Scale Effects of STATSGO and SSURGO on Flow and Water Quality Predictions

Soil information is one of the crucial inputs needed to assess the impacts of existing and alternative agricultural management practices on water quality. Therefore, it is important to understand the effects of spatial scale at which soil databases are developed on water quality evaluations. In the United States, STATSGO (State Soils Geographic) and SSURGO (Soil Survey Geographic) are the most commonly available soil databases. The purpose of this paper was to quantify the effect of scale by employing STATSGO (1:250,000) and SSURGO (1:24,000) soil databases in predicting and comparing flow, sediment, nitrate and phosphorus losses for High Island Creek. This watershed is predominately agricultural and located in south-central Minnesota. The ADAPT (Agricultural Drainage and Pesticide Transport), model was calibrated for flow, sediment, nitrate and phosphorus losses over two years (2001-2002) using STATSGO and SSURGO soil databases. Then the calibrated model was used to evaluate alternative tillage and fertilizer management practices such as adoption of conservation tillage, and rate, timing and method of N- and P-fertilizer applications. Statistical comparison of calibration results with observed data indicated excellent agreement for both soil databases (STATSGO with r2 of 0.95, 0.97, 0.77 and 0.92 and SSURGO with r2 of 0.90, 0.97, 0.82 and 0.99 for flow, sediment, nitrate and phosphorus losses, respectively). However, STATSGO based predictions of annual nitrate-N losses were consistently greater than those with SSURGO database and vice-versa for predicted annual phosphorus losses for the alternative management practice that were evaluated.

A Composite Index-Based Approach for Mapping Ecosystem Service Production Hotspots and Coldspots for Priority Setting in Integrated Watershed Management Programs

Despite the potential synergism, integrated watershed management and ecosystem services frameworks are rarely used jointly to address the myriad of current water-related issues. The two frameworks are used in this study to spatially identify ecosystem hotspots and coldspots for priority setting in natural resource management programs. Inferred proxies of carbon storage, groundwater supply, surface water supply, and soil retention ecosystem service production potentials were quantified for Texas, U.S., using two complimentary hydro-ecological models, and valued using a non-monetary multi-criteria valuation approach. Maps of individual and composite ecosystem service values showed that several services were co-located and unevenly distributed with most of the high-value hotspots clustered in the eastern part of the state. Individual impacts of land use, climatic and soil properties on the distribution and value of ecosystem services across space were discernable. The study underscored the need for holistic management of landscapes to take advantage of the multiplicity of benefits provided by nature. The approach can readily be incorporated into resource management programs to identify high-value ecosystem service production areas that need conservation, low-value areas that may need restoration, and anthropogenic activities influencing the distribution of ecosystem services.

Evaluating Biomass of Juniper Trees (Juniperus pinchotii) from Imagery-Derived Canopy Area Using the Support Vector Machine Classifier

Both temporal and spatial magnitude, structure, and distribution of rangeland aboveground biomass (AGB) are important inputs for many necessities, in particular for estimating terrestrial carbon amount, ecosystem productivity, climate change studies, and potential bioenergy uses. Much of the remote sensing research previously completed has focused on determining carbon stocks in forested ecosystems with little attention directed to estimate AGB amount in rangelands. Our objectives were to: 1) identify and delineate individual redberry juniper (Juniperus pinchotii) plants from surrounding live vegetation using the support vector machine method for classifying two-dimensional (2D) geospatial imagery with a 1-m spatial resolution at two sites;and 2) develop regression models relating imagery-derived and fieldmeasured single tree canopy area and diameter for dry AGB estimation. The regression results show that there were very close and significant relationships between field measured juniper plant AGB and canopy area derived from the image classification with r2 > 0.90. These results suggest that spectral reflectance recorded on 2D high resolution imagery is capable to assess and quantify AGB as a quick, repeatable, and unbiased method over large land areas.