Lloyd L. Coulter

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.

Connecting the Dots Between Health, Poverty and Place in Accra, Ghana

West Africa has a rapidly growing population, an increasing fraction of which lives in urban informal settlements characterized by inadequate infrastructure and relatively high health risks. Little is known, however, about the spatial or health characteristics of cities in this region or about the spatial inequalities in health within them. In this article we show how we have been creating a data-rich field laboratory in Accra, Ghana, to connect the dots between health, poverty, and place in a large city in West Africa. Our overarching goal is to test the hypothesis that satellite imagery, in combination with census and limited survey data, such as that found in demographic and health surveys (DHSs), can provide clues to the spatial distribution of health inequalities in cities where fewer data exist than those we have collected for Accra. To this end, we have created the first digital boundary file of the city, obtained high spatial resolution satellite imagery for two dates, collected data from a longitudinal panel of 3,200 women spatially distributed throughout Accra, and obtained microlevel data from the census. We have also acquired water, sewerage, and elevation layers and then coupled all of these data with extensive field research on the neighborhood structure of Accra. We show that the proportional abundance of vegetation in a neighborhood serves as a key indicator of local levels of health and well-being and that local perceptions of health risk are not always consistent with objective measures.

A frame centre matching approach to registration for change detection with fine spatial resolution multi-temporal imagery

A frame centre (FC) matching approach to the acquisition and geometric processing of multi-temporal datasets derived from airborne digital frame imagery was tested to determine if registration errors could be reduced relative to more ad hoc acquisition and registration approaches. The root mean square error (RMSE) of test points from image sets derived with the FC approach was at least 50% less than when frames for the second date of imagery were not centred on frames from the first date. The registration RMSE for FC images approached 2 pixels (2 m) for the central portion of image frames that is normally used to generate an image mosaic. The FC approach yielded visual and quantitative improvements in multi-temporal difference images that are commonly utilized for change detection analyses.

Irrigated Vegetation Assessment for Urban Environments

Assuring the availability of water in the southwestern United States is a major resource management problem. Irrigation of landscape vegetation within urban environments represents a large portion of the total urban water consumption for this region. Current estimates suggest that up to 50 percent of residential water is used for landscape irrigation. This paper examines the utility of Ikonos multispectral satellite imagery and expert classifier approaches for quantifying the amount and distribution of urban irrigated landscape vegetation. A decision tree, expert classifier model applied to Ikonos image and land-use GIS layer inputs was tested against conven tional image classification approaches. With all branches of the decision tree activated, percentage estimates of urban irrigated vegetation versus impervious cover differed from airborne image-derived reference data by less than 8 percent. Highest agreement was achieved using all model branches except a spatial structure rule, which utilized a texture metric derived from Ikonos 1-m panchromatic data. For this same product, proportion estimates of two growth form types (Tree/ Shrub and Grass) and impervious cover differed from reference data by less than 3 percent, and root-mean-square error (RMSE) values for all neighborhood-size sampling units were within 5 percent for all cover types. This “optimal” expert classifier product yielded areal proportion estimates and RMSE values that were approximately 2 percent closer to those of the reference data, compared to standard unsupervised classification applied to Ikonos multispectral data.

A frame center matching technique for precise registration of multitemporal airborne frame imagery

Accurate spatial registration between multitemporal imagery is necessary if pixel-level changes are to be detected. Registration of multitemporal airborne frame imagery is complicated by image distortions resulting from wide view angles and variable terrain. A novel technique for acquiring and precisely registering multitemporal airborne frame imagery is presented. This technique, referred to as frame center (FC) matching, may enable precise registration between a long series of multitemporal airborne imagery. Polynomial warping and orthorectification algorithms were tested for registering multitemporal imagery acquired with matched FCs. Registration results were compared to those derived using imagery not acquired with matched FC locations. The FC matching approach to image acquisition yielded substantially lower misregistration errors between all multitemporal image sets for each method evaluated. Special tools and protocols required for operationally replicating FCs in time sequential image acquisitions were evaluated. The effectiveness of these tools and protocols for frame-center matched acquisition and registration of multidate imagery is demonstrated.

Estimating lake area in an Arctic landscape using linear mixture modelling with AVHRR data

Estimates of lake cover fractions in Arctic tundra landscapes at regional or circumpolar scales are important for modelling carbon, energy and water fluxes. Data from the Advanced Very High Resolution Radiometer (AVHRR) are well suited to large-area studies but most lakes in the Arctic tend to be smaller than the size of the AVHRR ground elements (1.1km at nadir). This study demonstrates that lake fractions on the North Slope of Alaska may be estimated withacceptable accuracy using linear mixture modelling and AVHRR data. Landscape stratification, combining multi-date images and the inclusion of a thermal band do not appear to improve model estimates when compared to those based on single-date red and near-infrared endmembers.

Urban Vegetation Cover and Vegetation Change in Accra, Ghana: Connection to Housing Quality

The objectives are to (1) quantify, map, and analyze vegetation cover distributions and changes across Accra, Ghana, for 2002 and 2010; and (2) examine the statistical relationship between vegetation cover and a housing quality index (HQI) for 2000 at the neighborhood level. Pixel-level vegetation cover maps derived using threshold classification of 2002 and 2010 QuickBird normalized difference vegetation index images have very high overall accuracies and yield an estimate of 5.9 percent vegetation cover reduction over the study area between 2002 and 2010. A high degree of variance in vegetation cover for individual dates is explained by HQI at the neighborhood level, although minimal covariability between absolute or relative vegetation cover change and HQI for 2000 was observed.

The effect of input data transformations on object-based image analysis

The effect of using spectral transform images as input data on segmentation quality and its potential effect on products generated by object-based image analysis are explored in the context of land cover classification in Accra, Ghana. Five image data transformations are compared to untransformed spectral bands in terms of their effect on segmentation quality and final product accuracy. The relationship between segmentation quality and product accuracy is also briefly explored. Results suggest that input data transformations can aid in the delineation of landscape objects by image segmentation, but the effect is idiosyncratic to the transformation and object of interest.

Time–space radiometric normalization of TM/ETM+ images for land cover change detection

A novel approach to image radiometric normalization for change detection is presented. The approach referred to as stratified relative radiometric normalization (SRRN) uses a time-series of imagery to stratify the landscape for localized radiometric normalization. The goal is to improve the detection accuracy of abrupt land cover changes (human-induced, natural disaster, etc.) while decreasing false detection of natural vegetation changes that are not of interest. These vegetation changes may be associated with such phenomena as phenology, growth and stress (e.g. drought), which occur at varying spatial and temporal scales, depending on landscape position, vegetation type, season, precipitation history and historic episodes of local disturbance. The SRRN approach was tested for a study area on the Californian border between the USA and Mexico using Landsat Thematic Mapper and Enhanced Thematic Mapper Plus satellite imagery. Change products were generated from imagery radiometrically normalized using the SRRN procedure and with imagery normalized using a traditional empirical line technique. Reference data derived from high spatial resolution airborne imagery were utilized to validate the two change products. The SRRN procedure provided several benefits and was found to improve the overall accuracy of detecting abrupt land cover changes by nearly 20%.

Monitoring cross-border trails using airborne digital multispectral imagery and interactive image analysis techniques

The objective of this study was to evaluate image-based procedures for monitoring cross-border foot trails in the US – Mexico border zone in eastern San Diego County using airborne remote sensing techniques. Specifically, digital multi-spectral and multi-temporal imagery from an airborne digital multi-spectral imaging system, digital image processing, and visual image analysis techniques were explored in the context of detecting and delineating new trail features and updating trail GIS layers. Three trail updating approaches: map-to-image (M-I) overlay, map and image-to-image (M/I-I) differencing, map and image-to-image (M/I-I) swiping and two types of spectral transform, PCA and NDVI, were tested and compared. The M-I overlay was found to be the most reliable trail updating approach. The optimal image enhancement method for the M-I overlay approach varied with vegetation structure. PCA imagery yielded better results than NDVI imagery in a highly disturbed area and NDVI imagery performed better in a densely vegetated area. The M/I-I swiping approach was useful for distinguishing misregistered extant trails from new trail features.