ESTIMATING ANNUAL NET PRIMARY PRODUCTIVITY OF THE TALLGRASS PRAIRIE ECOSYSTEM OF THE CENTRAL GREAT PLAINS USING AVHRR NDVI

Abstract

Aboveground Net Primary Productivity (ANPP) is indicative of an ecosystem's ability to capture solar energy and store it in the form of carbon (or biomass). Annual and interannual ecosystem variation in ANPP is often linked to climatic dynamics and anthropogenic influences. The Great Plains grasslands occupy over 1.5 million km2 and are a primary resource for livestock production in North America. The tallgrass prairies are the most productive of the grasslands of the region and the Flint Hills of North America represent the largest contiguous area of unplowed tallgrass prairie (1.6 million ha) (Knapp and Seastead, 1998). Measurements of ANPP are of critical importance to the proper management and understanding of climatic and anthropogenic influences on tallgrass prairie, yet accurate, detailed, and systematic measurements of ANPP over large geographic regions of this system do not exist. For these reasons, this study was conducted to investigate the use of the Normalized Difference Vegetation Index (NDVI) to model ANPP for the tallgrass prairie. Many studies have established a positive relationship between the NDVI and ANPP, but the strength of this relationship is influenced by vegetation types and can significantly vary from year-to-year depending on land use and climatic conditions. The goal of this study is to develop a robust model using the Advanced Very High Resolution Radiometer (AVHRR) biweekly NDVI values to predict tallgrass ANPP. This study was conducted using the Konza Prairie Biological Station as the primary study area with data also from the Rannells Flint Hills Prairie Preserve and other sites near Manhattan, Kansas. The dominant study period was 1989 to 2005. The optimal period for estimating ANPP using AVHRR NDVI composite datasets is prairie 30 (late July). The Tallgrass ANPP Model (TAM) explained 53% (r2 = 0.53, r = 0.73) of the year-to-year variation. Efforts to validate the TAM results were frustrated by considerable variations among existing remote sensing based ANPP model estimates and in situ clipplot measurements of peak season tallgrass production. These findings support the conclusion that ecosystem specific ANPP models are needed to improve global scale ANPP estimates. The creation of 1 km x 1 km resolution ANPP maps for a four county (~7,000 ha) for years 1989 - 2007 showed considerable variation in annual and interannual ANPP spatial patterns suggesting complex interactions among factors influencing ANPP spatially and temporally. The observed patterns on these maps would be lost using the much coarser resolution ground weather recording stations.