Kevin P. Price

Estimating above-ground net primary productivity of the tallgrass prairie ecosystem of the Central Great Plains using AVHRR NDVI

Above-ground net primary productivity (ANPP) is indicative of an ecosystems ability to capture solar energy and convert it to organic carbon (or biomass), which may be used by consumers or decomposers, or stored in the form of living and nonliving organic matter. Annual and interannual variation in ANPP is often linked to climate dynamics and anthropogenic influences, such as fertilization, irrigation, above-ground biomass harvest, and so on. The Central 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 grasslands in this region, and the Flint Hills of North America represent the largest contiguous area of unploughed tallgrass prairie (1.6 million ha). 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 do not exist for this ecosystem. For these reasons, this study was conducted to investigate the use of the normalized difference vegetation index (NDVI) to model ANPP of 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 vary significantly from year to year depending on land use and climatic conditions. The goal of this study was 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 ANPP measurements from a watershed within the Konza Prairie Biological Station (KPBS) as the primary study area, with additional measurements from the Rannells Flint Hills Prairie Preserve (RFHPP) and biennial ANPP measurements by Kansas State University (KSU) students from tallgrass areas near Manhattan, Kansas. Data from the primary study site covered the period of 1989–2005. The optimal period for estimating ANPP using AVHRR NDVI composite data sets was found to be late July. The Tallgrass ANPP Model (TAM) explained 54% (coefficient of determination, R 2 = 0.54, p < 0.001) of the year-to-year variation in ANPP. The creation of 1.0 km × 1.0 km resolution ANPP maps for a four-county (∼7000 ha) area for years 1989–2007 showed considerable variation in annual and interannual ANPP spatial patterns, suggesting complex interactions among factors influencing ANPP spatially and temporally.

Mapping coffee plantations with Landsat imagery: an example from El Salvador

Considering the potential of shaded coffee plantations mixed with natural vegetation for promoting biodiversity conservation, this project assessed the utility of multi-date Landsat Thematic Mapper (TM) satellite imagery for the characterization of natural vegetation versus coffee plantations in western El Salvador. For assembling a multi-temporal Landsat TM data set, we applied a regression analysis model to remove cloud cover and cloud shadows. Then, through a hybrid classification approach, a nine-class land use/land cover (LULC) map was generated. We identified two types of coffee plantations (‘open-canopy’ and ‘close-canopy’) along with natural forest/shrubland, mangrove, water bodies, sandy coastal soils, bare soil, urban areas and agriculture. Notwithstanding the small sample size of the accuracy data, our assessment revealed an overall accuracy of 76.7% (Kappa coefficientu2009=u20090.68), considering only the four classes with independent field data. The overall classification accuracy for distinguishing coffee plantations from non-mangrove natural forest was 81.6% and the classification accuracy for distinguishing ‘open-canopy’ from ‘close-canopy’ coffee plantations was 85.7%. We are encouraged by the results of this prototype study. They indicate that remote-sensing techniques can be used to distinguish different classes of coffee production systems and to differentiate coffee from natural forest.

Harmful Algal Bloom Characterization at Ultra-High Spatial and Temporal Resolution Using Small Unmanned Aircraft Systems

Harmful algal blooms (HABs) degrade water quality and produce toxins. The spatial distribution of HAbs may change rapidly due to variations wind, water currents, and population dynamics. Risk assessments, based on traditional sampling methods, are hampered by the sparseness of water sample data points, and delays between sampling and the availability of results. There is a need for local risk assessment and risk management at the spatial and temporal resolution relevant to local human and animal interactions at specific sites and times. Small, unmanned aircraft systems can gather color-infrared reflectance data at appropriate spatial and temporal resolutions, with full control over data collection timing, and short intervals between data gathering and result availability. Data can be interpreted qualitatively, or by generating a blue normalized difference vegetation index (BNDVI) that is correlated with cyanobacterial biomass densities at the water surface, as estimated using a buoyant packed cell volume (BPCV). Correlations between BNDVI and BPCV follow a logarithmic model, with r2-values under field conditions from 0.77 to 0.87. These methods provide valuable information that is complimentary to risk assessment data derived from traditional risk assessment methods, and could help to improve risk management at the local level.

Using hyperspectral radiometry to predict the green leaf area index of turfgrass

The green leaf area index (LAI) is an important indicator of the photosynthetic capacity of turfgrass canopies. The measurement of LAI is typically destructive and requires large plots to allow for multiple sampling dates. Hyperspectral radiometry may provide a rapid, non-destructive means for estimating LAI. Our objectives were to: (1) evaluate the utility of hyperspectral radiometry to predict the LAI of Kentucky bluegrass (Poa Pratensis L.); and (2) determine regions of the spectrum that provide the best LAI predictions. An empirical prediction model of spectral data for LAI was conducted with partial least squares regression (PLSR). The PLSR method created viable, first-iteration models for five of 11 sampling dates (the coefficient of determination (R2) is 0.52–0.85). Each model had its own set of factors that were analysed to determine their ‘weights’, or specific regions of the spectrum by which they were most strongly influenced. Second iterations of each model were then created using only those regions most strongly influenced, centred on 600, 690, 761, 960, 1330, and 1420 nm (±10 nm). Four of the five second-iteration models had LAI estimation capabilities greater than or similar to the first-iteration models (R2 = 0.72–0.86), indicating that the information contained in all other wavelengths was redundant or irrelevant in regard to predictions of LAI. The robustness of prediction models varied over the growing season, possibly related to changes in canopy properties with environmental conditions. Results suggest hyperspectral radiometry has a significant potential to predict LAI in turfgrass, although different models may be required throughout the growing season.

Locating Amazonian Dark Earths (ADE) using vegetation vigour as a surrogate for soil type

Amazonian Dark Earths (ADE) are patches of archaeological soils scattered throughout the Amazon Basin. These soils are a mixture of charcoal, nutrient vegetable matter and the underlying Oxisol soil. ADE are extremely fertile in comparison to the surrounding soils and they are sought after by local residents for agricultural food production. Research is being conducted to learn how ADE were created and to explore the possibility of replicating them to sequester carbon and to reclaim depleted soils in the Amazon Basin. A factor limiting the success of this research is our current inability to locate ADE sites hidden beneath the tropical forest canopy. We use annual time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) satellite imagery from 2001 to 2005 and harmonic analysis (HA) to examine the spectral differences between forest vegetation growing on ADE and forest vegetation growing on non-ADE. There is a significant difference between the reflectances of vegetation growing on the two soil types, due primarily to lower EVI values over ADE during the dry season (multiple analysis of variance (MANOVA) p-valueu2009=u20090.040). A logistic model is used to create a predictive map of ADE location.

Locating Amazonian Dark Earths (ADE) Using Satellite Remote Sensing – A Possible Approach

Amazonian Dark Earths (ADE) are the result of preColumbian humans’ occupation of the Amazon Basin and are related to the need for fertile soils for growing crops (e.g. Glaser and Woods 2004). ADE soils contain highly elevated levels of organic matter, mostly in the form of very slowly decomposing charcoal, which retains water and nutrients, and makes ADE some of the most fertile soils in the world (Kern et al. 2003; Lehmann et al. 2003). When productivity of plants grown on ADE soil was contrasted with typical Amazonian soils, Major et al. (2005) found that maize yields were as much as 63 times greater, weed cover was 45 times greater, and plant species diversity was up to 11 times greater than for adjacent typical Amazonian soils. ADE soils contain up to 70 times more SOM than typical Amazonian soils (Mann 2002). Woods and McCann (1999) have shown that nutrient transfers from outside of ADE sites are necessary to explain current nutrient levels in ADE soils, suggesting that the formation of these soils ultimately became an intentional effort on the part of prehistoric Amerindian populations to improve the quality of their farmland. These nutrient sources may have been plant and animal food wastes, fish bones and other un-used fish matter, or human excrement, as well as a host plant materials used for fuel and construction. The presence of algae in ADE from c.1150 BP and later suggests that silt from riverbanks was incorporated into the ADE soils in at least one location (Mora et al. 1991). In addition to opening a window to the past, ADE soils may hold a key to the future. The most readily observed characteristic of ADE soils is their high concentration of charcoal, which gives them the distinctive dark brown-to-black coloration. Glaser et al. (2001) found 64 times more charcoal in ADE soils than in the surrounding soils. To meet the challenges of possible global climate change caused by greenhouse gases, atmospheric carbon concentrations must be reduced. Vegetation actively withdraws carbon from the atmosphere and stores it as organic matter. Biochar is created when organic matter is heated without oxygen and it contains twice the carbon content of ordinary biomass (Lehmann 2007). Biochar is much more resistant to decay and can store carbon for centennial timescales (Lehmann et al. 2006). The addition of biochar to the soil was part of the creation