Applications of Urban Tree Canopy Assessment and Prioritization Tools: Supporting Collaborative Decision Making to Achieve Urban Sustainability Goals

Urban Tree Canopy (UTC) Prioritizations can be both a set of geographic analysis tools and a planning process for collaborative decision-making. In this paper, we describe how UTC Prioritizations can be used as a planning process to provide decision support to multiple government agencies, civic groups and private businesses to aid in reaching a canopy target. Linkages to broader City-scale sustainability plans are explored. This article represents an extension and update to the UTC Canopy Goal Setting Guide by Raciti et al (2006). We conclude with recommendations for a market-like analysis of neighborhoods to better match planting initiatives to particular neighborhoods’ motivations, capacities and interests in order to improve the adoption of improved urban forestry practices

Tree Canopy Change in Coastal Los Angeles, 2009 - 2014

Los Angeles, California is prone to extreme climate events—e.g. drought, wildfires, and floods—that are only expected to increase with climate change. The establishment of green infrastructure, including a stable urban forest, is a strategy to improve resilience not only to these events, but also to contribute to other environmental, social, and economic goals. To this end, cities throughout Los Angeles County have tree planting programs and policies aimed to grow and maintain their urban forests. Despite the policy objectives and management goals of such programs, we know surprisingly little about the spatial distribution of the existing urban forest, how and where the canopy has changed over time, or the composition of the population living in places of canopy change. To examine these questions, we conducted an analysis of the Los Angeles Coast based on land cover data derived from high-resolution aerial imagery and LiDAR. In addition to characterizing the overall percentages of existing and possible tree canopy in 2014, we also characterized the change in tree canopy from 2009 to 2014 with five measures of tree canopy and change: total canopy, persistence, loss, gain, and net change. We used market segmentation data to analyze the relationship between tree canopy and the composition of communities. Results indicated that tree canopy covered about 15% of coastal Los Angeles, but this cover was unevenly distributed throughout the study area. The parcel-level analysis of change indicated that while the canopy did not change much from 2009-2014, the changes that did occur were localized and would have been missed at a coarser scale of analysis. Using geodemographic segments, we found that higher-income lifestyle groups tended to have more tree canopy and less loss over time. Change within land uses was consistent with overall change. These high-resolution, high-accuracy data and analyses can support valuable tools to guide decision-making about urban forests, especially as it relates to social equity

Developing methodology for efficient eelgrass habitat mapping across lidar systems

Super Storm Sandy, the second costliest hurricane in U.S. history, made landfall on the east coast of the U.S. in October 2012. In an attempt to assess the impacts of the storm on coastal ecosystems, several U.S. mapping agencies such as the National Oceanic and Atmospheric Administration (NOAA), the U.S. Geological Survey (USGS), and the U.S. Army Corps of Engineers (USACE) commenced data collection efforts using a variety of remotely-sensed data types including aerial imagery and topobathymetric lidar. The objective of this study was to investigate the applicability of object-based image analysis techniques for benthic habitat mapping. Bathymetry and reflectance data collected by a Riegl VQ-820-G system and the AHAB Chiroptera system along with aerial imagery (Applanix DSS) were compared using an objectbased image analysis (OBIA) technique to classify dense eelgrass beds, mixed sand and macroalgae, and sand habitats. In order to determine the efficacy of this method for benthic habitat classification it was also compared to a manual method of classification from aerial imagery. The resulting habitat maps were compared between systems to determine the feasibility of using one OBIA classification rule set across lidar systems and aerial imagery. Our preliminary results using the Riegl system suggest our methodology correctly classified 85% of benthic habitats. Preliminary results using the Chiroptera also suggests similar accuracy of classification. This methodology will allow streamlined creation of habitat maps for coastal managers and researchers using large sets of data collected by multiple sensors. Testing of this OBIA methodology is ongoing as new data from various sensors becomes available

Prioritizing Preferable Locations for Increasing Urban Tree Canopy in New York City

This paper presents a set of Geographic Information System (GIS) methods for identifying and prioritizing tree planting sites in urban environments. It uses an analytical approach created by a University of Vermont service-learning class called “GIS Analysis of New York City\u27s Ecology” that was designed to provide research support to the MillionTreesNYC tree planting campaign. These methods prioritize tree planting sites based on need (whether or not trees can help address specific issues in the community) and suitability (biophysical constraints and planting partners’ existing programmatic goals). Criteria for suitability and need were based on input from three New York City tree-planting organizations. Customized spatial analysis tools and maps were created to show where each organization may contribute to increasing urban tree canopy (UTC) while also achieving their own programmatic goals. These methods and associated custom tools can help decision-makers optimize urban forestry investments with respect to biophysical and socioeconomic outcomes in a clear and accountable manner. Additionally, the framework described here may be used in other cities, can track spatial characteristics of urban ecosystems over time, and may enable further tool development for collaborative decision-making in urban natural resource management

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\u27s 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

Ecological homogenization of oil Properties in the American Residential Macrosystem

The conversion of native ecosystems to residential ecosystems dominated by lawns has been a prevailing land-use change in the United States over the past 70 years. Similar development patterns and management of residential ecosystems cause many characteristics of residential ecosystems to be more similar to each other across broad continental gradients than that of former native ecosystems. For instance, similar lawn management by irrigation and fertilizer applications has the potential to influence soil carbon (C) and nitrogen (N) pools and processes. We evaluated the mean and variability of total soil C and N stocks, potential net N mineralization and nitrification, soil nitrite (NO2−)/nitrate (NO3−) and ammonium (NH4+) pools, microbial biomass C and N content, microbial respiration, bulk density, soil pH, and moisture content in residential lawns and native ecosystems in six metropolitan areas across a broad climatic gradient in the United States: Baltimore, MD (BAL); Boston, MA (BOS); Los Angeles, CA (LAX); Miami, FL (MIA); Minneapolis–St. Paul, MN (MSP); and Phoenix, AZ (PHX). We observed evidence of higher N cycling in lawn soils, including significant increases in soil NO2−/NO3−, microbial N pools, and potential net nitrification, and significant decreases in NH4+ pools. Self-reported yard fertilizer application in the previous year was linked with increased NO2−/ NO3− content and decreases in total soil N and C content. Self-reported irrigation in the previous year was associated with decreases in potential net mineralization and potential net nitrification and with increases in bulk density and pH. Residential topsoil had higher total soil C than native topsoil, and microbial biomass C was markedly higher in residential topsoil in the two driest cities (LAX and PHX). Coefficients of variation for most biogeochemical metrics were higher in native soils than in residential soils across all cities, suggesting that residential development homogenizes soil properties and processes at the continental scale

Continental-scale homogenization of residential lawn plant communities

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Landscape and Urban Planning 165 (2017): 54-63, doi:10.1016/j.landurbplan.2017.05.004.Residential lawns are highly managed ecosystems that occur in urbanized landscapes across the United States. Because they are ubiquitous, lawns are good systems in which to study the potential homogenizing effects of urban land use and management together with the continental-scale effects of climate on ecosystem structure and functioning. We hypothesized that similar homeowner preferences and management in residential areas across the United States would lead to low plant species diversity in lawns and relatively homogeneous vegetation across broad geographical regions. We also hypothesized that lawn plant species richness would increase with regional temperature and precipitation due to the presence of spontaneous, weedy vegetation, but would decrease with household income and fertilizer use. To test these predictions, we compared plant species composition and richness in residential lawns in seven U.S. metropolitan regions. We also compared species composition in lawns with understory vegetation in minimally-managed reference areas in each city. As expected, the composition of cultivated turfgrasses was more similar among lawns than among reference areas, but this pattern also held among spontaneous species. Plant species richness and diversity varied more among lawns than among reference areas, and more diverse lawns occurred in metropolitan areas with higher precipitation. Native forb diversity increased with precipitation and decreased with income, driving overall lawn diversity trends with these predictors as well. Our results showed that both management and regional climate shaped lawn species composition, but the overall homogeneity of species regardless of regional context strongly suggested that management was a more important driver.This research was supported by the Macrosystems Biology Program in the Emerging Frontiers Division of the Biological Sciences Directorate at the National Science Foundation (NSF) under grants EF-1065548, 1065737, 1065740, 1065741, 1065772, 1065785, 1065831, and 121238320

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-tocity variation). A multi-level modeling framework was useful for discerning these scaledependent 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

A Versatile, Production-Oriented Approach to High-Resolution Tree-Canopy Mapping in Urban and Suburban Landscapes Using GEOBIA and Data Fusion

The benefits of tree canopy in urban and suburban landscapes are increasingly well known: stormwater runoff control, air-pollution mitigation, temperature regulation, carbon storage, wildlife habitat, neighborhood cohesion, and other social indicators of quality of life. However, many urban areas lack high-resolution tree canopy maps that document baseline conditions or inform tree-planting programs, limiting effective study and management. This paper describes a GEOBIA approach to tree-canopy mapping that relies on existing public investments in LiDAR, multispectral imagery, and thematic GIS layers, thus eliminating or reducing data acquisition costs. This versatile approach accommodates datasets of varying content and quality, first using LiDAR derivatives to identify aboveground features and then a combination of LiDAR and imagery to differentiate trees from buildings and other anthropogenic structures. Initial tree canopy objects are then refined through contextual analysis, morphological smoothing, and small-gap filling. Case studies from locations in the United States and Canada show how a GEOBIA approach incorporating data fusion and enterprise processing can be used for producing high-accuracy, high-resolution maps for large geographic extents. These maps are designed specifically for practical application by planning and regulatory end users who expect not only high accuracy but also high realism and visual coherence