Remote Sensing for Fire Management. FOR 435: Remote Sensing for Fire Management

Transcription

1 Remote Sensing for Fire Management FOR 435: Remote Sensing for Fire Management 2. Remote Sensing Primer Primer A very Brief History Modern Applications As a young man, my fondest dream was to become a geographer. However, while working in the Patents Office, I thought deeply about the matter and concluded that it was far too difficult a subject. With some reluctance, I then turned to physics as an alternative. - Albert Einstein 1

2 FOR 435: Remote Sensing Primer A GIS is a system for capturing, storing, checking, integrating, manipulating, analyzing, and displaying data which are spatially referenced to the Earth. : FOR 435: Remote Sensing Primer Wildlife Management Hazard Assessment FOR 435: Remote Sensing Primer We aim to: Physically relate surface process to remotely derived measures 2

3 FOR 435: Remote Sensing Primer Remote sensing is the science of obtaining information about an object from measurements made at a distance from the object (i.e. without touching the object). FOR 435: Remote Sensing Primer Electromagnetic Spectrum: UV μm Visible μm IR Near μm Mid μm (SWIR) Far ,000 μm (Thermal) Microwave 1mm-30cm Radio >30cm Reflective spectrum μm - wavelengths A wavelength or frequency interval in the EMR is commonly referred to as a band. FOR 435: Remote Sensing Primer Radiance and Reflectance 3

4 FOR 435: Remote Sensing Primer - Reflectance 100% 5% 40% 10% 8% 5% FOR 435: Remote Sensing Primer - Reflectance FOR 435: Remote Sensing Primer - Emittance Energy emitted (q λ ) at a given wavelength and temperature is given by the Stefan-Boltzmann law: q λ = εσ T 4 [σ = 5.67 x 10-8 watts/m 2 /K 4 ] ε = emissivity, 0 <= ε <= 1, and is the efficiency that surface emits energy when compared to a black body 4

5 FOR 435: Remote Sensing Primer - Emittance Wooster et al 2005 FOR 435: Remote Sensing Primer - Scale Spatial Resolution High Low FOR 435: Remote Sensing Primer - Scale Extent Low High 5

6 FOR 435: Remote Sensing Primer - Scale Spectral Resolution Seeing the Light with Physics: Isaac Newton uses a prism to split day-light into the spectrum of the rainbow 1800: William Herschel discovers the infrared 1820s: The Photographic Age: 1826: Niepce Takes First Digital Photograph 1839: Photography begins to be widely used 1850s: First photographs taken from balloons Nadar "elevating photography to the condition of art", 1862, Honoré Daunier. 6

7 1860: Oldest Surviving Aerial Photograph As Nadar's pioneering work has been lost, the oldest surviving aerial photograph was acquired by James Wallace Black of Boston on October 13, 1860: Seeing the Light with Physics: 1860s: James Clerk Maxwell develops the Theory of Electromagnetic Radiation 1873: Hermen Vogel Develops Infrared Film 1900s: The Aviation Age: 1903: Wright Brothers Invent the Airplane 1910: Wilbur Wright takes the first Aerial photographs of Italy. 7

8 1906: Einstein and Max Planck Develop the photon model of light : First spy-remote sensing during WWI 1940s: The Development of Radar During WWII 1950s The Space Age: 1950s: US Military invents Thermal Remote Sensing 1957: USSR Launches Sputnik 1: First Man-made Satellite in Space 1958: Invention of the Laser at Bell Labs 8

9 1960: First ever satellite image of the Earth was taken by TIROS: TIROS = Television Infrared Observation Satellite 1972: Landsat Program Begins 1999: TERRA (MODIS) Launched 2005: Maps of area burned, vegetation mortality, and recovery developed NDVI = NIR RED NIR + RED NBR = NIR SWIR NIR + SWIR dnbr = NBR prefire -NBR postfire 9

10 MODIS and BIRD FRP data in Boreal Forest MODIS 3.9 μm channel images BIRD MODIS false alarms FRP data BIRD Zhukov, B., et al. (2005) Spaceborne detection and characterization of fires during the Bi-spectral Infrared Detection (BIRD) experimental small satellite mission ( ) Remote Sensing of Environment, 100, MSG SEVIRI MIR channel TIR channel MIR-TIR Fire Map 15 mins imaging frequency 10

11 Day of Burn 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: /2005JD Biomass = 3.2 million tonnes (1.5 Mtonnes C) Combusted ( million tonnes adj. for cloud) Cloud effe ect 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: /2005JD

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