Elissa Levine

Using nighttime DMSP/OLS images of city lights to estimate the impact of urban land use on soil resources in the United States

Abstract Nightime “city light” footprints derived from DMSP/OLS satellite images were merged with census data and a digital soils map in a continental-scale test of a remote sensing and geographic information system methodology for approximating the extent of built-up land and its potential impact on soil resources in the United States. Using image processing techniques and census data, we generated maps where the “city lights” class represented mean population densities of 947 persons km −2 and 392 housing units km −2 , areas clearly not available to agriculture. By our analysis, such “city lights” representing urban areas accounted for 2.7% of the surface area in the United States, an area approximately equal to the State of Minnesota or one half the size of California. Using the UN/FAO Fertility Capability Classification System to rank soils, results for the United States show that development appears to be following soil resources, with the better agricultural soils being the most urbanized. Some unique soil types appear to be on the verge of being entirely coopted by “urban sprawl.” Urban area figures derived from the DMSP/OLS imagery compare well to those derived from statistical sources. Further testing and refinement of the methodology remain but the technique shows promise for possible extension to global evaluations of urbanization, population and even global productivity.

THE USE OF MODELS TO INTEGRATE INFORMATION AND UNDERSTANDING OF SOIL C AT THE REGIONAL SCALE

Abstract Regional analysis of ecosystem properties, including soil C, is a rapidly developing area of research. Regional analyses are being used to quantify existing soil C stocks, predict changes in soil C as a function of changing landuse patterns, and assess possible responses to climate change. The tools necessary for such analyses are simulation models coupled with spatially-explicit databases of vegetation, soils, topography, landuse and climate. A general framework for regional analyses which integrates models with site-specific and spatially-resolved data is described. Two classes of models are currently being used for analyses at regional scales, ecosystem-level models, which were originally designed for local scale studies, and more aggregated “macro-scale” models developed for continental and global scale applications. A consideration in applying both classes of models is the need to minimize errors associated with aggregating information to apply to coarser spatial and temporal scales. For model input data, aggregation bias is most severe for variables which enter into non-linear model functions, such as soil textural effects on organic matter decomposition and water balance or the temperature response of decomposer organisms. Aggregation of model structure also needs to be considered, particularly for macro-scale models. For example, representations of litter and soil organic matter by only one or two pools may be suitable for representing equilibrium conditions but rates of change will tend to be overestimated for transient-state conditions using highly aggregated models. Geographic soils data, derived from field surveys, are a key component for regional analyses. Issues of data quality and interpretation of soil survey data are discussed in the context of regional analyses of soil C. Areas for further development of data and modeling capabilities, including refining soil C maps, developing spatial databases on landuse and management practices, using remotely sensed data in regional model applications, and linking terrestrial ecosystem models with global climate models, are discussed.

Specifying land surface characteristics in general circulation models: Soil profile data set and derived water‐holding capacities

A standardized global data set of soil horizon thicknesses and textures (particle size distributions) has been compiled from the Food and Agriculture Organization of the United Nations/United Nations Educational, Scientific, and Cultural Organization (FAO/UNESCO) Soil Map of the World, Vols. 2–10 [1971–1981]. This data set was developed for use by the improved land-surface hydrology parameterization designed by Abramopoulos et al. [1988] for the Goddard Institute for Space Studies General Circulation Model II (GISS GCM). The data set specifies the top and bottom depths and the percent abundance of sand, silt, and clay of individual soil horizons in each of the 106 soil types cataloged for nine continental divisions. When combined with the World Soil Data File [Zobler, 1986], the result is a l°×l° global data set of variations in physical properties throughout the soil profile. These properties are important in the determination of water storage in individual soil horizons and exchange of water with the lower atmosphere within global climate models. We have used these data sets, in conjunction with the Matthews [1983] global vegetation data set and texture-based estimates of available soil moisture, to calculate the global distributions of soil profile thickness, potential storage of water in the soil profile, potential storage of water in the root zone, and potential storage of water derived from soil texture. Comparisons with the water-holding capacities used in the GISS Model II show that our derived values for potential storage of water are consistently larger than those previously used in the GISS GCM. Preliminary analyses suggest that incorporation of this data set into the GISS GCM has improved the models performance by including more realistic variability in land surface properties.

Classifying soil structure using neural networks

Abstract Various feed forward artificial neural networks (ANNs) with back propagation were tested to classify 3 types of soil structure (granular, blocky, and massive) from 390 soil samples. The samples represented soils falling within the Ustoll taxonomic suborder that are part of the National Cooperative Soil Survey (USDA, NRCS) data base. The best network found to predict soil structure was one with 3 input nodes, 2 hidden nodes, and 3 output nodes with accuracies on the order of 79%. Inputs were percent organic carbon, silt, and clay. Simple perceptrons and simple linear perceptrons, e.g. networks with no hidden nodes (3 a 3), had accuracies of only 46%. Thus, ANNs are capable of learning soil structure from soil characterization data, and show a greater ability to classify soil structure types than simpler, linear methods. Classification of soil structure from commonly measured quantitative soil parameters is important because of the role structure plays in determining other soil properties, making it a critical component for modeling the soil system. This study shows the potential of artificial neural networks to recognize and learn complex relationships between quantitative soil parameters that can be used to correctly classify soil structure, and allow soil characterization data to be more effectively used for modeling activities.

Soil organic matter: Distribution, genesis, and management to reduce greenhouse gas emissions

In this paper we describe the accumulation of soil organic matter (SOM) during pedogenesis and the processes that can lead to the emission of greenhouse gases (CO2, CH4, N2O) to the atmosphere via SOM decomposition and denitrification. We discuss the role of management on SOM accumulation and loss, and the potential for controlling emission or comsumption of greenhouse gases by soils. We conclude that under current climate conditions there are global scale opportunities to reduce greenhouse gas emissions from soils and increase the indirect sequestration of greenhouse gases in soils through improved soil management.

Forest ecosystem dynamics: linking forest succession, soil process and radiation models

Abstract The Forest Ecosystem Dynamics (FED) project involves the development of an integrated mathematical model which links individual submodels of soil processes, forest growth and succession, and radiative transfer. The model will accommodate spatial scales from local to regional, and temporal scales from physiological to long term ecological processes. In its integrated form, the model is designed to simulate a forest ecosystem operating in a “process-response” manner whereby natural or anthropogenically induced changes in the environment will elicit responses within the soil, vegetation, and radiation regime of a forest that will impact and modify each other. In order to insure maximum flexibility for both the modeler and the user, an “object-oriented” structure will be implemented. In this way, the model will provide a tool with which patterns and processes within northern forests resulting from global change can be predicted.

Using seasonal variations in asthma hospitalizations in children to predict hospitalization frequency

Asthma hospitalization rates have increased in the United States since 1980. The exposure risk of many environmental factors, which contribute to respiratory disease, vary throughout the year. The objective of this study was to investigate the seasonal variation of pediatric asthma hospitalizations and predict hospitalization frequency. This was a longitudinal analysis of all pediatric asthma hospitalizations in the state of Maryland by age, gender, race, and residence using non-confidential discharge data sets from 1986 to 1999. Of the 631,422 pediatric hospitalizations in the state of Maryland during the years 1986-1999, 45,924 (7%) had a primary admission diagnosis of asthma. Frequency of hospitalization for asthma was lowest in the summer in all age groups, and highest in the fall. Seasonal variation in severe asthma episodes was least striking in children aged 15-18. This was in contrast to non-asthma admissions, which were highest in winter in preschool children, but relatively flat in school- and teenaged children. Using neural network modeling, weekly asthma hospitalizations could be predicted with an R2 between 0.71 and 0.8. Temporal trends in asthma hospitalizations were seen in each age group, gender, race, and location. The seasonal variability in asthma hospitalizations suggests that acute asthma is influenced by variables beyond socioeconomic factors and adherence to medical regimens. Strategies to combat exacerbations of asthma should take into consideration seasonal effects on a population. In addition, temporal trends examined over many years can be used to predict frequency of severe asthma episodes in a population.

Temporal dynamics of emergency department and hospital admissions of pediatric asthmatics

Asthma is a chronic disease that can result in exacerbations leading to urgent care in emergency departments (EDs) and hospitals. We examined seasonal and temporal trends in pediatric asthma ED (1997-1999) and hospital (1986-1999) admission data so as to identify periods of increased risk of urgent care by age group, gender, and race. All pediatric ED and hospital admission data for Maryland residents occurring within the state of Maryland were evaluated. Distinct peaks in pediatric ED and hospital asthma admissions occurred each year during the winter-spring and autumn seasons. Although the number and timing of these peaks were consistent across age and racial groups, the magnitude of the peaks differed by age and race. The same number, timing, and relative magnitude of the major peaks in asthma admissions occurred statewide, implying that the variables affecting these seasonal patterns of acute asthma exacerbations occur statewide. Similar gross seasonal trends are observed worldwide. Although several environmental, infectious, and psychosocial factors have been linked with increases in asthma exacerbations among children, thus far they have not explained these seasonal patterns of admissions. The striking temporal patterns of pediatric asthma admissions within Maryland, as described here, provide valuable information in the search for causes.

Relationships between soil properties and vegetation at the Northern Experimental Forest, Howland, Maine

Abstract This research relates the results of a survey and detailed analysis of soils in a northern mixed conifer forest to vegetation characteristics as represented by remotely sensed data. The work was conducted at International Papers Northern Experimental Forest (NEF) at Howland, Maine as part of NASAs Forest Ecosystem Dynamics (FED) project. An intensive soil survey was performed and relationships between soil properties (i.e., drainage class, depth of active zone, water holding capacity, carbon / nitrogen ratio, pH, and sum of bases), species composition, and normalized difference vegetation index (NDVI) from the Advanced Visible and Infrared Imaging Spectrometer (AVIRIS) were derived. Results showed that there was great variability in soil properties across the landscape due to complex regional glacial activity and recent alluvial events. Significant statistical differences were observed in species composition and NDVI between soil mapping units and with soil drainage class. However, other specific soil properties could not be used to explain these differences given the number of soil samples characterized, or without taking disturbance and management history into account. Simulation modeling, which would include soil data and stand history information as inputs, would provide an additional means of interpreting the relationship between remotely sensed imagery, inferred ecosystem properties, and complex, landscape-level patterns of soil characteristics.

Relationships between pediatric asthma and socioeconomic/urban variables in Baltimore, Maryland

Spatial relationships between clinical data for pediatric asthmatics (hospital and emergency department utilization rates), and socioeconomic and urban characteristics in Baltimore City were analyzed with the aim of identifying factors that contribute to increased asthma rates. Socioeconomic variables and urban characteristics derived from satellite data explained 95% of the spatial variation in hospital rates. The proportion of families headed by a single female was the most important variable accounting for 89% of the spatial variation. Evidence suggests that the high rates of hospital admissions and emergency department (ED) visits may partially be due to the difficulty of single parents with limited resources managing their childs asthma condition properly. This knowledge can be used for education towards mitigating ED and hospital events in Baltimore City.