Search

ABSTRACTRates of population growth and urbanization currently taking place in sub-Saharan Africa are and will be the highest in the world, providing impetus for remote-sensing-based monitoring of land cover and land use change (LCLUC). The objective of this study is to assess the utility of using segmentation and classification information derived from an object-based classification of an image at a later date (t = 2) to aid in the segmentation and classification of an image at an earlier date (t = 1) for the same area, in the context of a post-classification comparison for LCLUC analysis within Kumasi, Ghana. This object-based temporal inversion approach was found to increase the accuracy of the t = 1 classification and land use change classifications by 6.2% and 6.6%, respectively, compared to a traditional, non-constrained object-based image analysis (OBIA) approach.

To determine the spatial and demographic characteristics of pediatric patients who make nonurgent visits (NUVs) to an urban pediatric emergency department (ED). We hypothesized that the rate of NUVs would be inversely associated with the spatial density of primary care providers (PCPs).

AbstractPrevious research has indicated that modeling evapotranspiration (ET) in the Arctic Coastal Plain is challenging due to unique ecosystem conditions which include mosses, permafrost, and standing dead vegetation. A new version of the commonly used Biome–BGC (Biogeochemical Cycles) model (Arctic Biome–BGC) was developed that included: (1) a water storage and vertical drainage/infiltration routine that accounts for permafrost and mosses, (2) a modified representation of energy available at the surface which includes ground heat flux and simulates interception of incoming radiation by standing dead vegetation, and (3) a background evaporation routine that allows for moss and open water evaporation. In this study we investigated the sensitivity of model predictions to variations in parameter values, and to provide a conceptual validation of Arctic Biome–BGC. Using the generalized sensitivity analysis methodology, 13 parameters were evaluated. Results indicate that the model was sensitive to 8 of the 13 parameters. Seven of these parameters were introduced in the development of Arctic Biome–BGC and related to both energy reaching the ground surface and the amount of water stored within the soil and moss layers. The remaining sensitive parameter modulates the rate of snowmelt. These results validate the conceptual modifications included in the Arctic Biome–BGC model for estimating ET.

Ice-wedge polygons and drained thaw lake basins cover large portions of the Barrow Peninsula of Alaska. These landforms lead to variations in topography in the relatively flat coastal plain. These variations in topography, while small, lead to large variations in soil moisture, which have a substantial impact on carbon, water, and energy fluxes in these systems. In this study, we hypothesize that the spatial patterns of soil moisture control variations in vegetation production, and hence remotely sensed greenness. We investigate the relationship between soil moisture and a remotely sensed measure of vegetation greenness, the normalized difference vegetation index (NDVI). Soil moisture was measured in the summer of 2000 in the fetch of an eddy flux tower (0.5 km2footprint), and NDVI data were collected using an aerial digital multispectral camera system. Results indicate that the relationship between soil moisture and NDVI was significant in areas dominated by high-centered polygons and troughs, and where the general slope of the terrain promoted the redistribution of soil moisture. However, in areas with little to no relief, the correlation between soil moisture and NDVI was not significant.