Socioeconomic Factors and Urban Tree Cover Policies in a ...

Socioeconomic Factors and Urban Tree Cover Policies in a Subtropical Urban Forest Zoltan Szantoi1

University of Florida–Geomatics Program, School of Forest Resources and Conservation, Reed Lab 404A, Gainesville, Florida 32611-0806

Francisco Escobedo

University of Florida–IFAS, School of Forest Resources and Conservation, P.O. Box 110410, Gainesville, Florida 32611-0410

John Wagner

State University of New York, College of Environmental Science and Forestry, 304 Bray Hall, One Forestry Drive, Syracuse, New York 13210-2788

Joysee M. Rodriguez

University of Florida–School of Natural Resources and Environment, Gainesville, Florida 32611

Scot Smith

University of Florida–Geomatics Program, School of Forest Resources and Conservation, P.O. Box 110565, Gainesville, Florida 32611-0806

Abstract: Cities are increasingly promoting policies that increase and conserve urban forests based largely on biophysical and land use-cover metrics. This study demonstrates how socioeconomic factors need to be considered in geospatial analyses when formulating urban greening policies. Using remote sensing, geographical information systems, spatial field and census data, and policy analyses, we analyzed the effectiveness of urban forest cover policies that included socioeconomic factors when quantifying urban forest cover. We found that urban forest cover was heterogeneous across the study area and non-white residents younger than 19 and greater than 45 years old living in rentals were more likely to reside in areas with less urban forest cover than any other age cohort. Our analyses also indicated that urban forest cover was temporally variable and demographic factors unique to Miami-Dade County bring to light the complexity of establishing homogenous, county-wide “tree canopy” and urban greening policy goals. We present a localized socioeconomic and ecologically based geospatial approach for formulating urban forest cover goals.

Corresponding author; email: [email protected]. Present address: Land Resource Management Unit, European Commission, Joint Research Centre, Ispra, Italy: [email protected]

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428 GIScience & Remote Sensing, 2012, 49, No. 3, p. 428–449. http://dx.doi.org/10.2747/1548-1603.49.3.428 Copyright © 2012 by Bellwether Publishing, Ltd. All rights reserved.

urban tree cover and socioeconomic factors

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INTRODUCTION Urban areas across the world are experiencing rapid population growth, increased impervious surfaces, and an overall decrease in green spaces (Grimm et al., 2008). Miami-Dade County in Florida is an example of this phenomenon and is one of the most densely urbanized areas in the United States (Perry and Mackun, 2001). Loss of green space in the form of urban forests is important because it is an indicator of the amount of ecosystem services being provided to citizens (Dobbs et al., 2011; Flocks et al., 2011). Several local and regional governments in Florida have formulated urban greening policy goals as part of their urban sustainability initiatives. Both the Florida State and Miami-Dade County governments have recognized the importance of urban green space and urban forest cover by developing policies that include tree cover goals (Miami-Dade County, 2007; D’Arelli et al., 2009). Specifically, Miami–Dade County has established a policy to increase urban forest cover to 30 percent of its surface area from the current level of 14 percent (Zhao et al., 2010). However, these urban forest cover goals are often based on temporally static urban tree cover goals without factoring in climate and community characteristics like those of subtropical, Miami-Dade (American Forests, 2010). For example, Miami-Dade’s forest cover has been affected by hurricanes and pest-disease outbreaks such as citrus canker (Szantoi et al., 2008). Furthermore, urban ecosystems such as Miami-Dade’s are constantly being influenced by socioeconomic factors such as human management preferences and changing economic trends (e.g., fluctuating housing prices, immigration and emigration trends) (Tratalos et al., 2007; Davies et al., 2008; Szantoi et al., 2008) that have resulted in unequal distribution of tree cover and ecosystem services (Flocks et al., 2011). Studies have used satellite imagery to quantify spatial distributions and multitemporal changes in urban land covers (Guindon et al., 2004; Yuan et al., 2005), urban expansion (Forsythe, 2005; Xian and Crane, 2005) and measuring urban forest function (Myeong et al., 2006). Many of these utilized spectral vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), as a quantitative measure of vegetation abundance and to better understand spatial and temporal urban vegetation cover (Myeong et al., 2006; Davies et al., 2008). Kumagai (2011) used NDVI as a proxy for vegetation abundance and Jenerette et al. (2007) used census data and remote sensing to analyze social characteristics of neighborhoods and surface temperatures. Other studies have used conventional multispectral methods based on image recognition systems for classification and detection of urban forest change (Chavez and MacKinnon, 1994; Gitelson et al., 1998; Lefsky et al., 2001; Coburn and Roberts, 2004; Magnusson and Fransson, 2005; Kumagai, 2011). Detecting changes in urban forests (i.e., the sum of tree, palm, and shrub cover in a city) requires differentiating between urban, non-urban, and vegetation cover (Key et al., 2001; Sugumaran et al., 2003; Lipscomb et al., 2006). Urban environments are highly heterogeneous (Forster, 1985; Small, 2002), thus distinguishing between vegetation classes such as tree and herbaceous cover is problematic. Davies et al. (2008) found that the areal extent of green space is a poor predictor of tree cover when compared to grassy areas versus wooded areas, while plant productivity as measured by NDVI is better related to absolute levels of tree cover.

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Urban morphology and natural factors have also been used in geospatial analyses that integrate remote sensing, geographical information systems (GIS), and land use/ cover data to formulate urban forest cover goals (Miami-Dade County, 2007; D’Arelli et al., 2009). Geospatial analyses of urban forest cover, as adjusted by spatial socioeconomic data, are also important for comprehending the distribution of urban forests (Grove and Burch, 1997; Iverson and Cook, 2000; Pedlowski et al., 2002; Heynen and Lindsey, 2003; Conway and Urbani, 2007; Heynen et al., 2006; Tratalos et al., 2007; Davies et al., 2008). However, to our knowledge there are few studies such as these from hurricane-prone cities of the subtropics (Dobbs et al., 2011; Flocks et al., 2011). Iverson and Cook (2000), for example, found that household income and building density were strongly related to tree cover in temperate-climate cites. Tratalos et al. (2007) and Flocks et al. (2011) also found that increased household income and race were related to greater tree cover and different levels of ecosystem service provision. Heynen and Lindsey (2003) investigated other factors such as formal education and found that it was positively correlated with tree cover, while population density, income, and municipal tree policies had no appreciable effect. These authors also found that regional tree cover, land ownership, topography, and house age and type are also statistically related to tree canopy cover. Conway and Urbani (2007) and Heynen et al. (2006) also found that land ownership and policies affected urban green spaces. Other research has studied the preferences and values of urban demographic groups toward trees (Lohr et al., 2004; Elmendorf et al., 2005). Miami-Dade, however, is located on a highly urbanized coastline, has a subtropical climate, and an urban forest structure consisting of diverse evergreen trees and palms (Zhao et al., 2010). As previously mentioned, hurricanes have severely damaged its urban forests and have generated substantial amounts of tree debris (Escobedo et al., 2009; Staudhammer et al., 2009). Thus, Miami-Dade is different in many respects to previous studies that have measured and established tree cover in temperate urban forests. Despite these differences, Florida permits tree preservation and coverage goals as a policy option for complying with energy conservation requirements for growth management and development plans (D’Arelli et al., 2009). Furthermore, MiamiDade County’s stated policy goal is “… to enhance the county tree canopy to a minimum of 30 percent coverage, countywide …” and “Promote the expansion of the overall canopy in Miami-Dade County to at least 30 percent by 2020” (Miami-Dade County, 2007). Policy goals such as these are often based on static, city-wide, temperate tree cover estimates using aerial photography stratified by land use and land covers (American Forests, 2010). But both the Miami-Dade County’s 2007 policy goal of increasing urban forest cover and the State of Florida’s House Bill 697 (D’Arelli et al., 2009) do not include any recognition of the potential influence that socioeconomic factors might have on achieving tree cover policy goals (Miami-Dade County, 2007; American Forests, 2010). To address this lack of information on the integration of socioeconomic factors in geospatial analyses of tree cover and policies, this study had two objectives. First, we explored the amount, distribution, and temporal change of Miami-Dade’s urban forest cover—both tree and palm cover—using conventional remote sensing and GIS approaches. We also studied the relationship between ethnicity, age, income, education, and housing tenure on this urban forest cover. To achieve this first objective


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we: (1) used photo-interpretation of Digital Orthophoto Quarter Quadrangle (DOQQ) images to analyze temporal changes in tree cover; (2) correlated amounts of urban forests (e.g., tree and palm cover) as measured using random field plots to available U.S. Census tract data; and (3) statistically analyzed the relationship between NDVI and socioeconomic factors (i.e., subclasses) from U.S. Census spatial data. We used NDVI because of its common use in estimating vegetation cover (i.e., trees, palms, and other canopy-forming non-woody, tropical plants) and condition (Curran, 1980). However, the DOQQ images were used to standardize raw NDVI values to absolute levels of tree cover (i.e., 0–100%) that are commonly used as metrics for urban tree canopy assessments (Davies et al., 2008; Kumagai, 2011) and also to better compare this study’s findings to recent results from a parallel urban forest study (Zhao et al., 2010). Second, we analyzed the effectiveness of achieving tree cover policy goals under two alternatives: (1) based on biophysical, land use-cover, tree-specific criteria alone; or (2) with the inclusion of additional socioeconomic factors. This second objective will be stated and tested in the form of two related hypotheses that examine the relationship between urban forest cover and a defined set of socioeconomic factors. Our first hypothesis (H1) is that urban forest cover was temporally static and uniform over the study area. Our second hypothesis (H2) was that urban forest cover was not related to the defined set of socioeconomic factors. Testing and failing to reject H1 and H2 would imply that there would be no relationship between urban forest cover and a defined set of socioeconomic factors. The implication would be that Miami-Dade County’s policy goal could be achieved by absolute increases in tree canopy. However, testing and rejecting H1 then testing and rejecting H2 would imply there was a correlation between socioeconomic factors— such as income and ethnic groups for example—and urban forest cover and as such absolute urban forest cover increase goals would not be most effective at achieving the policy goal. Results should assist urban planners and urban forest managers in establishing targeted urban forest canopy goals and present an approach on how to meet these goals most cost effectively. METHODS Study Area Miami-Dade County covers approximately 6,000 km². Approximately 20 percent is urban and the rest is a variety of wetland, agricultural, non-urban residential, and barren land uses (Fig. 1). We limited our study area to those 1,260 km2 that roughly corresponds to the county-designated urban development boundary that encompasses several urban municipalities and unincorporated sections. Urban unincorporated areas do not fall within municipal boundaries, but are administered by the county government. Population density in the study area has leveled off recently, but between 1980 and 2000 it increased by 17% and then another 7% between 2001 and 2006 (U.S. Census Bureau, 2008). Miami-Dade County’s population is ethnically diverse and nearly 50% of the population is foreign born (U.S. Census Bureau, 2000; Perry and Mackun, 2001). The predominance of shallow and alkaline soils, urbanization, and hurricanes have affected the county’s urban forests detrimentally (Florida Soil and Water Conservation

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Fig. 1. Study area and the distribution of aggregated municipalities, or communities within Miami-Dade County, Florida.

Staff, 1984; Miami-Dade County, 2007; Escobedo et al., 2009). While most of the study area is comprised of “urban” land cover, other cover types include agricultural and natural areas such as salt marshes, sawgrass, freshwater marshes, mangrove swamps, and sloughs. Remnant areas of South Florida’s pine rocklands, flatwoods, and interspersed hammock communities are also found throughout the study area (Florida Soil and Water Conservation Staff, 1984). Palms also comprise a substantial portion of Miami-Dade’s urban forest structure (Zhao et al., 2010). Urban Tree Cover Estimates Urban forest cover in Miami-Dade was estimated for the years 1984, 2004, and 2008 using photo-interpreted, digital aerial photographs. The 1984 images were 1:58,000 scale color infrared (CIR) from the National High Altitude Photography Program and were taken between March 1984 and February 1985. The photography was subsequently scanned from the diapositive and ortho-rectified with a ground


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sampling distance of 2 × 2 meters and then mosaiced. The ortho-rectification process followed standards established by the United States Geological Survey (USGS, 1996) for Digital Orthophotos in which the digital elevation model of the subject area, the coordinates of identifiable ground control points, the camera calibration information, and orientation parameters are included in the processing. The 2004 imagery was 1:40,000 color and CIR, was taken on January 15, 2004, and was ortho-rectified (DOQQs) with a ground sampling size of 1 × 1 meter. Urban forest cover plus percent plantable space (e.g., area in a predefined plot where a tree could be planted and become established) was based on very high resolution three-band (red-green-blue) digital aerial imagery (0.08 m ground sample distance) taken in 2007. Palm and tall shrub cover were not easily discernible in the 1984 images. In order to better estimate changes in urban forest cover from 1984 to 2004, we used a photo-interpretation point-plot scheme (Escobedo et al., 2006; Heynen et al., 2006) and 1000 random points to classify points into the following categories: (1) urban forest cover (e.g., trees, palms, and shrubs); (2) buildings; (3) pervious (i.e., bare soil and herbaceous vegetation); (4) impervious (e.g., concrete or asphalt); and (5) water. To asses urban forest cover changes from 2004 to 2008, we used 250 (229 field measured and 21 plots where access was not allowed), 0.04 ha, georeferenced, permanent circular plots that were randomly distributed throughout the study area in 2008 as part of a parallel study (Zhao et al., 2010). For each of these 250 plots, we photo-interpreted plot-level urban forest (i.e., tree, palm, and tall shrub) following methods outlined in Szantoi et al. (2008). The 2008 field measurements of percentage tree, palm, and tall shrub cover from Zhao et al.’s (2010) 229 plots were also used in our analyses as well. A Pearson’s chi-square test was used to determine significant differences (α < 0.05) between photo-interpreted tree cover during these time periods. Image Processing and Classification Landsat Thematic Mapper (TM) imagery taken on March 21, 1996; December 27, 1999; and April 23, 2008 was corrected for radiometric and geometric distortion and projected onto the Universal Transverse Mercator (UTM, 17 North) projection system and co-registered to the corresponding DOQQ imagery using 55 evenly distributed control points to a root mean square (RMS) accuracy of 0.5 pixel (15.0 m). The study area was a subset of the full TM scene and was corrected for atmospheric conditions and converted to calibrated reflectance using the ENVI (ITT Visual Information Solutions, Boulder, Colorado, version 4.4) software’s atmospheric correction model and the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) module which incorporates the MODTRAN4 radiation transfer code (Matthew et al., 2000). The urban forest cover in the images were analyzed using NDVI, which is a measure of vegetation cover and condition (Tucker, 1979). Therefore, healthy green vegetation (i.e., urban forest) should have a relatively high NDVI ratio, whereas areas with a lack of vegetation would have a low NDVI. The NDVI was calculated from the spectral radiance in the visible red (R) and near-infrared (NIR) bands using equation (1):

NDVI = (NIR – R)/(NIR + R)

(1)

Our NDVI values were calculated using ERDAS Imagine 8.7 (Leica Geosystems, Atlanta, Georgia) image processing software. Raw values ranged between –1 and 1,

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with values of 0.50 and greater indicating dense vegetation and values less than 0 indicating areas with no vegetation (Leica Geosytems, 2004). For our study, NDVI values were standardized to a scale of 0 to 200 to provide integer data for statistical analyses, eliminate the use of negative numbers, and to better compare our results with scaled metrics based on absolute levels of tree cover (i.e., 0–100% tree cover or square meters of tree cover per study area). Theoretically, all land NDVI values are positive, but some urban values lie marginally below the theoretical level of zero. To scale the raw NDVI values to the standardized NDVI values, the raw values were computed, where –1.0 is 0, 0 is 100, and 1.0 is 200, based on the following equation (2):

(Raw NDVI value + 1) • 100

(2)

As a result, NDVI values less than 100 mainly represent water or bare and impervious surfaces while values above 100 represent live vegetation and values above 150 represent very dense vegetation. The resulting image depicts scaled NDVI values across urban Miami-Dade County (Fig. 2) for 1996 and 1999. Although many geospatial studies regularly use raw NDVI values, by using these rescaled NDVI values (i.e., 0–200), we were better able to compare our findings to other urban ecosystem studies that discuss urban forest structure and urban tree canopy assessment methods in terms of absolute levels of tree cover (Davies et al., 2008; Flocks et al., 2011; Tratalos et al., 2007; Zhao et al., 2010). To better determine an NDVI value that differentiated urban forest cover from areas where there was no or minimal urban forest cover, we analyzed central tendency and performed a simple regression test of the derived NDVI values from the 2008 Landsat TM 5 image. The NDVI values were standardized and compared against field-measured urban forest cover estimates from the 2008 parallel study (Zhao et al., 2010). The NDVI values ranged from 89.6 to 164.7 (25% quartile = 112.1 NDVI) and had a mean value of 123.8 (std. dev. = 14.8, N = 159). Regression analysis utilizing only urban forest cover to predict NDVI yielded a line with an intercept at 118.2 (p < 0.0001), a positive coefficient for urban forest cover of 0.30 (p < 0.0001), and a mean of 122.5. Also a pairwise Pearson product-moment correlation analysis revealed that NDVI and urban forest cover were positively correlated (0.5398; p < 0.0001). Therefore, since Davies et al. (2008) found that NDVI was determined by absolute levels of tree cover and Kumagai (2011) used NDVI as a proxy for vegetation abundance, we considered that an NDVI value of 120—since the urban forest cover to NDVI intercept was 118.2 and the mean was 122.5—is a conservative value that can be used for comparison with other urban tree cover studies and for testing our hypothesis as previously explained. Socioeconomic Variables Socioeconomic variables were based on 2000 U.S. Census Bureau data in the form of census block groups (CBGs). The CBGs are the smallest statistical subdivisions within a census tract and therefore represent unique communities of people. These CBGs usually have approximately 1,500 persons and are designed to be homogeneous with respect to population demographics such as socioeconomic status and living conditions (U.S. Census Bureau, 2000). Socioeconomic subclasses for each


Fig. 2. Normalized Difference Vegetation Index of the Miami-Dade County, Florida study area for 1996 and 1999.

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CBG were created for (a) ethnicity, (b) formal education level, (c) income, (d) age, and (e) housing tenure and are represented as percentages of the total population in each tract (reported in Table 2 below). Topologically Integrated Geographic Encoding and Referencing (TIGER) files from the 2000 US census were used to spatially represent each CBG as a polygon. Mean NDVI values were calculated for each CBG polygon and data were analyzed at the raster cell level using the zonal statistics in ArcGIS. Using the zonal statistics tool, the NDVI value of each CBG was calculated and analyzed relative to the tract’s socioeconomic subclasses. The 1,204 CBG polygons in the study area were defined by the TIGER polygon shapefiles. Hypotheses Testing and Policy Analysis Figure 1 depicts urban Miami-Dade County, its municipalities/communities, and unincorporated areas while Figure 2 displays the distribution of urban forest cover (i.e., vegetation productivity) across this same study area, with the darker areas representing areas with greater urban forest cover. The first hypothesis (i.e., urban forest cover NDVI = 120 is uniform over the 344 census tracts in the study area) was tested using a t-test. To facilitate statistical analyses, we grouped the 344 census tracts into 12 geographically distinct strata using the CBG numbering identification scheme (U.S. Census Bureau, 2000). For the second hypothesis (i.e., urban forest cover was not related to socioeconomic factors), Pearson and Spearman weighted correlation coefficients analyzed statistical relationships between rescaled NDVI and socioeconomic U.S. Census subclasses using SAS (SAS Institute Inc., 2007), in which a correlation of (+1) indicated a perfect positive linear relationship and (–1) indicated a perfect negative linear relationship between NDVI and each socioeconomic subclass. Data were checked for normality using Levene’s test, and individual pair-wise correlations and a correlation matrix were calculated and analyzed to reduce multicollinearity among explanatory variables. We used a weighted multiple linear regression analysis (MLR) to determine the significance of subclass variables on NDVI. The weighting was based on the number of corresponding pixels within each CBG. Using the number of pixels per CBG might bias the estimates because larger CBGs might have lower population and a greater potential for more urban forest cover. However, because population density was computed by dividing the total population within a geographic entity (i.e., CBG; U.S. Census, 2000) and urban forest cover was calculated either by dividing tree-palm cover within a geographic entity or plot, the proportion of tree-palm cover in the CBGs can be accounted for. We defined effectiveness as the “effort” and inconsistency relative to reaching a defined policy goal. To examine the effectiveness of different alternatives, relative to the existing urban forest cover, we determined the average NDVI of all 12 CBG strata in Miami-Dade County. As NDVI is being used as a proxy for percent urban forest cover, we fixed hypothetical NDVI values to represent Miami-Dade County’s goal of 30 percent tree cover. These various values allowed for a comparative analysis between two urban forest cover increase alternatives—flat and targeted. “Effort” in our analysis was measured as the difference between the existing urban forest cover and the policy goal and is expressed in equation (3):


E = 12 ⋅ ( G – N )

437 (3)

where E denotes effort, G denotes the urban forest cover goal, and N denotes the average NDVI for Miami-Dade County’s urban forest cover based on the 12 CBG strata. Equation (4) was then used to predict the urban forest cover of the i-th stratum using a flat increase ( Fˆ i ) alternative: Fˆ i = N i + ( G – N ) ; i = 1 ,… ,12

(4)

To predict increases in urban forest cover using the targeted alternative requires first developing weights to distribute the effort based on existing urban forest cover, then predicting the urban forest cover using the targeted increase. Equations (5.1) to (5.3) define the logic we used for assigning these weights. D i = G – N ; i = 1 ,… ,12 ⎧ ⎪ If D i > 0 ; D = D + D i ⎨ If D i ≤ 0 ; D = D ⎪ ⎩ ⎧ D ⎪ If D i > 0 ; w i = -----i D ⎨ ⎪ ≤ 0 ; w = 0 If D ⎩ i i

⎫ ⎪ ⎬; i = 1 ,… ,12 ⎪ ⎭

⎫ ⎪ ⎬; i = 1 ,… ,12 ⎪ ⎭

(5.1)

(5.2)

(5.3)

Equation (5.1) defines the distance from the i-th CBG strata’s existing urban forest cover to the goal; equation (5.2) targets only those strata whose existing urban forest cover is below the goal; and equation (5.3) defines the weight given to i-th stratum. Equations (5.1) and (5.2) require that the sum of the weights given in equation (5.3) sum to one. Equation (6) is used to predict the urban forest cover of the i-th stratum using a targeted increase ( Tˆ i ) alternative: Tˆ i = w i ⋅ E + N i ; i = 1 ,… ,12

(6)

Comparing equations (4) and (6) shows that the same amount of effort is used in the flat and targeted increase alternatives; that is, in equation (6) effort is constant and given by equation (3) while in equation (4) the effort is the cumulative of (G – N). Also, while the flat and targeted increase alternatives will result in the same average NDVI for all the strata, the predicted urban forest cover among the strata might be inconsistent with Miami-Dade County’s goal. RESULTS Tree Cover and NDVI Estimates The 12.4% urban tree and palm cover in 2004 is statistically different and a decrease from the 31.3% cover estimated using the 1984 images (Table 1). According

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Table 1. Estimates of Urban Forest Cover Change in Miami-Dade County between 1984 and 2004, Using 1000 Random Photo-interpretation Points Cover class

Percent cover (standard error) 1984

2004

31.3a (1.21)

12.4a (0.99)

Building

8.6 (0.80)

13.0 (0.94)

Pervious

40.8 (1.31)

45.9 (1.31)

Impervious

15.8 (1.00)

22.3 (1.11)

Water

3.5 (0.55)

6.4 (0.65)

Urban forest

Significantly different: Pearson chi-square = 71.178.

a

to a parallel study in 2008, tree and palm cover was 14% (Zhao et al., 2010), well below the stated goal of 30% and about one-third of this consisted of palm cover. Figure 3 illustrates an increase in Miami-Dade County’s urban forest cover to an average of 125, 126.4, and 130 NDVI. The 125 NDVI value was selected as it was above the average NDVI value of all the CBG strata but below the CBG stratum with the highest urban forest cover. The 126.4 NDVI value was chosen as it was the same stratum with the highest urban forest cover. Finally, the 130 NDVI value was used to illustrate having to increase the urban forest cover in all CBG strata. Hypotheses Tests The results indicate that urban forest cover varies in relation to age, ethnicity, education, and housing tenure across Miami-Dade County (Table 2). County-wide NDVI and socioeconomic subclass variables are positively correlated with the category “whites,” while 1996 and 1999 NDVI values were negatively correlated with African Americans and Hispanics. In 1996 and 1999, Hispanics had the greatest negative correlation to NDVI of the three ethnic groups. Miami-Dade County residents with post-high school or higher degrees had strong positive correlation to NDVI, while people with at least a primary school education had a negative correlation to NDVI. Conversely, households with an average annual income above US$40,000 were correlated with the greatest amounts of urban forest per unit area as measured by this subclass’s NDVI. When examining age groups, the results indicate that persons less than 21 years old and persons between 40 and 64 years old are correlated as living in high urban forest cover areas compared to persons in other age subclasses. When examining for housing tenure, owner-occupied homes have greater tree cover than do rental homes, as expected. The statistical results for the first hypothesis test showed that 9 of the 12 CBG strata did not have an NDVI > 120 (α > 0.001) and hence had insufficient urban forest cover; thus our first hypothesis was rejected. For the second hypothesis test, the correlation matrix revealed that income and education were highly correlated; therefore income variables were reduced to 3 subclasses while the education variable was completely removed from the analysis. Also, the African-American ethnicity subclass was


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Fig. 3. Increase Miami-Dade County’s urban forest cover to an (A) average of 125 NDVI, (B) an average of 126.4 NDVI, and (C) an average of 130 NDVI.

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Table 2. Socioeconomic Factors and Raw Normalized Difference Vegetation Index Correlation Coefficients for the Urbanized Portion of Miami-Dade County, Florida for 1996 and 1999 Socioeconomic factor Ethnicity

Education

Mean annual household income (US$ 1,000)

Age (years)

Housing tenure

Subclasses

1996 NDVI image

1999 NDVI image

Corr. coeff.a

P

Corr. coeff.

P

Hispanics

–0.5992