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As the epigraph above indicates, cities have become an important part of human existence, and they represent and support most human activity. Urban areas have been the primary locations for social movements, intellectual discoveries, and the rise and fall of nations and civilizations (). It is projected that cities will only become more important as societies continue to go through the demographic transformation process. Geospatial technologies will probably play a critical role throughout this because of their ability to examine things synoptically, help manage existing infrastructure and services, and predict and model future growth.

Timely and accurate change information in the urban environment is essential for successful planning and management. The change detection may range from 1) monitoring general land cover/land use found in multiple dates of imagery, to 2) anomaly (e.g., subsidence) detection on hazardous waste sites. Remote sensing approaches to change detection have been widely used due to its cost-effectiveness, extensibility, and temporal frequency. Since the advent of high-spatial resolution satellite imagery, it has become increasing popular to detect, analyze, and monitor detailed changes such as new buildings, roads, and even patios in the urban environment. Basically, there are two types of change detection methods: 1) detection of the change using various image enhancement methods, and 2) extraction of detailed types of land-cover change based on the use of classification techniques (; )

Geospatial technologies are focused around the acquisition, integration, analysis, visualization, management and distribution of data having an explicit spatial and temporal context (). These data are usually analyzed within geographic information systems (GIS). These technologies have grown to include a wide array of technologies, many of which are actively used in urban risk assessment. First, these geospatial tools and technologies are often used for the identification of “hazards” or the establishment of “risk” parameters, like height above flood stage (elevation derived through photogrammetric methods or LIDAR) and proximity to hazards (distance). Second, they can be used to actively map risk (e.g., active wildfires detected through remote sensing). Finally, these geospatial technologies can be integrative through visualization tools and models often delivered through internet-based mapping and services.

Of the Earth’s 6.5 billion human inhabitants, nearly three billion live in urban settlements (). Natural increase, land tenure practices, political policy, environmental degradation, and the dynamics of regional / global economics are largely responsible for the ongoing population shift from rural agrarian regions to cities. This increased urbanization is not just a developing country phenomenon. Urban areas of North America in 1900 were home to only 50% of the continent’s population. In 2000, the percentage of North American urban inhabitants rose to 75%.

The social value of the urban forest to local urban populations has long been recognized. In contrast, the impact of the urban forest on global and local environments is not clearly understood, and the impact of urban trees on carbon sequestration, mitigation of urban heat, and removal of pollution remain topics of contemporary scientific study. Land cover conversion in urban areas is typically faster than in wildland areas, thus there is a need for rapid measurement methods of urban biophysical variables that are repeatable and economically efficient.

Urban environments offer unique opportunities for researchers in the spatial sciences. Urban systems create their own microclimate, are the realization of intensive human activity and are strongly associated with high levels of human population (). These aspects among others afford the opportunity for geospatial technologies to monitor and enhance humankind’s relationship with the environment. However, far too often is this monitoring done by researchers or interested groups which are empowered to do so (). Marginalized groups within urban settings rarely are offered the opportunity to participate in the development of a monitoring system, which could include geospatial technologies. Often these marginalized groups are not just lacking in the sense of policy decisions but their quality of health is often times inferior to more prominent groups within the city. This level of poor health is not defined and in order for it to be understood sufficiently, the level of spatial differentiation in health needs to be measured ().

Emerging research suggests that the built environment has potential to influence physical activity which, in turn, can have a protective effect against obesity and a positive impact on public health (; ). As a result, research on the association between the built environment and health is receiving increased attention in a variety of disciplines. Most research on the associations between the built environment and physical activity to date has focused on adults, but the potential links in children are largely unexplored. The present study examines how GIS and remote sensing can be used to enhance understanding of the relationships between physical activity and the built environ ment for a cohort of children from low-income urban neighborhoods in Indianapolis, Indiana.

Immediately after World War II, developers in the United States took advantage of market demand and government incentives to build new housing subdivisions for returning soldiers anxious to marry, begin families, and resume civilian life. New developments such as Levittown (New York), Park Forest (Illinois) and Lakewood (California) sprang up and were quickly filled with affordable cookie-cutter homes for veterans seeking the American Dream of suburban home ownership (Hayden 2003). The baby boom followed. As a result of the boom and international immigration, the U.S. population grew from 151 million to 300 million between 1950 and 2007. To accommodate this expanding population growth, cities and towns in the U.S. rapidly spread into their rural hinterlands.

Deer-Vehicle Collisions (DVCs) are a significant problem in many areas of the United States (). In 2002 alone, there were over 1.5 million DVCs resulting in over 1 billion dollars in damages, 150 human fatalities, and approximately 1.5 million white-tailed deer deaths (). In sum, there are roughly 4,100 accidents/day resulting in over 2.7 million dollars of damage/day. DVCs are an increasing hazard to motorists as human development spreads into rural areas where residents commute daily to urban locations. Furthermore, through urban sprawl cities further encroach into environments where wildlife have no choice but to interact with humans. In many cases these interactions can have negative impacts for both humans and wildlife.

One of the challenges for urban and regional planners and other users of remotely sensed imagery is how to select the appropriate data for a particular monitoring or mapping problem. In the past, the dearth of available imagery meant that the problem itself usually had to be adapted to fit the data, which was typically limited to either high spatial resolution film-based aerial imagery, or coarse-spatial resolution digital satellite imagery. Today, a vast range of aerial and satellite imagery is available (), opening a new range of potential scales of problems that can be investigated. However, these new options also place additional burdens on the remote sensing user, who, in selecting data, has to consider differences in spectral, temporal, radiometric, and spatial characteristics of the imagery. Spatial properties are particularly important, and the pixel size of current sensors varies over more than three orders of magnitude (from 0.6 m to 1 km and larger) ().