Scaling Biomass Measurements for Examining MODIS Derived Vegetation Products Matthew C. Reeves and Maosheng Zhao Numerical Terradynamic Simulation Group University of Montana Missoula, MT

Objectives… Part 1: Discuss conversion of plot level biomass measurements to regional scales Part 2: Characterize effectiveness of MODIS products for monitoring grassland vegetation

Part 1 Scaling Biomass Measurements

 Focuses on Little Missouri National Grasslands  Climate: continental/semi-arid  Vegetation: mixed grass prairie common in the northern Great Plains  C3 : C4 ~ 70:30  Sampling was constrained to Federal Lands comprised of rolling prairie Study Area

Little Missouri National Grasslands

 Focuses on Little Missouri National Grasslands  Climate: continental/semi-arid  Vegetation: mixed grass prairie common in the northern Great Plains  C3 : C4 ~ 70:30  Sampling constrained to Federal grasslands Study Area

Woody Stringer

Methods… Collecting and Processing Field Measurements  Data were collected at 2,200 plots (473 transects) during 2001 growing season Sampling Periods May 26 – 30 June 13 – 17 July 13 – 17 August 9 – 13  Measurements included: Beginning and ending GPS locations Clipping herbaceous biomass within 0.5m 2 quadrat Species composition Bare ground estimate Percentage of living vegetation estimated for each plot  All biomass dried at 65°C for 48 hours

 ETM+ (30 m spatial resolution) approximately corresponding to each period of sampling was acquired (all image data clipped to grasslands)  All four periods of ETM + imagery converted to NDVI (NIR – Red)/(NIR + Red)  Spatial relations between NDVI and observed biomass explored included: 3*3, 5*5, 7*7 zonal mean Average NDVI by allotment Pt. In cell extraction 90, 150, 500 meter buffers around transects Average NDVI by zone of met. Influence Methods … scaling from plot frame to pixel ETM+ Imagery

Methods … scaling from plot frame to pixel Meteorological Data  Weather station data chosen within and adjacent to the LMNG  Met data were screened  Thiessen polygons were created around retained met. stations  Information derived from meteorlogical data included: Summation of precipitation across varying time frames Water balance (Ppt. - P et ) Growing Degrees Days (T avg – T base )

NCDC Weather Station Distribution Surrounding the Little Missouri National Grasslands Montana North Dakota

Spatial Arrangement of Thiesson Polygons Montana North Dakota

Montana North Dakota Vegetation Mosaic ~ 182,000 ha

 Biomass modeled as a function of ETM NDVI Precipitation Growing degree days NDVI = ETM+ Normalized difference vegetation index PPT sum = Summation of Precipitation from day 0 – time of sampling midpoint GDD = Summation of growing-degree day GDD opt =number of growing degree days required for peak of greenness Methods…scaling from plot frame to pixel Building the scaling model

Methods…scaling from plot frame to pixel Validating the scaling model MAE = 4.22 Bias = -0.08

Results… Regional Biomass prediction Influence of C4 species

Results… Zonal Biomass prediction

Part 2 Comparing and Characterizing MODIS Data

 250, 500, or 1000 meter spatial resolution  GLOBAL COVERAGE EVERY 1-2 DAYS (Landsat 16 days)  on-board calibration + 36 spectral channels ( AVHRR 5, TM 7, ETM+ 8)  More accurate geo-location (within 0.1 pixels)  Unprecedented processing and quality assurance tests before distribution  DATA ARE FREE! MODIS MODERATE RESOLUTION IMAGING SPECTRORADIOMETER

 MOD 09Surface Reflectance  MOD 11Land Surf. Temp. / Emissivity  MOD 12Land Cover / Change  MOD 13Vegetation Indices  MOD 14Thermal Anomalies / Fire  MOD 15 Leaf Area Index / FPAR  MOD 17 Net Primary Production  MOD 43BRDF / Albedo  MOD 44Vegetation Continuous Fields MODIS LAND PRODUCTS

Mod 15 Leaf Area Index (LAI)  Conceptualized as a spatially continuous photosynthetically active layer  Measures vertical density of projected leaf area  Example: LAI of 2 = Two meters of vertically distributed leaf area per unit of land.

Relating Temporal Trends of Leaf Area Index to Modeled Biomass LAI appears insensitive to small changes in biomass

Some Zones Include High Proportion of Agricultural land

Improving the Spatial Relations Between MODIS LAI and Modeled Biomass Agricultural zones removed NOTE:…relationship is strongest when biomass is at peak

LAI and Biomass Change Through Time  May - June  June - July  July - August LAI Biomass

250 m footprint 1 km footprint Approximate location of Road MODIS Receives a More Spatially Averaged Spectral Response Than ETM

Conclusions 1.Reliable conversion of plot level measurements to landscape scales possible 2.Spatial patterns of MODIS LAI are tightly linked with biomass 3.Temporal Trajectory of LAI with biomass is reliable 4.Comparing LAI with biomass is inherently difficult 5.Success of smaller regional studies depends on: intimate local knowledge Relatively large differences in biomass in a given time frame