Accelerations in population growth and urban expansion are transforming landscapes worldwide and represent a major sustainability challenge. In the United States, land conversion to impervious ...surfaces has outpaced population increases, yet there are few spatial metrics of urbanization and per capita land change available nationwide for assessing local to regional trends in human footprint. We quantified changes (2000–2010) in housing density, imperviousness, per capita land consumption, and land-use efficiency for block groups of the contiguous U.S. and examined national patterns and variation in these metrics along the urban–rural gradient and by megaregion. Growth in housing (+13.6%) and impervious development (+10.7%) resulted in losses of rural lands, primarily due to exurbanization and suburbanization. Mean per capita consumption increased in all density classes but was over 8.5 times greater in rural lands than in exurban, suburban, and urban areas. Urban and suburban areas had significantly lower mean consumption, yet change was unsustainable in 60% of these areas. Megaregions across the sprawling Sun Belt, spanning from Arizona to North Carolina, grew most unsustainably, especially compared to regions in the Pacific Northwest and Front Range. This work establishes 21st-century benchmarks that decision-makers can use to track local and regional per capita land change and sustainable growth in the U.S.; however, these metrics of the form, extent, rate, and efficiency of urbanization can be applied anywhere concurrent built-up area and population data are available over time. Our web mapping application allows anyone to explore spatial and temporal trends in human footprint and download metrics, and it is designed to be easily updatable with future releases of validated developed land cover, protected areas, and decennial Census data.
The ways in which energy and security have been framed in Brussels since the early days of the European Coal and Steel Community through to recent developments in climate policy are considered, with ...a main focus upon the European Commission, which prepares policy for decision by the Council and Parliament. Both in terms of institutions and ideas, energy, security, and environmental policy have evolved separately. However, since 2005, there has been a growing convergence as the Commission attempts to develop the internal and external dimensions of EU climate policy. The reasons for this and the potential implications of such a 'synergistic' approach are briefly explored.
Engaging citizen scientists is becoming an increasingly popular technique for collecting large amounts of ecological data while also creating an avenue for outreach and public support for research. ...Here we describe a unique study, in which citizen scientists played a key role in the spatial prediction of an emerging infectious disease. The yearly citizen-science program called "Sudden Oak Death (SOD) Blitz" engages and educates volunteers in detecting the causal pathogen during peak windows of seasonal disease expression. We used these data - many of which were collected from under-sampled urban ecosystems - to develop predictive maps of disease risk and to inform stakeholders on where they should prioritize management efforts. We found that continuing the SOD Blitz program over 6 consecutive years improved our understanding of disease dynamics and increased the accuracy of our predictive models. We also found that self-identified non-professionals were just as capable of detecting the disease as were professionals. Our results indicate that using long-term citizen-science data to predict the risk of emerging infectious plant diseases in urban ecosystems holds substantial promise.
The structural characteristics of Light Detection and Ranging (LiDAR) data are increasingly used to classify urban environments at fine scales, but have been underutilized for distinguishing ...heterogeneous land covers over large urban regions due to high cost, limited spectral information, and the computational difficulties posed by inherently large data volumes. Here we explore tradeoffs between potential gains in mapping accuracy with computational costs by integrating structural and intensity surface models extracted from LiDAR data with Landsat Thematic Mapper (TM) imagery and evaluating the degree to which TM, LiDAR, and LiDAR-TM fusion data discriminated land covers in the rapidly urbanizing region of Charlotte, North Carolina, USA. Using supervised maximum likelihood (ML) and classification tree (CT) methods, we classified TM data at 30m and LiDAR data and LiDAR-TM fusions at 1m, 5m, 10m, 15m and 30m resolutions. We assessed the relative contributions of LiDAR structural and intensity surface models to classification map accuracy and identified optimal spatial resolution of LiDAR surface models for large-area assessments of urban land cover. ML classification of 1m LiDAR-TM fusions using both structural and intensity surface models increased total accuracy by 32% compared to LiDAR alone and by 8% over TM at 30m. Fusion data using all LiDAR surface models improved class discrimination of spectrally similar forest, farmland, and managed clearings and produced the highest total accuracies at 1m, 5m, and 10m resolutions (87.2%, 86.3% and 85.4%, respectively). At all resolutions of fusion data and using either ML or CT classifier, the relative contribution of the LiDAR structural surface models (canopy height and normalized digital surface model) to classification accuracy is greater than the intensity surface. Our evaluation of tradeoffs between data volume and thematic map accuracy for this study system suggests that a spatial resolution of 5m for LiDAR surface models best balances classification performance and the computational challenges posed by large-area assessments of land cover.
Low-density exurban development represents a unique form of landscape change motivated by aesthetics and individual choice, whether driven by perceptions of beauty or more broadly as worldviews ...expressed through outward appearance and actions. However, little is known about how individual preferences for new home sites manifest in landscape patterns of exurbanization. In this study, we examine the extent to which viewscapes - the visible part of a landscape that creates connection between people and their surroundings - drive patterns of development in the Sonoita Plain of Arizona. We mapped the locations of over 2,000 homes built before and after the Great Recession (~2010) and calculated line-of-sight viewscapes of each home with four metrics: viewscape area, privacy (number of visible neighbors), greenness (NDVI), and terrain ruggedness. We found that exurban homes have significantly larger and more private viewscapes compared to suburban homes and what would be expected by chance. After 2010, exurban homes were built at locations with yet larger and more private viewscapes even as settlement density increased. An autologistic model of post-2010 settlement patterns showed that viewscape privacy is positively associated with the probability of exurban development after accounting for road proximity and the area and greenness of viewscapes. Application of the predictive model was made possible through a new open-source algorithm that computes spatially continuous, all-possible vantage points (1.3M). Our algorithm allows planners to visualize wall-to-wall spatial patterns of viewscape drivers across a large region and more comprehensively consider the roles that viewscapes play in landscape change.
Probability of future development based on auto-logistic model eq. (Y = −9.63 + (0.03*X1) + (0.001*X2) + (−0.004*X3) + (−0.009*X4) + (yW*10.58)) from high (red) to low (blue). High probability of development is found in areas that are very private (A), are private and have larger viewscapes (B) and are private, have larger viewscapes, and see more green (C). Low probability of development is found in areas that are less private, despite being more green (D). X1 = viewscape area (km2), X2 = average maximum NDVI (0.4–0.7), X3 = privacy (number of visible neighbors), X4 = distance to primary roads, yW = auto-covariate. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Display omitted
•Exurban viewscapes are larger and more private than suburban or expected by chance.•New homes post-2010 have the largest and most private viewscapes yet.•New algorithm computes all-possible spatially continuous viewscapes for entire region.•Model application reveals role of privacy relative to other visual quality metrics.•We predict high probability of exurbanization on 43% of developable lands.
Abstract
Impacts of sea level rise will last for centuries; therefore, flood risk modeling must transition from identifying risky locations to assessing how populations can best cope. We present the ...first spatially interactive (i.e., what happens at one location affects another) land change model (FUTURES 3.0) that can probabilistically predict urban growth while simulating human migration and other responses to flooding, essentially depicting the geography of impact and response. Accounting for human migration reduced total amounts of projected developed land exposed to flooding by 2050 by 5%–24%, depending on flood hazard zone (50%–0.2% annual probability). We simulated various “what-if” scenarios and found managed retreat to be the only intervention with predicted exposure below baseline conditions. In the business-as-usual scenario, existing and future development must be either protected or abandoned to cope with future flooding. Our open framework can be applied to different regions and advances local to regional-scale efforts to evaluate potential risks and tradeoffs.
This paper summarizes land-cover and land-use change at eight sites in Thailand, Yunnan (China), Vietnam, Cambodia, and Laos over the last 50 years. Project methodology included incorporating ...information collected from a combination of semiformal, key informant, and formal household interviews with the development of spatial databases based on aerial photographs, satellite images, topographic maps, and GPS data. Results suggest that land use (e.g. swidden cultivation) and land cover (e.g. secondary vegetation) have remained stable and the minor amount of land-use change that has occurred has been a change from swidden to monocultural cash crops. Results suggest that two forces will increasingly determine land-use systems in this region. First, national land tenure policies-the nationalization of forest lands and efforts to increase control over upland resources by central governments-will provide a push factor making it increasingly difficult for farmers to maintain their traditional swidden land-use practices. Second, market pressures-the commercialization of subsistence resources and the substitution of commercial crops for subsistence crops-will provide a pull factor encouraging farmers to engage in new and different forms of commercial agriculture. These results appear to be robust as they come from eight studies conducted over the last decade. But important questions remain in terms of what research protocols are needed, if any, when linking social science data with remotely sensed data for understanding human-environment interactions.
We used the conversion of land use and its effects (CLUE-s) model to simulate scenarios of land-cover change in Montane mainland southeast Asia (MMSEA), a region in the midst of transformation due to ...rapid intensification of agriculture and expansion of regional trade markets. Simulated changes affected approximately 10 % of the MMSEA landscape between 2001 and 2025 and 16 % between 2001 and 2050. Roughly 9 % of the current vegetation, which consists of native species of trees, shrubs, and grasses, is projected to be replaced by tree plantations, tea, and other evergreen shrubs during the 50 years period. Importantly, 4 % of this transition is expected to be due to the expansion of rubber (
Hevea brasiliensis
), a tree plantation crop that may have important implications for local-to-regional scale hydrology because of its potentially high water consumption in the dry season.
Increasing population and rural to urban migration are accelerating urbanization globally, permanently transforming natural systems over large extents. Modelling landscape change over large regions, ...however, presents particular challenges due to local-scale variations in social and environmental factors that drive land change. We simulated urban development across the South Atlantic States (SAS), a region experiencing rapid population growth and urbanization, using FUTURES—an open source land change model that uses demand for development, local development suitability factors, and a stochastic patch growing algorithm for projecting alternative futures of urban form and landscape change. New advances to the FUTURES modelling framework allow for high resolution projections over large spatial extents by leveraging parallel computing. We simulated the adoption of different urban growth strategies that encourage settlement densification in the SAS as alternatives to the region’s increasing sprawl. Evaluation of projected patterns indicate a 15% increase in urban lands by 2050 given a status quo development scenario compared to a 14.8% increase for the Infill strategy. Status quo development resulted in a 3.72% loss of total forests, 2.97% loss of highly suitable agricultural land, and 3.69% loss of ecologically significant lands. An alternative Infill scenario resulted in similar losses of total forest (3.62%) and ecologically significant lands (3.63%) yet consumed less agricultural lands (1.23% loss). Moreover, infill development patterns differed qualitatively from the status quo and resulted in less fragmentation of the landscape.
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