Ryan R. Jensen

Using Remote Sensing and Geographic Information Systems to Study Urban Quality of Life and Urban Forest Amenities

This study examines urban quality of life by assessing the relationship between observed socioeconomic conditions and urban forest amenities in Terre Haute, Indiana, USA. Using remote-sensing methods and techniques, and ordinary least squares regression, the paper determines the relationship between urban leaf area and a population density parameter with median income and median housing value. Results demonstrate positive correlations between urban leaf area, population density, and their interaction with median income and median housing value. Furthermore, leaf area, density, and their interaction statistically account for observed variance in median income and median housing value, indicating that these variables may be used to study observed quality-of-life metrics. The methods used in this study may be useful to city managers, planners, and foresters who are concerned with urban quality-of-life issues, and who are interested in developing and implementing alternative policy assessment regimes.

Small-Scale Unmanned Aerial Vehicles in Environmental Remote Sensing: Challenges and Opportunities

Although potential applications abound, small-scale unmanned aerial vehicles have not yet been widely used for environmental remote sensing. Several challenges remain to be overcome until widespread adoption is possible. One problem is the challenge inherent in flying fragile small-scale aircraft with low weight limits and narrow center of gravity tolerances. Other challenges include: (1) the hostile natural environment in which the aircraft fly; (2) the limits of on-board power; (3) the paucity of commercially available sensors; (4) the difficulties involved in managing and analyzing the large imagery volume generated during a sortie; and (5) the federal regulations in the United States designed to ensure the safety of commercial and private air travel. Each of these challenges is formidable, and overcoming them will require the use of technologies that are currently experimental. However, within each challenge are opportunities for researchers willing to act as innovative pioneers in the remote sensing community.

Growth through greening: developing and assessing alternative economic development programmes

Abstract The paper articulates how communities can capitalize on the specific benefits of urban forestry and assesses the outcomes of urban forestry efforts. To accomplish this, the paper defines the context of local economic development and urban forestry; outlines the economic, aesthetic, and ecological benefits of a smart-growth agenda that includes urban forestry; and presents two brief case studies that empirically assess the viability of urban forestry policy by measuring the dynamics of the urban canopy. The research methodology presented here can be used by policy-makers to assess policy outcomes and the overall success of smarter and greener economic development strategies.

Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf coast.

Mangrove wetlands are economically and ecologically important ecosystems and accurate assessment of these wetlands with remote sensing can assist in their management and conservation. This study was conducted to evaluate airborne AISA hyperspectral imagery and image transformation and classification techniques for mapping black mangrove populations on the south Texas Gulf coast. AISA hyperspectral imagery was acquired from two study sites and both minimum noise fraction (MNF) and inverse MNF transforms were performed. Four classification methods, including minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM), were applied to the noise-reduced hyperspectral imagery and to the band-reduced MNF imagery for distinguishing black mangrove from associated plant species and other cover types. Accuracy assessment showed that overall accuracy varied from 84 percent to 95 percent for site 1 and from 69 percent to 91 percent for site 2 among the eight classifications for each site. The MNF images provided similar or better classification results compared with the hyperspectral images among the four classifiers. Kappa analysis showed that there were no significant differences among the four classifiers with the MNF imagery, though maximum likelihood provided excellent overall and class accuracies for both sites. Producer’s and user’s accuracies for black mangrove were 91 percent and 94 percent, respectively, for site 1 and both 91 percent for site 2 based on maximum likelihood applied to the MNF imagery. These results indicate that airborne hyperspectral imagery combined with image transformation and classification techniques can be a useful tool for monitoring and mapping black mangrove distributions in coastal environments.

Spectral analysis of coastal vegetation and land cover using AISA + hyperspectral data

This paper describes a spectral analysis of several coastal land cover types in South Padre Island, Texas using AISA+ hyperspectral remote sensing data. AISA+ hyperspectral data (1.5 metre) were acquired throughout the area on 9 March 2005. Data over mangrove areas were converted to percent reflectance using four 8×8 metre reflectance tarps (4%, 16%, 32% and 48%) and empirical line calibration. These data were then compared to percent reflectance values of other terrestrial features to determine the ability of AISA+ data to distinguish features in coastal environments. Results suggest that these data may be appropriate to discriminate coastal mangrove vegetation and provide researchers with high resolution spatial and spectral information to more effectively manage coastal ecosystems.

Measurement and comparison of Leaf Area Index estimators derived from satellite remote sensing techniques

Leaf Area Index (LAI) is an important biophysical characteristic of vegetation that is directly related to rates of atmospheric gas exchange, biomass partitioning, and productivity. Mapping and monitoring LAI over scales from landscapes to regions is essential for understanding medium-scale biophysical properties and how these properties affect biogeochemical cycling, biomass accumulation, and primary productivity. This study developed and verified several models to estimate LAI using in situ field measurements, Landsat Thematic Mapper imagery, vegetation indices, simple and multiple regression, and artificial neural networks (ANNs). It was shown that while multiple band regression and regression with individual vegetation indices can estimate LAI, the most accurate way to estimate regional scale LAI is to train an ANN using in situ LAI data and remote sensing brightness values.

Geo-spatial technologies in urban environments

Applying Geo-Spatial Technologies in Urban Environments.- Remote Sensing of Impervious Surfaces and Building Infrastructure.- Policy Implications of Remote Sensing in Understanding Urban Environments.- Making Spatial Data Usable to the General Public.-Modeling Human-Environment Interactions with the Expansion Method.- The Relationship Between Urban Leaf Area and Summertime Household Energy Usage.- The Urban Environment, Socioeconomic Conditions, and Quality of Life: An Alternative Framework for Understanding and Assessing Environmental Justice.- Image Homogeneity and Urban Demographics: An Integrated Approach to Applied Geotechniques.- Local Government Perceptions of Urban Forestry.- Satellite Remote Sensing of Urban Heat Islands.- Remote Sensing as a Program Assessment Device.- Urban Sprawl Detection Using Satellite Imagery and Geographically Weighted Regression.- Satellites, Census, and the Quality of Life.- Urban Environmental Approaches: Policy, Application and Method.

Vegetation Cover Analysis of Hazardous Waste Sites in Utah and Arizona Using Hyperspectral Remote Sensing

This study investigated the usability of hyperspectral remote sensing for characterizing vegetation at hazardous waste sites. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using three different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. HyMap airborne data (126 bands at 2.3 × 2.3 m spatial resolution), collected over the U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona, were used. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. Regression trees resulted in the best calibration performance of LAI estimation (R 2 > 0.80. The use of REPs failed to accurately predict LAI (R 2 < 0.2). The use of the MTMF-derived metrics (matched

People, pixels and weights in Vanderburgh County, Indiana: toward a new urban geography of human–environment interactions

This research examines the social-spatial dynamics of human–environment interactions in Evansville, Indiana, USA as well as the surrounding Vanderburgh County. Employing geographically weighted regression, this paper models the observed relationship between the Normalized Difference Vegetation Index (NDVI) and key sociodemographic parameters (housing value, median household income, percent of residents in poverty, population density and percent of population white). Further, this paper demonstrates that geographically weighted regression utilized within a GISci framework can be effectively used to visualize urban human–environment interactions and that the spatial distribution of environmental resources co-varies with socioeconomic conditions. Finally, the paper demonstrates that greenness indicators derived from remote sensing data can be used as proxy measures for observed sociodemographic variables.

Introduction—Small-Scale Unmanned Aerial Systems for Environmental Remote Sensing

In a recently published article entitled “How UAVs Will Change Aviation,” David Esler (2010) discussed many ways that unmanned aerial systems (UAS) have changed (and will continue to change) traditional aviation. Simply stated, Esler believed that UAS are not only here now, but they are here to stay. We share that opinion regarding small-scale UAS for environmental remote sensing—while they may currently be experimental, their use in environmental monitoring, mapping, and management will certainly increase in the future. In their present state of development, UAS are popular in academic and research remote sensing where they have been successfully used to acquire high-spatial-resolution imagery and other environmental data. The appeal of UAS to researchers is obvious. Not only can UAS potentially obtain timely imagery over areas that are difficult or dangerous to access by traditional means, this imagery can usually be acquired at a lower cost relative to other collection methods (Hardin and Hardin, 2010). The popularity of unmanned aerial systems has coincided with the rapid deployment and use of unmanned vehicles by the military (Newcome, 2004). Militaries in many countries worldwide are either flying UAS on an operational basis or are actively seeking that capability. With this increased interest in UAS, the underpinning technologies—airframe design, materials, aviation electronics, sensor systems, etc.—have also continued to improve. However, the success and proliferation of military UAS has come with an unexpected consequence—more active governmental oversight over all UAS operations. The resulting regulatory burden that must be borne by practitioners wanting to fly civilian UAS represents a substantial impediment to widespread adoption of environmental remote sensing from small unmanned aircraft. Given the promise and challenges of using UAS in environmental remote sensing, more research is called for to: (1) remove the current limitations of the technology; and (2) build effective systems and methodologies for its successful use in environmental applications. Within the foregoing context, this special issue of GIScience & Remote Sensing focuses on the use of UAS for environmental remote sensing. The processing and analysis of the imagery acquired by small-scale UAS remains a significant challenge to efficient use of that imagery. The paper by Laliberte and Rango (2011) continues to advance the leading edge of mapping ground cover from very large scale aerial imagery using automated and object-oriented approaches. In the