MODIS Imagery & Aerial Detection Survey

Transcription

1 MODIS Imagery & Aerial Detection Survey Jim Ellenwood Remote Sensing Program Manager USDA Forest Service Forest Health Technology Enterprise Team Fort Collins, CO Eric Nielsen (formerly) Remote Sensing Analyst USDA Forest Service Remote Sensing Applications Center RedCastle Resources, Inc. Salt Lake City, UT image courtesy of British Columbia Ministry of Forests image courtesy of Natural Resources Canada Dr. Robert Chastain Soon to be 01/2009

2 Purpose and Need Basic purpose is to provide information to prioritize aerial detection survey flights Provide operational information with a 2-4 week response time Focus not specifically on detection, but where there is likely disturbance events - Omission vs. commission error, or where to go vs. where not to go? The need is to create a standardized and repeatable method for producing spatial summaries of forest disturbance Varying intensity of state surveys creates spatial biases in results using current methods Satellite remote sensing can be synoptic, but regional variability in damage signatures and interfering factors (clouds, for example) may create biases.

3 Damage signatures Mountain pine beetle Gypsy moth defoliation Balsam bark beetle Larch casebearer

4 MODIS MODIS sensors are on the Terra (2001) and Aqua (2003) satellites. Each platform images most of the earth each day; Terra overpass in the late morning, Aqua in the early afternoon. Land sensing bands at 250- meter and 500-meter resolution. Free data, many standard products available.

5 NAIP imagery, Montana MODIS spatial resolution as a limiting factor

6 1 acre grid shown for reference MODIS spatial resolution as a limiting factor

7 MODIS resolution (231.6 m) MODIS spatial resolution as a limiting factor

8 Hurricane KATRINA image difference change detection

9 Summer 2007 Wisconsin Tornado

10 Minnesota Harvest Activity MODIS 250-m m percent pixel percent disturbance

11 Minnesota Harvest Activity MN DNR classified 30-m m forest disturbance map

12 Reflectance difference, 06/225 05/225

13 Reflectance difference, spruce budworm

14 Challenges Normal change detection applications would involve an analyst choosing optimal (clear sky, appropriate phenology) pre- and post-event images, in order to minimize noise and permit wall- to-wall coverage (no missing pixels) An operational MODIS-based disturbance monitoring system has to work with what data is available, when it s s available Missing data Corrupt data (e.g., clouds, shadows, BRDF effects, other garbage) which may be confused with disturbance

15 RSAC/FHTET Disturbance Detection, Mapping, and Estimation

16 RSAC/FHTET Risk and Anomaly Detection Subsystem

17 Baseline creation Extract (n-1)/2 th lowest reflectance value from the available observations (n)( ) for the timestep Repeat for each 8-day 8 timestep and spectral band Intentionally biased toward lower reflectances because of the greater preponderance of residual clouds than of shadows in standard MODIS composites MOVIE MOVIE baseline = med ( 2001, 2002, 2003, 2004, 2005 )

18 Possible analysis class groupings Clustering on forest type could eliminate noise associated with natural phenology variability.

19 Possible analysis class groupings Clustering on day of image acquisition* could reduce temporally varying but spatially homogeneous atmospheric noise. 19 November 2008 * dynamic class CA Forest Pest Council, Sacramento, CA

20 Possible analysis class groupings Clustering on signed view zenith angle* or scattering angle* could reduce BRDF effects, if we also cluster on forest type. 19 November 2008 * dynamic class CA Forest Pest Council, Sacramento, CA

21 Binning pixels by scattering angle All data combined, best fit polynomial.

22 Dynamic class determination Extract solar zenith angle (θs), view zenith angle (θv), and relative azimuth (φs φv) for each pixel in incoming image Calculate back-scattering angle: 180 Θ = cos -1 [cosθs cosθv + cos(φs φv) sinθs sinθv] Use scattering angle thresholds to assign BRDF analysis class to each pixel Intersect BRDF analysis class with forest type class for each pixel

23 Analysis Procedure (2) (1) RZ cbi RZ cbi, baseline = = x cbi ~ x ~ cb σ cb x cbi, baseline ~ σ cb ~ x cb ( 3) ΔRZ cbi = RZ cbi RZ cbi, baseline Class-pervasive noise is removed Signals are preserved Non-pervasive noise (e.g., clouds) is preserved, but the change yardstick is not significantly affected

24 Anomaly Stack = current RZ reflectances - baseline RZ reflectances = reflectance anomalies

25 NDFI Anomaly Score ΔNDFI = ΔRZ ΔRZ NIR NIR + ΔRZ ΔRZ RED RED + ΔRZ ΔRZ NIR NIR + ΔRZ ΔRZ MIR MIR Reflectance Anomalies NDFI Anomalies

26 Comparison of Results ZOOM DIFFERENCE Disturbance intensity (NDFI change) 13-Aug-2006 DCΔRZA Severe Moderate 19 November 2008 CA Forest Pest Council, Sacramento, No change CA Regrowth/recovery

27 DCΔRZA Disturbance Intensity severe moderate no change regrowth/ recovery

28 DCΔRZA Disturbance Intensity MOVIE with 2006 spruce budworm defoliation severe moderate no change regrowth/ recovery

29 pinyon juniper high elevation conifers (spruce, fir, lodgepole pine) ponderosa pine aspen birch western oak Douglas fir eastern pines (mainly loblolly & shortleaf) oak hickory oak pine deciduous bottomland forests

30 8-day anomaly detections (western half of tile)

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36 Coming Soon 2009!

37 Priority Process + Prior Aerial Detection Survey + National Forest Insect/Disease Risk Assessment + Anomaly Detection = Aerial Survey of Prioritized Areas