Variation the in relationship between urban tree canopy and air temperature reduction under a range of daily weather conditions.

Mitigating heat is a vital ecosystem service of trees, particularly with climate change. Land surface temperature measures captured at a single time of day (in the morning) dominate the urban heat island literature. Less is known about how local tree canopy and impervious surface regulate air temperature throughout the day, and/or across many days with varied weather conditions, including cloud cover. We use bike-mounted air temperature sensors throughout the day in New Haven, Connecticut, USA, from 2019 to 2021 and generalized additive mixed models across 156 rides to estimate the daily variation in cooling benefits associated with tree canopy cover, and warming from impervious surface cover in 90 m buffers surrounding bike observations. Cooling is inferred by subtracting the bicycle-observed temperature from a reference station. The cooling benefits from tree canopy cover were strongest in the midday (11:00-14:00, -1.62 °C), afternoon (14:00-17:00, -1.19 °C), and morning (8:00-11:00, -1.15 °C) on clear days. The cooling effect was comparatively smaller on cloudy mornings -0.92 °C and afternoons -0.51 °C. Warming from impervious surfaces was most pronounced in the evening (17:00-20:00, 1.11 °C) irrespective of clouds, and during cloudy nights (20:00-23:00) and cloudy mornings 1.03 °C 95 % CI [1.03, 1.04]. Among the hottest observed days (top 25th percentile of reference station daily maxima), tree canopy was associated with lower temperatures on clear afternoons -1.78 °C [-1.78, -1.78], cloudy midday -1.17 °C [-1.19, -1.15], clear midday -1.12 °C [-1.12, -1.11]. We add a broader spectrum of weather conditions by explicitly including clouds, and greater temporal resolution by measuring throughout the day to bike-based urban heat research. Future mobile sampling campaigns may broaden the spatial extent with more environmental variation, representing an opportunity for public science and engagement.

Using high-resolution land cover data to assess structure loss in the 2018 Woolsey Fire in Southern California.

There are growing concerns about increases in the size, frequency, and destructiveness of wildfire events. One commonly used mitigation strategy is the creation and maintenance of defensible space, a zone around buildings where vegetation is managed to increase potential for structures to survive during wildfires. Despite widespread acceptance and advocacy of defensible space, few studies provide empirical evidence documenting the efficacy of different fuel modification practices under real wildfire conditions. The 2018 Woolsey Fire in Los Angeles County, California, occurred a short time after high-resolution (0.07 m2) land cover data were created, providing a unique opportunity to quantify vegetation before the fire. We integrated measurements from this high-resolution land cover data with parcel data, building attributes, and environmental context. We then used Random Forests models to analyze the extent to which these factors predicted structure loss in the wildfire. Variable importance scores showed vegetation around buildings was not a strong predictor of building-level damage outcomes compared to building materials and landscape features such as paved land cover per parcel, elevation, building density, and distance to road networks. Among building materials, multi-paned windows and enclosed eaves were most highly associated with building survival. These results are consistent with other studies that conclude building materials and environmental context are more related to survivorship than defensible space.

Hydro-bio-geo-socio-chemical interactions and the sustainability of residential landscapes.

Residential landscapes are essential to the sustainability of large areas of the United States. However, spatial and temporal variation across multiple domains complicates developing policies to balance these systems' environmental, economic, and equity dimensions. We conducted multidisciplinary studies in the Baltimore, MD, USA, metropolitan area to identify locations (hotspots) or times (hot moments) with a disproportionate influence on nitrogen export, a widespread environmental concern. Results showed high variation in the inherent vulnerability/sensitivity of individual parcels to cause environmental damage and in the knowledge and practices of individual managers. To the extent that hotspots are the result of management choices by homeowners, there are straightforward approaches to improve outcomes, e.g. fertilizer restrictions and incentives to reduce fertilizer use. If, however, hotspots arise from the configuration and inherent characteristics of parcels and neighborhoods, efforts to improve outcomes may involve more intensive and complex interventions, such as conversion to alternative ecosystem types.

Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling.

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.

Gauging the happiness benefit of US urban parks through Twitter.

The relationship between nature contact and mental well-being has received increasing attention in recent years. While a body of evidence has accumulated demonstrating a positive relationship between time in nature and mental well-being, there have been few studies comparing this relationship in different locations over long periods of time. In this study, we analyze over 1.5 million tweets to estimate a happiness benefit, the difference in expressed happiness between in- and out-of-park tweets, for the 25 largest cities in the US by population. People write happier words during park visits when compared with non-park user tweets collected around the same time. While the words people write are happier in parks on average and in most cities, we find considerable variation across cities. Tweets are happier in parks at all times of the day, week, and year, not just during the weekend or summer vacation. Across all cities, we find that the happiness benefit is highest in parks larger than 100 acres. Overall, our study suggests the happiness benefit associated with park visitation is on par with US holidays such as Thanksgiving and New Year's Day.

Spatial changes in park visitation at the onset of the pandemic.

The COVID-19 pandemic disrupted the mobility patterns of a majority of Americans beginning in March 2020. Despite the beneficial, socially distanced activity offered by outdoor recreation, confusing and contradictory public health messaging complicated access to natural spaces. Working with a dataset comprising the locations of roughly 50 million distinct mobile devices in 2019 and 2020, we analyze weekly visitation patterns for 8,135 parks across the United States. Using Bayesian inference, we identify regions that experienced a substantial change in visitation in the first few weeks of the pandemic. We find that regions that did not exhibit a change were likely to have smaller populations, and to have voted more republican than democrat in the 2020 elections. Our study contributes to a growing body of literature using passive observations to explore who benefits from access to nature.

Residential household yard care practices along urban-exurban gradients in six climatically-diverse U.S. metropolitan areas.

Residential land is expanding in the United States, and lawn now covers more area than the country's leading irrigated crop by area. Given that lawns are widespread across diverse climatic regions and there is rising concern about the environmental impacts associated with their management, there is a clear need to understand the geographic variation, drivers, and outcomes of common yard care practices. We hypothesized that 1) income, age, and the number of neighbors known by name will be positively associated with the odds of having irrigated, fertilized, or applied pesticides in the last year, 2) irrigation, fertilization, and pesticide application will vary quadratically with population density, with the highest odds in suburban areas, and 3) the odds of irrigating will vary by climate, but fertilization and pesticide application will not. We used multi-level models to systematically address nested spatial scales within and across six U.S. metropolitan areas-Boston, Baltimore, Miami, Minneapolis-St. Paul, Phoenix, and Los Angeles. We found significant variation in yard care practices at the household (the relationship with income was positive), urban-exurban gradient (the relationship with population density was an inverted U), and regional scales (city-to-city variation). A multi-level modeling framework was useful for discerning these scale-dependent outcomes because this approach controls for autocorrelation at multiple spatial scales. Our findings may guide policies or programs seeking to mitigate the potentially deleterious outcomes associated with water use and chemical application, by identifying the subpopulations most likely to irrigate, fertilize, and/or apply pesticides.

Climate and lawn management interact to control C4 plant distribution in residential lawns across seven U.S. cities.

In natural grasslands, C4 plant dominance increases with growing season temperatures and reflects distinct differences in plant growth rates and water use efficiencies of C3 vs. C4 photosynthetic pathways. However, in lawns, management decisions influence interactions between planted turfgrass and weed species, leading to some uncertainty about the degree of human vs. climatic controls on lawn species distributions. We measured herbaceous plant carbon isotope ratios (δ13 C, index of C3 /C4 relative abundance) and C4 cover in residential lawns across seven U.S. cities to determine how climate, lawn plant management, or interactions between climate and plant management influenced C4 lawn cover. We also calculated theoretical C4 carbon gain predicted by a plant physiological model as an index of expected C4 cover due to growing season climatic conditions in each city. Contrary to theoretical predictions, plant δ13 C and C4 cover in urban lawns were more strongly related to mean annual temperature than to growing season temperature. Wintertime temperatures influenced the distribution of C4 lawn turf plants, contrary to natural ecosystems where growing season temperatures primarily drive C4 distributions. C4 cover in lawns was greatest in the three warmest cities, due to an interaction between climate and homeowner plant management (e.g., planting C4 turf species) in these cities. The proportion of C4 lawn species was similar to the proportion of C4 species in the regional grass flora. However, the majority of C4 species were nonnative turf grasses, and not of regional origin. While temperature was a strong control on lawn species composition across the United States, cities differed as to whether these patterns were driven by cultivated lawn grasses vs. weedy species. In some cities, biotic interactions with weedy plants appeared to dominate, while in other cities, C4 plants were predominantly imported and cultivated. Elevated CO2 and temperature in cities can influence C3 /C4 competitive outcomes; however, this study provides evidence that climate and plant management dynamics influence biogeography and ecology of C3 /C4 plants in lawns. Their differing water and nutrient use efficiency may have substantial impacts on carbon, water, energy, and nutrient budgets across cities.

Multiple health benefits of urban tree canopy: The mounting evidence for a green prescription.

The purpose of this study was to enhance the understanding of the health-promoting potential of trees in an urbanized region of the United States. This was done using high-resolution LiDAR and imagery data to quantify tree cover within 250m of the residence of 7910 adult participants in the California Health Interview Survey, then testing for main and mediating associations between tree cover and multiple health measures. The results indicated that more neighborhood tree cover, independent from green space access, was related to better overall health, primarily mediated by lower overweight/obesity and better social cohesion, and to a lesser extent by less type 2 diabetes, high blood pressure, and asthma. These findings suggest an important role for trees and nature in improving holistic population health in urban areas.

Comparative assessment of methods for estimating tree canopy cover across a rural-to-urban gradient in the mid-Atlantic region of the USA.

Tree canopy cover significantly affects human and wildlife habitats, local hydrology, carbon cycles, fire behavior, and ecosystem services of all types. In addition, changes in tree canopy cover are both indicators and consequences of a wide variety of disturbances from urban development to climate change. There is growing demand for this information nationwide and across all land uses. The extensive inventory plot system managed by the USDA Forest Service Forest Inventory and Analysis (FIA) offers a unique opportunity for acquiring unbiased tree canopy cover information across broad areas. However, the estimates it produces had not yet been examined for comparative accuracy with other sources. In this study, we compared four different methods readily available and with significant potential for application over broad areas. The first two, field-collected and photointerpreted, are currently acquired by FIA on approximately 44,000 plots annually nationwide. The third method is a stem-mapping approach that models tree canopy cover from variables regularly measured on forested plots and is efficient enough to calculate nationwide. The fourth is a Geographic-Object-Based Image Analysis (GEOBIA) approach that uses both high-resolution imagery and leaf-off LiDAR data and has reported very high accuracies and spatial detail at state-wide levels of application. Differences in the spatial and temporal resolution and coverage of these four datasets suggest that they could provide complementary information if their relationships could be better understood. Plot- and county-level estimates of tree canopy cover derived from each of the four data sources were compared for 11 counties in Maryland, Pennsylvania, and West Virginia across a range of urbanization levels. We found high levels of systematic agreement between field and photointerpreted, stem-mapped and field, photointerpreted and GEOBIA estimates. In several cases, the relationship changed with the level of tree canopy cover. GEOBIA produced the highest tree cover estimates of all the methods compared. Results are discussed with respect to known differences between the methods and ground conditions found in both forest and nonforest areas.

Integrating LIDAR and forest inventories to fill the trees outside forests data gap.

Forest inventories are commonly used to estimate total tree biomass of forest land even though they are not traditionally designed to measure biomass of trees outside forests (TOF). The consequence may be an inaccurate representation of all of the aboveground biomass, which propagates error to the outputs of spatial and process models that rely on the inventory data. An ideal approach to fill this data gap would be to integrate TOF measurements within a traditional forest inventory for a parsimonious estimate of total tree biomass. In this study, Light Detection and Ranging (LIDAR) data were used to predict biomass of TOF in all "nonforest" Forest Inventory and Analysis (FIA) plots in the state of Maryland. To validate the LIDAR-based biomass predictions, a field crew was sent to measure TOF on nonforest plots in three Maryland counties, revealing close agreement at both the plot and county scales between the two estimates. Total tree biomass in Maryland increased by 25.5 Tg, or 15.6%, when biomass of TOF were included. In two counties (Carroll and Howard), there was a 47% increase. In contrast, counties located further away from the interstate highway corridor showed only a modest increase in biomass when TOF were added because nonforest conditions were less common in those areas. The advantage of this approach for estimating biomass of TOF is that it is compatible with, and explicitly separates TOF biomass from, forest biomass already measured by FIA crews. By predicting biomass of TOF at actual FIA plots, this approach is directly compatible with traditionally reported FIA forest biomass, providing a framework for other states to follow, and should improve carbon reporting and modeling activities in Maryland.

Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA.

BACKGROUND: Continental-scale aboveground biomass maps are increasingly available, but their estimates vary widely, particularly at high resolution. A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making. To this end, we compare four continental-scale maps with a recent high-resolution lidar-derived biomass map over Maryland, USA. We conduct detailed comparisons at pixel-, county-, and state-level. RESULTS: Spatial patterns of biomass are broadly consistent in all maps, but there are large differences at fine scales (RMSD 48.5-92.7 Mg ha-1). Discrepancies reduce with aggregation and the agreement among products improves at the county level. However, continental scale maps exhibit residual negative biases in mean (33.0-54.6 Mg ha-1) and total biomass (3.5-5.8 Tg) when compared to the high-resolution lidar biomass map. Three of the four continental scale maps reach near-perfect agreement at ~4 km and onward but do not converge with the high-resolution biomass map even at county scale. At the State level, these maps underestimate biomass by 30-80 Tg in forested and 40-50 Tg in non-forested areas. CONCLUSIONS: Local discrepancies in continental scale biomass maps are caused by factors including data inputs, modeling approaches, forest/non-forest definitions and time lags. There is a net underestimation over high biomass forests and non-forested areas that could impact carbon accounting at all levels. Local, high-resolution lidar-derived biomass maps provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale maps produced in carbon monitoring systems.

Trees grow on money: urban tree canopy cover and environmental justice.

This study examines the distributional equity of urban tree canopy (UTC) cover for Baltimore, MD, Los Angeles, CA, New York, NY, Philadelphia, PA, Raleigh, NC, Sacramento, CA, and Washington, D.C. using high spatial resolution land cover data and census data. Data are analyzed at the Census Block Group levels using Spearman's correlation, ordinary least squares regression (OLS), and a spatial autoregressive model (SAR). Across all cities there is a strong positive correlation between UTC cover and median household income. Negative correlations between race and UTC cover exist in bivariate models for some cities, but they are generally not observed using multivariate regressions that include additional variables on income, education, and housing age. SAR models result in higher r-square values compared to the OLS models across all cities, suggesting that spatial autocorrelation is an important feature of our data. Similarities among cities can be found based on shared characteristics of climate, race/ethnicity, and size. Our findings suggest that a suite of variables, including income, contribute to the distribution of UTC cover. These findings can help target simultaneous strategies for UTC goals and environmental justice concerns.

An ecology of prestige in New York City: examining the relationships among population density, socio-economic status, group identity, and residential canopy cover.

Several social theories have been proposed to explain the uneven distribution of vegetation in urban residential areas: population density, social stratification, luxury effect, and ecology of prestige. We evaluate these theories using a combination of demographic and socio-economic predictors of vegetative cover on all residential lands in New York City. We use diverse data sources including the City's property database, time-series demographic and socio-economic data from the US Census, and land cover data from the University of Vermont's Spatial Analysis Lab (SAL). These data are analyzed using a multi-model inferential, spatial econometrics approach. We also examine the distribution of vegetation within distinct market categories using Claritas' Potential Rating Index for Zipcode Markets (PRIZM™) database. These categories can be disaggregated, corresponding to the four social theories. We compare the econometric and categorical results for validation. Models associated with ecology of prestige theory are more effective for predicting the distribution of vegetation. This suggests that private, residential patterns of vegetation, reflecting the consumption of environmentally relevant goods and services, are associated with different lifestyles and lifestages. Further, our spatial and temporal analyses suggest that there are significant spatial and temporal dependencies that have theoretical and methodological implications for understanding urban ecological systems. These findings may have policy implications. Decision makers may need to consider how to most effectively reach different social groups in terms of messages and messengers in order to advance land management practices and achieve urban sustainability.

Assessing the homogenization of urban land management with an application to US residential lawn care.

Changes in land use, land cover, and land management present some of the greatest potential global environmental challenges of the 21st century. Urbanization, one of the principal drivers of these transformations, is commonly thought to be generating land changes that are increasingly similar. An implication of this multiscale homogenization hypothesis is that the ecosystem structure and function and human behaviors associated with urbanization should be more similar in certain kinds of urbanized locations across biogeophysical gradients than across urbanization gradients in places with similar biogeophysical characteristics. This paper introduces an analytical framework for testing this hypothesis, and applies the framework to the case of residential lawn care. This set of land management behaviors are often assumed--not demonstrated--to exhibit homogeneity. Multivariate analyses are conducted on telephone survey responses from a geographically stratified random sample of homeowners (n = 9,480), equally distributed across six US metropolitan areas. Two behaviors are examined: lawn fertilizing and irrigating. Limited support for strong homogenization is found at two scales (i.e., multi- and single-city; 2 of 36 cases), but significant support is found for homogenization at only one scale (22 cases) or at neither scale (12 cases). These results suggest that US lawn care behaviors are more differentiated in practice than in theory. Thus, even if the biophysical outcomes of urbanization are homogenizing, managing the associated sustainability implications may require a multiscale, differentiated approach because the underlying social practices appear relatively varied. The analytical approach introduced here should also be productive for other facets of urban-ecological homogenization.

Urban tree canopy and asthma, wheeze, rhinitis, and allergic sensitization to tree pollen in a New York City birth cohort.

BACKGROUND: Urban landscape elements, particularly trees, have the potential to affect airflow, air quality, and production of aeroallergens. Several large-scale urban tree planting projects have sought to promote respiratory health, yet evidence linking tree cover to human health is limited. OBJECTIVES: We sought to investigate the association of tree canopy cover with subsequent development of childhood asthma, wheeze, rhinitis, and allergic sensitization. METHODS: Birth cohort study data were linked to detailed geographic information systems data characterizing 2001 tree canopy coverage based on LiDAR (light detection and ranging) and multispectral imagery within 0.25 km of the prenatal address. A total of 549 Dominican or African-American children born in 1998-2006 had outcome data assessed by validated questionnaire or based on IgE antibody response to specific allergens, including a tree pollen mix. RESULTS: Tree canopy coverage did not significantly predict outcomes at 5 years of age, but was positively associated with asthma and allergic sensitization at 7 years. Adjusted risk ratios (RRs) per standard deviation of tree canopy coverage were 1.17 for asthma (95% CI: 1.02, 1.33), 1.20 for any specific allergic sensitization (95% CI: 1.05, 1.37), and 1.43 for tree pollen allergic sensitization (95% CI: 1.19, 1.72). CONCLUSIONS: Results did not support the hypothesized protective association of urban tree canopy coverage with asthma or allergy-related outcomes. Tree canopy cover near the prenatal address was associated with higher prevalence of allergic sensitization to tree pollen. Information was not available on sensitization to specific tree species or individual pollen exposures, and results may not be generalizable to other populations or geographic areas.

ElderSmile: A comprehensive approach to improving oral health for seniors.

Societal changes, including the aging of the US population and the lack of routine dental service coverage under Medicare, have left many seniors unable to afford any dental care whatsoever, let alone the most advanced treatments.(1) In 2004, the Columbia University College of Dental Medicine and its partners instituted the ElderSmile program in the largely impoverished communities of Harlem and Washington Heights/Inwood in New York City. The long-term goal of this program is to improve access to and delivery of oral health care for seniors; the short-term goal is to establish and operate a network of prevention centers surrounding a limited number of treatment centers. Preliminary results indicate substantial unmet dental needs in this largely Hispanic and Black elderly population.

Predicting opportunities for greening and patterns of vegetation on private urban lands.

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 market segmentation data were tested as categorical predictors of possible and realized stewardship and yard expenditures. PRIZM 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.