growth


Remote Sensing of Fires: Regional Emissions/Air Quality ModelingSurface Changes

Long Near-infrared (TM bands 5 and 7): increase post-fire

Thermal (TM band 6): increase post-fire


Remote Sensing of Fires: Regional Emissions/Air Quality ModelingSurface Changes

These noticeable changes allow us to easily produce maps of the area burned


Remote Sensing of Fires: Regional Emissions/Air Quality ModelingSurface Changes

Several options exist: One of the most popular in N. America is the dNBR method

NIR – SWIR

NIR + SWIR

NBR =

Where, dNBR = NBRprefire – NBRpostfire

In terms of fire management, dNBR maps are often used to produce Burned Area


Fire Intensity, Fire Severity, and Burn Severity… Regional Emissions/Air Quality Modeling

From Jain T, Pilliod D, Graham R (2004) Tongue-tied. Wildfire. 4, 22-26. [After: DeBano LF, Neary DG, Ffolliott PF (1998) ‘Fire’s effects on ecosystems.’ (John Wiley and Sons: New York) 333 pp.

Source of Confusion: The Terms Fire Severity and Burn Severity are used inconsistently in the Remote Sensing literature


The severity concern

Van Wagtendonk et al (2004); Epting et al (2005) Lentile et al (2006)

Subjective & Value Laden Term

∆NBR: non-linear asymptotic relationship with CBI that varies with sensor spatial resolution and environment

The Severity Concern

∆NBR

Highlights need to evaluate alternative methods


The Burn Severity Map Concern al (2006)

Multiple Agency’s use the dNBR method

dNBR is a good Measure of Current Canopy Condition

dNBR

BAER


The Severity Concern al (2006)

  • Value Laden Term

  • Negative Connotations: severity = bad

  • Public & Policy Miscommunication

  • Multiple Definitions in the Literature

* Fire duration and heat transfer

* Vegetation consumption

* White ash production

* Change in surface reflectance

* Alteration in soil properties

* Changes in the litter and duff layers

* Long-term vegetation mortality and recovery



Pre-Fire Condition al (2006)

Post Fire Effects

Active Fire Characteristics

During Combustion

Following Combustion

Simplifying the Fire Disturbance Continuum:

  • Limit use of the Terms Fire Severity & Burn Severity

  • Describe and Quantify the Actual Processes Being Assessed

  • Make sure that satellites CAN also measure these processes


Field measures of post fire effects ryan and noste

The method describes fire severity in terms al (2006)of the heat received by overstory vegetation and the soil.

  • 5 flame length classes (feet)

  • Class scorch ht. tree mortality (dbh)

  • 1. 0-2 ft 0-9 < 1.0

  • 2. 2-4 ft 9-24 1 - 4.9

  • 3. 4-8 ft 24-64 5 - 8.9

  • 4. 8-12 ft 64-116 9 - 13

  • 5. >12 ft >116 > 13

  • Ground char classes:

  • %Deep %Mod %Light

  • Light Char <2% <15% >80%

  • Moderate <10% >15%

  • Deep >10% >80%

Field Measures of Post-Fire Effects: Ryan and Noste


Field measures of post fire effects ryan and noste1

The method describes fire severity in terms al (2006)of the heat received by overstory vegetation and the soil.

Field Measures of Post-Fire Effects: Ryan and Noste


Field measures of post fire effects cbi

  • Used with dNBR al (2006)

  • Measures scaled 0-3

  • 15m radius plots

  • Uses 5 Strata:

  • Soil

  • Understory

  • Shrubs / saplings

  • sub-canopy trees

  • overstory

Field Measures of Post-Fire Effects: CBI

With dNBR or CBI how do you know whether effects are caused by the fire and if they are what magnitude of those effects are due to the fire?


“Byram’s Fire Intensity equation contains about as much information about a fire’s behavior as can be crammed into one number.”

Van Wagner (1977)


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