30 Years of Tree Canopy Cover Change in Unincorporated and Incorporated Areas of Orange County,

Transcription

1 30 Years of Tree Canopy Cover Change in Unincorporated and Incorporated Areas of Orange County, Final Report to Orange County July 2017 Authors Dr. Shawn Landry, USF Water Institute, University of South Florida Qiuyan Yu, USF Water Institute, University of South Florida Project Contributors David D. Jones, P.E., Orange County Environmental Protection Division Cody Winter, USF Water Institute, University of South Florida Citation for this Report Landry, S., Yu, Q. (2017). 30 Years of Tree Canopy Cover Change in Unincorporated and Incorporated Areas of Orange County, Report to Orange County, June Orlando, FL.

2 Table of Contents Introduction... 2 Accurate Estimate of Tree Canopy Cover... 4 Moderate Resolution Tree Canopy Cover Mapping... 6 Landsat Data Download... 6 Landsat Image Processing... 7 Tree Canopy Classification Using Decision Tree... 8 Tree Canopy Cover Results... 9 Tree Canopy Cover Change Recommended Next Steps References Appendix Page 1 of 24

3 Introduction The purpose of this project was to map and estimate 30 years of tree canopy change in Orange County (OC), from 1986 to Landsat satellite images were used to map tree cover using remote sensing techniques at approximately 10-year intervals based on the availability of cloud-free images: 1986, 1994, 2005 and The original intent was to use 1986, 1996, 2006, and 2016, but cloud-free Landsat imagery was unavailable for the 1996 and 2006 (see Appendix Figure 1 and 2). Although the use of Landsat images has been shown to underestimate urban tree cover, the methods used to map tree cover are consistent over time and can produce an accurate picture of temporal tree canopy change. The tree canopy cover analysis results were summarized for specific geographic divisions of interest to Orange County. Most analyses excluded the individual jurisdictions within Orange County and included only the areas of Unincorporated Orange. However, jurisdictions separate from Unincorporated Orange County were grouped together as Incorporated Areas within the analysis of Unincorporated and Incorporated Areas. Maps and tables to show change for three (3) separate divisions in Orange County were generated: 1) Unincorporated Orange County compared to the Incorporated areas of OC (Figure 1) 2) The Six Market Areas of Unincorporated OC (Figure 2) 3) The Urban and Rural Service Areas within Unincorporated OC (Figure 3). Figure 1 Unincorporated and Incorporated areas of Orange County. Page 2 of 24

4 Figure 2 Six Market Areas of Unincorporated Orange County. Figure 3 Urban and Rural Service Areas of Unincorporated Orange County. The Landsat analysis was complimented by a dot-based quantification of tree canopy cover in the Unincorporated Urban Service Area of Orange County using very high resolution aerial imagery from The dot-based method produced a very accurate estimate of total tree canopy cover that can be compared to the temporal change results to understand the amount of underestimation. Page 3 of 24

5 Accurate Estimate of Tree Canopy Cover Tree canopy in the Unincorporated Urban Service Area of Orange County was accurately estimated using a dot-based simple sampling approach with aerial imagery acquired in early January, 2016 (Figure 4). This approach to estimate tree canopy cover followed the dot-based estimation methods described by David Nowak and colleagues from the U.S. Forest Service (Nowak et al. 1996, Nowak and Greenfield 2012). The dot-based approach has been shown to be a very accurate and consistent method of characterizing canopy cover and change (Landry et al. 2013). A total of 3000 dots were randomly placed within the Unincorporated Urban Service Area boundary. At each location, a dot was independently photo-interpreted as canopy or not canopy by two trained photo-interpreters. Dots located within the tree canopy were classified as canopy, while dots located on other vegetation, water, roads or other surfaces were classified as no canopy. The classification results are based on only the points where the technicians agreed on the assessment (Figure 5). Figure 4 Orange County with aerial images. Page 4 of 24

6 Figure 5 Dot-based tree canopy estimate from 2016 aerial imagery in Unincorporated Urban Service Area. Table 1 Count of canopy and non-canopy of 2016 by two photo-interpreters. Number of Dots with Agreement Percentage Canopy at Confidence Level 95% Canopy % +/- 1.7% Non-Canopy % Total % Based on the 2016 aerial imagery, 858 out of 2830 dots were classified as canopy by both photo-interpreters (Table 1). As with any photo-interpretation effort, the two independent photo-interpreters did not agree for the classification of canopy/non-canopy for all 3000 dots. There was a lack of agreement for 170 dots of the 3000 classified, and therefore those dots were not excluded. Based on a 95% statistical confidence interval, the results indicate that we are 95% confident that the true tree canopy cover within the Unincorporated Urban Service Area was between 28.6% and 32%, with the mean of 30.3% canopy cover. Page 5 of 24

7 Moderate Resolution Tree Canopy Cover Mapping Measurements of tree canopy cover over time can provide an indicator of the geographic distribution of urban forest benefits within different areas and how those benefits have changed over time. Landsat satellite imagery is one of the only consistent data sources from which long-term (i.e., several decades) tree canopy cover can be derived. Unfortunately, the use of moderate-resolution satellite data such as the 30 meter resolution Landsat imagery has been shown to underestimate urban tree canopy cover. Figure 6 provides an illustration of the lower resolution of the Landsat image. Note that individual trees are impossible to detect; only larger areas of vegetation are detectable. However, the use of Landsat provides a consistent long-term measurement of change for several decades prior to the availability of highresolution mapping techniques. Figure 6. Comparison of the spatial resolution of Landsat and Aerial imagery for the "castle" in Magic Kingdom. The geographic extent of the area shown is the same in both images. Landsat Data Download We downloaded Landsat Surface Reflectance data from United States Geological Survey ( Collection 1 Higher Level) for 08/24/1986, 05/26/1994, 04/22/2005, and 05/06/2016 (Figure 7). It is important to note that the dates for all Landsat images represent leaf-on conditions within Orange County. Although the original intent was to focus on 1996 and 2006, Landsat scenes acquired in summer of 1996 and 2006 were not usable due to cloud cover over the study area, as shown in the Appendix. Page 6 of 24

8 08/24/ Path 16, Row 40 - Landsat 5 05/26/ Path 16, Row 40 - Landsat 5 04/22/ Path 16, Row 40 - Landsat 5 5/6/ Path 16, Row 40 - Landsat 8 Figure 7 natural color preview images of Landsat data used. Landsat Image Processing The Landsat data was downloaded as surface reflectance data, which had been atmospheric and radiometric corrected. Normalization was then used to minimize the differences caused by atmospheric or solar conditions between images so that the tree canopy mapping method would be consistent through time (1986, 1994, 2005 and 2016). The Landsat image from 2005 was employed as the standard image, from which the other three images (1986, 1994, and 2016) were normalized. Normalization was completed using a linear model generated for each band of the analysis image with the corresponding band of the 2005 image. Page 7 of 24

9 The general model is as shown in in Equation 1: y = x + b (1) where y is band reflectance of 2005, and x stands for corresponding band reflectance of 1986, 1994, or The linear models of normalization for each band based on the image of 2005 are in Table 2. The R squares are higher than 75%, except band 1 of Landsat image of 1994 with R square (73.5%), which is very close to 75%. Therefore, all the regression models are acceptable. Table 2 Linear models of normalization for each band, image of 2005 as standard image. 2005_B1 2005_B2 2005_B3 2005_B4 2005_B5 2005_B7 a b R² a b R² a b R² Tree Canopy Classification Using Decision Tree To determine the amount and distribution of canopy from the Landsat images, a decision tree with biophysical composition index and normalized difference vegetation index (NDVI) was applied to classify canopy and no-canopy. The decision tree classifiers were generated for each Landsat image using training samples of canopy and non-canopy which remained consistent in every image. Decision tree classification is a popular remote sensing technique to classify land cover types. It is defined as a classification procedure that recursively partitions a data set into smaller subdivisions on the basis of a set of tests defined at each branch in the tree (Friedl and Brodley 1997). Biophysical composition index (BCI) is a quantitative spectral indicator designed for characterizing major urban land cover compositions following Ridd's conceptual vegetation impervious surface soil (V I S) triangle model. It could be derived with the help of the normalized Tasseled Cap spectral, as shown in Equations 2-5. Tasseled Cap transformation for Landsat data, which could transform spectral reflectance to brightness, greenness and wetness (the first three components), is able to highlight relevant vegetation variance (Healey, Cohen et al. 2005). The combination of BCI and NDVI is able to reduce within-class variation and enhance between-class variation among various urban compositions. This method was successfully used to extract endmembers of urban land cover types in urban areas of Franklin County, Ohio (Deng and Wu 2013). Page 8 of 24

10 (H + L)/2 V (2) BCI = (H + L)/2 + V H = TC1 TC1 min (3) TC1 max TC1 min V = TC2 TC2 min (4) TC2 max TC2 min L = TC3 TC3 min (5) TC3 max TC3 min where H, V, and L are the normalized Tasseled Cap (TC) components 1, 2 and 3, indicating high albedo material, vegetation, and low albedo material, respectively; TCi (i = 1, 2, 3) are the first three original Tasseled Cap spectra; TCimax and TCimin are the maximum and minimum values of the ith Tasseled Cap component, respectively. The extraction rule for canopy and non-canopy from a decision tree was obtained using decision tree classification in Rstudio programming software. Classification accuracy was assessed by comparing classification results to visually interpreted randomly selected 1500 pixels from Landsat images. The classification overall accuracy for 1986, 1994, 2005 and 2016 is 78.34%, 75.88%, 78.49% and 79.45%, respectively. Tree Canopy Cover Results The decision tree classifier was applied to the whole study area (Orange County) for 1986, 1994, 2005 and The distribution of canopy and non-canopy for 1986, 1994, 2005 and 2016 is shown in Figures Tree canopy was summarized by three divisions within the County: 1) Unincorporated versus Incorporated areas; 2) Six Market Areas of Unincorporated area; and 3) Urban versus Rural areas of Unincorporated area. Tables 3-5 provide a summary of the percentage and acres of tree canopy from each geographic division for each analysis year. Because of the lower accuracy of Landsat-based results, all numbers are rounded to the nearest percent or nearest acre. Page 9 of 24

11 Figure 8. Tree canopy cover for August 24, Figure 9. Tree canopy cover for May 26, Page 10 of 24

12 Figure 10. Tree canopy cover for April 22, Figure 11. Tree canopy cover for May 6, Page 11 of 24

13 Table 3 Percent and acres of Tree Canopy from Landsat images within Unincorporated and Incorporated Areas. LANDSAT 1986 LANDSAT 1994 LANDSAT 2005 LANDSAT 2016 UNINCORPORATED (%) 24% (117,166 acre) 25% (123,366 acre) 30% (145,927 acre) 30% (144,112 acre) INCORPORATED (%) 20% (30,790 acre) 18% (28,250 acre) 19% (28,592 acre) 20% (30,722 acre) Table 4 Percent and acres of Tree Canopy from Landsat images within Six Market Areas in Unincorporated Orange County. LANDSAT 1986 LANDSAT 1994 LANDSAT 2005 LANDSAT 2016 CORE (%) 6% (2,377 acre) 9% (3,471 acre) 8% (2,946 acre) 10% (3,798 acre) EAST (%) 24% (13,004 acre) 20% (10,754 acre) 25% (13,646 acre) 26% (14,052 acre) NORTHWEST (%) 20% (18,238 acre) 24% (21,727 acre) 28% (24,823 acre) 27% (23,949 acre) RURAL EAST (%) 33% (55,456 acre) 35% (59,039 acre) 45% (75,572 acre) 41% (68,432 acre) SOUTH (%) 22% (13,385 acre) 23% (13,709 acre) 24% (14,568 acre) 27% (16,337 acre) SOUTHWEST (%) 18% (14,703 acre) 18% (14,663 acre) 18% (14,370 acre) 22% (17,540 acre) Table 5 Percent and acres of Tree Canopy from Landsat images within Rural and Urban Service Areas in Unincorporated Orange County. LANDSAT 1986 LANDSAT 1994 LANDSAT 2005 LANDSAT 2016 RURAL (%) 28% (88,907 acre) 30% (95,626 acre) 37% (117,745 acre) 35% (111,379 acre) URBAN (%) 17% (28,187 acre) 17% (27,672 acre) 17% (28,105 acre) 20% (32,660 acre) Tree Canopy Cover Change Post-classification change analysis was conducted to quantify and create maps of temporal change (i.e., loss, gain, no-change) from , , , and The maps (Figure 12-15) show the gains and the losses of tree canopy for each time period, and the overall gains and losses for the 30 year of change between 1986 and 2016 in Orange County. Page 12 of 24

14 Figure 12 Tree canopy change from Figure 13 Tree canopy change from Page 13 of 24

15 Figure 14 Tree canopy change from Figure 15 Tree canopy change from Page 14 of 24

16 Tree canopy change was summarized by three divisions within the County: 1) Unincorporated versus Incorporated areas; 2) Six Market Areas of the Unincorporated area; and 3) urban versus rural service areas of Unincorporated area. The canopy in the Unincorporated area increased from and , but decreased from (Table 6). Tree canopy within incorporated areas remained essentially unchanged since There was an overall increase of 6% in tree canopy in Unincorporated Orange County from approximately 24% in 1986 to 30% in Table 6 Tree Canopy change in Unincorporated and Incorporated areas. Parentheses indicate a decrease. JURISDICTION TOTAL LAND NET CANOPY CHANGE IN ACRES AREA (ACRES) UNINCORPORATED 487,967 6,200 22,561 (1,815) 26,946 INCORPORATED 153,797 (2,540) 342 2,130 (68) TOTAL 641,764 3,660 22, ,878 Canopy gains and losses occurred at different time periods within each of the Six Market Areas of Unincorporated Orange County (Table 7). Overall change from showed an increase in canopy cover since 1986 in all Six Market Areas. Large losses occurred in the East market area from and in the Rural East market area from Table 7 Tree Canopy change in Six Market Areas of Unincorporated OC. Parentheses indicate a decrease. MARKET TOTAL LAND NET CANOPY CHANGE IN ACRES AREA AREA (ACRES) CORE 37,109 1,094 (525) 852 1,421 EAST 54,043 (2,250) 2, ,049 NORTHWEST 90,073 3,488 3,096 (873) 5,711 RURAL EAST 166,630 3,583 16,533 (7,139) 12,976 SOUTH 60, ,769 2,952 SOUTHWEST 79,593 (40) (293) 3,170 2,837 TOTAL 487,964* 6,199 22,562 (1,815) 26,946 * Note that Total Land Area listed in Tables 7 and 8 does not exactly match due to differences in geographic boundary spatial datasets. Both the Urban and Rural Services Areas of Unincorporated Orange County showed an overall increase in tree canopy cover from (Table 8), with the urban service area experiencing a 3% increase and the rural service area indicating a 7% increase. In the Urban Service Area, tree canopy decreased from , then increase slightly in and Page 15 of 24

17 again in In the Rural Service Area, tree canopy cover increased slightly from , then there was a large increase from followed by a smaller decrease from Table 8 Tree Canopy change in Rural and Urban Service Areas of Unincorporated OC. Parentheses indicate a decrease. SERVICE TOTAL LAND NET CANOPY CHANGE IN ACRES AREA AREA (ACRES) RURAL 321,000 6,719 22,119 (6,366) 22,471 URBAN 166,885 (515) 433 4,554 4,473 TOTAL 487,885* 6,204 22,552 (1,812) 26,944 * Note that Total Land Area listed in Tables 7 and 8 does not exactly match due to differences in geographic boundary spatial datasets. A summary of the land area included within each analysis and the acres and percent net tree cover change from is shown in Table 9. Net tree cover increased in all geographic divisions that were analyzed, except the slight decrease in the incorporated areas of the County. As might be expected, net percent change in tree cover from 1986 to 2016 was highest in the rural service area and the rural east market area. Although differences in the spatial datasets resulted in minor differences in the total land area included within each analysis, net change results were affected by less than 0.001%. Table 9. Summary of land area and net tree cover change for all geographic divisions. MARKET AREA TOTAL LAND AREA (ACRES) NET TREE COVER CHANGE (ACRES) NET CHANGE (%) ENTIRE ORANGE COUNTY 641,764 26,878 4% UNINCORPORATED 487,967 26,946 6% INCORPORATED 153,797 (68) 0% MARKET AREAS IN UNINCORPORATED COUNTY 487,964* 26,946 6% CORE 37,109 1,421 4% EAST 54,043 1,049 2% NORTHWEST 90,073 5,711 6% RURAL EAST 166,630 12,976 8% SOUTH 60,516 2,952 5% SOUTHWEST 79,593 2,837 4% SERVICE AREAS IN UNINCORPORATED COUNTY 487,885* 26,944 6% RURAL 321,000 22,471 7% URBAN 166,885 4,473 3% * Note that Total Land Area varied slightly as a result of differences in geographic boundary spatial datasets. For example, the analysis area for market areas included 79 more acres (355 more Landsat pixels) than the service areas. Page 16 of 24

18 The results of the Landsat classification, as expected, show less canopy than the dot-based analysis that used the higher resolution aerial imagery. Results from the 2016 aerial imagery showed 30.3% tree canopy (95% CI: 28.6% to 32%) within the unincorporated Urban Service Area, compared to 20% from the Landsat analysis. This results suggests an approximate 10% underrepresentation of tree canopy cover using the Landsat images; smaller patches of trees are simply not detected by the larger pixel size of the Landsat image. The United States Forest Service has also found similar 10% underestimates of tree cover when using Landsat data (Nowak 2012). However, despite the underestimation of canopy from satellite data, the tree canopy change trends should be accurate because the underestimation is consistent for each Landsat image. One of the possible causes of increases and decreases of tree cover from one year to the next is differences in land development. For example, when agricultural land becomes fallow (i.e., no longer plowed) we can expect an increase in tree cover as smaller weedy trees grow. Similarly, as these former agricultural lands are developed then we might see a decrease in tree canopy associated with the removal of trees during land clearing. As new developments mature, trees planted during housing construction typically increase in canopy over time until the trees become fully mature. Additional research would be required to identify the underlying explanations for the changes in tree canopy cover within specific areas of Orange County. Recommended Next Steps Urban forest management is arguably most successful when an adaptive management approach is adopted. In other words, the effectiveness of management efforts and regulations should be evaluated on a periodic basis and used to make improvements to the urban forest management strategies. Cities such as the City of Tampa have adopted such an approach as part of their Urban Forest Management Plan (Northrop et al 2013). It is strongly suggested that Orange County conduct a tree canopy analysis similar to the one published in this report every 5-10 years in order to monitor change. The methods utilized in this study provide an efficient approach to evaluating long-term change for the entire County and for large geographic divisions such as urban service areas and market areas. The evaluation of specific urban forest policies and regulations also requires analysis using a policy relevant unit of analysis (Stone 2004). For example, using a very high resolution tree canopy cover map (i.e., 1 meter), Landry and Pu (2010) were able to evaluate the effectiveness of a tree ordinance on increasing parcel-level residential tree cover. It is also recommended that Orange County develop a very high resolution tree canopy cover analysis similar to the efforts conducted in Tampa (Landry et al 2013; Landry et al. 2009). Detailed tree cover maps Page 17 of 24

19 generated from analyses such as these can provide valuable information about existing and possible tree cover on every parcel within the County. Parcel-level data allow urban forest managers to examine the differences in tree cover by land use and zoning, relationships with building age or neighborhood design, comparisons associated with sociodemographic characteristics of neighborhoods (i.e., Census data), and many other analyses. More importantly, very high resolution analysis provides the tool necessary to target urban forest management strategies to the properties or type of properties where improvements are desired. References Deng, C. and C. Wu (2013). "A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution." Remote Sensing of Environment 133: Friedl, M. A. and C. E. Brodley (1997). "Decision tree classification of land cover from remotely sensed data." Remote Sensing of Environment 61(3): Healey, S. P., W. B. Cohen, Y. Zhiqiang and O. N. Krankina (2005). "Comparison of Tasseled Capbased Landsat data structures for use in forest disturbance detection." Remote Sensing of Environment 97(3): Landry, S. M., Andreu, M. G., Friedman, M. H., & Northrop, R. J. (2009). A Report on the City of Tampa s Existing and Possible Urban Tree Canopy. Final Report to the City of Tampa, February 19, City of Tampa, Florida. 23 p. Landry, S. M., & Pu, R. (2010). The impact of land development regulation on residential tree cover: An empirical evaluation using high-resolution IKONOS imagery. Landscape and Urban Planning, 94(2), Landry, S., Northrop, R. J., Andreu, M., & Rhodes, C. C. (2013). City of Tampa 2011 Urban Forest Analysis: The Structure, Composition, Function and Economic Benefits of Trees and the Urban Forest. Final Report to the City of Tampa, September City of Tampa, Florida. 63 p. Northrop, R. J., Beck, K., Irving, R., Landry, S., & Andreu, M. (2013). City of Tampa Urban Forest Management Plan. November City of Tampa, Florida. 65 p. Page 18 of 24

20 Nowak, D. J., R. A. Rowntree, E. G. McPherson, S. M. Sisinni, E. R. Kerkmann and J. C. Stevens (1996). "Measuring and analyzing urban tree cover." Landscape and Urban Planning 36(1): Nowak, D. J. and E. J. Greenfield (2012). "Tree and impervious cover change in U.S. cities." Urban Forestry & Urban Greening 11(1): Nowak, D. J. (2012). A Guide to Assessing Urban Forests. NRS-INF Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 4 p. Stone, B. (2004). Paving over paradise: how land use regulations promote residential imperviousness. Landscape and Urban Planning, 69(1), Page 19 of 24

21 Appendix The following images show the possible Landsat images available in 1996 and 2006, clearly demonstrating the challenges caused by cloud cover. As a result of the extensive cloud cover, the years 1994 and 2005 were chosen for analysis. 29-APR MAY MAY JUN JUL JUL-96 Page 20 of 24

22 03-AUG AUG SEP SEP-96 Figure 1 Landsat TM data acquired in summer of 1996 covered by clouds. Page 21 of 24

23 25-APR MAY MAY JUN JUN JUL JUL AUG-06 Page 22 of 24

24 31-AUG SEP-06 Figure 2 Landsat TM data acquired in summer of 2006 covered by clouds. Page 23 of 24