Joel Kuszmaul Henrique Momm Greg Easson The University of Mississippi

1. Rapid Prototyping of NASA Next Generation Sensors for the SERVIR System of Fire Detection in Mesoamerica Joel Kuszmaul Henrique Momm Greg Easson The University of Mississippi Collaborators: Dan Irwin, NASA-MSFC Tim Gubbels, SSAI-Goddard Bob Ryan, SSAI-Stennis

2. Objectives • We do not seek to validate or evaluate the MODIS active fire detection algorithm • This has been done by other scientists • We seek to compare results from MODIS to results from VIIRS with the goal of identifying issues of active fire detection

3. MODIS Active Fire Product (SERVIR) Fires in Mesoamerica

4. MODIS Active Fire Product (MOD14)Production Code, Version 4.3.2

5. The Kappa Statistic Useful for assessing agreement between two sets of classification Corrects for chance agreement An improvement on the proportion of correct classification (simplest measure of agreement) Calculated in the general case as: k = 0 chance agreement k < 0 worse than chance k > 0 better than chance

6. MODIS FIRE ALGORITHM Giglio et al (2003)

7. Comparison between MODIS and VIIRS spectral and spatial resolution

8. Saturation Differences MODIS VIIRS Channel 22 (331K) M-13 (634K) Channel 21 (500K) Channel 31 (400K – 340K) M-15 (343K) TERRA AQUA

9. Study Site

10. Date Selection Criteria for the selection of dates Guatemala has to be covered – entirely if possible Low cloud coverage – as little as possible Lots of fires – Comparison with SERVIR online data Availability of imagery with higher spectral resolution for validation Availability of the required data: Level 1B and Geolocation files (MOD021KM and MOD03)

11. Available auxiliary datasets

12. Comparing MODIS- and VIIRS-based Detection Tools • 8 MODIS fire products (one for each sensor, Terra and Aqua, on each of the four study days) • 16 simulated VIIRS fire products (with two simulated VIIRS products for every one MODIS product due to the two different errors models)

13. Comparing MODIS- and VIIRS-based Detection Tools (continued) The error matrix result comparing the MODIS and simulated VIIRS fire products for March 20, 2003, using the Terra sensor data and the extended error model for the simulated VIIRS data. Overall Accuracy: 0.999578148 Kappa: 0.6989

14. Comparing MODIS- and VIIRS-based Detection Tools (continued) Results from the overall kappa calculations for the case of the point source error model

15. Comparing MODIS- and VIIRS-based Detection Tools (continued) Overall results comparison for the ability to detect fires using the four different definitions of fires

16. Low and Nominal Confidence Fires • Nominal confidence fires found only 20% as often using VIIRS • Low confidence fires not found at all

17. Comparing Detection Tools Using Validation Data Sets: Results from the Aster Imagery Fire location and the 25 nearest MODIS pixels collected to investigate agreement with field data

19. Comparing Detection Tools Using Validation Data Sets: Results from the Landsat Imagery Examples of Landsat false color composite images showing active fires in Guatemala Two independent image analysts

20. Comparing Detection Tools Using Validation Data Sets: Results from the Landsat Imagery (continued) Comparison of both large and small fires identified in Landsat-7 imagery and fires detected by the MODIS- and VIIRS-based DST

21. Summary and Findings • The highest values were obtained when the MODIS- and VIIRS-based assessments of high confidence fires • The VIIRS-based fire detection system finds few nominal-confidence fires and no low-confidence fires

22. Summary and Findings • Previous researchers had identified the potential difficulty of the proposed VIIRS thermal band (3.95 m) in finding small and low intensity fires. Our results confirm their expectations. • We recommend a change in the sensor-algorithm combination from what is currently planned.

23. Questions