Predicting Opportunities for Greening and Patterns of Vegetation on Private Urban Lands

This paper examines predictors of vegetative cover on private lands in Baltimore, Maryland. Using high-resolution spatial data, we generated two measures: “possible stewardship,” which is the proportion of private land that does not have built structures on it and hence has the possibility of supporting vegetation, and “realized stewardship,” which is the proportion of possible stewardship land upon which vegetation is growing. These measures were calculated at the parcel level and averaged by US Census block group. Realized stewardship was further defined by proportion of tree canopy and grass. Expenditures on yard supplies and services, available by block group, were used to help understand where vegetation condition appears to be the result of current activity, past legacies, or abandonment. PRIZM™ market segmentation data were tested as categorical predictors of possible and realized stewardship and yard expenditures. PRIZM™ segmentations are hierarchically clustered into 5, 15, and 62 categories, which correspond to population density, social stratification (income and education), and lifestyle clusters, respectively. We found that PRIZM 15 best predicted variation in possible stewardship and PRIZM 62 best predicted variation in realized stewardship. These results were further analyzed by regressing each dependent variable against a set of continuous variables reflective of each of the three PRIZM groupings. Housing age, vacancy, and population density were found to be critical determinants of both stewardship metrics. A number of lifestyle factors, such as average family size, marriage rates, and percentage of single-family detached homes, were strongly related to realized stewardship. The percentage of African Americans by block group was positively related to realized stewardship but negatively related to yard expenditures.     

Using GIS to Generate Spatially Balanced Random Survey Designs for Natural Resource Applications

Sampling of a population is frequently required to understand trends and patterns in natural resource management because financial and time constraints preclude a complete census. A rigorous probability-based survey design specifies where to sample so that inferences from the sample apply to the entire population. Probability survey designs should be used in natural resource and environmental management situations because they provide the mathematical foundation for statistical inference. Development of long-term monitoring designs demand survey designs that achieve statistical rigor and are efficient but remain flexible to inevitable logistical or practical constraints during field data collection. Here we describe an approach to probability-based survey design, called the Reversed Randomized Quadrant-Recursive Raster, based on the concept of spatially balanced sampling and implemented in a geographic information system. This provides environmental managers a practical tool to generate flexible and efficient survey designs for natural resource applications. Factors commonly used to modify sampling intensity, such as categories, gradients, or accessibility, can be readily incorporated into the spatially balanced sample design.     

A framework for assessing the impact of land use policy on community exposure to air toxics

Our research focuses on the linkage between land use planning policy and the spatial pattern of exposure to air toxics emissions. Our objective is to develop a modeling framework for assessment of the community health risk implications of land use policy. The modeling framework is not intended to be a regulatory tool for small-scale land use decisions, but a long-range planning tool to assess the community health risk implications of alternative land use scenarios at a regional or subregional scale. This paper describes the development and application of an air toxic source model for generating aggregate emission factors for industrial and commercial zoning districts as a function of permitted uses. To address the uncertainty of estimating air toxics emission rates for planned general land use or zoning districts, the source model uses an emissions probability mass function that weights each incremental permitted land use activity by the likelihood of occurrence. We thus reduce the uncertainty involved in planning for development with no prior knowledge of the specific industries that may locate within the land use district. These air toxics emission factors can then be used to estimate pollutant atmospheric mass flux from land use zoning districts, which can then be input to air dispersion and human health risk assessment models to simulate the spatial pattern of air toxics exposure risk. The model database was constructed using the California Air Toxics Inventory, 1997 US Economic Census, and land assessment records from several California counties. The database contains information on more than 200 air toxics at the 2-digit Standard Industrial Classification (SIC) level. We present a case study to illustrate application of the model. LUAIRTOX, the interactive spreadsheet model that applies our methodology to the California data, is available at http://www2.bren.ucsb.edu/~mwillis/LUAIRTOX.htm.     

Evaluating farm performance using agri-environmental indicators: recent experiences for nitrogen management in The Netherlands

Intensive agriculture, characterized by high inputs, has serious implications on the environment. Monitoring and evaluation of projects aiming at designing, testing and applying more sustainable practices require instruments to asses agronomic as well as environmental performance. Guidelines for Good Agricultural Practice (GAP) or Good Farming Practice (GFP) define sustainable practices but give limited insight into their environmental performance. Agri-environmental indicators (AEIs) provide information on environmental as well as agronomic performance, which allows them to serve as analytical instruments in research and provide thresholds for legislation purposes. Effective AEIs are quantifiable and scientifically sound, relevant, acceptable to target groups, easy to interpret and cost-effective. This paper discusses application of four AEIs for nitrogen (N) management in three Dutch research projects: 'De Marke', 'Cows and Opportunities' and 'Farming with a future'. 'De Marke' applied Nitrogen Surplus and Groundwater Nitrate Concentration in the design and testing of environmentally sound dairy systems. 'Cows and Opportunities', testing and disseminating dairy systems designed at 'De Marke', mainly applied Nitrogen Surplus, while 'Farming with a future' used Nitrogen Surplus, Groundwater Nitrate Concentration and Residual Mineral Soil Nitrogen to support arable farmers in complying with Dutch legislation (MINAS). Nitrogen Surplus is quantifiable, appealing and easy to interpret, but lacks scientific soundness or a good relationship with groundwater quality. Nitrogen Use Efficiency is sensitive to changes in management, while Residual Mineral Soil Nitrogen is appealing and cheap, but has difficulties in scaling. Groundwater Nitrate Concentration lacks clear rules for sampling, is labor consuming, expensive and mainly used in combination with other indicators. AEIs enhanced improvements in N management by facilitating (i) definition of project goals, (ii) design of desired systems, (iii) evaluation of applied systems and (iv) improving effective communication. AEI applications in other countries show a similar pattern as found in The Netherlands. Limitations to AEI application relate to inconsistencies between different indicators, heterogeneity of soil characteristics and linkages of N, carbon and water management. AEIs should be applied in an integrated evaluation, at a scale that reflects the farm's spatial variability. Simple AEIs like Nitrogen Surplus should be supported by other indicators and/or model calculations. The paper concludes that AEIs proved their value in design, implementation and testing of farming systems, but they should be used with care, always keeping in mind that indicators are simplifications of complex and variable processes.     

Assessing slope stability in unplanned settlements in developing countries

Unplanned housing in developing countries is often located on steep slopes. Frequently no building code is enforced for such housing and mains water is provided with no drainage provision. Both of these factors can be particularly significant in terms of landslide risk if, as is so often the case, such slopes lack any planned drainage provision. There is thus a need to develop a model that facilitates the assessment of slope stability in an holistic context, incorporating a wide range of factors (including surface cover, soil water topographic convergence, slope loading and point source water leakage) in order that appropriate advice can be given as to the general controls on slope stability in such circumstances. This paper outlines a model configured for this specific purpose and describes an application to a site in St. Lucia, West Indies, where there is active slope movement in an unplanned housing development on relatively steep topography. The model findings are in accord with the nature of the current failure at the site, provide guidance as to the significance of slope drainage and correspond to inferences drawn from an application of resistance envelope methods to the site. In being able to scenario test a uniquely wide range of combinations of factors, the model structure is shown to be highly valuable in assessing dominant slope stability process controls in such complex environments.     

The integrated project AquaTerra of the EU sixth framework lays foundations for better understanding of river-sediment-soil-groundwater systems

The integrated project "AquaTerra" with the full title "integrated modeling of the river-sediment-soil-groundwater system; advanced tools for the management of catchment areas and river basins in the context of global change" is among the first environmental projects within the sixth Framework Program of the European Union. Commencing in June 2004, it brought together a multidisciplinary team of 45 partner organizations from 12 EU countries, Romania, Switzerland, Serbia and Montenegro. AquaTerra is an ambitious project with the primary objective of laying the foundations for a better understanding of the behavior of environmental pollutants and their fluxes in the soil-sediment-water system with respect to climate and land use changes. The project performs research as well as modeling on river-sediment-soil-groundwater systems through quantification of deposition, sorption and turnover rates and the development of numerical models to reveal fluxes and trends in soil and sediment functioning. Scales ranging from the laboratory to river basins are addressed with the potential to provide improved river basin management, enhanced soil and groundwater monitoring as well as the early identification and forecasting of impacts on water quantity and quality. Study areas are the catchments of the Ebro, Meuse, Elbe and Danube Rivers and the Brévilles Spring. Here we outline the general structure of the project and the activities conducted within eleven existing sub-projects of AquaTerra.     

Wood carbon content of tree species in Eastern China: interspecific variability and the importance of the volatile fraction

Data on the mass density and carbon content of tree organs, and in particular stem wood, are essential for accurate assessments of forest carbon sequestration. However most available data, including that for East Asia, has neglected the volatile C fraction. Wood samples were collected and assayed for C content from 14 native tree species in Jilin Province, NE China. C content showed statistically significant variation among species, ranging from 48.4% to 51.0%. The volatile C fraction was non-negligible, averaging 2.2%, and showed high variation among species. As found in prior studies, wood C content was appreciably higher in conifer than hardwood (angiosperm) species (50.8+/-0.1% vs. 49.5+/-0.2%, respectively). Wood carbon density (gC/cm(3)) showed very high inter-specific variation, due mainly to differences in wood specific gravity. Our analyses, in conjunction with recently published data from North America, indicate a global mean value of 47.5+/-0.5% wood C content exclusive of volatile C; the widely used 50% figure corresponds more closely to total wood C inclusive of the volatile fraction. Failure to include volatile C or to use species- or higher-taxon-specific C content values in forest C assessments is likely to introduce biases on the order approximately 4-6%. In addition, the stocks and flows of the volatile C fraction in wood are in themselves an important and sorely neglected aspect of forest C processes likely to be strongly impacted by harvests and other management practices.     

Spatial variation of soil phosphorus within a drainage ditch network

Agricultural drainage ditches serve as P transport pathways from fields to surface waters. Little is known about the spatial variation of P at the soil-water interface within ditch networks. We quantified the spatial variation of surficial (0-5 cm) soil P within vegetated agricultural ditches on a farm in Princess Anne, MD with an approximately 30-yr history of poultry litter application. Ditch soils from 10 ditches were sampled at 10-m intervals and analyzed for acid ammonium oxalate-extractable P, Fe, Al (P(ox), Fe(ox), Al(ox)), and pH. These variables were spatially autocorrelated. Oxalate-P (min = 135 mg kg(-1), max = 6919 mg kg(-1), mean = 700 mg kg(-1)) exhibited a high standard deviation across the study area (overall 580 mg kg(-1)) and within individual ditches (maximum 1383 mg kg(-1)). Several ditches contained distinct areas of high P(ox), which were associated with either point- or nonpoint-P sources. Phosphorus was correlated with Al(ox) or Fe(ox) within specific ditches. Across all ditches, Al(ox) (r = 0.80; p < 0.001) was better correlated with P(ox) than was Fe(ox) (r = 0.44; p < 0.001). The high level of spatial variation of soil P observed in this ditch network suggests that spatially distributed sampling may be necessary to target best management practices and to model P transport and fate in ditch networks.     

Lability of drinking water treatment residuals (WTR) immobilized phosphorus: aging and pH effects

Time constraints associated with conducting long-term (>20 yr) field experiments to test the stability of drinking water treatment residuals (WTR) sorbed phosphorus (P) inhibit improved understanding of the fate of sorbed P in soils when important soil properties (e.g., pH) change. We used artificially aged samples to evaluate aging and pH effects on lability of WTR-immobilized P. Artificial aging was achieved through incubation at elevated temperatures (46 or 70 degrees C) for 4.5 yr, and through repeated wetting and drying for 2 yr. Using a modified isotopic ((32)P) dilution technique, coupled with a stepwise acidification procedure, we monitored changes in labile P concentrations over time. This technique enabled evaluation of the effect of pH on the lability of WTR-immobilized P. Within the pH range of 4 to 7, WTR amendment, coupled with artificial aging, ultimately reduced labile P concentrations by > or = 75% relative to the control (no-WTR) samples. Soil samples with different physicochemical properties from two 7.5-yr-old, one-time WTR-amended field sites were utilized to validate the trends observed with the artificially aged samples. Despite the differences in physicochemical properties among the three (two field-aged and one artificially aged) soil samples, similar trends of aging and pH effects on lability of WTR-immobilized P were observed. Labile P concentrations of the WTR-amended field-aged samples of the two sites decreased 6 mo after WTR amendment and the reduction persisted for 7.5 yr, ultimately resulting in > or = 70% reduction, compared to the control plots. We conclude that WTR application is capable of reducing labile P concentration in P-impacted soils, doing so for a long time, and that within the commonly encountered range of pH values for agricultural soils WTR-immobilized P should be stable.     

Assessment of vegetation stress using reflectance or fluorescence measurements

Current methods for large-scale vegetation monitoring rely on multispectral remote sensing, which has serious limitation for the detection of vegetation stress. To contribute to the establishment of a generalized spectral approach for vegetation stress detection, this study compares the ability of high-spectral-resolution reflectance (R) and fluorescence (F) foliar measurements to detect vegetation changes associated with common environmental factors affecting plant growth and productivity. To obtain a spectral dataset from a broad range of species and stress conditions, plant material from three experiments was examined, including (i) corn, nitrogen (N) deficiency/excess; (ii) soybean, elevated carbon dioxide, and ozone levels; and (iii) red maple, augmented ultraviolet irradiation. Fluorescence and R spectra (400-800 nm) were measured on the same foliar samples in conjunction with photosynthetic pigments, carbon, and N content. For separation of a wide range of treatment levels, hyperspectral (5-10 nm) R indices were superior compared with F or broadband R indices, with the derivative parameters providing optimal results. For the detection of changes in vegetation physiology, hyperspectral indices can provide a significant improvement over broadband indices. The relationship of treatment levels to R was linear, whereas that to F was curvilinear. Using reflectance measurements, it was not possible to identify the unstressed vegetation condition, which was accomplished in all three experiments using F indices. Large-scale monitoring of vegetation condition and the detection of vegetation stress could be improved by using hyperspectral R and F information, a possible strategy for future remote sensing missions.