comparison

The U.S. Geological Survey (USGS) 2001 National Land Cover Database (NLCD) was compared to the U.S. Department of Agriculture (USDA) 2002 Census of Agriculture. Wecompared areal estimates for cropland at the state and county level for 14 States in the Upper Midwest region of the United States. Absolute differences between the NLCD and Census cropland areal estimates at the state level ranged from 1.3% (Minnesota) to 37.0% (Wisconsin). The majority of counties (74.5%) had differences of less than 100 km2. 7.2% of the counties had differences of more than 200 km2. Regions where the largest areal differences occurred were in southern Illinois, North Dakota, South Dakota, and Wisconsin, and generally occurred in areas with the lowest proportions of cropland (i.e., dominated by forest or grassland). Before using the 2001 NLCD for agricultural applications, such as mapping of specific crop types, users should be aware of the potential for misclassification errors, especially where the proportion of cropland to other land cover types is fairly low.

Land-use change models are used by researchers and professionals to explore the dynamics and drivers of land-use/land-cover change and to inform policies affecting such change. A broad array of models and modeling methods are available to researchers, and each type has certain advantages and disadvantages depending on the objective of the research. This report presents a review of different types of models as a means of exploring the functionality and ability of different approaches. In this review, we try to explicitly incorporate human processes, because of their centrality in land-use/land-cover change. We present a framework to compare land-use change models in terms of scale (both spatial and temporal) and complexity, and how well they incorporate space, time, and human decisionmaking. Initially, we examined a summary set of 250 relevant citations and developed a bibliography of 136 papers. From these 136 papers a set of 19 land-use models were reviewed in detail as
representative of the broader set of models identified from the more comprehensive review of literature. Using a tabular approach, we summarize and discuss the 19 models in terms of dynamic (temporal) and spatial interactions, as well as human decisionmaking as defined by the earlier framework. To eliminate the general confusion surrounding the term scale, we evaluate each model with respect to pairs of analogous parameters of spatial, temporal, and decisionmaking scales: (1) spatial resolution and extent, (2) time step and duration, and (3) decisionmaking agent and domain. Although a wide range of spatial and temporal scales is
covered collectively by the models examined, we find most individual models occupy a much more limited spatio temporal niche. Many raster models we examined mirror the extent and resolution of common remote-sensing data. The broadest-scale models are, in general, not spatially explicit. We also find that models incorporating higher levels of human decision making are more centrally located with respect to spatial and temporal scales,
probably due to the lack of data availability at more extreme scales. Further, we examine the social drivers of land-use change and methodological trends exemplified in the models we reviewed. Finally, we conclude with some proposals for future directions in land-use modeling.

This study presents the results of comparing land use estimates between three different data sets for the Upper Mississippi River Basin (UMRB). The comparisons were performed between the U.S. Department of Agriculture (USDA) Natural Resource Conservation Service (NRCS) National Resource Inventory (NRI), the U.S. Geological Survey (USGS) National Land Cover Data (NLCD) database, and a combined USDA National Agricultural Statistics Service (NASS) Agricultural Census – NLCD dataset created to support applications of the Hydrologic Unit Model for the U.S. (HUMUS). The comparison was performed for 1992 versions of the datasets because that was the only consistent year available among all three data sources. The results show that differences in land use area estimates increased as comparisons shifted from the entire UMRB to smaller 4- and 8-digit watershed regions (as expected). However, the area estimates for the major land use categories remained generally consistent among all three data sets across each level of spatial comparison. Differences in specific crop and grass/forage land use categories were magnified with increasing refinement of the spatial unit of comparison, especially for close-grown crops, pasture, and alfalfa/hayland.

Power generation emits significant amounts of greenhouse gases (GHGs), mainly carbon dioxide (CO2). Sequestering CO2 from the power plant flue gas can significantly reduce the GHGs from the power plant itself, but this is not the total picture. CO2 capture and sequestration consumes additional energy, thus lowering the plant's fuel-to-electricity efficiency. To compensate for this, more fossil fuel must be procured and consumed to make up for lost capacity. Taking this into consideration, the global warming potential (GWP), which is a combination of CO2, methane (CH4), and nitrous oxide (N2O) emissions, and energy balance of the system need to be examined using a life cycle approach. This takes into account the upstream processes which remain constant after CO2 sequestration as well as the steps required for additional power generation.