Digital Remote Sensing Burn Severity and Remote Sensing

BARC Use Training 2010

Overview • Defining burn intensity and severity • Measures of severity • Remote sensing basics / Sensor properties

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Fire Intensity • The amount of energy or heat release per unit time or area and encompasses several specific types of fire intensity measures. • Byram (1959): “The rate of energy or heat release per unit time, per unit length of fire front, regardless of its depth.”

Byram, G.M. 1959. Combustion of forest fuels. In: Davis, K.P. (ed.). Forest fire: control and use. McGraw-Hill, New York. p. 61-89.

Photo courtesy of NPS Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Fire (Burn) Severity • The effect of a fire on ecosystem properties, often defined by the degree of mortality of vegetation. – Relates to soil heating, large fuel and duff consumption, consumption of the litter and organic layer beneath trees and isolated shrubs, and mortality of buried plant parts.

• Degree to which a site has been altered or disrupted by fire; loosely, a product of fire intensity and residence time.

Photo courtesy of Stefan Doerr Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Soil Burn Severity • The fire-induced changes in physical, chemical, and biological soil properties that impact hydrological and biological soil functions

Photo courtesy of Stefan Doerr Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Example in Pictures

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Field Perspective Ground-based severity assessments may include: • Composite Burn Index (CBI) • Hiking through and observing burn scar mosaic • Water repellency tests

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Satellite Perspective Imagery

Severity

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Connecting the Dots • How do we connect pixels in a satellite image to burn severity on the ground?

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

What is Remote Sensing? Remote Sensing can be defined as: the collection and interpretation of information about objects based on the measurement of electromagnetic energy reflected or emitted from those objects.

We can collect remotely sensed data in a number of ways: Our eyes are sensitive to a portion of the EM spectrum, airborne and spaceborne sensors can carry instruments to record EM energy... Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

What is EM Energy? 0.4

0.5

0.6

0.7 µm

Visible λ

Wavelength (µm)

10-6

10-5

X-Rays

Wavelength (µm)

10-4

10-3

10-2

10-1

Ultraviolet

1

10

102

Infrared

103

104

105

Microwave

106

107

108

TV/Radio

EM energy is a continuum which we (somewhat arbitrarily) classify according to wavelength. Wavelengths extend from very, very short (cosmic and X rays) to very, very long (thermal, radar, etc...).

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Remote Sensing and EM Energy

Conifer

Remote sensing relies on the fact that different targets have unique responses to EM energy—allowing us to visually distinguish one thing from Asphalt another.

Water

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Response to EM Energy Spectral Response Curves, aka Spectral Signatures

Reflectance

Graphically, the spectral reflectance of green vegetation in the visible wavelengths may be represented as shown

0.4

2.6

Wavelength (µm)

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

The Sun Emits a Full Spectrum of EM Energy Thus our tree has a spectral signature that extends beyond the visible

Wavelength (µm)

10-6 10-5

X-Rays

Wavelength (µm)

10-4

10-3

10-2

10-1

Ultraviolet

1

10

102

Infrared

103

104

105

Microwave

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

106

107

108

TV/Radio

Response to EM Energy Spectral response curve of typical vegetation from 0.4 to 2.6 µm High near infrared response due to healthy plant cell structure

Reflectance

Relatively high green response due to chlorophyll pigmentation

Relatively low responses in the mid-infrared due to water absorption

0.4

2.6

Wavelength (µm) Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Typical Spectral Signatures Typical Spectral Response Curves in the 0.4 to 2.6 µm Region... Healthy Vegetation

Reflectance

Dry, Bare Soil

Clear Water 0.4

2.6

Wavelength (µm) Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Healthy Vegetation vs. Burned Areas Exploiting Spectral Response Curves High Burn Severity

Unburned

Mod. Burn Severity

Reflectance

Low Burn Severity

0.4

2.6

Wavelength (µm)

The goal of remote sensing is to take advantage of differences in spectral response curves to distinguish one thing from another. Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Important Satellite Sensor Properties • Spatial Properties – Resolution • How small of an object can we see?

– Extent • How large of an area is covered?

• Revisit time • How often can we see the same area?

• Spectral sensitivity • How many “colors” can we see?

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Sensor Spatial Properties • Spatial Resolution – Measured as the Ground Sample Distance or more commonly Pixel Size • The distance on the ground covered by the Instantaneous Field Of View (IFOV) of the sensor detectors

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Sensor Spatial Properties • Pixels in a Raster Format Columns

Rows

Single Pixel

A Pixel (picture element) is an individual cell in a raster image. Each pixel has three dimensions: 1. Length 2. Width 3. Digital number. The value of the digital number relates directly to the average integrated brightness of all the surface objects and material contained within the pixel.

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Sensor Properties—Spatial Resolution • Spatial Resolution - Pixel Sizes of selected sensors Landsat (30 m)

SPOT 1-4 (20 m)

SPOT 5 (10 m)

Ikonos (4 m)

Quickbird (2.4 m)

30m

20m

10m

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Sensor Spatial Properties • Spatial Extent (Area Covered) – Generally, there is a direct relationship between pixel size and image extent • Sensors that have a large IFOV (large pixel size) usually produce imagery that covers large areas • Sensors that have a small IFOV (small pixel size) usually produce imagery that covers small areas

Sensor

Pixel Size (m)

Extent (sq km)

AWiFS

56

547,600

Landsat

30

34,225

SPOT 5

10

3,600

Quickbird

2.4

272

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Sensor Properties—Revisit Time • How often does the sensor gather an image of the same ground area? Depends on: – – – –

Orbital characteristics Image Swath width Off-nadir viewing capabilities (i.e., pointable optics) Number of satellites in the family

Sensor: Landsat/ASTER Revisit Time

8 days

SPOT 4,5

AWiFS

Quickbird/IKONOS

3-4 days

5 days

3-4 days

Landsat 7 and ASTER

Imagery is typically

Imagery is typically

Areas can be

have a revisit time of 16

acquired 48-72 hours

acquired 48-72 hours

revisited every 2 to

days each.

after an order is

11 days depending on

submitted. Clouds

after an order is submitted. Clouds

Landsat 5 images an

and smoke can delay

and smoke can delay

tolerance.

area 8 days after

useful acquisition.

useful acquisition.

Notes:

Landsat 7.

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

latitude and look angle

Sensor Properties—Spectral Sensitivity • Spectral Sensitivity: – The size, number, and position of imaging bands. – How many “colors” the sensor sees – Example: .1 UV

.4 .5 .6 .7 .8 .9 1

Example:

Near Infrared

1.5

2

SWIR

3

4

5

6 7

Mid Infrared

8

9

10

11 12 µ

Far Infrared

Sensor 1

Relatively Coarse Spectral Resolution

Sensor 2

Relatively Fine Spectral Resolution

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Sensor Properties—Spectral Sensitivity • Multispectral Imagery in Ecosystem Management .1 UV

.4 .5 .6 .7 .8 .9 1 Near Infrared

1.5

2

SWIR

3

4

5

6 7

8

Mid Infrared

Visible Region (Blue, Green, Red): Cultural features, soil versus water, hydrography, vegetation.

Near Infrared (Reflected Infrared): Vegetation discrimination, biomass, soil, snow from clouds

9

10

11 12 µ

Far Infrared

Far Infrared: Includes the longwave thermal window, vegetation stress, thermal Shortwave-Infrared (Partly reflected-Partly emitted): Moisture absorption, the high temperature thermal window, wildfires, vehicles, exhausts. Longer (e.g., Radar) wavelengths: Surface Texture, Interferometry, topography

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Sensor Properties—Spectral Sensitivity • Spectral Sensitivity of Common Sensor Systems .1

UV

.4 .5 .6 .7 .8 .9 1

1.5

2

Near Infrared SWIR

3

4

5

Mid IR

Moderate Resolution

SPOT 4 (20m) SPOT 5 (10m) AWiFS (56m) Landsat (30m)

High Resolution

ASTER (30m)

Ikonos (4m) Quickbird (2.4m)

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

6

7 8

9

10 11 12 Far Infrared

µm

Healthy Vegetation vs Burned Areas • Exploiting Spectral Response Curves

Reflectance

Healthy Vegetation

Burned Areas

0.4

2.6 Landsat band 4

Landsat band 7 Wavelength (µm)

Key spectral differences to exploit. What is the best way to take advantage of these differences ? --i.e., is there a way to accentuate the differences? Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Band Ratios used for Severity Mapping • Normalized Burn Ratio (NBR) (B4 – B7) / (B4 + B7) Pre Refl Pre NBR Post Refl Post NBR

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Change Detection • Differenced Normalized Burn Ratio (dNBR) Prefire NBR – Postfire NBR

Monitoring Trends in Burn Severity Project, http://www.mtbs.gov

Questions??

Exercise 1: Image Viewing Tools and Techniques Exercise