Ryan Baum, MS Candidate, ISU Matthew Germino, Assistant Professor, ISU

Transcription

1 Spatial and temporal variation in remotely sensed vegetation indices on the INEEL from Ryan Baum, MS Candidate, ISU Matthew Germino, Assistant Professor, ISU

2 Importance How does vegetation vary among years? Need for understanding of ecological processes and mechanisms driving change (Anderson and Inouye, 2001) Vegetation types that show least variation during egetat o types t at s o east a at o du g climate anomalies may be most suitable for barrier caps for protecting hazardous waste

3 Sagebrush-Steppe Steppe Rangelands Information regarding variation in vegetation exists primarily at small-plot scale Anderson and Inouye (2001) state the need for large-scale perspective to better understand vegetation dynamics of sagebrush-steppe Little is known about responses to climate variation or disturbance at large-scales

4 Project Goals Examine temporal and spatial variation of vegetation in sagebrush steppe rangelands on the INEEL from Investigate response to climate variability or g p y disturbances (fire and/or grazing)

5 Remote Sensing Platforms Landsat 5 TM and 7 ETM+ - Provide regional-level and monthly coverage - Measure electromagnetic radiation reflected from Earth s surface - 185km swath width - 30m pixel resolution - continuous, 16-day repeat cycle - 7 spectral bands ( µm)

6 Image Date Selection One cloud-free image selected per year from 1984 to 2002 for 30-day window centered over Julian day 183 (~1-July) - missing years: 1987, 1992, 1995, 1999 Multiple l images from April September for 2000 to 2002 Allows for comparisons of non-phenological differences in vegetation between years

7 Image Pre-processing The following pre-processing was performed on all images using ENVI 3.6 software: 1.) Reflectance conversion 2)I 2.) Image-to-image registration ti 3.) Radiometric normalization 4.) Vegetation index calculation

8 Image Co-registration Geometric correction procedure that aligns corresponding pixels of one image to another Allows for the comparison of pixels between multiple images Performed using 20 GCPs RMS error < 1.0

9 Radiometric Normalization Accounts for radiometric differences due to nonsurface factors Applied multi-date linear regression method (Jensen 1996) - 20 pseudo-invariant invariant ground targets - targets selected based on acceptance criteria Removing non-surface factors allows differences in reflectance values between pixels of multiple images to reflect actual changes in vegetation

10 Vegetation Indices Dimensionless, radiometric measures that function as indicators of relative abundance and activity of green vegetation Rely heavily on the vegetation s ability to reflect y y g y highly in the near-infrared (NIR) region of the electromagnetic spectrum

11 Leaf Cross-section: section: absorbs red and blue, reflects some green, reflects mostly near-ir ~60% ~15% Green Near-IR Blue/Red Upper epidermis Chloroplasts Spongy Mesophyll Lower epidermis stoma

12 Soil-Adjusted Vegetation Index (SAVI) Introduces a soil calibration factor to minimize soil background influences SAVI = (1 + L) (NIR red) NIR + red + L Results range from +1 to 1 Correlates with number of leaf layers per ground area (LAI)

13 Methods Used ENVI 3.6 software Created regions of interest t (ROIs) for vegetation ti and disturbance types from GIS shapefiles and exported SAVI values to ASCII text

14 Examining Variation in SAVI TEMPORAL SPATIAL - Inter-annual variation - Community type (climate) (eg. Sage,grassland) - Intra-annual variation - Disturbance type (weather, phenology) (fire, grazing)

15 Interactions of Spatial and Temporal Variation CV A vs CV B al pora Tem CV C vs CV D Spatial

16 Inter-annual Variation for entire INEEL ( ) 35 n SAVI Mea Time (Year) al ge Annua m) y Averag ation (m ative July Precipit Cumula

17 0.3 Inter-annual Variation and Precipitation Mea an SAVI R 2 = p-value = 0.003, 003 F = Cummulative July Precipitation (mm)

18 Intra-annual annual Variation for entire INEEL ( )

19 Histogram of Temporal Variation 1.2e+6 8.0e+5 4.0e Number of Pixels 0.0 Temporal Variation (%)

20 Map of Temporal Variation

21 Spatial Determinants of Temporal Variation: effect of community type Drought Years (1988, 1994, 2001, 2002) Wet Years (1984, 1986, 1993, 1998) Basin Wildrye Grassland Steppe Sage Temporal Variation (%) 0 Grasslands Combined Sage/ Low Sage Rabbitbrush Sagebrush & Rabbitbrush Sage/Lava Sage/Winterfat Sage Combined Vegetation Type

22 Sagebrush-SteppeSteppe comparison of burned and burned/grazed 0.30 Mean SAVI Burned 1994 Burned 1994, Grazed 0.25 Mean SAVI Time (Year)

23 Sagebrush-SteppeSteppe comparison of burned and burned/grazed 0.30 Mean SAVI Burned 1996 Burned 1996, Grazed 0.25 Mean SAVI Time (Year)

24 Conclusions Range of inter-annual variation higher than intraannual variation in SAVI for entire INEEL Variation among vegetation types four times higher in wet compared to dry years, but no consistent variation between functional groups No consistent differences in temporal variation among lands with grazing and/or fire effects

25 Acknowledgements Ron Rope, Randy Lee, Shane Cherry, Ryan Hruska; INEEL Ecological and Cultural Resources Dr. Edwin House, ISU Office of Research Dr. Matthew Germino, ISU Biological Sciences Dr. Nancy Glenn, ISU Geosciences ISU GISTreC Jerry Tagestad, Battelle Pacific Northwest National Lab

26 Questions?