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Microsoft released a U.S.-wide vector building dataset in 2018. Although the vector building layers provide relatively accurate geometries, their use in large-extent geospatial analysis comes at a high computational cost. We used High-Performance Computing (HPC) to develop an algorithm that calculates six summary values for each cell in a raster representation of each U.S. state, excluding Alaska and Hawaii: (1) total footprint coverage, (2) number of unique buildings intersecting each cell, (3) number of building centroids falling inside each cell, and area of the (4) average, (5) smallest, and (6) largest area of buildings that intersect each cell. These values are represented as raster layers with 30 m cell size covering the 48 conterminous states. We also identify errors in the original building dataset. We evaluate precision and recall in the data for three large U.S. urban areas. Precision is high and comparable to results reported by Microsoft while recall is high for buildings with footprints larger than 200 m2 but lower for progressively smaller buildings. Measurement(s) building • building footprint • area • building count Technology Type(s) computational modeling technique Sample Characteristic - Environment city Sample Characteristic - Location contiguous United States of America Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12444776

Despite the intensity of the 2018 Camp Fire, many structures survived in heavily burned areas. Logistic regressions were run to determine which structural and parcel characteristics predicted structure survival using two data sets. The first, CAL FIRE’s Damage Inspections (DINS) dataset, included 14 518 destroyed and 622 partially damaged structures. The second, combining information from the DINS and Defensible Space (DINS+DSPACE) databases, had many more attributes and was better balanced between destroyed (n = 728) and surviving (n = 676) structures, but was much smaller. Several approaches were compared for filtering out records with null values. Results were largely consistent with previously literature, finding that structural hardness factors (e.g. double-paned windows, enclosed eaves, ignition-resistant roofs and siding, no vents, etc.) are important in determining structure survival. Newer structures, built after California’s recent (2005 and 2007) fire safe building code updates, were more likely to survive, as were homes with higher improvement values. Mobile homes were far more likely to be destroyed. The role of fuel mitigation around structures was less conclusive; defensible space clearance had only a weak association with structure survival, although DINS+DSPACE results suggested a slight reduction in risk due to removing leaves and needles from gutters/roofs and keeping surrounding dead grass mowed.

BackgroundA recent study of licensed drivers found a non-linear relationship between density of non-residential destinations (NRDs), a proxy for walkability and body mass index (BMI) across a wide range of development patterns. It is unclear if this relationship can be replicated in a population with multiple chronic conditions or translated to health outcomes other than BMI.MethodsWe obtained health data and home addresses for 2405 adults with multiple chronic conditions from 44 primary care clinics across 13 states using the Integrating Behavioral health and Primary Care Trial. In this cross-sectional study, the relationships between density of NRDs (from a commercial database) within 1 km of the home address and self-reported BMI, and mental and physical health indices were assessed using several non-linear methods, including restricted cubic splines, LOWESS smoothing curves, non-parametric regression with a spline basis and piecewise linear regression.ResultsAll methods demonstrated similar non-linear relationships. Piecewise linear regression was selected for ease of interpretation. BMI had a positive marginal rate of change below the NRD density inflection point of 15 establishments/hectare (β=+0.09 kg/m2/non-residential buildings ha-1; 95% CI +0.01 to +0.14), and a negative marginal rate of change above the inflection point (β=−0.02; 95% CI −0.06 to 0.02). Mental health decreased with NRD density below the inflection point (β=−0.24; 95% CI −0.31 to −0.17) and increased above it (β=+0.03; 95% CI −0.00 to +0.07). Results were similar for physical health (β= −0.28; 95% CI −0.35 to −0.20) and (β=+0.06; 95% CI 0.01 to +0.10).ConclusionHealth indicators were the lowest in middle density (typically suburban) areas and got progressively better moving in either direction from the peak. NRDs may affect health differently depending on home-address NRD density.Trial registration numberNCT02868983.

Accurate and timely information about land cover pattern and change in urbanareas is crucial for urban land management decision-making, ecosystem monitoring andurban planning. This paper presents the methods and results of an object-basedclassification and post-classification change detection of multitemporal high-spatialresolution Emerge aerial imagery in the Gwynns Falls watershed from 1999 to 2004. TheGwynns Falls watershed includes portions of Baltimore City and Baltimore County,Maryland, USA. An object-based approach was first applied to implement the land coverclassification separately for each of the two years. The overall accuracies of theclassification maps of 1999 and 2004 were 92.3% and 93.7%, respectively. Following theclassification, we conducted a comparison of two different land cover change detectionmethods: traditional (i.e., pixel-based) post-classification comparison and object-basedpost-classification comparison. The results from our analyses indicated that an objectbasedapproach provides a better means for change detection than a pixel based methodbecause it provides an effective way to incorporate spatial information and expertknowledge into the change detection process. The overall accuracy of the change mapproduced by the object-based method was 90.0%, with Kappa statistic of 0.854, whereasthe overall accuracy and Kappa statistic of that by the pixel-based method were 81.3% and0.712, respectively.

The National Land Cover Database (NLCD) provides time-series data characterizing the land surface for the United States, including land cover and tree canopy cover (NLCD-TC). NLCD-TC was first published for 2001, followed by versions for 2011 (released in 2016) and 2011 and 2016 (released in 2019). As the only nationwide tree canopy layer, there is value in assessing NLCD-TC accuracy, given the need for cross-city comparisons of urban forest characteristics. Accuracy assessments have only been conducted for the 2001 data and suggest substantial inaccuracies for that dataset in cities. For the most recent NLCD-TC version, we used various datasets that characterize the built environment, weather, and climate to assess their accuracy in different contexts within 27 cities. Overall, NLCD underestimates tree canopy in urban areas by 9.9% when compared to estimates derived from those high-resolution datasets. Underestimation is greater in higher-density urban areas (13.9%) than in suburban areas (11.0%) and undeveloped areas (6.4%). To evaluate how NLCD-TC error in cities could be reduced, we developed a decision tree model that uses various remotely sensed and built-environment datasets such as building footprints, urban morphology types, NDVI (Normalized Difference Vegetation Index), and surface temperature as explanatory variables. This predictive model removes bias and improves the accuracy of NLCD-TC by about 3%. Finally, we show the potential applications of improved urban tree cover data through the examples of ecosystem accounting in Seattle, WA, and Denver, CO. The outputs of rainfall interception and urban heat mitigation models were highly sensitive to the choice of tree cover input data. Corrected data brought results closer to those from high-resolution model runs in all cases, with some variation by city, model, and ecosystem type. This suggests paths forward for improving the quality of urban environmental models that require tree canopy data as a key model input.

This paper examines predictors of vegetative cover on private lands in Baltimore, Maryland. Using high-resolution spatial data, we generated two measures: “possible stewardship,” which is the proportion of private land that does not have built structures on it and hence has the possibility of supporting vegetation, and “realized stewardship,” which is the proportion of possible stewardship land upon which vegetation is growing. These measures were calculated at the parcel level and averaged by US Census block group. Realized stewardship was further defined by proportion of tree canopy and grass. Expenditures on yard supplies and services, available by block group, were used to help understand where vegetation condition appears to be the result of current activity, past legacies, or abandonment. PRIZM[trade mark sign] market segmentation data were tested as categorical predictors of possible and realized stewardship and yard expenditures. PRIZM[trade mark sign] segmentations are hierarchically clustered into 5, 15, and 62 categories, which correspond to population density, social stratification (income and education), and lifestyle clusters, respectively. We found that PRIZM 15 best predicted variation in possible stewardship and PRIZM 62 best predicted variation in realized stewardship. These results were further analyzed by regressing each dependent variable against a set of continuous variables reflective of each of the three PRIZM groupings. Housing age, vacancy, and population density were found to be critical determinants of both stewardship metrics. A number of lifestyle factors, such as average family size, marriage rates, and percentage of single-family detached homes, were strongly related to realized stewardship. The percentage of African Americans by block group was positively related to realized stewardship but negatively related to yard expenditures.

We intend to estimate the value of ecosystem services in the U.S. State of New Jersey using spatially explicit benefit transfer. The aggregated net rent, a conservative underestimate for the total economic value of the state's natural environment, ranged from $11.6 to $19.6 billion/year, conditional on how inclusive we were in selecting the primary studies used to calculate the central tendency values to transfer. In addition to calculating the range, mean, and standard deviation for each of 12 ecosystem services for 11 Land Use/Land Cover (LULC) types, we also conduct a gap analysis of how well ecosystem service values are represented in the literature. We then map these values by assuming a mean value for each LULC and apply this to spatial data. As to sensitivity analysis, we calculate the net present value of New Jersey's natural environment utilizing three different methods of discounting. These research results provide a useful, albeit imperfect, basis for assessing the value of ecosystem services and natural capital, and their comparison with the value of conventional human and built capitals.

In this study, we develop a vegetation monitoring framework which is applicable at a planetary scale, and is based on the BACI (Before-After, Control-Impact) design. This approach utilizes Google Earth Engine, a state-of-the-art cloud computing platform. A web-based application for users named EcoDash was developed. EcoDash maps vegetation using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) products (the MOD13A1 and MYD13A1 collections) from both Terra and Aqua sensors from the years 2000 and 2002, respectively. to detect change in vegetation, we define an EVI baseline period, and then draw results at a planetary scale using the web-based application by measuring improvement or degradation in vegetation based on the user-defined baseline periods. We also used EcoDash to measure the impact of deforestation and mitigation efforts by the Vietnam Forests and Deltas (VFD) program for the Nghe An and Thanh Hoa provinces in Vietnam. Using the period before 2012 as a baseline, we found that as of March 2017, 86% of the geographical area within the VFD program shows improvement, compared to only a 24% improvement in forest cover for all of Vietnam. Overall, we show how using satellite imagery for monitoring vegetation in a cloud-computing environment could be a cost-effective and useful tool for land managers and other practitioners

This article outlines an approach, based on ecosystem services, for assessing the trade-offs inherent in managing humans embedded in ecological systems. Evaluating these trade-offs requires an understanding of the biophysical magnitudes of the changes in ecosystem services that result from human actions, and of the impact of these changes on human welfare. We summarize the state of the art of ecosystem services–based management and the information needs for applying it. Three case studies of Long Term Ecological Research (LTER) sites—coastal, urban, and agricultural—illustrate the usefulness, information needs, quantification possibilities, and methods for this approach. One example of the application of this approach, with rigorously established service changes and valuations taken from the literature, is used to illustrate the potential for full economic valuation of several agricultural landscape management options, including managing for water quality, biodiversity, and crop productivity.

Moderate-resolution (30-m) national map products have limited capacity to represent fine-scale, heterogeneous urban forms and processes, yet improvements from incorporating higher resolution predictor data remain rare. In this study, we applied random forest models to high-resolution land cover data for 71 U.S. urban areas, moderate-resolution National Land Cover Database (NLCD) Tree Canopy Cover (TCC), and additional explanatory climatic and structural data to develop an enhanced urban TCC dataset for U.S. urban areas. With a coefficient of determination (R 2 ) of 0.747, our model estimated TCC within 3% for 62 urban areas and added 13.4% more city-level TCC on average, compared to the native NLCD TCC product. Cross validations indicated model stability suitable for building a national-scale TCC dataset (median R 2 of 0.752, 0.675, and 0.743 for 1,000-fold cross validation, urban area leave-one-out cross validation, and cross validation by Census block group median year built, respectively). Additionally, our model code can be used to improve moderate-resolution TCC in other parts of the world where high-resolution land cover data have limited spatiotemporal availability.