Charles W. Emerson

An evaluation of fractal methods for characterizing image complexity

Previously, we developed an integrated software package called ICAMS (Image Characterization and Modeling System) to provide specialized spatial analytical functions for interpreting remote sensing data. This paper evaluates three fractal dimension measurement methods that have been implemented in ICAMS: isarithm, variogram, and a modified version of triangular prism. To provide insights into how the fractal methods compare with conventional spatial techniques in measuring landscape complexity, the performance of two spatial autocorrelation methods, Morans I and Gearys C, is also evaluated. Results from analyzing 25 simulated surfaces having known fractal dimensions show that both the isarithm and triangular prism methods can accurately measure a range of fractal surfaces. The triangular prism method is most accurate at estimating the fractal dimension of surfaces having higher spatial complexity, but it is sensitive to contrast stretching. The variogram method is a comparatively poor estimator for all surfaces, particularly those with high fractal dimensions. As with the fractal techniques, spatial autocorrelation techniques have been found to be useful for measuring complex images, but not images with low dimensionality. Fractal measurement methods, as well as spatial autocorrelation techniques, can be applied directly to unclassified images and could serve as a tool for change detection and data mining.

A comparison of local variance, fractal dimension, and Moran's I as aids to multispectral image classification

The accuracy of traditional multispectral maximum‐likelihood image classification is limited by the multi‐modal statistical distributions of digital numbers from the complex, heterogenous mixture of land cover types in urban areas. This work examines the utility of local variance, fractal dimension and Morans I index of spatial autocorrelation in segmenting multispectral satellite imagery with the goal of improving urban land cover classification accuracy. Tools available in the ERDAS ImagineTM software package and the Image Characterization and Modeling System (ICAMS) were used to analyse Landsat ETM + imagery of Atlanta, Georgia. Images were created from the ETM + panchromatic band using the three texture indices. These texture images were added to the stack of multispectral bands and classified using a supervised, maximum likelihood technique. Although each texture band improved the classification accuracy over a multispectral only effort, the addition of fractal dimension measures is particularly effective at resolving land cover classes within urbanized areas, as compared to per‐pixel spectral classification techniques.

GIS and Paleoanthropology: Incorporating New Approaches from the Geospatial Sciences in the Analysis of Primate and Human Evolution.

The incorporation of research tools and analytical approaches from the geospatial sciences is a welcome trend for the study of primate and human evolution. The use of remote sensing (RS) imagery and geographic information systems (GIS) allows vertebrate paleontologists, paleoanthropologists, and functional morphologists to study fossil localities, landscapes, and individual specimens in new and innovative ways that recognize and analyze the spatial nature of much paleoanthropological data. Whether one is interested in locating and mapping fossiliferous rock units in the field, creating a searchable and georeferenced database to catalog fossil localities and specimens, or studying the functional morphology of fossil teeth, bones, or artifacts, the new geospatial sciences provide an essential element in modern paleoanthropological inquiry. In this article we review recent successful applications of RS and GIS within paleoanthropology and related fields and argue for the importance of these methods for the study of human evolution in the twenty first century. We argue that the time has come for inclusion of geospatial specialists in all interdisciplinary field research in paleoanthropology, and suggest some promising areas of development and application of the methods of geospatial science to the science of human evolution.

Finding fossils in new ways: An artificial neural network approach to predicting the location of productive fossil localities

Chance and serendipity have long played a role in the location of productive fossil localities by vertebrate paleontologists and paleoanthropologists. We offer an alternative approach, informed by methods borrowed from the geographic information sciences and using recent advances in computer science, to more efficiently predict where fossil localities might be found. Our model uses an artificial neural network (ANN) that is trained to recognize the spectral characteristics of known productive localities and other land cover classes, such as forest, wetlands, and scrubland, within a study area based on the analysis of remotely sensed (RS) imagery. Using these spectral signatures, the model then classifies other pixels throughout the study area. The results of the neural network classification can be examined and further manipulated within a geographic information systems (GIS) software package. While we have developed and tested this model on fossil mammal localities in deposits of Paleocene and Eocene age in the Great Divide Basin of southwestern Wyoming, a similar analytical approach can be easily applied to fossil‐bearing sedimentary deposits of any age in any part of the world. We suggest that new analytical tools and methods of the geographic sciences, including remote sensing and geographic information systems, are poised to greatly enrich paleoanthropological investigations, and that these new methods should be embraced by field workers in the search for, and geospatial analysis of, fossil primates and hominins.

An artificial neural network-based approach to identifying mammalian fossil localities in the Great Divide Basin, Wyoming

Successful identification of fossil-bearing sedimentary deposits in the field typically requires expert knowledge in geology and anatomy and some degree of luck. One way to reduce the role of serendipity is to develop an empirical model that increases the likelihood of locating productive fossil-bearing deposits by identifying combinations of geological, geospatial and spectral features that are common to productive localities. In this example, a neural network classifier successfully identified Eocene mammalian fossil localities in the Great Divide Basin, Wyoming. This approach has broad implications for many other types of anthropological field research that also involve unique geospatially distributed phenomena.

Measuring toxic emissions from landfills using sequential screening

Comparisons of alternate spatial sampling strategies for characterizing hazardous waste sites can be performed using GIS-based stochastic simulation techniques. This work compares a traditional sampling design that uses only precise laboratory analytical methods to a sequential screening strategy that uses field screening methods to focus quantitative analysis on areas having pollutant concentrations above the analytical detection limit. Cost savings that result from eliminating uncontaminated areas from detailed analysis are spent on obtaining additional measurements until a fixed sampling budget is depleted. The screening strategy produces less biased and more precise estimates of volatile organic compounds emissions from an example landfill.

“DEVELOP THE WEST” ASSESSED: ECONOMIC AND ENVIRONMENTAL CHANGE IN THE INNER MONGOLIA AUTONOMOUS REGION, PEOPLE'S REPUBLIC OF CHINA 2000–2005

Abstract In late 1999, President Jiang Zemin requested that a comprehensive regional development campaign be designed and implemented to address growing disparities in income, development, and quality of life across three of Chinas macro-regions: eastern lowland China, central China, and western China. Through the resulting “Strategy of Developing Chinas West” Campaign, massive amounts of capital have been made available across the 12 provincial-order units now defined as “West China” to promote economic development, improve social equity across urban and rural places and mitigate environmental degradation and pollution. The Inner Mongolia Autonomous Region (IMAR), the focus of our assessment of this program, offers an ideal location to evaluate the changes that have occurred since the inception of the program in 2000 through an analysis of available socioeconomic and environmental data. Progress has been made in a number of areas. Industrial output has increased dramatically, and extra-local government investments in rural areas for infrastructure and other projects are being funded at unprecedented levels. Important problems still remain. Specifically we identify growing inequity between urban and rural places and little progress in pasture improvement as measured by MODIS-generated NDVI indices for 2000 to 2005.

An Object-Oriented Approach to Extracting Productive Fossil Localities from Remotely Sensed Imagery

Most vertebrate fossils are rare and difficult to find and although paleontologists and paleoanthropologists use geological maps to identify potential fossil-bearing deposits, the process of locating fossiliferous localities often involves a great deal of luck. One way to reduce the role of serendipity is to develop predictive models that increase the likelihood of locating fossils by identifying combinations of geological, geospatial, and spectral features that are common to productive localities. We applied GEographic Object-Based Image Analysis (GEOBIA) of high resolution QuickBird and medium resolution images from the Landsat 8 Operational Land Imager (OLI) along with GIS data such as slope and surface geology layers to identify potentially productive Eocene vertebrate fossil localities in the Great Divide Basin, Wyoming. The spectral and spatial characteristics of the image objects that represent a highly productive locality (WMU-VP-222) were used to extract similar image objects in the area covered by the high resolution imagery and throughout the basin using the Landsat imagery. During the 2013 summer field season, twenty-six locations that would not have been spotted from the road in a traditional ground survey were visited. Fourteen of the eighteen localities that were fossiliferous were identified by the predictive model. In 2014, the GEOBIA techniques were applied to Landsat 8 imagery of the entire basin, correctly identifying six new productive localities in a previously unsurveyed part of the basin.

Scaling Geocomplexity and Remote Sensing

Remotely sensed radiances from planetary surfaces or atmospheres reveal elds of immense complexity with structures typically spanning the range of scales from planetary to submillimetric: 10 or more orders of magnitude. The natural framework for analyzing, modeling, and indeed understanding such hierarchies of structures within structures is scale invariance: fractal sets and multifractal elds. A little over 15 years ago, several colleagues and CONTENTS

Integrating Scale in Remote Sensing and GIS

Integrating Scale in Remote Sensing and GIS serves as the most comprehensive documentation of the scientific and methodological advances that have taken place in integrating scale and remote sensing data. This work addresses the invariants of scale, the ability to change scale, measures of the impact of scale, scale as a parameter in process models, and the implementation of multiscale approaches as methods and techniques for integrating multiple kinds of remote sensing data collected at varying spatial, temporal, and radiometric scales. Researchers, instructors, and students alike will benefit from a guide that has been pragmatically divided into four thematic groups: scale issues and multiple scaling; physical scale as applied to natural resources; urban scale; and human health/social scale. Teeming with insights that elucidate the significance of scale as a foundation for geographic analysis, this book is a vital resource to those seriously involved in the field of GIScience.