Types and spatial contexts of neighborhood greenery matter in associations with weight status in women across 28 U.S. communities

Excess body weight is a risk factor for many chronic diseases. Studies have identified neighborhood greenery as supportive of healthy weight. However, few have considered plausible effect pathways for ecosystem services (e.g., heat mitigation, landscape aesthetics, and venues for physical activities) or potential variations by climate. This study examined associations between weight status and neighborhood greenery that capture ecosystem services most relevant to weight status across 28 U.S. communities. Weight status was defined by body mass index (BMI) reported for 6591 women from the U.S. Sister Study cohort. Measures of greenery within street and circular areas at 500 m and 2000 m buffer distances from homes were derived for each participant using 1 m land cover data. Street area was defined as a 25 m-wide zone on both sides of street centerlines multiplied by the buffer distances, and circular area was the area of the circle centered on a home within each of the buffer distances. Measures of street greenery characterized the pedestrian environment to capture physically and visually accessible greenery for shade and aesthetics. Circular greenery was generated for comparison. Greenery types of tree and herbaceous cover were quantified separately, and a combined measure of tree and herbaceous cover (i.e., aggregate greenery) was also included. Mixed models accounting for the clustering at the community level were applied to evaluate the associations between neighborhood greenery and the odds of being overweight or obese (BMI > 25) with adjustment for covariates selected using gradient boosted regression trees. Analyses were stratified by climate zone (arid, continental, and temperate). Tree cover was consistently associated with decreased odds of being overweight or obese. For example, the adjusted odds ratio [AOR] was 0.92, 95% Confidence Interval [CI]: 0.88-0.96, given a 10% increase in street tree cover at the 2000 m buffer across the 28 U.S. communities. These associations held across climate zones, with the lowest AOR in the arid climate (AOR: 0.74, 95% CI: 0.54-1.01). In contrast, associations with herbaceous cover varied by climate zone. For the arid climate, a 10% increase in street herbaceous cover at the 2000 m buffer was associated with lower odds of being overweight or obese (AOR: 0.75, 95% CI: 0.55-1.03), whereas the association was reversed for the temperate climate, the odds increased (AOR: 1.19, 95% CI: 1.05-1.35). Associations between greenery and overweight/obesity varied by type and spatial context of greenery, and climate. Our findings add to a growing body of evidence that greenery design in urban planning can support public health. These findings also justify further defining the mechanism that underlies the observed associations.

Ecosystem services consideration in the remediation process for contaminated sites

In 2009, the U.S. Environmental Protection Agency's Science Advisory Board recommended activities to advance consideration of ecosystem services (ES) to enhance existing remediation and redevelopment processes in the U.S. This article examines advancements in the decade since, focusing on providing those involved in cleanup of contaminated sites a basic understanding of ES concepts and guidelines for considering ES at cleanup sites using a new, four-step transferable framework. Descriptions, including activities for site teams and case study applications of ES tools, are presented for each step: (1) identify site-specific ES; (2) quantify relevant ES; (3) examine how cleanup activities affect ES; and (4) identify, select, and implement solutions (e.g., Best Management Practices). The goal of this article is to provide site cleanup stakeholders, including project managers, contractors, and site responsible parties, with a stronger foundation and shared understanding to consider ES during the cleanup process for their given site. Anticipated outcomes include identifying ES benefits to inform management and tradeoff analyses, a reduction in unintended impacts on ES during site operations, and attention to developing a robust suite of ES relevant for site reuse.

A landscape assessment and associated dataset of stream confluences for the conterminous U.S

Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution. These changes often increase habitat heterogeneity relative to upstream and downstream locations, which have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns upstream and downstream of confluences. We have produced a publicly available dataset of stream confluences and associated watershed attributes for the conterminous USA. The dataset includes 1,085,629 stream confluences and 383 attributes for each confluence organized into 15 dataset tables for both tributary and mainstem upstream catchments and watersheds. Themes in the dataset include hydrology (e.g., stream order), land cover, land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). Additionally, we used measures of ecological integrity to assess the condition of the stream confluences. Aside from a generally positive east-to-west gradient in ecological condition, we found that approximately one-third of the confluences had markedly contrasting ecological conditions between mainstem and tributary, catchment and watershed, or both. The dataset should support many, multifaceted studies of stream confluence ecology.

Sediment retention by natural landscapes in the conterminous United States

Soils provide vital ecosystem services, from sequestering carbon to providing food and moderating floods. Soil erosion threatens the provisioning of these services and degrades downstream water quality. Vegetation plays an important role in soil retention: by holding it in place, soil can continue to provide ecosystem goods and services and protect water resources. The aims of this study were to: (1) develop a 30-meter resolution map of erosion in the conterminous United States, and (2) quantify the soil retention service of natural vegetation. Using the Revised Universal Soil Loss Equation and physiographic and remote sensing datasets, we estimated sheet and rill erosion. We also developed a map of sediment delivery ratio to connect erosion to downstream delivery using hydrologic connectivity. The estimated sheet and rill erosion in the conterminous United States was 1.55 Pg yr^-1, of which 0.52 Pg yr^-1 reached waterbodies. Natural land cover prevents 12.3 Pg yr^-1 of sheet and rill erosion and 5.1 Pg yr^-1 in delivery to waterbodies. The value of natural land cover in retaining sediment is a function of the land cover, physiographic characteristics, and spatial context. This study has implications for spatial prioritization of natural land cover preservation and agricultural land management to minimize sediment erosion and delivery.

Earth Observation-Based Ecosystem Services Indicators for National and Subnational Reporting of the Sustainable Development Goals

Decision-making that impacts sustainability occurs at national and subnational levels, highlighting the need for multi-scale Earth observations (EO) and geospatial data for assessing the United Nations' Sustainable Development Goals (SDGs). EnviroAtlas, developed by the United States Environmental Protection Agency and partners, provides a collection of web-based, interactive maps of environmental and socio-economic data relevant to the SDGs. EnviroAtlas maps ecosystem services indicators at national, regional, and local extents that can contribute to targets set forth in numerous goals, such as SDG 6 for clean water, SDG 11 for sustainable cities and communities, and SDG 15 for life on land. Examples of EnviroAtlas indicators that provide a way to view spatial inequalities, help fill gaps in environmental indicators, and integrate socio-economic and environmental data for the SDGs are explored herein. Remotely sensed EO data are essential for producing these indicators and informing planning and decision-making for the SDGs at subnational scales. The National Land Cover Dataset is the basis for many EnviroAtlas maps at the national extent, while National Agriculture Imagery Program and Light Detection and Ranging (LiDAR) data are used to classify Meter-scale Urban Land Cover in select US metro areas. These 30 meter and 1 meter land cover products are combined with demographic and other geospatial data (remotely sensed and otherwise) to produce integrated indicators that can aid in target setting of the SDGs. Though EnviroAtlas was created for the conterminous US, the methods for indicator creation are transferable, and the open-source code for the EnviroAtlas resource may serve as an example for other nations. Achieving the SDGs means assessing targets and decision-making outcomes at local, regional, and national levels using consistent and accurate data. Geospatial resources like EnviroAtlas that provide open access to indicators based on EO data and allow for assessment at multiple extents and resolutions are critical to broadly addressing national to subnational SDG goals and targets.

US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance

This article defines the land cover classes used in Meter-scale Urban Land Cover (MULC), a unique, high resolution (one meter2 per pixel) land cover dataset developed for 30 US communities for the United States Environmental Protection Agency (US EPA) EnviroAtlas. MULC data categorize the landscape into these land cover classes: impervious surface, tree, grass-herbaceous, shrub, soil-barren, water, wetland and agriculture. MULC data are used to calculate approximately 100 EnviroAtlas metrics that serve as indicators of nature's benefits (ecosystem goods and services). MULC, a dataset for which development is ongoing, is produced by multiple classification methods using aerial photo and LiDAR datasets. The mean overall fuzzy accuracy across the EnviroAtlas communities is 88% and mean Kappa coefficient is 0.84. MULC is available in EnviroAtlas via web browser, web map service (WMS) in the user's geographic information system (GIS), and as downloadable data at EPA Environmental Data Gateway. Fact Sheets and metadata for each MULC Community are available through EnviroAtlas. Some MULC applications include mapping green and grey infrastructure, connecting land cover with socioeconomic/demographic variables, street tree planting, urban heat island analysis, mosquito habitat risk mapping and bikeway planning. This article provides practical guidance for using MULC effectively and developing similar high resolution (HR) land cover data.

Accuracy Assessment of NLCD 2011 percent impervious cover for selected USA Metropolitan Areas

The emergence of high-resolution land cover data has created the opportunity to assess the accuracy of impervious cover (IC) provided by the National Land Cover Database (NLCD). We assessed the accuracy of the 900 m² NLCD2011 %IC for 18 metropolitan areas throughout the conterminous United States using reference data from 1 m² land cover data developed as part of the United States Environmental Protection Agency's EnviroAtlas project. Agreement was assessed from two perspectives: 1) sensitivity to the size of the assessment unit used for the comparison, and 2) utility of NLCD %IC to serve as a proxy for high-resolution IC. The former perspective was considered because statistical relationships can be sensitive to assessment unit size and shape, and the latter perspective was considered because high resolution (reference) %IC data are not available nationwide. The utility of NLCD %IC as a proxy for the high resolution data was assessed for seven lattice (square) cell sizes ranging from 1 ha to 200 ha using four EnviroAtlas IC indicators: 1) %IC per 100 ha (1 km²); 2) %IC by Census block group; 3) %IC within a 15 m (radius) of the riparian zone, and; 4) %IC within a 50 m (radius) of the riparian zone. Agreement was quantified as per assessment unit deviation (NLCD %IC - reference %IC) and summarized as Mean Absolute Deviation (MAD) and Mean Deviation (MD) both within and across the 18 metropolitan areas. Ordinary least squares (OLS) regression (y = reference %IC and x = NLCD %IC) was also used to evaluate the quality of the NLCD %IC data. MAD was ≤ 5% for six of the seven lattice cell sizes. MAD was also ≤ 5% for Census block groups > 100 ha and for both riparian units. These results suggest that uncertainty attributable to the measurement of %IC was no greater than the uncertainty related to the effect of IC on aquatic resources that have been derived from studies of aquatic condition (e.g., benthic fauna) over a range of %IC. Overall, agreement was variable from one metropolitan area to the next. Agreement improved as assessment unit size increased and declined as the level of urbanization (NLCD %IC) increased. NLCD %IC tended to underestimate reference %IC overall, but NLCD %IC was sometimes greater than reference %IC in urbanized settings.

Associations between types of greenery along neighborhood roads and weight status in different climates

Obesity is a major international health concern. Neighborhood greenery has been identified as a critical factor for promoting health in urban areas, due in part to its apparent role in facilitating healthy weight by promoting physical activity. However, studies have used diverse greenery measures and spatial analysis units to ascertain this relationship. This study examined associations between street greenery and weight status at the residential address level across 500 to 2000m buffers in two climatically distinct communities, Phoenix, AZ, and Portland, OR. Greenery was measured using one-meter landcover data. Street greenery measures were designed to quantify the pedestrian environment along a gradient of suitability for promoting physical exercise. Weight status was defined by body mass index (BMI) calculated from weight and height information on driver's license records. BMI values were dichotomized at 25 into overweight or obese vs. neither. Approximately 500,000 BMI values in Phoenix and 225,000 in Portland were modelled by community using logistic regression. Street tree cover was consistently protective for healthy weight status across all buffer sizes after adjusting for potential confounders. Herbaceous street cover showed protective associations in Phoenix but harmful associations in Portland. Every 10% increase in street tree cover within 2000m was associated with 18% lower odds of being overweight or obese (adjusted odds ratio [AOR]: 0.82, 95% CI: 0.81 - 0.84 in Phoenix; 0.82, 95% CI: 0.81 - 0.83 in Portland). When compared to residents with less than 10% street tree cover within 2000m, those with greater than 10% tree cover had at least 13% (AOR for Portland: 0.87, 95% CI: 0.81 - 0.92) lower odds of being overweight or obese. Findings support the importance of urban street trees in very different climates for facilitating healthy weight status. They can inform greenery management to prioritize vegetation type and allocation decisions in limited urban spaces.

Development of a spatially complete floodplain map of the conterminous United States using random forest

Floodplains perform several important ecosystem services, including storing water during precipitation events and reducing peak flows, thus reducing flooding of downstream communities. Understanding the relationship between flood inundation and floodplains is critical for ecosystem and community health and well-being, as well as targeting floodplain and riparian restoration. Many communities in the United States, particularly those in rural areas, lack inundation maps due to the high cost of flood modeling. Only 60% of the conterminous United States has Flood Insurance Rate Maps (FIRMs) through the U.S. Federal Emergency Management Agency (FEMA). We developed a 30-meter resolution flood inundation map of the conterminous United States (CONUS) using random forest classification to fill the gaps in the FIRM. Input datasets included digital elevation model (DEM)-derived variables, flood-related soil characteristics, and land cover. The existing FIRM 100-year floodplains, called the Special Flood Hazard Area (SHFA), were used to train and test the random forests for fluvial and coastal flooding. Models were developed for each hydrologic unit code level four (HUC-4) watershed and each 30-meter pixel in the CONUS was classified as floodplain or non-floodplain. The most important variables were DEM-derivatives and flood-based soil characteristics. Models captured 79% of the SFHA in the CONUS. The overall F1 score, which balances precision and recall, was 0.78. Performance varied geographically, exceeding the CONUS scores in temperate and coastal watersheds but were less robust in the arid southwest. The models also consistently identified headwater floodplains not present in the SFHA, lowering performance measures but providing critical information missing in many low-order stream systems. The performance of the random forest models demonstrates the method's ability to successfully fill in the remaining unmapped floodplains in the CONUS, while using only publicly available data and open source software.

A Needs-Driven, Multi-Objective Approach to Allocate Urban Ecosystem Services from 10,000 Trees

Urban areas face challenges including vehicular emissions, stormwater runoff, and sedentary lifestyles. Communities recognize the value of trees in mitigating these challenges by absorbing pollution and enhancing walkability. However, siting trees to optimize multiple benefits requires a systems approach that may cross sectors of management and expertise. We present a spatially-explicit method to optimize tree planting in Durham, NC, a rapidly growing urban area with an aging tree stock. Using GIS data and a ranking approach, we explored where Durham could augment its current stock of willow oaks through its plans to install 10,000 mid-sized deciduous trees. Data included high-resolution landcover metrics developed by the U.S. Environmental Protection Agency (EPA), demographics from the U.S. Census, an attributed roads dataset licensed to the EPA, and sidewalk information from the City of Durham. Census block groups (CBGs) were ranked for tree planting according to single and multiple objectives including stormwater reduction, emissions buffering, walkability, and protection of vulnerable populations. Prioritizing tree planting based on single objectives led to four sets of locations with limited geographic overlap. Prioritizing tree planting based on multiple objectives tended to favor historically disadvantaged CBGs. The four-objective strategy met the largest proportion of estimated regional need. Based on this analysis, the City of Durham has implemented a seven-year plan to plant 10,000 trees in priority neighborhoods. This analysis also found that any strategy which included the protection of vulnerable populations generated more benefits than others.

Building a potential wetland restoration indicator for the contiguous United States

Wetlands provide key functions in the landscape from improving water quality, to regulating flows, to providing wildlife habitat. Over half of the wetlands in the contiguous United States (CONUS) have been converted to agricultural and urban land uses. However, over the last several decades, research has shown the benefits of wetlands to hydrologic, chemical, biological processes, spurring the creation of government programs and private initiatives to restore wetlands. Initiatives tend to focus on individual wetland creation, yet the greatest benefits are achieved when strategic restoration planning occurs across a watershed or multiple watersheds. For watershed-level wetland restoration planning to occur, informative data layers on potential wetland areas are needed. We created an indicator of potential wetland areas (PWA), using nationally available datasets to identify characteristics that could support wetland ecosystems, including: poorly drained soils and low-relief landscape positions as indicated by a derived topographic data layer. We compared our PWA with the National Wetlands Inventory (NWI) from 11 states throughout the CONUS to evaluate their alignment. The state-level percentage of NWI-designated wetlands directly overlapping the PWA ranged from 39 to 95%. When we included NWI that was immediately adjacent to the overlapping NWI, our range of correspondence to NWI ranged from 60 to 99%. Wetland restoration is more likely on certain landscapes (e.g., agriculture) than others due to the lack of substantive infrastructure and the potential for the restoration of hydrology; therefore, we combined the National Land Cover Dataset (NLCD) with the PWA to identify potentially restorable wetlands on agricultural land (PRW-Ag). The PRW-Ag identified a total of over 46 million ha with the potential to support wetlands. The largest concentrations of PRW-Ag occurred in the glaciated corn belt of the upper Mississippi River from Ohio to the Dakotas and in the Mississippi Alluvial Valley. The PRW-Ag layer could assist land managers in identifying sites that may qualify for enrollment in conservation programs, where planners can coordinate restoration efforts, or where decision makers can target resources to optimize the services provided across a watershed or multiple watersheds.