Mapping of Fires Over North America Using Satellite Data Sean Raffuse CAPITA, Washington University September,

Introduction Motivation for study Fire detection method Temporal pattern of fires Temporal and spatial distribution of fires in North America Future Work Conclusion

Why study fires? Fires endanger humans and destroy property Fires play a major role in the transport and conversion of nutrients in the global biogeochemical cycle Fires produce smoke –Reduced visibility –Health concerns Particulate matter Potential toxins (Hg)

Remote Sensing of Fires A sensor on the European Space Agency’s ERS-2 satellite is used to detect fires –This sensor detects the intensity of the light at 3.7  m, which is the peak wavelength –If an area has a high enough intensity, the pixel is flagged as a “fire pixel” –Pixel resolution is ~ 1 km

Here will be a diagram of the satellite detecting fires. Remote Sensing of Fires Sensing is done at night to avoid interference from the sun Clouds block fire signals

Problems Incomplete coverage –Clouds preclude detection –Fires are more prominent in the daytime Sensor is active at night –Sensor does not cover entire globe every day False Detection –Not all signals are from fires –Certain urban areas may produce counts Non-periodic noise Low signal counts Large fires are counted multiple times

California Fires ( ) Large fires are evident as clusters of ‘fire pixels’

Temporal Pattern of Fires Seasonal –Heavy burning –Highly regional –Burning season lasts a few months Sporadic –Occur during regional dry periods –Do not cover a large region –Not annual in a given location –Bad time charts – connected

FIRE and Norm. Diff. Veg. Index, NDVI The ‘Northern’ zone from Alaska to Newfoundland has large fire ‘patches’, evidence of large, contiguous fires. The ‘Northwestern’ zone (W. Canada, ID, MT, CA) is a mixture of large and small fires The ‘Southeastern’ fire zone (TX–NC–FL) has a moderate density of uniformly distributed small fires. The ‘Mexican’ zone over low elevation C America is the most intense fire zone, sharply separated from arid and the lush regions. Fires are absent in arid low-vegetation areas (yellow) and over areas of heavy, moist vegetation (blue). Fire Zones of North America

Seasonality of Fire Dec, Jan, Feb is generally fire-free except in Mexico, and W. Canada Mar, Apr, May is the peak fire season in Mexico and Cuba; fires occur also in Alberta-Manitoba and in OK-MO region Jun, Jul, Aug is the peak fire season in N. Canada, Alaska and the NW US. Sep, Oct, Nov is fire over the ‘Northwest’ and the “Southeast’

Seasonal Pattern of Fires over the North (52N-72N) The number of satellite-observed fires peaks in May and July-August The daily fire counts data shows a significant fluctuation North

Seasonal Pattern of Fires over the ‘South’ (12N-30N) The fire count gradually increases from March through May and sharply declines by June The daily fire counts over the entire South varies by a factor of five within a month South

Seasonal Pattern of Fires over the ‘West’ (30N-52N, > 100W) The fire count gradually increases from March through May and sharply declines by June The daily fire counts over the entire West varies by an order of magnitude during the fire season West

Seasonal Pattern of Fires over the ‘East’ (30N-52N, < 100W) About fires are recorded from March-November. The fire counts over the East are low compared to other regions of NAM. East

Future Work Develop algorithm to distinguish and eliminate false detection Fusion of fire data with other data –Smoke detected by other remote sensors –Air quality monitoring networks –US Forest Service fire data

Data Fusion: Fire and Smoke

Conclusion Fires are an important mechanism of material transport and conversion Smoke from fire poses a risk to visibility and human health The ATSR-2 sensor on the ERS-2 satellite detects fires via light intensity at 3.7  m –Coverage is incomplete not 100% accurate Fires are seasonal and regional –In North America, the most intense fires seasons are in Mexico in the spring and the Northwest in the fall Future work will focus on improving the dataset and integrating it with data from other sources