forest fire oil spill floods biogeochemical cycle class 13. remote sensing applications

25
Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Upload: bethany-todd

Post on 29-Dec-2015

217 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Forest Fire

Oil Spill

Floods

Biogeochemical Cycle

Class 13. Remote Sensing Applications

Page 2: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Fire is part of the natural reproductive cycle of many forests revitalizing growth by opening seeds and releasing nutrients

from the soil.

However, fires can also spread quickly and threaten settlements and wildlife, eliminate timber supplies, and

temporarily damage conservation areas.

Information is needed to help control the extent of fire, and to assess how well the forest is recovering following a burn.

CCRS WWW

Page 3: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Fire Monitoring, Mapping Fire Monitoring, Mapping and Modeling System:and Modeling System:

Fire M3Fire M3

CCRS/CFS

Page 4: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Wild Fire in Canada

• 10,000 fires per year

• 2.5 million ha burned annually

• $500 million fire management cost

• 20% of forest management costs

CCRS/CFS

Page 5: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

CFS

(1999 not included)

Page 6: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Fire M3 detection Algorithm (NOAA-14 AVHRR)Single date AVHRR

Calibration, radiometric andgeometric correction

Temperature band 3 (T3) > 315 K NO

Yes

Fire pixel

Fire clear pixel

Li et al., 1998CCRS WWW

First test: Marking potential forest fires

using thermal band (3)

Page 7: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Forest Fires - Aug 11, 1998

CCRS

Page 8: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Burn Mapping NDVI Composite May 21-30, 1995

NDVI Composite September 11-19, 1995

CCRS

Page 9: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

High Resolution ImagesHigh Resolution Images

CCRS

Page 10: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Oil spill detection

Oil spectral properties arevery different than that of water.

Many sensors can be used for oil spill detection

Page 11: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Electromagnetic Energy-Oil Interaction

UV

Visibleand reflected IR

Black orBrown Signature

Energy largelyAbsorbed by oil

Incident sunlight

Dark Signature

UV energy simulatesfluorescence;

bright signature

Energy reflectedby clean water

(in part specular)

UV energy istransmitted and absorbed

Sabins 9

Blue or greensignature

Blue or greensignature

Page 12: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Electromagnetic Energy-Oil Interaction

Sabins 9

Radiant TemperatureTrad = 17.4o CRadiant Temperature

Trad = 15.9o C

Emissivity of oil = 0.972

Emissivity of water = 0.993

Thermal Infrared

Oil and water kinetic temperature Tkin = 18oC

Radiant Temperature Trad = 1/4 Tkin

Page 13: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Electromagnetic Energy-Oil Interaction

Sabins 9

Radar

Smooth Rough

Incident radar energy

Strong backscatter;bright signature

Specular reflection;dark signature

h < 25 sin

h > 4.4 sin

h = surface roughness = radar wavelength = depression angle

Page 14: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

"Sea Empress" Oil Spill MonitoringMilford Haven, Wales, United Kingdom

February 22, 1996

CCRS WWW

http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/radarsat/images/uk/ruk01e.html

A: Oil Spill, B: Tywi River, C: Ocean waves, D: Oil spill with waves, F: Refinery wharves, E: the city of Milford Haven

Page 15: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Biogeochemical Cycles

Issues that requires global perspectives.

Page 16: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Biogeochemical Cycles

Hydrological Cycle

Campbell 20.2

PrecipitationOceans

Precipitation

Ocean

Atmosphere

EvaporationLand Evaporation

Oceans

Saline Lakes andInland Seas

Fresh Water

StreamChannels

GroundWater

Runoff and GroundWater Return

SoilMoistures

Ice Capsand Glaciers

Page 17: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Brightness values

Reflectance from

Water Bodies

Reflectance from Land

Campbell 18

HydrologyRemote sensing provides a straightforward means to map

the extent of water bodies and their changes over time

Open Water

V V

Land

IRIR

Page 18: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Biogeochemical CyclesNitrogen Cycle

Campbell 20.2

Atmosphere

N2

JuvenileAddition

Atmospheric Fixation

Biological Fixation

Industrial Fixation

DenitrificationDenitrification

Atmospheric Fixation

Diffusion

Biological Fixation

N2

MarineOrganisms

DisolvedNitrogen

InorganicNitrogen

DecayingOrganicMatter

InorganicNitrogen, land

Organisms,land

Crust

Sedimentary Rocks

Page 19: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Biogeochemical CyclesCarbon Cycle

Campbell 20.2

Fossil Fuel Combustion

Net Primary Production (NPP)

Atmosphere

Organic SoilEnrichment

Runoff and Ground Water FlownDiffusion

FossilFuels

CarbonateSediments

Dead Organic Matter, Land

DiagenesisDiagenesis

Precipitation

NPP

Sed.Resp.

OceanSurfaceLayer

CO2

OrganismsOceans

DeepOceanLayer

OrganicSediment

Accumulation

decomposition

Page 20: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Biogeochemical CyclesCarbon Cycle

Remote Sensing instruments assist scientistsin understanding the carbon cycle by estimating the

areas covered by plants, identifying the kinds of plants, and estimating the period for which they are

photosynthetically active.

Campbell 20.2

Page 21: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

NPP is the difference between plant photosynthesis and respiration which releases part of the carbon

absorbed: NPP = Photosynthesis Rate - Plant Respiration Rate

(expressed in units of gram carbon/ m2/year)

Net Primary Productivity (NPP)

CCRS WWW

NPP is a parameter used to quantify the net carbon absorption rate by living plants.

Net Carbon Flow to/from Terrestrial Ecosystems Net Ecosystem Productivity (NEP)

= NPP - Soil Respiration (gram carbon/m2/year)

Page 22: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

NPP quantifies the carbon absorption by plants only, while

NEP includes carbon absorption by plants and carbon release by soils.

NPP is a component of the carbon cycle, while NEP is net carbon exchange

between the ecosystem and the atmosphere; NEP quantifies the various carbon sinks and sources.

CCRS WWW

Page 23: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

CCRS WWW

NPPDistribution

The Boreal Ecosystem Productivity Simulator (BEPS)

AVHRR

Page 24: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

NPP1994

Liu/Chen/Cihlar, 2002. Global Ecology and Biogeography

0.01 0.1 0.2 0.3 0.4 0.5 kg C/m2/year

Page 25: Forest Fire Oil Spill Floods Biogeochemical Cycle Class 13. Remote Sensing Applications

Carbon Source and Sink Distribution Based on Remote Sensing

Chen et al., 2003. Tellus