Digital Remote Sensing

Transcription

1 Digital Remote Sensing Burn Severity and Remote Sensing BARC Use Training 2010

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

3 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 Combustion of forest fuels. In: Davis, K.P. (ed.). Forest fire: control and use. McGraw-Hill, New York. p Photo courtesy of NPS

4 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

5 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

6 Example in Pictures

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

8 Satellite Perspective Imagery Severity

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

10 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...

11 What is EM Energy? µm Visible λ Wavelength (µm) Wavelength (µm) X-Rays Ultraviolet Infrared Microwave 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...).

12 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

13 Response to EM Energy Spectral Response Curves, aka Spectral Signatures Graphically, the spectral reflectance of green vegetation in the visible wavelengths may be represented as shown Reflectance Wavelength (µm)

14 The Sun Emits a Full Spectrum of EM Energy Thus our tree has a spectral signature that extends beyond the visible Wavelength (µm) Wavelength (µm) X-Rays Ultraviolet Infrared Microwave TV/Radio

15 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 Relatively high green response due to chlorophyll pigmentation Relatively low responses in the mid-infrared due to water absorption Reflectance 0.4 Wavelength (µm) 2.6

16 Typical Spectral Signatures Typical Spectral Response Curves in the 0.4 to 2.6 µm Region... Healthy Vegetation Dry, Bare Soil Reflectance 0.4 Clear Water 2.6 Wavelength (µm)

17 Healthy Vegetation vs. Burned Areas Exploiting Spectral Response Curves Unburned High Burn Severity Mod. Burn Severity Low Burn Severity Reflectance 0.4 Wavelength (µm) 2.6 The goal of remote sensing is to take advantage of differences in spectral response curves to distinguish one thing from another.

18 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?

19 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

20 Sensor Spatial Properties Pixels in a Raster Format Columns Single Pixel Rows 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.

21 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

22 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 ,600 Landsat 30 34,225 SPOT ,600 Quickbird

23 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 SPOT 4,5 AWiFS Quickbird/IKONOS Revisit Time 8 days 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 hours acquired hours revisited every 2 to Notes: days each. Landsat 5 images an area 8 days after Landsat 7. after an order is submitted. Clouds and smoke can delay useful acquisition. after an order is submitted. Clouds and smoke can delay useful acquisition. 11 days depending on latitude and look angle tolerance.

24 Sensor Properties Spectral Sensitivity Spectral Sensitivity: Sensor 1 The size, number, and position of imaging bands. How many colors the sensor sees Example: µ UV Near Infrared SWIR Mid Infrared Far Infrared Example: Relatively Coarse Spectral Resolution Sensor 2 Relatively Fine Spectral Resolution

25 Sensor Properties Spectral Sensitivity Multispectral Imagery in Ecosystem Management µ UV Near Infrared SWIR Mid Infrared Far Infrared Visible Region (Blue, Green, Red): Cultural features, soil versus water, hydrography, vegetation. Far Infrared: Includes the longwave thermal window, vegetation stress, thermal Near Infrared (Reflected Infrared): Vegetation discrimination, biomass, soil, snow from clouds 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

26 Sensor Properties Spectral Sensitivity Spectral Sensitivity of Common Sensor Systems µm UV Near Infrared SWIR Mid IR Far Infrared SPOT 4 (20m) Moderate Resolu ution SPOT 5 (10m) AWiFS (56m) Landsat (30m) ASTER (30m) High Resolution Ikonos (4m) Quickbird (2.4m)

27 Healthy Vegetation vs Burned Areas Exploiting Spectral Response Curves Healthy Vegetation Burned Areas Reflectance 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?

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

29 Change Detection Differenced Normalized Burn Ratio (dnbr) Prefire NBR Postfire NBR

30 Questions??

31 Exercise 1: Image Viewing Tools and Techniques Exercise