Urban Tree Canopy Assessment Purcellville, Virginia

Transcription

1 GLOBAL ECOSYSTEM CENTER Urban Tree Canopy Assessment Purcellville, Virginia Table of Contents 1. Project Background 2. Project Goal 3. Assessment Procedure 4. Economic Benefits 5. Results 6. Recommendations

2 Purcellville, Virginia - Urban Tree Canopy Assessment 1. Project Background Purcellville is a small town in Loudoun County, Virginia with a population of 6,945 according to the 2010 census. The Global Ecosystem Center (GEC) conducted an Urban Tree Canopy Assessment (UTCA) for the town using digital imagery and Remote Sensing technology to produce an accurate green infrastructure data layer. 2. Project Goal The goal of this project was to accurately and inexpensively document urban forest canopy and ecosystem service values so the canopy value as infrastructure can be considered in policy decision making, budget deliberations, and resource management. As a tool, canopy analysis enables managers effectively measure, monitor and communicate the effectiveness of their programs and practices. Spectral NAIP imagery (R,G,B) of Purcellville. 2 GLOBAL ECOSYSTEM CENTER

3 3.0 Assessment Procedure The assessment of Purcellville involved the following processes: 3.1 Imagery Acquisition 3.2 Data Processing 3.3 Land Cover Classification 3.4 Quality Assurance/Quality Control 3.5 Canopy Assessment Key Terms Land Cover: The physical cover on the Earth s surface such trees, grass, concrete, bare ground and water. Land use is a description of how people utilize the land (urban, agricultural etc.) NAIP: National Agriculture Imagery Program. Ecosystem Analysis: Calculations of benefits provided by natural systems 3.1 Imagery Acquisition GEC used 4-band NAIP imagery of 1 meter resolution dated The NAIP acquires aerial imagery during the agricultural growing seasons in the continental United States. A primary goal of the NAIP program is to make digital ortho-imagery is available to communities so it is used to maintain the Common Land Unit (CLU) boundaries. 3.2 Data Processing NAIP imagery comes in compressed format (MrSID or JP2). In order to best utilize this imagery, it is necessary to process it into friendlier format (IMAGINE). The imagery is downloaded Ortho-Imagery: Geo-referenced image data of the Earth s surface from. The image can be collected by satellite or airborne sensors. TR-55: The stormwater runoff calculations incorporate volume of runoff formulas from the Urban Hydrology of small Watersheds model (TR-55) developed by the U.S. Natural Resources Conservation Service (NRCS), formerly known as the U. S. Soil Conservation Service. Don Woodward, P. E., a hydrologic engineer with NRCS, customized the formulas to determine the benefits of trees and other urban vegetation with respect to stormwater management. L-THIA: Long-Term Hydrological Impact Assessment model developed by Purdue University to estimate the change in the concentration of the pollutants in runoff during a typical storm event given the change in the land cover from existing trees to a no tree condition. UFORE: The Urban Forest Effect model developed by USDA Forest Service to estimate mass of greenhouse gases stored in tree canopies. UFORE model is based on data collected in 55 U. S. cities. NAIP imagery (left) and processed imagery cliped to project boundary (right). 3

4 Purcellville, Virginia - Urban Tree Canopy Assessment and processed. This involved clipping the image to fit the city boundary and re-sampling the image into a 3 meter pixel resolution. GEC s extensive testing of the Canopy Assessment accuracy has resulted in this methodology. We discovered that down sampling of 1-meter pixel 3.3 Land Cover Classification In order to create consistent and accurate land cover products, automated and semi-automated processes are used to conduct classifications. Automated processes provide precise and accurate assessments while eliminating analyst bias. Once the imagery was clipped and re-sampled, a supervised classification was conducted to extract land cover features. Graphic models were applied to reduce speckle and correct some misclassifications. The final classification was reviewed and edited as needed. In order to create consistent and accurate land cover products, automated and semi-automated processes are used to conduct classifications. Automated processes provide precise and accurate assessments while eliminating analyst bias. The 5-class land cover classification draped over near-infrared imagery. 3.4 Quality Assurance and Quality Control Custom models were used to ensure product quality and accuracy. The final land cover classification was validated against randomly selected sample points. The minimum mapping unit was set to 3 meters and 95%+ accuracy for land cover categories overall. 4 GLOBAL ECOSYSTEM CENTER

5 As more objective approaches have been adopted in the classification process, the resulting land cover classification has increasingly realistic and accurate land cover features. To ensure the quality of land cover classifications, hand edits are performed only at the final stage of the classification. Comparision between existing 0.6 meter-resolution imagery (center) with GEC s 3-meter resolution (right). Despite having a resolution of 3-meters, GEC s classification was able to extract land cover features accurately and realistically due to the ground-breaking semi-automated processes. 3.5 Canopy Assessment Using the land cover data interpreted from the NAIP imagery along with soil and weather data provided by the NRCS and the National Weather Service, ecosystem services are calculated. The GEC s analysis showed that Purcellville urban tree canopy covered acres (25.7%) of the total town area in The largest land cover area was covered by Open Spaces, total of acres (46.5%); while Impervious Surfaces covered acres (27.3%) of the town. 5

6 Purcellville, Virginia - Urban Tree Canopy Assessment 4. Economic Benefits In addition to classifying the spectral image into land cover classes so canopy measurements can be established, the GEC utilizes GIS and the land cover data to calculate ecosystem services. Using the scientific and engineering algorithms, GEC has been able to translate land cover data into economic values. Economic benefits are calculated in terms of stormwater management, and air quality. Additionally, water quality is calculated in terms of specific nutrients add to the water. 5.0 Results The data provided by the assessment provides decision makers and resource managers with the framework for improving their urban forest and increasing the economic values produced by the resource in the future. The classified geo-referenced data can be used in an ArcGIS project to plan growth and development that includes improving the green infrastructure. 5.1 Stormwater Management Stormwater management using green infrastructure as non structural devices (trees etc) offers huge financial benefits to a community and can be accomplished during the urban planning process. GEC s ecosystem services use a hydrological model (TR-55) to calculate stormwater numbers for any given urban areas. Results shows that 25.7% of the tree coverage have been saving 3.5 million cubic feet of rain water from running off, and saving $608, 259 annually. 5.2 Air Pollution and Carbon Part of ecosystem analysis was to calculate air pollution removal and carbon storage and sequestration. Using an UFORE model with the land cover data, results were produced for each pollutant. Results show that existing 25.7% canopy coverage helped removed total of 43,348 Lbs pollutant per year. In the mean time, same canopy coverage stored 18,518 ton of carbon. 6 GLOBAL ECOSYSTEM CENTER

7 6.0 Recommendations Having canopy coverage mapped is a first step for documenting ecosystem services and managing the natural systems. The next step is to determine change in the canopy over time. This is done by classifying imagery into land cover from images several years apart. With this data, managers can investigate the location and reasons for losses. A final step is to develop trend of change to guide long term plans. This is done by classifying three or more years of change and extrapolating the rate of change into the future. In addition, it is recommended that data from land use plans can be incorporated into the GIS analysis for scenario development. 6.1 Scenario Modeling GEC has developed a scenario modeling tool that enables decision-makers to create hypothetical scenarios of land cover change, and the resulting impacts on ecosystem services and future costs. This powerful tool can be applied to other GIS data layers as well. The best application of this model is to apply it to future planning maps to compute ecosystem service values. 6.2 Change Analysis The GEC has developed a methodology to conduct low-cost change analysis. The data from the original UTCA provides the base land cover classification for all future analysis. When newer imagery is obtained, change detection is conducted, and only areas that have land cover changed are classified, creating an updated land cover classification. This method allows for effecient and inexpensive land cover updates. 6.3 Trend Analysis As change analysis gives valuable information on land cover land use for two dates, it cannot be used to draw decisive projection of future growth. Based on series of change over multiple years of data, trend analysis can provide crucial information on the state of the land use management and pin-point the areas of concern. Furthermore, trend analysis can give detailed cost/benefit information for decision making. 7