Performance anomalies are a relative measure of ecosystem services

Transcription

1 Performance anomalies are a relative measure of ecosystem services Bruce K. Wylie 1, J. Rover 2, and E. Fosnight 1 1 ASRC Research and Technology Solutions, contractor to the USGS at the EROS Center. Work performed under USGS contract 08HQCN0007. Sioux Falls, SD, USA 2 USGS EROS Center, Sioux Falls, SD, USA Funding: USGS Land Remote Sensing and Earth Surface Dynamics, Climate Effects Network, and Geographic Analysis and Monitoring U.S. Department of the Interior U.S. Geological Survey

2 Disturbance and management Important local variations which impact ecosystem services Timber harvest Land cover change Fire Mining Urban expansion Ecosystem condition Grazing intensity Ecosystem stress Vulnerability for crossing ecosystem thresholds Insects and disease Degrading permafrost Localized effects often not included in regional models

3 Separating interannual weather variations and management / disturbance effects is difficult.

4 Proxy for ecosystem performance: Growing Season NDVI (gndvi) vegetation soil Growing Season NDVI (gndvi) gndvi: A synoptic ecological variable consistently repeatable in time and space.

5 A model to predict gndvi from weather gndvi = f (site potential, weather) Site potential Long-term gndvi modeled from elevation, soils, surface geology, slope, aspect, CTI, permafrost, etc. Spatially variable but static in time. Weather Precipitation, maximum and minimum temperature for seasons Winter: Nov. Feb. Spring: March May Summer: June July Varies in space and time

6 Site Potential in Shrub Areas of Wyoming (R 2 = 0.96, n = 13,000 random pixels) Site Potential = long-term AVHRR gndvi estimated from 3 different regression tree models using combinations of: Sage Land Cover (NLCD 2001) Sage Types (Univ. of Wyoming) Elevation, Aspect, and Slope Compound Terrain Index STATSGO Soil Data (Percent Clay, Carbonates, Available Water) MLRA Standard Deviation of the Yearly Mean TIN Percent Bare, Herb, and Tree Cover (M. Hanson, 2001) High Low Site Potential Non-shrub Areas IDAHO UTAH MONTANA WYOMING COLORADO NORTH DAKOT SOUTH DAKO NEBRASKA

7 Model to predict gndvi from climate and site potential Large numbers of random points (~10,000) Stratified by site potential Robust for a wide range of sites Stratified by years Robust for a wide range of weather conditions Boreal forest site potential high low state / province Yukon River Basin Yukon River random pixels in unburned areas

8 Regression tree / piecewise regression model gndvi if site_pot <= 135 and pptspr > 19 then gndvi = site_pot - 19 pptspr - 89 tmxwin - 35 tmnsum Site potential if site_pot <= 135 and pptspr <= 19 then gndvi = site_pot - 26 pptspr + 58 tmnspr + 17 pptwin - 39 tmxsum - 46 tmxwin if site_pot > 147 then gndvi= tmxsum pptsum + 52 tmxspr Weather expected gndvi

9 Regression trees stratify in space and time to optimize regression prediction Carbon flux in the Northern Great Plains example Rule 1: low NDVI and senescence Rule 2: high NDVI and low PAR Rule 3: low NDVI and early green -up Rule 4: high NDVI and high temperature Rule 5: high NDVI, low temperature, and low PAR Rule 6: high NDVI, high temperature, and low PAR

10 Performance anomalies: use confidence limits to quantify anomalous performance Actual gndvi (units of NDVI) Climate Disturbance overperformance normal underperformance Is this pixel responding to weather conditions as expected? Expected gndvi (units of NDVI)

11 Model predictions of gndvi (expected performance) are robust across years Boreal forest MODIS 250m gndvi Regression 2000 and 2001 were drier years (lower maximums and minimums) than the wetter years in 2003 and 2004

12 Separating climatic effects from anthropogenic and other disturbances (e.g., fire) 2004 Yukon Basin Performance Anomalies Overperforming Fire Scars (post 1996) Underperforming Overperformance Normal Performance Underperformance Anomaly = difference between Actual and Expected Performance

13 Performance anomalies validated with known disturbances (fires) using composite burn index observations overperformance Witch Fire normal underperformance Years Fire (n) Statistic (11) R Witch fire MSE (13) R Jessica fire MSE (14) R 2 * Otter Creek MSE * * Burned 2001

14 Interannual frequency of boreal forest anomalies (2000 to 2005) in the Yukon River Basin underperform 5/6 yrs underperform 4/6 yrs underperform 3/ last 3 yrs underperform 2/last 2 yrs mixed and normal overperform 2/last 2 yrs overperform 3/last 3 yrs overperform 4/6 yrs overperform 5/6 yrs low elevation high elevation

15 Interannual trends in performance anomaly 1996 to 2004 (negative is decreasing and positive is increasing)

16 Performance anomalies detect insect Damage? Trend Significance Fires + 95% Overperform Normal Underperform 2004 Anomaly + 90% + 80% No trend -80% -90% -95% Annual Trend ( ) Landsat 1986 (5,4,3) Landsat 2004 (5,4,3)

17 Boreal forest performance anomaly overperforming underperforming

18 Shrub performance anomaly multi-year frequency in Wyoming Shrub areas of Wyoming Overperformance 3/5 years 4/5 years 5/5 years Underperformance 3/5 years 4/5 years 5/5 years Non-Sage Areas 60% + Sage Cover (NLCD 2001, Shrub and Bare)

19 Underperforming anomalies tended to have higher percent bare ground (Homer et al. 2007) at comparable site potentials. Percent bare soil from Landsat and field measurements R 2 = R = normal 70 normal underperormance 80% Conf % Conf. (n=230) 80% Conf % Conf. Poly. (normal) Poly. (normal) % % 2% Site Site potential Underperforming anomalies within CI under CI over CI

20 Shrublands in Idaho: 2007 performance anomalies agreed with fence lines, grazing pressure (R 2 = 0.74), and historical fires. Fences Selected Fires (2005, 2006) Overperforming Normal Underperforming

21 Future directions Multiple land cover applications Owyhee in sagebrush systems in southern Idaho Yukon River Basin Standard MODIS emodis Alaska North slope Refinements emodis and CCRS MODIS Climate data sets (Ecocast) Work toward a national monitoring program