Heather M. Cheshire

Water quality mapping of Augusta Bay, Italy from Landsat-TM data

Abstract Landsat Thematic Mapper (TM) digital data were used to map the distributions and concentrations of selected water quality indicators in and around Augusta Bay, Sicily. The general approach involved near-simultaneous aquisition of TM data and water quality samples from 42 sites, laboratory analysis of samples, extraction of sample site digital numbers from the TM data, development and validation of regression models based on sample data, application of models to the entire study area, and generation of colour-coded output maps. Results were good for modelling temperature, turbidity, Secchi disk depth and chlorophyll-a, and indicate that remotely-sensed data may be applicable to monitoring water quality in this geographic area.

Comparson of Landsat MSS and TM Data for Urban Land-Use Classification

A supervised classification of digital Landsat Multispectral Scanner (MSS) data for the Raleigh, North Carolina, metropolitan area was conducted in 1982. These data were categorized into 10 land-use/land-cover types representative of the area. Digital Landsat Thematic Mapper (TM) data, for the Raleigh metropolitan area, were obtained in 1985 and analyzed for comparison to the MSS data. A stratified classification based upon principal components analysis was applied to the TM data, classifying the data into the 10 land-use/land-cover categories used in the analysis of the MSS data. Comparison of photo-interpreted land-use types and Landsat derived land-use types indicates that TM data provides significantly higher classification accuracies than can be obtained from MSS data. However, an increase in confusion between urban cover types was observed for the classified TM data over the MSS data. It is felt that the stratified classification approach was instrumental in reducing classification errors between general land-use/land-cover types such as urban areas, coniferous forests, and deciduous forests. It is not clear that the information extracted from the TM data regarding the urban environment will be of much more use to city planners than that obtained from MSS data.

Spatial Analysis of Aedes albopictus (Diptera: Culicidae) Oviposition in Suburban Neighborhoods of a Piedmont Community in North Carolina

Abstract Temporal and spatial distribution of egg-laying by Aedes albopictus (Skuse) (Diptera: Culicidae) was investigated in suburban neighborhoods in Raleigh, NC, by using oviposition traps (ovitraps) at fixed sampling stations during the 2002 and 2003 mosquito seasons. Variations in the phenology of oviposition between the two mosquito seasons resulted from differences in the patterns and amounts of rainfall early in the season. Aerial images of each study neighborhood were digitized, and the proportions of specific types of land cover within buffer zones encompassing ovitraps were estimated. Retrospective analyses showed that in some neighborhoods, oviposition intensity was significantly associated with specific types of land cover. However, in general, it seemed that gravid Ae. albopictus searched throughout the landscape for water-filled containers in which to lay eggs. Peridomestic surveys were carried out concurrently with ovitrap collections to estimate production of Ae. albopictus pupae in discarded water-filled containers and the abundance of females in vegetation that made up the resting habitat. Results of linear regression analyses indicated that the mean standing crop of pupae (total and per container) per residence was not a significant predictor of mean egg densities in ovitraps. However, the mean standing crop of adult females was a significant but weak predictor variable, because the magnitude and sign of regression coefficients varied between neighborhoods. Linear spatial regression analyses revealed that oviposition intensity was not spatially dependent on pupal standing crop or the numbers of pupae-positive containers distributed peridomestically. However, a weak spatial dependence on the standing crop of adult females was found in some neighborhoods. Based on spherical variogram models, kriging was carried out to predict the spatial patterns of oviposition in suburban neighborhoods. Focal areas of high and low oviposition intensity were evident in most neighborhoods; however, the spatial patterns of oviposition changed between mosquito seasons. Kriging predictions were evaluated, using cross-validation, by systematically removing each data point from our data set and predicting the removed point by using the remaining points. The root mean square (standardized) error values of best fitting variogram models approximated 1, and plots of standardized PRESS residuals showed no distinct pattern for most neighborhoods, indicating that predictions of the spatial distribution of oviposition intensity were valid. Spherical variogram models are a satisfactory method for describing the spatial distribution of Ae. albopictus oviposition, and kriging can be a useful technique for predicting oviposition intensity at locations that have not been sampled.

High-resolution land cover change detection based on fuzzy uncertainty analysis and change reasoning

Land cover change detection is an important research and application area for analysts of remote sensing data. The primary objective of the research described here was to develop a change detection method capable of accommodating spatial and classification uncertainty in generating an accurate map of land cover change using high resolution satellite imagery. As a secondary objective, this method was designed to facilitate the mapping of particular types and locations of change based on specific study goals. Urban land cover change pertinent to surface water quality in Raleigh, North Carolina, was assessed using land cover classifications derived from pan-sharpened, 0.61 m QuickBird images from 2002 and 2005. Post-classification map errors were evaluated using a fuzzy logic approach. First, a ‘change index’ representing a quantitative gradient along which land cover change is characterized by both certainty and relevance, was created. The result was a continuous representation of change, a product type that retains more information and flexibility than discrete maps of change. Finally, fuzzy logic and change reasoning results were integrated into a binary change/no change map that quantified the most certain, likely, and relevant change regions within the study area. A ‘from-to’ change map was developed from this binary map inserting the type of change identified in the raw post-classification map. A from-to change map had an overall accuracy of 78.9% (κ = 0.747) and effectively mapped land cover changes posing a threat to water quality, including increases in impervious surface. This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and practical change analysis.

Automated image registration for change detection from Landsat Thematic Mapper imagery

Image registration is a critical operation in digital change detection to correct for spatial image-to-image displacement. One challenging problem in this area is the automated registration with higher levels of accuracy. In this paper, the authors explore the basic elements for an automated image registration system based on image segmentation. The technique used region boundaries and strong edges as the matching primitives, and chain-code correlation as the similarity function. A mapping between the two images is formed by interpolation and used to resample the images. The method is automatic and computationally efficient.

Database development from spectral mixture analysis for use in biogenic emission models

Abstract A methodology designed to generate a spatial data layer which can be used as input to a forest biogenic emission model is described. A geographic information system was utilized to integrate forest inventory data of varying spatial resolutions including field assessments of forest species composition, color infrared aerial photography, Landsat Thematic Mapper Imagery, and Advanced Very High Resolution Radiometer imagery These data were used to calibrate a series of least squares linear mixture models capable of generating detailed information describing the spatial distribution of eight forest species group cover types within a three state study site encompassing North Carolina, South Carolina, and Georgia, USA.