Helping Preserve Atlanta s Urban Tree Canopy

Helping Preserve Atlanta s Urban Tree Canopy [Extended Abstract] Jordan Belknap jbelknap3@gatech.edu Alex Beasley alex@treesatlanta.org Caroline Foster cfoster2@gatech.edu Anthony Giarrusso tonyg@gatech.edu Sanat Moningi smoningi3@gatech.edu Bistra Dilkina bdilkina@cc.gatech.edu ABSTRACT Urban trees provide important ecological, economic, social, and health benefits for cities. Many cities create and implement policies to preserve and expand urban tree canopy cover through conservation, tree planting, and educational campaigns; these efforts are typically mandated by municipal ordinances. Policy decisions, however, are often made with limited information. This paper details three datadriven tools we created for the City of Atlanta and Trees Atlanta, a non-profit volunteer organization dedicated to preserving Atlanta s urban forest. To support preservation of Atlanta s urban tree canopy, we created three web-based tools with complimentary functions. The first tool is an interactive map of trees, displayed by genus, species, and/or cultivar. The purpose of this tool is to display the distribution and biodiversity of existing street trees planted by Trees Atlanta across the city, or to display a tree inventory of public trees conducted by the City. The second tool is a web-based Dynamic Tree Planting Prioritization application, which allows decision makers to assign weights to the different factors and dynamically recompute parcel prioritization scores in order to identify potential planting sites meeting various criteria such as percent urban tree canopy and percent of impervious land cover. The third tool supports urban forest conservation planning. This interactive application identifies contiguous forested regions spanning multiple parcels. It allows the user to filter the regions based on desired tree canopy and acreage thresholds, and provides information regarding assessed value, size, proximity to streams, and other factors. Stakeholders interested in conservation can use the tool to identify parcels that can then be further evaluated based upon capacity to provide ecological services such as watershed protection, wildlife habitat, support of biodiversity and rare species, heat island reduction, and educational and recreational value. Using diverse sources of data such as processed satellite imagery, GIS parcel data, ecological information from USGS, tree inventory spreadsheets, and other information, we developed three interactive data-driven tools to aid our partners in their efforts to maintain, protect, and expand Atlanta s urban forest. Bloomberg Data for Good Exchange Conference. 28-Sep-2015, New York City, NY, USA. Categories and Subject Descriptors H.4 [Information Systems Applications]: Miscellaneous Keywords Environment, Urban Trees, Decision Support System, Webbased applications, Geospatial 1. INTRODUCTION Urban forests are an integral part of the green infrastructure of cities. In the Piedmont region, where Atlanta is located, the natural landscape is almost completely forested. To the extent that trees can remain in the built environment, residents of urban areas experience significant benefits both from urban street trees as well as larger forested areas. Trees provide shade in the summer, cooling paved surfaces and buildings and help to mitigate the heat island effect, which is caused by higher temperature in urban areas where impervious surfaces retain heat longer than the cooler pervious surfaces found in the natural environment [4] [2] [1]. Trees also trap airborne particulates, provide a stormwater management benefit by intercepting rainfall that could otherwise cause erosion, and make neighborhoods and urban areas more livable by providing aesthetic and social benefits for residents [8]. The presence of trees also increases property values. Because urban forests are so valuable, policies related to the protection and maintenance of trees are incredibly important. Cities face many challenges in effectively maintaining urban forests. In the context of limited resources, data required to make the best management decisions may not be available in a usable format. Urban forest maintenance in many cities involves collaboration of multiple organizations, including various divisions of government and non-profit organizations. Many cities have begun using GIS and satellite data to gain a better understanding of the urban forest and to inform decision making. In this paper, we outline our work in helping the city of Atlanta build data-driven decision support tools for urban tree canopy preservation. The urban forest of Atlanta, Georgia has been a landmark environmental feature. While the city s urban core has fewer trees [5], many areas of the city benefit from the presence of large deciduous shade trees native to the area and extensive tree canopy. The health of the canopy is challenged by urban development, competition for planting space, and harsh

weather conditions. When trees are removed for building projects, the City s tree ordinance requires that trees be replanted on site, or if there is not sufficient space for replanting, recompense fees are collected in a trust fund for the purpose of planting and maintaining trees throughout the city. In addition to tree planting supported by the tree trust fund, the non-profit volunteer organization, Trees Atlanta, founded in 1985, plants trees in Atlanta and the surrounding area. Targeting where trees are needed and identifying the best and most appropriate species for each site requires a great deal of information. We identify three important tasks for Atlanta s urban tree canopy preservation: 1) understanding the current distribution and species composition of trees; 2) deciding where to plant more trees; 3) identifying and conserving remaining large urban forest tracts. We develop data-driven web-based tools to support each of these tasks. 2. WEB-BASED INTERACTIVE TOOLS 2.1 Atlanta s Street Trees Visualization Our first web-based tool is an interactive visualization of street trees. Similarly to existing efforts for New York City (see http : //jillhubley.com/blog/nyctrees), our tool displays the spatial distribution of trees color coded by species, and allow for filtering by species. This interactive map allows the user to observe the overall distribution of street trees, as well as the distribution of each species of tree throughout the area and assess whether various species of trees are over- or under-represented in various areas. Maintaining a diverse tree canopy is vital to the health of the forest- it means better resistance to pests and disease and less loss of benefit if a disease strikes a particular species [9]. The tool was used to visualize a tree inventory, commissioned by the City, of all street trees in downtown Atlanta. The City is in the process of contracting an expanded tree inventory of metro Atlanta, and the new data will be incorporated as it becomes available. The visualization tool is also used with a subset of data on past plantings of Trees Atlanta ranging from 1994 2014. The dataset includes locations, genus, species, cultivar and planting dates, and was previously only available in the form of spreadsheets. Our tool presents the results of two decades of Trees Atlanta s work in a cohesive, easily understandable format to both Figure 1: Street Trees Visualization Tool: mapping tree species biodiversity and distribution of trees planted by Trees Atlanta in 1994-2014. professionals and the layman. Having the web-based visualization linked on Trees Atlanta s website is helpful to their end-users if they are curious what has been planted in their neighborhood, what species is dominant, how to further diversify their community s canopy. It helps Trees Atlanta employees by providing an easily understandable, mobile tool that can be presented to individual homeowners or community leaders to convince them, based on past data in other neighborhoods, just how much a series of tree plantings can impact their overall Urban Tree Canopy (UTC). 2.2 Dynamic Tree Planting Prioritization Tool Recently, many cities have recognized the need to preserve urban tree canopy and have initiated tree planting campaigns. Cities have begun using GIS and satellite data for urban tree canopy assessments to inform targeting tree planting decisions. Examples of such cities are Los Angeles with the Million Trees Los Angeles campaign [7], New York with the MillionTreesNYC campaign [6], Minneapolis [3], and many more. Utilizing resources from nearby universities, both Minneapolis and New York City utilized GISbased data to learn more about existing and potential urban tree canopy (UTC). Minneapolis conducted an urban tree canopy analysis following USDA Forest Service protocol. The analysis was completed on a parcel layer to relate tree canopy to land use. Similar to the program in Los Angeles, New York City instituted a MillionTreesNYC campaign. Locke et. al. [6] outline a method to identify and prioritize neighborhoods for planting sites in New York city and recommends using the same method with updated local data or applying the method to other cities. Such studies usually produce a GIS database and/or a set of maps with priority scores for neighborhoods, parcels or specific sites, depending on the level of analysis. Since each city is unique in terms of structure, budget, and ecological needs, each city requires a unique approach [10]. Even within a city, different stakeholders, agencies and organizations with different goals and objectives would usually benefit from different prioritization schemes. Extending the methodologies developed in previous studies, in this work we focus on creating a web-based interactive tool, that allow stakeholders to express their preferences across factors affecting urban tree canopy and obtain dynamic prioritization results. In this way, instead of a one shot urban tree canopy assessment and corresponding agency report, we provide a data-drive web-based interactive tool that allows the underlying data to be relevant and useful for longer and by a broader community of stakeholders, beyond the immediate partners of the project. The main purpose of our Dynamic Tree Planting Prioritization tool was to show users where trees should be planted on the parcel and the neighborhood level. This tool will save an immense amount of time in targeting potential planting areas rather than requiring on-site evaluations without information about factors affecting suitability for planting. Users, whether arborists, citizens and neighborhood organization or policy makers, can prioritize tree planting using their own weighting of a variety of factors. In particular, this tool will be used in helping Trees Atlanta NeighborWoods staff in prioritizing locations in the city that are in need of a higher percentage of canopy. Traditionally,

Figure 2: Web-based Dynamic Tree Planting Prioritization Tool: on neighborhood scale across the city (left) and on parcel scale within a neighborhood (right). Trees Atlanta staff, either by reputation or suggestion, drove throughout the city looking for areas in need of shade. The Dynamic Tree Planting Prioritization Tool allows Trees Atlanta to prioritize the scouting of potential planting areas by adjusting the importance of UTC and impervious surface ratios on a community or parcel level. If low UTC and low impervious surface are present, the location is considered high priority and is to be scouted in person. In areas where a higher UTC exists, it is more difficult to find planting locations, thus, the weighted characteristics become increasingly important. Once high priority parcels are identified, the tool provides way to directly link to Google Street View and Bing Maps to visually inspect available satellite images of the parcel and surrounding areas. In summation this tool allows the ability to, on a large scale, digitally prioritize canopy-needy locations without having to physically visit them. This saves Trees Atlanta time and money. 2.2.1 GIS Data Borrowing from previous studies, such as Minneapolis s parcellevel analysis of tree cover and New York City s planting prioritization model, we collected all of the necessary data available. Data sources included tax parcel data, urban tree canopy raster, parks, watershed, streams and thermal heat data. Processed urban tree canopy raster data was obtained from the City s study of 2008 analysis of high resolution, multi-spectral, leaf-on Quickbird satellite imagery (analyzed in a contract with researchers at the Center for GIS and the Center for Quality Growth and Regional Development at the Georgia Institute of Technology) [5]. We used tax parcel data from the City of Atlanta, which is open to the public on a site of GIS services for the city. Limitations of the data include temporal differences - tax data were for 2011, satellite imagery was from 2008. In addition, the parcel data lacks a data catalog with descriptions of each attribute, so novice users, and sometimes even GIS experts, might find it difficult to understand. Lack of documentation is common for GIS data, which can lead to confusion. There also are data inaccuracies due to imperfections in the GIS geometry, which sometimes shows odd parcels like tiny dots in the middle of roads, or irregular-shaped parcels on top of one another or overlapping. Because of these inaccuracies, extensive data cleaning was performed before starting the data analysis. For the cleaned parcels layer, for every parcel we calculated additional parcel attributes, including the percent urban tree cover, percent impervious surface, thermal information, watershed information, proximity to parks and proximity to streams, by joining GIS data for the 2008 urban tree canopy study, a thermal heat map taken on May 30th, 2011, and watershed data ( all three data from ), parks (from the City of Atlanta), and waterbody and stream data (from the USGS). All processing was done using ESRI 10.1. Because of the variety of data sources and the various dates, the result is not entirely accurate. However, comparing these results to Google/Bing satellite information and local knowledge suggests it is a good approximation of current conditions. We select three parcel attributes to be included as variables in the tree planting prioritization: Urban Tree Canopy (UTC), Impervious Surface, and Temperature. Urban Tree Canopy is the percentage of tree canopy in the city based on satellite imagery. Impervious surface is the percentage of impenetrable materials, such as asphalt, concrete, brick, and stone, based on satellite imagery. Temperature is the average degrees Celsius of a region from a thermal heat map taken on May 30th, 2011. Though there are more variables to planting prioritization, data for those factors was not accessible at the time of the application s creation. However, software stack makes adding variables an easy process. The values of variables used in the prioritization must be normalized to allow easy comparison and combination of scores. For each parcel, we compute additional attributes corresponding to the normalized values of the prioritization variables. For normalization, we use z-scores defined as: ˆx i = xi µx σ x where x i is the value of the variable x for a parcel i, µ x is the mean of variable x across all parcels in the city, and σ x is the standard deviation of the variable. The Tree Planting score of a parcel will be a weighted sum of the normalized scores of the prioritization variables. For example, some users want to plant in areas that have the least urban tree canopy and a slightly low impervious surface so there is plenty of space to plant trees. Other users might want slightly low urban tree canopy and a high impervious surface to help reduce surface runoff water. Normalized scores of the prioritization variables were also computed on a coarser scale for each neighborhood planning unit (NPU). 2.2.2 Application Development

The Planting Prioritization application was built with PostgreSQL/PostGIS, PHP, and Javascript (d3.js, Leaflet.js) stack. Once data was processed with ESRI ArcGIS, we took the shapefiles (containing parcel information of each NPU and a shapefile of the NPUs themselves) and imported them into a database using the PostGIS extension of PostgreSQL. Due to the large number of parcels (approximately 120,000) browsers typically can not handle drawing all the polygons (i.e. parcels) at once. In addition, viewing all the parcels simultaneously can overwhelm users with information. We solve this problem by allowing users to first select an NPU. When they do this on the front end comprised of HTML, CSS, and Javascript (d3.js and Leaflet.js being the main libraries used), we use PHP to retrieve parcel data of that specific NPU and draw it on the front end. PHP queries the database and creates GeoJSON to send to the front end. On the front end, we use d3.js to draw the parcels and link appropriate parcel information, such as parcel IDs, owner, land usage, etc. Then we take the normalized values of prioritization variables (now on the front end) and determine the tree planting score of the parcel based on weights determined by the user via sliders. We then split up the tree planting scores into five bins, and assign colors to parcels that correlate to lowest priority, low priority, medium priority, high priority, and highest priority. Users can move the sliders anytime during the application to place negative or positive preferences on the different prioritization variables and the parcel colors update accordingly. 2.3 Urban Forest Conservation Tool The third tool was design to support urban forest conservation decisions. To identify regions of potential high conservation value as urban forest, one needs to find tracts of land with high percentage of urban tree canopy, that are large enough to provide the ecosystem services of a forest, but that are also economically feasible. Our Urban Forest Conservation Tool is also an interactive web-based application, which utilizes the processed GIS parcel data developed for the Tree Planting Prioritization tool. This interactive application identifies forested regions spanning multiple parcels. We start by first filtering out any parcel with UTC below 80%, and of the remaining parcels keeping only those that participate in contiguous forested regions of size at least five acres. The tool displays these candidate parcels and corresponding contiguous regions they form, and allows the user to further filter based on higher levels of desired tree canopy coverage, acreage of forested region, or by estimated price per square foot of the forested region. The data includes information about assessed value, acreage, UTC, proximity to streams, proximity to parks, and other factors for each selected parcel and each forested region. Stakeholders interested in conservation can use the tool to select land for further assessment to determine its conservation value for such varied purposes as watershed protection, wildlife habitat protection, heat island reduction, and educational and recreational value. 3. CONCLUSIONS Our three web-based data-driven applications are designed with our partners in mind, however they could easily be adopted in other cities to serve a similar purpose. Having a tool that dynamically prioritizes planting locations based on variables that are important to the user cuts down on Figure 3: Web-based Urban Forest Conservation Decision Support Tool: dynamically filter contiguous forested tracts of land by desired acreage, urban tree canopy percent coverage, or land cost. time spent in the field. It also visually identifies trends in tree canopy, allowing users to make informed decisions on what sections of the city are in the greatest need of trees. Visualizing the biodiversity of the tree population aids organizations in deciding which species of tree should be planted and where, which is vital for maintaining a robust diseaseresistant forest. Identifying contiguous tracts of forested land facilitates thoughtful forest preservation by allowing the user to implement land preservation polices based upon proximity to streams, parks, and watersheds. Variables of interest may vary from city to city, but the procedures for creating these applications remain the same. Our partners are pleased to have access to these tools. As we watch how our partners integrate the tools into their work, we hope to identify areas for improvement. Studies and projects from other cities, particularly New York City, influenced our decisions. In turn, we hope what we have done can inspire other cities. Future work in Atlanta might include expansions of our applications and updates with new data. 4. ACKNOWLEDGMENTS We want to thank our partners at the Atlanta Tree Conservation Commission and Trees Atlanta for their support and guidance, especially Kathy Evans with Tree Conservation Commission. We also want to thank the Georgia Institute of Technology and the Data Science for Social Good program for their funding and academic support for this research. 5. REFERENCES [1] H. Akbari, S. Bretz, J. Hanford, D. M. Kurn, B. L. Fishman, H. G. Taha, and W. Bos. Monitoring peak power and cooling-energy savings of shade trees and white surfaces in the sacramento municipal utility district (smud) service area: Data analysis, simulations, and results, 1993. [2] H. Akbari and H. Taha. Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas. Solar Energy, 70(3):295 310, 2001. [3] M. Bauer, D. Kilberg, and M. Martin. Mapping minneapolis urban tree canopy, 2013. [4] U. S. Environmental Protection Agency. Urban heat island mitigation, 2013. [5] A. Giarrusso. Assessing urban tree canopy in the city of atlanta; a baseline canopy study. City of Atlanta,

Department of Planning and Community Development, Arborist Division, 2014. [6] D. H. Locke, J. M. Grove, J. W. Lu, A. Troy, J. P. O Neil-Dunne, and B. D. Beck. Prioritizing preferable locations for increasing urban tree canopy in new york city. Cities and the Environment (CATE), 3(1):4, 2011. [7] E. G. McPherson. Los Angeles 1-million tree canopy cover assessment. DIANE Publishing, 2010. [8] J. D. Nowak. The effects of urban trees on air quality, 2002. [9] M. Pautasso, O. Holdenrieder, and J. Stenlid. Susceptibility to fungal pathogens of forests differing in tree diversity. In Ecological Studies 176, pages 263 270. Springer, 2005. [10] S. Pincetl. Urban ecology and natures services infrastructure: Policy implications of the million trees initiative of the city of los angeles. In Urbanization and Sustainability, pages 61 74. Springer, 2013.