D. Stow

Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case

The purpose of this paper is to evaluate spatial resolution effects on image classification. Classification maps were generated with a maximum likelihood (ML) classifier applied to three multi-spectral bands and variance texture images. A total of eight urban land use/cover classes were obtained at six spatial resolution levels based on a series of aggregated Colour Infrared Digital Orthophoto Quarter Quadrangle (DOQQ) subsets in urban and rural fringe areas of the San Diego metropolitan area. The classification results were compared using overall and individual classification accuracies. Classification accuracies were shown to be influenced by image spatial resolution, window size used in texture extraction and differences in spatial structure within and between categories. The more heterogeneous are the land use/cover units and the more fragmented are the landscapes, the finer the resolution required. Texture was more effective for improving the classification accuracy of land use classes at finer resolution levels. For spectrally homogeneous classes, a small window is preferable. But for spectrally heterogeneous classes, a large window size is required.

Variability of the Seasonally Integrated Normalized Difference Vegetation Index Across the North Slope of Alaska in the 1990s

The interannual variability and trend of above-ground photosynthetic activity of Arctic tundra vegetation in the 1990s is examined for the north slope region of Alaska, based on the seasonally integrated normalized difference vegetation index (SINDVI) derived from local area coverage (LAC) National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data. Smaller SINDVI values occurred during the three years (1992-1994) following the volcanic eruption of Mt Pinatubo. Even after implementing corrections for this stratospheric aerosol effect and adjusting for changes in radiometric calibration coefficients, an apparent increasing trend of SINDVI in the 1990s is evident for the entire north slope. The most pronounced increase was observed for the foothills physiographical province.

Land-Cover Change Monitoring with Classification Trees Using Landsat TM and Ancillary Data

We monitored land-cover change in San Diego County (1990‐1996) using multitemporal Landsat TM data. Change vectors of Kauth Thomas features were combined with stable multitemporal Kauth Thomas features and a suite of ancillary variables within a classification tree classifier. A combination of aerial photointerpretation and field measurements yielded training and validation data. Maps of land-cover change were generated for three hierarchical levels of change classification of increasing detail: change vs. no-change; four classes representing broad increase and decrease classes; and nine classes distinguishing increases or decreases in tree canopy cover, shrub cover, and urban change. The multitemporal Kauth Thomas (both stable and change features representing brightness, greenness, and wetness) provided information for magnitude and direction of land-cover change. Overall accuracies of the land-cover change maps were high (72 to 92 percent). Ancillary variables representing elevation, fire history, and slope were most significant in mapping the most complicated level of land-cover change, contributing 15 percent to overall accuracy. Classification trees have not previously been used operationally with remotely sensed and ancillary data to map land-cover change at this level of thematic detail.

Object-based classification of residential land use within Accra, Ghana based on QuickBird satellite data

A segmentation and hierarchical classification approach applied to QuickBird multispectral satellite data was implemented, with the goal of delineating residential land use polygons and identifying low and high socio‐economic status of neighbourhoods within Accra, Ghana. Two types of object‐based classification strategies were tested, one based on spatial frequency characteristics of multispectral data, and the other based on proportions of Vegetation–Impervious–Soil sub‐objects. Both approaches yielded residential land‐use maps with similar overall percentage accuracy (75%) and kappa index of agreement (0.62) values, based on test objects from visual interpretation of QuickBird panchromatic imagery.

Measuring temporal compositions of urban morphology through spectral mixture analysis: toward a soft approach to change analysis in crowded cities

This paper reports on preliminary results from a study applying the technique of spectral mixture analysis (SMA) to the measurement of temporal changes in the composition of urban morphology in the metropolitan area of Greater Cairo, Egypt, between 1987 and 1998. Although several remote sensing techniques have been used successfully for urban change analysis, most of these focus on change ‘between’ classes measured in a discrete, crisp way through which each pixel is assigned to a label indicating either a change or no change in the class to which the pixel originally belonged. In many major cities, such as Cairo, change also occurs within classes (e.g. vertical growth of buildings, increase in housing density, decrease in open spaces) and is reflected by an aggregation of land cover and urban materials. None of these materials may seem important in isolation. Rather, the significance of these urban land covers arises from the way they interweave with each other to structure the morphology of the urban place. In this paper, a ‘soft’ approach is presented to identify and measure the composition of changing morphology from multi‐temporal, multi‐spectral satellite images. SMA is demonstrated to be capable of deriving spatially continuous variables quantified at the sub‐pixel level. These variables represent measures that can be compared across urban places and at different time periods. They can be integrated readily into a wide range of applications and models concerned with physical, economic and/or socio‐demographic phenomena in the city.

Interannual growth dynamics of vegetation in the Kuparuk River watershed, Alaska based on the Normalized Difference Vegetation Index

Interannual above-ground production patterns are characterized for three tundra ecosystems in the Kuparuk River watershed of Alaska using NOAA-AVHRR Normalized Difference Vegetation Index (NDVI) data. NDVI values integrated over each growing season (SINDVI) were used to represent seasonal production patterns between 1989 and 1996. Spatial differences in ecosystem production were expected to follow north-south climatic and soil gradients, while interannual differences in production were expected to vary with variations in seasonal precipitation and temperature. It was hypothesized that the increased vegetation growth in high latitudes between 1981 and 1991 previously reported would continue through the period of investigation for the study watershed. Zonal differences in vegetation production were confirmed but interannual variations did not covary with seasonal precipitation or temperature totals. A sharp reduction in the SINDVI in 1992 followed by a consistent increase up to 1996 led to a further hypothesis that the interannual variations in SINDVI were associated with variations in stratospheric optical depth. Using published stratospheric optical depth values derived from the SAGE and SAGE-II satellites, it is demonstrated that variations in these depths are likely the primary cause of SINDVI interannual variability.

PHYSIOLOGICAL MODELS FOR SCALING PLOT MEASUREMENTS OF CO2 FLUX ACROSS AN ARCTIC TUNDRA LANDSCAPE

Regional estimates of arctic ecosystem CO2 exchange are required because of the large soil carbon stocks located in arctic regions, the potentially large global-scale feedbacks associated with climate-change-induced alterations in arctic ecosystem C sequestration, and the substantial small-scale (1–10 m2) heterogeneity of arctic vegetation and hydrology. Because the majority of CO2 flux data for arctic ecosystems are derived from plot-scale studies, a scaling routine that can provide reliable estimates of regional CO2 flux is required. This study combined data collected from chamber measurements of CO2 exchange, meteorology, hydrology, and surface reflectance with simple physiological models to quantify the diurnal and seasonal dynamics of whole-ecosystem respiration (R), gross primary production (GPP), and net CO2 exchange (F) of wet- and moist-sedge tundra ecosystems of arctic Alaska. Diurnal fluctuations in R were expressed as exponential functions of air temperature, whereas diurnal fluctuations in GPP were described as hyperbolic functions of diurnal photosynthetic photon flux density (PPFD). Daily integrated rates of R were expressed as an exponential function of average daily water table depth and temperature, whereas daily fluctuations in GPP were described as a hyperbolic function of average daily PPFD and a sigmoidal function of the normalized difference vegetation index (NDVI) calculated from satellite imagery. These models described, on average, 75–97% of the variance in diurnal R and GPP, and 78–95% of the variance in total daily R and GPP. Model results suggest that diurnal F can be reliably predicted from meteorology (radiation and temperature), but over seasonal time scales, information on hydrology and phenology is required to constrain the response of GPP and R to variations in temperature and radiation. Using these physiological relationships and information about the spatial variance in surface features across the landscape, measurements of CO2 exchange in 0.5-m2 plots were extrapolated to the hectare scale. Compared to direct measurements of hectare-scale F made using eddy covariance, the scaled estimate of seasonally integrated F was within 20% of the observed value. With a minimum of input data, these models allowed plot measurements of arctic ecosystem CO2 exchange to be confidently scaled in space and time.

Greenness trends of Arctic tundra vegetation in the 1990s: comparison of two NDVI data sets from NOAA AVHRR systems

The primary objective of this study was to compare the sensitivity of two different normalized difference vegetation index (NDVI) time series derived from Local Area Coverage (LAC) and Global Areal Coverage (GAC) data sets of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellite system. This comparison was conducted in the context of analysing spatiotemporal patterns of Arctic tundra vegetation greenness change in the 1990s within the North Slope of Alaska. A second objective was to examine patterns of greenness change with respect to the distribution of vegetation association types. An 8 km spatial resolution NDVI series was produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center based on a GAC data set and corrected for stratospheric aerosol effects from the eruption of Mt Pinatubo. The LAC (1 km spatial resolution) NDVI time series was generated through recalibration and fine‐tuning of image registration of a twice‐monthly time series produced by the US Geological Survey, and was cross‐calibrated with the GIMMS data set to reduce stratospheric aerosol effects from the Mt Pinatubo eruption. While the general patterns of pixels exhibiting significant increase in seasonally integrated NDVI over the 1990s were similar from both data sets, many of the more localized areas of more rapidly increasing greenness (i.e. ‘hotspots’) between 1990 and 1999 were lost with the product from the GIMMS data set. The majority of the ‘hotspots’ of greenness increase within the North Slope region are located in the southern portions of the foothills physiographic province and within vegetation units composed primarily of prostrate or dwarf shrubs with a mixture of graminoid species. Notably fewer hotspots of greenness increase were detected in Arctic tundra areas of the Seward Peninsula and none in the Chukotka Peninsula of the Russian Far East, an area that had not experienced the same warming trend in the 1990s and preceding decades as the Alaskan Arctic.

MODIS‐derived visible atmospherically resistant index for monitoring chaparral moisture content

Time series of normalized difference indices (NDIs) derived from MODIS surface reflectance data provide potentially useful information for monitoring fuel moisture content (FMC) for fire risk assessment. The visible atmospherically resistant index (VARI) and normalized difference water index (NDWI) were compared for monitoring live FMC of chaparral shrublands. Regression coefficients are encouraging given disparate spatial resolutions of ground‐based FMC measurements and MODIS‐derived NDIs. VARI exhibited greater temporal co‐variability (0.79>r 2<0.94) and spatial robustness with FMC than NDWI, even though the former is based solely on visible waveband reflectance data.

Estimating CO2 exchange at two sites in Arctic tundra ecosystems during the growing season using a spectral vegetation index

Measurements of carbon fluxes in Arctic tundra landscapes are generally obtained through intensive field work and involve the use of chamber and/or micrometeorological tower techniques. However, findings in a variety of nonArctic ecosystems have demonstrated the potential of remote sensing-based techniques (particularly spectral vegetation indices) to provide estimates of CO2 exchange in a more timely and efficient manner. As the firststep towards modelling Arctic regional and circumpolar fluxes of CO2 using remotely sensed data, we investigated the relationships between plot-level fluxes of CO2 and a vegetation spectral reflectance index derived from hand-held radiometric data at two sites. These relationships were evaluated for variations in vegetation cover type and environmental factors using data collected during the short Arctic growing season. Overall, this study demonstrated a relationship between the Normalized Difference Vegetation Index (NDVI) and measurements of mean site gross photosynthesis ...