We study the extent to which U. S. urban development is sprawling and what determines differences in sprawl across space. Using remote-sensing data to track the evolution of land use on a grid of 8.7 billion 30 30 meter cells, we measure sprawl as the amount of undeveloped land surrounding an average urban dwell-ing. The extent of sprawl remained roughly unchanged between 1976 and 1992, although it varied dramatically across metropolitan areas. Ground water avail-ability, temperate climate, rugged terrain, decentralized employment, early pub-lic transport infrastructure, uncertainty about metropolitan growth, and unincor-porated land in the urban fringe all increase sprawl. I.

Abstract: We study the relationship between urban sprawl and obesity. Using data that tracks individuals over time, we find no evidence that urban sprawl causes obesity. We show that previous findings of a positive relationship most likely reflect a failure to properly control for the fact the individuals who are more likely to be obese choose to live in more sprawling neighborhoods. Our results indicate that current interest in changing the built environment to counter the rise in obesity is misguided.

Abstract.................................... Status of available> information................ Methods of preparing and reporting coal reserve estimates.................... Classification according to

Forest cover is of great interest to a variety of scientific and land management applications, many of which require not only information on forest categories, but also tree canopy density. In previous studies, large area tree canopy density had been estimated at spatial resolutions of 1km or coarser using coarse resolution satellite images. In this study, a strategy is developed for estimating tree canopy density at a spatial resolution of 30 m. This strategy is based on empirical relationships between tree canopy density and Landsat data, established using linear regression and regression tree techniques. One-meter digital orthophoto quadrangles were used to derive reference tree canopy density data needed for calibrating the relationships between canopy density and Landsat spectral data. This strategy was tested over three areas of the United States. In general, models derived using both linear regression and regression tree techniques were statistically significant. The regression tree was found more robust than linear regression, primary due to its capability of approximating complex non-linear relationships using a set of linear equations. This strategy will be recommended for use in developing a nation wide tree canopy density data set at a 30 m resolution as part of the Multi-Resolution Land

Federal wildland fire policy in the United States has been substantially revised over the past 10 years and new emphasis has been given to the wildland-urban interface (WUI), which creates a need for information about the WUI's location and extent. We operationalized a policy definition published in the Federal Register (US Department of the Interior [USDI] and US Department of Agriculture [USDA]), 2001, Urban wildland interface communities within vicinity of federal lands that are at high risk from wildfire. Fed. Regist. 66(3):751-777) to create national maps and statistics of the WUI to guide strategic planning. Using geographic information system analysis, we evaluate the national WUI by altering the definition's parameters to assess the influence of individual parameters (i.e., housing density, vegetation type and density, and interface buffer distance) and stability of outcomes. The most sensitive parameter was the housing density threshold. Changes in outputs (WUI homes and area) were much smaller than parameter variations suggesting the WUI definition generates stable results on most landscapes. Overall, modifying the WUI definition resulted in a similar amount of WUI area and number of homes and affected the precise location of the WUI.

Abstract Thematic mapping via a classification analysis is one of the most common applications of remote sensing. The accuracy of image classifications is, however, often viewed negatively. Here, it is suggested that the approach to the evaluation of image classification accuracy typically adopted in remote sensing may often be unfair, commonly being rather harsh and misleading. It is stressed that the widely used target accuracy of 85% can be inappropriate and that the approach to accuracy assessment adopted commonly in remote sensing is pessimistically biased. Moreover, the maps produced by other communities, which are often used unquestioningly, may have a low accuracy if evaluated from the standard perspective adopted in remote sensing. A greater awareness of the problems encountered in accuracy assessment may help ensure that perceptions of classification accuracy are realistic and reduce unfair criticism of thematic maps derived from remote sensing. 3

Treves et al. Predicting Human-Carnivore Conflict 115 de información geográfica para discriminaráreas afectadas deáreas no afectadas a dos escalas espaciales (municipios y establecimientos). Los lobos (Canis lupus) depredaron selectivamente el ganado en municipios con proporciones altas de pasto y altas densidades de venado (Odocoileus

Abstract: We present the first global inventory of the spatial distribution and density of constructed impervious surface area (ISA). Examples of ISA include roads, parking lots, buildings, driveways, sidewalks and other manmade surfaces. While high spatial resolution is required to observe these features, the product we made is at one km2 resolution and is based on two coarse resolution indicators of ISA. Inputs into the product include the brightness of satellite observed nighttime lights and population count. The reference data used in the calibration were derived from 30 meter resolution ISA estimates of the USA from the U.S. Geological Survey. Nominally the product is for the years 2000-01 since both the nighttime lights and reference data are from those two years. We found that 1.05% of the United States land area is impervious surface (83,337 km2) and 0.43 % of the world's land Sensors 2006, 6 2 2 surface (579,703 km2) is constructed impervious surface. China has more ISA than any other country (87,182 km2), but has only 67 m2 of ISA per person, compared to 297 m2 per person in the USA. Hyrdologic and environmental impacts of ISA begin to be exhibited when the density of ISA reaches 10% of the land surface. An examination of the areas with 10% or more ISA in watersheds finds that with the exception of Europe, the majority of watershed areas have less than 0.4% of their area at or above the 10% ISA threshold. The authors believe the next step for improving the product is to include reference ISA data from many more areas around the world.

Abstract. Despite its very large territory and the best Landsat archive in the world, Canada has made very limited use of Landsat data for land cover mapping. The primary difficulty has been the prohibitive cost of information extraction and the earlier (and now overcome for Landsat-7 enhanced thematic mapper plus data) high cost of data purchase. The solution to this remaining obstacle lies in decreasing the cost of Landsat data processing and analysis while ensuring the high quality of the extracted information. In this paper, we present an efficient and effective approach to mapping land cover in Canada from Landsat thematic mapper data (single or multiple satellites). The key features of this approach are an increase in the ratio of computer to human analysis and automation for high data volume or large area processing. However, it is essential that the final product quality not suffer because of the greater reliance on computer processing, thus the algorithm performance becomes critical. We describe the overall approach, discuss key challenges, explain the principles behind key algorithms developed to respond to the challenges, present evidence demonstrating the effectiveness of these algorithms in a boreal landscape setting, and consider implementation issues. With a processing system developed to handle large numbers (tens to hundreds) of Landsat scenes, which incorporates most of the algorithms discussed here, the stage is nearly set for large-scale processing leading to a Landsat-based land cover classification product(s) for Canada. Résumé. En dépit de l’étendue de son territoire et de la disponibilité de la meilleure archive Landsat au monde, le Canada a jusqu’à maintenant très peu fait usage des données Landsat pour les besoins de la cartographie du couvert. La difficulté

Phytophthora ramorum has the potential to infect many forest types found throughout the United States. Efforts to model the potential habitat for P. ramorum and Sudden Oak Death (SOD) are important for disease regulation and management. Yet, spatial models using identical data can have differing results. In this paper we examine the results from five types of models generated from common input parameters, and investigate model agreement for distribution of risk for P. ramorum. We examine five models: (1) Rule-based, (2) Logistic regression, (3) Classification and Regression Trees, (4) Genetic Algorithm modeling, and (5) Support Vector Machines. The models differed in terms of parametric and non-parametric requirements, necessity for presence/absence data, and whether or not the explanatory variables were determined a priori or revealed during the model process. Nationwide input data included vegetation/host (hardwood diversity and hardwood density), topography, and climate (e.g. precipitation, frost days, temperature, and many other layers). We developed a risk map for the conterminous United States in which probabilities for SOD disease establishment were based not on one model, but on agreement between multiple models. The five models were consistent in their prediction of some SOD risk in coastal CA, OR and WA. All models predicted some risk in the northern foothills of the Sierra Nevada mountains in CA. Outside of the west coast, the combined models predicted highest risk for SOD in a east-west oriented band including eastern OK, central AR,