- Email: [email protected]
Evaluating the efficacy of a local tree protection policy using LiDAR remote sensing data
Landscape and Urban Planning 104 (2012) 19–25
Contents lists available at SciVerse ScienceDirect
Landscape and Urban Planning journal homepage: www.elsevier.com/locate/landurbplan
Evaluating the efficacy of a local tree protection policy using LiDAR remote sensing data Chan Yong Sung ∗ Texas Transportation Institute, Texas A&M University, 3135 TAMU, College Station, TX 77843-3135, USA
a r t i c l e
i n f o
Article history: Received 23 March 2011 Received in revised form 31 August 2011 Accepted 24 September 2011 Available online 18 October 2011 Keywords: Tree removal permit Local environmental policy Urban forest management Urban remote sensing Object-based image segmentation
a b s t r a c t This study evaluated the efficacy of a tree removal permit regulation that the city of Lakeway adopted in 2002 to protect trees in private lands by comparing the levels of tree protection in Lakeway with those in the village of The Hills, a neighboring municipality without such a regulation. The level of tree protection was assessed by mean canopy height (MCH) and percent canopy cover (PCC) of a parcel derived from light detection and ranging (LiDAR) data and aerial photography, respectively. MCHs and PCCs were estimated for single family residential parcels where houses were constructed for four years before (1998–2001) and after (2003–2006) the adoption of the tree removal permit regulation by Lakeway. Holding parcel size and vegetation condition before housing construction constant, MCHs of the parcels developed in 2003–2006 were statistically significantly higher in Lakeway than Hills. Because the difference in MCHs was not found in the parcels developed in 1998–2001, it is concluded that Lakeway’s tree removal permit regulation has been successful to protect trees in private lands. Unlike MCHs, PCCs between the two municipalities were not statistically significantly different in the parcels developed in each of the two study periods. This result may indicate that PCC is not as accurate in assessing the level of tree protection as MCH due to the inability of PCC to distinguish existing mature trees from young ones planted after construction. Based on these findings, it is recommended that urban planners use LiDAR data to study urban forests. Published by Elsevier B.V.
1. Introduction Urban trees provide many valuable benefits. Urban trees mitigate climate change by absorbing carbon from the atmosphere and store it in plant biomass (Churkina, Brown, & Keoleian, 2010; Escobedo, Varela, Zhao, Wagner, & Zipperer, 2010; Pataki et al., 2006). Nowak and Crane (2002) estimated that trees in urban areas of the United States stores 700 million tonnes of carbon. Urban trees also save energy consumption by shading buildings (Akbari, Kurn, Bretz, & Hanford, 1997; Simpson & McPherson, 1998), improve air quality (Nowak, Crane, & Stevens, 2006), decrease stormwater runoff (Booth, Hartley, & Jackson, 2002; Sung & Li, 2010), maintain biodiversity by serving as habitats for wildlife (Angold et al., 2006), enhance human health (Ulrich, 1984), and raise property values (Sander, Polasky, & Haight, 2010). To protect trees in urban areas, local government has adopted various tree protection policies, such as tree planting program and minimum landscape area (Kim & Ellis, 2009; McPherson, Simpson, Xiao, & Wu, 2011; Schmied & Pillmann, 2003). One of the widely used policies is a tree removal permit regulation that requires a
∗ Tel.: +1 979 575 7747. E-mail address: [email protected] 0169-2046/$ – see front matter. Published by Elsevier B.V. doi:10.1016/j.landurbplan.2011.09.009
landowner to obtain a permit to remove trees from private lands. The types of trees protected by this regulation vary with local conditions, but are usually defined based on the sizes, species, and locations of trees (Cooper, 1996; Coughlin, Mendes, & Strong, 1988). A growing number of local governments have adopted tree removal permit regulations, but only few studies have evaluated the efficacies of these regulations. Results of the previous studies were even inconsistent with each other. Some studies found the positive effect of the tree removal permit regulation (Landry & Pu, 2010) or similar tree protection policies (Hill, Dorfman, & Kramer, 2010; Kim & Ellis, 2009), while others did not (Conway & Urbani, 2007; Heynen & Lindsey, 2003; Taylor, Brown, & Larsen, 2007). The contrasting results may be attributed to insufficient information on the extent to which existing trees were protected. Most studies evaluated a tree removal permit regulation using the level of tree protection assessed by percent canopy cover (PCC) derived from airborne and satellite images. However, a high PCC does not necessarily indicate the success of a tree removal permit regulation. For instance, if a landowner removed existing trees and planted nursery ones after housing construction, this property had a high PCC even though existing trees were not protected. Mature trees are more beneficial than nursery ones (e.g., storing a larger amount of carbon and saving more energy by shading buildings), and therefore distinguishing existing and nursery trees is needed for evaluating
20
C.Y. Sung / Landscape and Urban Planning 104 (2012) 19–25
a tree removal permit regulation, especially in recently developed areas where nursery trees did not yet reach their mature heights. Light detection and ranging (LiDAR) can provide useful data to assess the level of tree protection in private lands. LiDAR is an active remote sensing system that emits light and detects reflected energy from the earth surface. The emitted light either returns from a tree canopy or penetrates the canopy and returns from the ground. Using these multiple returns, previous studies has estimated the heights of tree canopies (Lefsky, Cohen, Parker, & Harding, 2002; Lim, Treitz, Wulder, St-Onge, & Flood, 2003). Because existing trees are likely to be taller than newly planted ones after construction, LiDAR-derived canopy heights can be used to assess the level of tree protection. LiDAR has also been applied to diverse fields in urban research including urban tree detection (Goodwin, Coops, Tooke, Christen, & Voogt, 2009; Secord & Zakhor, 2007), land cover classification (Zhou, Troy, & Grove, 2008), and impervious surface mapping (Hodgson, Jensen, Tullis, Riordan, & Archer, 2003; Smith, Zhou, Cadenasso, Grove, & Band, 2010). This study evaluated the efficacy of a tree removal permit regulation by comparing tree canopy heights derived from LiDAR data between two municipalities with and without a regulation. The primary research question of this study is whether trees are taller in the municipality with a tree removal permit regulation than one without such a regulation. For comparison to the previous studies (e.g., Conway & Urbani, 2007; Heynen & Lindsey, 2003; Landry & Pu, 2010), this study also questions whether canopy cover estimated from high resolution aerial photography is higher in the municipality with a permit regulation than one without such a regulation. Fig. 1. Illustrations of (a) 2008 aerial photography and (b) triangulated irregular network (TIN) created from the 2007 LiDAR data.
2. Materials and methods 2.1. Study area The study area is two neighboring municipalities (the City of Lakeway, and the Village of The Hills) located near the city of Austin, TX, USA. The two municipalities are typical North American suburban communities predominantly used for single family residences. In both municipalities, more than 90% of populations were white and more than 70% of households were family households. Income and education levels were higher than US averages (Table 1). In 2002, Lakeway amended a land development ordinance and adopted a tree removal permit regulation. This ordinance requires a landowner to obtain a permit before removing trees larger than 41 cm in diameter at breast height (DBH) from a private land. Tree species that are either very common or invasive in this region are exempted from this permit. Those trees were tree of heaven (Ailanthus altissima), mimosa (Albizia julibrissin), Bois d’Arc (Maclura pomifera), Chinaberry (Melia azedarach), black willow (Salix nigra), hackberry (Celtis spp.), and Ashe’s juniper (Juniperus ashei). Once a landowner applies for a permit, a city arborist visits the site and reviews if tree removal is necessary for site development. Even if the permit is issued, the removed trees must be replaced by trees larger than 2.5 cm in DBH. This ordinance also requires installing fences to prevent damage to existing trees from construction activities (City of Lakeway, 2011). Hills is a small municipality that has its own ordinances. Hills was selected as a control site because it is similar in many socioeconomic characteristics to Lakeway (Table 1). Hills does not have any regulation to protect trees in private lands (Village of The Hills, 2011). Climate is semi-arid and subtropical with hot and dry summer. Mean annual temperature is 21 ◦ C and mean annual precipitation is 882 mm. During the hottest month (August), the mean temperature reaches 35 ◦ C with precipitation of 64 mm (US National Climatic Data Center, 2003). Because of hot and dry summer, tree growth in this region is limited by water (Sung, Li, Rogers, Volder, & Wang,
2011). As a result, the study area was covered by patches of grasslands and forests depending on local water availability. Dominant tree species are Ashe’s juniper (Juniperus ashei) and live oak (Quercus virginiana), both of which can grow up to 10–15 m (Sung and Li, in press). 2.2. Tree canopy cover Tree canopy cover was classified from aerial photography taken on June 1, 2008, as a part of the US national agricultural imagery program (NAIP) (available from http://tnris.state.tx.us) (Fig. 1). The NAIP images were acquired using a Leica ADS40 SHH52 digital camera sensor that can detect solar radiation reflected from the earth surface in four spectral bands (blue, green, red, and near infrared). The NAIP images had a pixel size of 0.5 m. A two-step classification procedure was used for land cover classification. First, the images were pre-processed by an object-based image segmentation (OBIS), an image processing algorithm that extracts geographic objects by merging contiguous pixels based on the spectral similarity (Benz, Hofmann, Willhauck, Lingenfelder, & Heynen, 2004). OBIS has been widely used to classify land covers from fine-resolution remote sensing images because, unlike a per-pixel based algorithm, it does not suffer from a salt and pepper noise problem (Blaschke, 2010; Carleer, Debeir, & Wolff, 2005). Second, the OBIS objects were assigned into one of five land cover types (impervious surface, tree canopy, grass, bareground, and water) using an iterative self-organizing data analysis (ISODATA), an unsupervised classification algorithm that automatically identifies spectrally distinct clusters (Jensen, 2005). BerkeleyImageSeg (Environmental Software Developers, 2010) and ENVI 4.7 (ITT VIS, 2009) were used for OBIS and ISODATA, respectively. User-specific parameters of the OBIS (threshold = 20 pixels, shape = 0.5, compactness = 0.5) and ISODATA (the maximum number of classes = 20, the minimum number of pixels in each class = 1, the maximum class standard deviation = 1, the minimum class distance = 5, the maximum number of merge
C.Y. Sung / Landscape and Urban Planning 104 (2012) 19–25
21
Table 1 Socioeconomic characteristics of the city of Lakeway and the village of the Hills.a Characteristics
Hills
Lakeway
US
Total population White population Family households (families) With own children under 18 years Average household size Total housing unit Occupied housing units Owner-occupied housing units Bachelor’s degree or higher (percent of the population older than 25) Per capita income (in 2009 inflation-adjusted US dollars)
2485 90.7% 86.6% 36.3% 2.69 1028 89.9% 91.3% 73.7% 62,811
10,367 94.8% 73.5% 31.2% 2.46 4,979 84.5% 84.3% 62.0% 51,648
– 74.5% 66.7% 31.0% 2.60 – 88.2% 66.9% 73.7% 27,041
a
2005–2009 estimates by US Census Bureau (2011).
pairs = 2) were determined by trials and errors so that the final land cover map had the highest classification accuracy. The classification accuracy was calculated using 300 reference pixels randomly selected from the NAIP images. The overall classification accuracy of the final land cover map was 84.0% and kappa coefficient was 0.78 (Table 2). Tree canopy was the most accurately classified land cover type. Of 127 pixels, 117 were correctly classified as tree canopies (both producer and user accuracies were 92.1%).
2.3. Canopy heights Canopy heights were derived from LiDAR data obtained in 2007 by the US Federal Emergency Management Agency (FEMA) (available from http://www.capcog.org) (Fig. 1). The LiDAR data were collected, on average, at 1.4 m ground sampling distance (GSD) by three systems (Optech 2050, ALS49, and ALS50 LH). Previous studies that were conducted in natural forests derived canopy heights by subtracting a digital elevation model (DEM) from a digital surface model (DSM) (Means et al., 2000; Næsset, 1997). DEM and DSM are grid models whose cell values represented the elevations of the ground and the uppermost surface that were estimated by taking the minimum and maximum LiDAR elevations within a cell, respectively. However, this approach cannot be directly applied to urban areas where the uppermost surface is not always tree canopy but other artificial materials (e.g., building roofs). To avoid an error that mistakenly identifies the height of an artificial material as the height of a tree canopy, DSM was calculated using LiDAR data falling only on tree canopy cover classified in Section 2.2. Because the LiDAR did not collect ground samples uniformly but in a zigzag fashion, the final DEM and DSM were created to have 5 m cell size, which is about four times larger than the average GSD. Canopy height model (CHM) was calculated by taking the difference between DEM and DSM. To exclude pixels that may represent the heights of other materials underneath tree canopy, CHM cells whose values were less than 1 m were excluded from the analysis (Næsset, 1997).
2.4. Comparing canopy height and canopy cover between two study municipalities The effect of Lakeway’s tree removal permit regulation was evaluated by comparing mean canopy heights (MCHs) and PCCs of parcels where houses were constructed for four years after the adoption of the regulation (2003–2006) in Lakeway with those parcels in Hills. Parcel boundaries were obtained from Travis County Appraisal District. The 2007 FEMA LiDAR data did not covered about one fifth of property parcels located in the southwest corner of the study area. Thus, only parcels that were completely covered by the LiDAR data were used in this analysis. This study focused on parcels used for single family residences. Parcels along the shoreline of the Lake Travis were also excluded because many of those parcels extended into lake water where tree cannot grow. Remaining parcels were used to develop a general linear model (GLM) with MCH and PCC as dependent variables, municipality (0 for Hills and 1 for Lakeway) as a factor, and percent canopy cover in 1997 (PCC97) and parcel size as covariates. PCC97 were included to estimate vegetation conditions before houses were constructed. Because 1997 aerial photography was a black-and-white image (available from http://www.capcog.org), canopy cover was manually digitized on screen. Parcel size was included to control the luxury effect, which refers to the phenomenon that a wealthy resident tends to spend more money to maintain gardens with denser and more diverse plants (Grove et al., 2006; Hope et al., 2003; Larsen & Harlan, 2006; Luck, Smallbone, & O’Brien, 2009; Martin, Warren, & Kinzig, 2004). Initially, appraised property value was considered as a separate covariate but was excluded in the final analysis due to its severe multicollinearity with parcel size. Pearson’s correlation coefficient between parcel size and appraised property value was 0.441, which indicates a strong positive relationship between the two variables. Parcel size was selected as a covariate because it is a better predictor for the level of tree protection than appraised property value. The higher predictability of the parcel size may be attributed to space availability, i.e., in a
Table 2 Accuracy assessment of land cover classification. Classified
Tree canopy Grass Impervious surface Bareground Water Column total Producer’s accuracy (%)
Reference Tree canopy
Grass
Impervious surface
Bareground
Water
Row total
User’s accuracy (%)
117 10 0 0 0 127 92.1
1 56 1 3 0 61 91.8
7 4 39 7 3 60 65.0
1 0 10 30 0 41 73.2
1 0 0 0 10 11 90.9
127 70 50 40 13 300 –
92.1% 80.0% 78.0% 75.0% 76.9% – –
Overall accuracy = 84.0%; kappa coefficient = 0.778.
22
C.Y. Sung / Landscape and Urban Planning 104 (2012) 19–25
large parcel, more space is not occupied by houses and other builtup structures, and existing trees were less likely damaged during construction. Property value poorly explains the variation in the level of tree protection in the study area (correlation coefficients of appraised value were 0.021 with MCH and −0.067 with PCC). All statistical inferences were based on a bootstrapping procedure, a computationally expensive statistical technique that constructs a confidence interval using an empirical distribution of a statistic of interest derived from a number of datasets resampled with replacement from an original dataset (Efron, 1993). The bootstrapping procedure was used because residuals of the GLMs were not normally distributed. Confidence intervals for the parameters of the GLMs were constructed by bias corrected and accelerated (BCa) method using 999 resampled datasets generated by ‘boot’ package in R (Canty & Ripley, 2009). To explore if the difference in MCH and PCC had existed between the two municipalities even before Lakeway adopted the tree removal permit regulation, the same analyses were also conducted with parcels where houses were constructed for four years before the amendment of the ordinance (1998–2001). Because canopy heights were estimated from the 2007 LiDAR data, MCHs of the parcels developed in 1998–2001 and in 2003–2006 represented vegetation conditions six to nine years and one to four years since housing constructions, respectively (seven to ten years and two to five years for PCCs).
3. Results and discussion 3.1. Descriptive statistics There were 419 and 246 single family residential parcels where houses were constructed for four years before and after Lakeway adopted the tree removal permit regulation in 2002, respectively (Table 3). For the parcels developed in 2003–2006, Lakeway (5.1 m) had, on average, higher MCH than Hills (4.2 m). The difference in MCHs between the two municipalities was not observed in the parcels developed in 1998–2001. Average MCHs of the parcels developed in 1998–2001 were similar between Lakeway (5.2 m) and Hills (5.3 m). PCC had a similar trend to MCH. Average PCCs were similar between Lakeway (40.6%) and Hills (42.4%) for the parcels developed in 1998–2001, while higher in Lakeway (33.2%) than Hills (28.6%) for the parcels developed in 2003–2006. Vegetation conditions before housing construction and parcel size were different between the two municipalities. For the parcels developed in 2003–2006, Lakeway had, on average, higher PCC97 and smaller parcel size than Hills. Average PCC97s were 71.1% for Lakeway and 59.2% for Hills, and average parcel sizes were 0.18 ha for Lakeway and 0.10 ha for Hills.
can be attributed to the effect of the tree removal permit regulation adopted by Lakeway in 2002. This argument can be further supported by comparison between the parcels developed before and after the adoption of the regulation within same municipality. Previous studies reported that tree canopy cover continuously increases for the first decades since the initial site development (Grove et al., 2006; Hope et al., 2003; Martin et al., 2004; Troy, Grove, O’Neil-Dunne, Pickett, & Cadenasso, 2007). A similar result was observed in Hills in that the parcels developed in 1998–2001 had higher MCHs than those developed in 2003–2006. The growth of nursery trees that were planted after housing construction may result in the higher MCHs in the parcels developed in 1998–2001. Tree heights in Lakeway did not follow this trend. In Lakeway, trees in the parcels developed in 2003–2006 were as tall as those developed in 1998–2001, which suggests that landowners in Lakeway protected more existing trees after Lakeway adopted the tree removal permit regulation. Parcel size and vegetation condition before housing construction had effects on MCHs in the two municipalities. PCC97 was positively related to MCHs (statistically significant at ˛ = 0.01 with the parcels developed in each of the two study periods). As PCC97 increased by 1%, MCH increased by 0.011 m in a parcel developed in 1998–2001 and 0.016 m in a parcel developed in 2003–2006, respectively (Table 4). Parcel size was also positively related to MCH in the parcels developed in 2003–2006 (statistically significant at ˛ = 0.05). As parcel size increased by 1 ha, MCH increased by 4.57 m in a parcel developed in 2003–2006. To control the unwanted variations in parcel size and PCC97, this study selected the control site that has similar to Lakeway in natural and socioeconomic characteristics and included the two covariates in the GLMs. Unfortunately, GLM can only partially control the effects of covariates. To further eliminate the potential effects of the two covariates and examine the net effect of the tree removal permit, additional analyses were conducted only with parcels whose parcel size and PCC97 were within one standard deviation from the means of the control site (Hills). The results with the subset of the dataset lead to the similar conclusion with those with the full dataset. After excluding parcels that had exceptionally high or low values from the mean parcel size and PCC97 of the control site and then controlling the remaining variations in the two covariates, Lakeway had, on average, 1.61 m higher MCH than Hills in the parcels developed in 2003–2006 (statistically significant at ˛ = 0.01), while two municipalities had similar MCHs in the parcels developed in 1998–2001 (Table 5). Considering that mature heights of the two dominant tree species, Ashe’s juniper and live oak, were 10–15 m in this region, the increase in canopy height by 1.61 m must be a substantial benefit to Lakeway. 3.3. The effect of Lakeway’s tree removal permit on canopy cover
3.2. The effect of the tree removal permit on canopy height The results of the GLMs illustrate that Lakeway’s tree removal permit regulation had a positive effect on tree protection. Holding the two covariates constant, MCHs of the parcels developed in 2003–2006 in Lakeway were, on average, 0.58 m higher than those parcels in Hills (statistically significant at ˛ = 0.05), while MCHs of the parcels developed in 1998–2001 was not statistically significantly different between Lakeway and Hills (Table 4). Because trees with similar ages are likely to have similar canopy heights, the lack of difference in MCHs of the parcels developed in 1998–2001 suggests that landowners in the two municipalities made similar decisions on tree protection before Lakeway adopted the tree removal permit regulation. Because there was no other change that may affect tree protection in Hills between the two study periods, the higher MCHs of the parcel developed in 2003–2006 in Lakeway
Contrary to MCH, PCC was not statistically significantly different between the two municipalities for the parcels developed in each of the two study periods (Table 4). This result may suggest the weakness of using PCC in the analysis of urban trees. As mentioned earlier, aerial photography cannot distinguish mature and young trees. Thus, if a landowner removed existing trees and planted nursery ones, PCC of this parcel was similar to that of other parcels where existing trees were protected. Urban planners should be cautious when making a decision on urban tree management based on canopy cover derived from aerial photography. 3.4. Limitations of this study Finally, it is worth noting the limitation of this study. The GLMs only moderately explain the variations in MCHs (R2 of the GLMs with the full dataset ranged from 0.114 to 0.461) (Table 4), which
C.Y. Sung / Landscape and Urban Planning 104 (2012) 19–25
23
Table 3 Descriptive statistics of single family residential parcels developed before (1998–2001) and after (2003–2006) the adoption of the tree removal permit regulation by Lakeway. The number of parcels
Mean canopy height in 2007 (m)
sd
Mean canopy cover in 2008 (%)
sd
Mean canopy cover in 1997 (%)
sd
Mean parcel size (ha)
sd
1998–2001 Hills Lakeway
112 307
5.3 5.2
0.9 1.2
40.6 42.4
11.3 16.5
71.0 66.7
23.8 27.7
0.129 0.153
0.074 0.068
2003–2006 Hills Lakeway
51 195
4.2 5.1
1.2 1.9
28.6 33.2
17.3 11.6
59.2 71.1
25.5 26.5
0.104 0.181
0.044 0.092
Table 4 Parameter estimates and bootstrapping confidence intervals (CI) of general linear models (GLMs) with the full data. Variables
Parameter estimates
95% CI Lower bound (2.5%)
99% CI Upper bound (97.5%)
Lower bound (0.5%)
Upper bound (99.5%)
1998–2001 mean canopy height (m) (R2 = 0.282) Intercept 4.21 Municipality 0.15 Parcel size 1.34 PCC97** 0.011
−0.11 −0.21 0.007
0.37 3.22 0.016
−0.17 −0.56 0.005
0.44 3.83 0.017
2003–2006 mean canopy height (m) (R2 = 0.143) Intercept 3.16 0.58 Municipality* 4.57 Parcel size* PCC97** 0.016
0.01 0.14 0.001
0.88 6.82 0.024
−0.14 −1.37 0.006
1.03 7.58 0.027
1998–2001% canopy cover (R2 = 0.461) Intercept 19.6 Municipality 1.99 Parcel size** 33.0 0.24 PCC97**
−0.52 16.9 0.18
4.83 49.3 0.29
−1.53 9.7 0.16
5.95 54.5 0.31
2003–2006% canopy cover (R2 = 0.115) 16.8 Intercept 0.01 Municipality 37.4 Parcel size** PCC97** 0.13
−0.32 1.29 0.06
0.19 5.22 0.20
−0.42 0.76 0.03
0.25 6.34 0.22
* **
Statistically significant at ˛ = 0.05. Statistically significant at ˛ = 0.01.
indicates the presence of other factors that affected MCHs in the study area. For instance, landscape maintenance after construction may influence MCHs. If, for some reason, residents in Lakeway irrigate more often than those in Hills, this may lead to the higher MCHs in Lakeway. Water conservation policy was similar (irrigation is restricted twice a week for Lakeway and three times a week for Hills), and therefore, unlikely to make the difference in irrigation practices between the two municipalities. This study also
controlled the wealth of a resident, which is known to affect landscape maintenance, by selecting the control site that is similar in many socioeconomic characteristics to Lakeway and including the parcel size as a covariate (Grove et al., 2006; Larsen & Harlan, 2006; Larson, Casagrande, Harlan, & Yabiku, 2009; Luck et al., 2009; Troy et al., 2007). However, the possibility that there was disparity in landscape maintenance between the two municipalities cannot be completely ruled out without detailed data on water usage
Table 5 Parameter estimates and bootstrapping confidence intervals (CI) of general linear models (GLMs) with the subset of data whose parcel size and percent canopy cover in 1997 (PCC97) were one standard deviation from the means of parcels in Hills (N = 196 for parcels developed in 1998–2001 and N = 72 for parcels developed in 2003–2006). Variables
Parameter estimates
95% CI Lower bound (2.5%)
99% CI Upper bound (97.5%)
Lower bound (0.5%)
Upper bound (99.5%)
2
1998–2001 mean canopy height (m) (R = 0.059) 3.94 Intercept 0.35 Municipality −1.88 Parcel size PCC97** 0.019
−0.02 −6.33 0.007
0.72 2.88 0.029
−0.13 −7.61 0.004
0.84 4.09 0.032
2003–2006 mean canopy height (m) (R2 = 0.299) 1.58 Intercept 1.61 Municipality** −4.62 Parcel size PCC97** 0.040
0.72 −27.50 0.015
2.53 17.85 0.066
0.40 −37.94 0.004
2.94 24.98 0.073
**
Statistically significant at ˛ = 0.01.
24
C.Y. Sung / Landscape and Urban Planning 104 (2012) 19–25
for irrigation in the parcel level. Readers should be aware of this limitation when interpreting the results of this study. 4. Conclusion Lakeway’s tree removal permit regulation has been successful to protect trees in private lands. Using LiDAR-derived tree canopy heights, this study found that trees in the parcels developed after Lakeway adopted the tree removal permit regulation in 2002 were, on average, taller in Lakeway than a neighboring municipality without such a regulation. In contrast, tree heights in the parcels developed before the regulation were similar between the two municipalities. Combining these two results, it can be concluded that the tree removal permit regulation had a positive effect on tree protection from housing development in Lakeway. This result provides a strong evidence of the efficacy of a tree removal permit regulation, but there are still many questions to be answered before urban planners and policy makers adopt a similar tree protection policy to protect urban trees in their municipality. For instance, Lakeway’s tree removal permit regulation does not specify criteria for issuing a permit but relies on the arborist’s subjective decision. Without criteria, the success of a tree removal permit regulation will largely depend on the arborist’s judgment on the need for tree removal. Further studies are required to elaborate implementation mechanisms of a tree removal permit regulation. The result of this study also shows that LiDAR technology can provide an unprecedented opportunity to investigate the three dimensional structures of urban trees. It is recommended that future studies on urban forest management utilize more LiDAR technology. References Akbari, H., Kurn, D. M., Bretz, S. E. & Hanford, J. W. (1997). Peak power and cooling energy savings of shade trees. Energy and Buildings, 25, 139–148. Angold, P. G., Sadler, J. P., Hill, M. O., Pullin, A., Rushton, S., Austin, K., Small, E., Wood, B. & Wadsworth, R. (2006). Biodiversity in urban habitat patches. Science of The Total Environment, 360, 196–204. Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I. & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GISready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58, 239–258. Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 2–16. Booth, D. B., Hartley, D. & Jackson, R. (2002). Forest cover, impervious-surface area, and the mitigation of stormwater impacts. Journal of the American Water Resources Association, 38, 835–845. Canty, A. & Ripley, B. (2009). R package ‘boot’ (Version 1.2-41) [computer software]. Available from:. http://cran.r-project.org/web/packages/boot/boot.pdf Carleer, A. P., Debeir, O. & Wolff, E. (2005). Assessment of very high spatial resolution satellite image segmentation. Photogrammetric Engineering & Remote Sensing, 71, 1285–1294. Churkina, G., Brown, D. G. & Keoleian, G. (2010). Carbon stored in human settlements: The conterminous United States. Global Change Biology, 16, 135–143. City of Lakeway. (2011). City of Lakeway Ordinances. Available from:. http://www.cityoflakeway.com/index.aspx?nid=108 (retrieved 13.07.11) Conway, T. M. & Urbani, L. (2007). Variations in municipal urban forestry policies, a case study of Toronto, Canada. Urban Forestry & Urban Greening, 6, 181–192. Cooper, J. C. (1996). Legislation to protect and replace trees on private land: Ordinances in Westchester County, New York. Journal of Arboriculture, 22, 270–278. Coughlin, R. E., Mendes, D. C. & Strong, A. L. (1988). Local programs in the United States for preventing the destruction of trees on private land. Landscape and Urban Planning, 15, 165–171. Efron, B. (1993). An Introduction to the Bootstrap. New York: Chapman & Hall. Environmental Software Developers. (2010). BerkeleyImageSeg (Version 1.0rc8) [computer software]. (Berkeley, CA). Escobedo, F., Varela, S., Zhao, M., Wagner, J. E. & Zipperer, W. (2010). Analyzing the efficacy of subtropical urban forests in offsetting carbon emissions from cities. Environmental Science & Policy, 13, 362–372. Goodwin, N. R., Coops, N. C., Tooke, T. R., Christen, A. & Voogt, J. A. (2009). Characterizing urban surface cover and structure with airborne lidar technology. Canadian Journal of Remote Sensing, 35, 297–309.
Grove, J. M., Troy, A. R., O’Neil-Dunne, J. P. M., Burch, W. R., Jr., Cadenasso, M. L. & Pickett, S. T. A. (2006). Characterization of households and its implications for the vegetation of urban ecosystems. Ecosystems, 9, 578–597. Heynen, N. C. & Lindsey, G. (2003). Correlates of urban forest canopy cover: Implications for local public works. Public Works Management & Policy, 8, 33–47. Hill, E., Dorfman, J. H. & Kramer, E. (2010). Evaluating the impact of government land use policies on tree canopy coverage. Land Use Policy, 27, 407–414. Hodgson, M. E., Jensen, J. R., Tullis, J. A., Riordan, K. D. & Archer, C. M. (2003). Synergistic use of Lidar and color aerial photography for mapping urban parcel imperviousness. Photogrammetric Engineering & Remote Sensing, 69, 973–980. Hope, D., Gries, C., Zhu, W., Fagan, W. F., Redman, C. L., Grimm, N. B., Nelson, A. L., Martin, C. & Kinzig, A. (2003). Socioeconomics drive urban plant diversity. Proceedings of the National Academy of Sciences of the United States of America, 100, 8788–8792. ITT VIS. (2009). ENVI (Version 4.7) [computer software]. (Boulder, CO). Jensen, J. R. (2005). Introductory digital image processing (third ed.). Upper Saddle River, NJ: Prentice Hall. Kim, J. & Ellis, C. D. (2009). Determining the effects of local development regulations on landscape structure: Comparison of The Woodlands and North Houston, TX. Landscape and Urban Planning, 92, 293–303. Landry, S. & 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, 94–104. Larsen, L. & Harlan, S. L. (2006). Desert dreamscapes: Residential landscape preference and behavior. Landscape and Urban Planning, 78, 85–100. Larson, K. L., Casagrande, D., Harlan, S. L. & Yabiku, S. T. (2009). Residents’ yard choices and rationales in a desert city: Social proirities, ecological impacts, and decision tradeoffs. Environmental Management, 44, 921–937. Lefsky, M. A., Cohen, W. B., Parker, G. G. & Harding, D. J. (2002). Lidar remote sensing for ecosystem studies. BioScience, 52, 19–30. Lim, K., Treitz, P., Wulder, M., St-Onge, B. & Flood, M. (2003). LiDAR remote sensing of forest structure. Progress in Physical Geography, 27, 88–106. Luck, G. W., Smallbone, L. T. & O’Brien, R. (2009). Socio-economics and vegetation change in urban ecosystems: Pattern in space and time. Ecosystems, 12, 604–620. Martin, C. A., Warren, P. S. & Kinzig, A. P. (2004). Neighborhood socioeconomic status is a useful predictor of perennial landscape vegetation in residential neighborhoods and embedded small parks of Phoenix, AZ. Landscape and Urban Planning, 69, 355–368. McPherson, E. G., Simpson, J. R., Xiao, Q. & Wu, C. (2011). Million tree Los Angeles canopy cover and benefit assessment. Landscape and Urban Planning, 99, 40–50. Means, J. E., Acker, S. A., Fitt, B. J., Renslow, M., Emerson, L. & Hendrix, C. J. (2000). Predicting forest stand characteristics with airborne scanning lidar. Photogrammetric Engineering & Remote Sensing, 66, 1367–1371. Næsset, E. (1997). Determination of mean tree height of forest stands using airborne laser scanner data ISPRS. Journal of Photogrammetry and Remote Sensing, 52, 49–56. Nowak, D. J. & Crane, D. E. (2002). Carbon storage and sequestration by urban trees in the USA. Environmental Pollution, 116, 381–389. Nowak, D. J., Crane, D. E. & Stevens, J. C. (2006). Air pollution removal by urban trees and shrubs in the United States. Urban Forestry & Urban Greening, 4, 115–123. Pataki, D. E., Alig, R. J., Fung, A. S., Golubiewski, N. E., Kennedy, C. A., McPherson, E. G., Nowak, D. J., Pouyat, R. V. & Romero Lankao, P. (2006). Urban ecosystems and the North American carbon cycle. Global Change Biology, 12, 2092–2102. Sander, H., Polasky, S. & Haight, R. G. (2010). The value of urban tree cover: A hedonic property price model in Ramsey and Dakota counties, MN, USA. Ecological Economics, 69, 1646–1656. Schmied, A. & Pillmann, W. (2003). Tree protection legislation in European cities. Urban Forestry & Urban Greening, 2, 115–124. Secord, J. & Zakhor, A. (2007). Tree detection in urban regions using aerial lidar and image data. IEEE Geoscience and Remote Sensing Letters, 4, 196–200. Simpson, J. R. & McPherson, E. G. (1998). Simulation of tree shade impacts on residential energy use for space conditioning in Sacramento. Atmospheric Environment, 32, 69–74. Smith, M. L., Zhou, W., Cadenasso, M., Grove, M. & Band, L. E. (2010). Evaluation of the national land cover database for hydrologic applications in urban and suburban Baltimore, Maryland. Journal of the American Water Resources Association, 46, 429–442. Sung, C. Y. & Li, M.-H. (2010). The effect of urbanization on stream hydrology in hillslope watersheds in central Texas. Hydrological Processes, 24, 3706-2717. Sung, C. Y., Li, M. H. Considering plant phenology for improving the accuracy of urban impervious surface mapping in subtropical climate regions. International Journal of Remote Sensing, doi:10.1080/01431161.2011.591445, in press. Sung, C. Y., Li, M.-H., Rogers, G. O., Volder, A. & Wang, Z. (2011). Investigating alien plant invasion in urban riparian forests in a hot and semi-arid region. Landscape and Urban Planning, 100, 278–286. Taylor, J. J., Brown, D. G. & Larsen, L. (2007). Preserving natural features: A GIS-based evaluation of a local open-space ordinance. Landscape and Urban Planning, 82, 1–16. Troy, A. R., Grove, J. M., O’Neil-Dunne, J. P. M., Pickett, S. T. A. & Cadenasso, M. L. (2007). Predicting opportunities for greening and patterns of vegetation on private urban lands. Environmental Management, 40, 394–412.
C.Y. Sung / Landscape and Urban Planning 104 (2012) 19–25 Ulrich, R. S. (1984). View through a window may influence recovery from surgery. Science, 224, 420–421. US Census Bureau. (2011). American FactFinder. Available from:. http:// factfinder.census.gov/home/saff/main.html? lang=en (retrieved 03.07.11) US National Climatic Data Center. (2003). US Climatic Normals 1971–2000. Available from:. http://cdo.ncdc.noaa.gov/climatenormals/clim81/TXnorm.txt (retrieved 10.07.11)
25
Village of The Hills. (2011). Rules and regulations. Available from: http://www. thehillspoa.com/thehills/sub category list.asp?category=17&title=Rules+and+ Regulations (retrieved 18.08.11) Zhou, W., Troy, A. & Grove, M. (2008). Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors, 8, 1613–1636.
Contents lists available at SciVerse ScienceDirect
Landscape and Urban Planning journal homepage: www.elsevier.com/locate/landurbplan
Evaluating the efficacy of a local tree protection policy using LiDAR remote sensing data Chan Yong Sung ∗ Texas Transportation Institute, Texas A&M University, 3135 TAMU, College Station, TX 77843-3135, USA
a r t i c l e
i n f o
Article history: Received 23 March 2011 Received in revised form 31 August 2011 Accepted 24 September 2011 Available online 18 October 2011 Keywords: Tree removal permit Local environmental policy Urban forest management Urban remote sensing Object-based image segmentation
a b s t r a c t This study evaluated the efficacy of a tree removal permit regulation that the city of Lakeway adopted in 2002 to protect trees in private lands by comparing the levels of tree protection in Lakeway with those in the village of The Hills, a neighboring municipality without such a regulation. The level of tree protection was assessed by mean canopy height (MCH) and percent canopy cover (PCC) of a parcel derived from light detection and ranging (LiDAR) data and aerial photography, respectively. MCHs and PCCs were estimated for single family residential parcels where houses were constructed for four years before (1998–2001) and after (2003–2006) the adoption of the tree removal permit regulation by Lakeway. Holding parcel size and vegetation condition before housing construction constant, MCHs of the parcels developed in 2003–2006 were statistically significantly higher in Lakeway than Hills. Because the difference in MCHs was not found in the parcels developed in 1998–2001, it is concluded that Lakeway’s tree removal permit regulation has been successful to protect trees in private lands. Unlike MCHs, PCCs between the two municipalities were not statistically significantly different in the parcels developed in each of the two study periods. This result may indicate that PCC is not as accurate in assessing the level of tree protection as MCH due to the inability of PCC to distinguish existing mature trees from young ones planted after construction. Based on these findings, it is recommended that urban planners use LiDAR data to study urban forests. Published by Elsevier B.V.
1. Introduction Urban trees provide many valuable benefits. Urban trees mitigate climate change by absorbing carbon from the atmosphere and store it in plant biomass (Churkina, Brown, & Keoleian, 2010; Escobedo, Varela, Zhao, Wagner, & Zipperer, 2010; Pataki et al., 2006). Nowak and Crane (2002) estimated that trees in urban areas of the United States stores 700 million tonnes of carbon. Urban trees also save energy consumption by shading buildings (Akbari, Kurn, Bretz, & Hanford, 1997; Simpson & McPherson, 1998), improve air quality (Nowak, Crane, & Stevens, 2006), decrease stormwater runoff (Booth, Hartley, & Jackson, 2002; Sung & Li, 2010), maintain biodiversity by serving as habitats for wildlife (Angold et al., 2006), enhance human health (Ulrich, 1984), and raise property values (Sander, Polasky, & Haight, 2010). To protect trees in urban areas, local government has adopted various tree protection policies, such as tree planting program and minimum landscape area (Kim & Ellis, 2009; McPherson, Simpson, Xiao, & Wu, 2011; Schmied & Pillmann, 2003). One of the widely used policies is a tree removal permit regulation that requires a
∗ Tel.: +1 979 575 7747. E-mail address: [email protected] 0169-2046/$ – see front matter. Published by Elsevier B.V. doi:10.1016/j.landurbplan.2011.09.009
landowner to obtain a permit to remove trees from private lands. The types of trees protected by this regulation vary with local conditions, but are usually defined based on the sizes, species, and locations of trees (Cooper, 1996; Coughlin, Mendes, & Strong, 1988). A growing number of local governments have adopted tree removal permit regulations, but only few studies have evaluated the efficacies of these regulations. Results of the previous studies were even inconsistent with each other. Some studies found the positive effect of the tree removal permit regulation (Landry & Pu, 2010) or similar tree protection policies (Hill, Dorfman, & Kramer, 2010; Kim & Ellis, 2009), while others did not (Conway & Urbani, 2007; Heynen & Lindsey, 2003; Taylor, Brown, & Larsen, 2007). The contrasting results may be attributed to insufficient information on the extent to which existing trees were protected. Most studies evaluated a tree removal permit regulation using the level of tree protection assessed by percent canopy cover (PCC) derived from airborne and satellite images. However, a high PCC does not necessarily indicate the success of a tree removal permit regulation. For instance, if a landowner removed existing trees and planted nursery ones after housing construction, this property had a high PCC even though existing trees were not protected. Mature trees are more beneficial than nursery ones (e.g., storing a larger amount of carbon and saving more energy by shading buildings), and therefore distinguishing existing and nursery trees is needed for evaluating
20
C.Y. Sung / Landscape and Urban Planning 104 (2012) 19–25
a tree removal permit regulation, especially in recently developed areas where nursery trees did not yet reach their mature heights. Light detection and ranging (LiDAR) can provide useful data to assess the level of tree protection in private lands. LiDAR is an active remote sensing system that emits light and detects reflected energy from the earth surface. The emitted light either returns from a tree canopy or penetrates the canopy and returns from the ground. Using these multiple returns, previous studies has estimated the heights of tree canopies (Lefsky, Cohen, Parker, & Harding, 2002; Lim, Treitz, Wulder, St-Onge, & Flood, 2003). Because existing trees are likely to be taller than newly planted ones after construction, LiDAR-derived canopy heights can be used to assess the level of tree protection. LiDAR has also been applied to diverse fields in urban research including urban tree detection (Goodwin, Coops, Tooke, Christen, & Voogt, 2009; Secord & Zakhor, 2007), land cover classification (Zhou, Troy, & Grove, 2008), and impervious surface mapping (Hodgson, Jensen, Tullis, Riordan, & Archer, 2003; Smith, Zhou, Cadenasso, Grove, & Band, 2010). This study evaluated the efficacy of a tree removal permit regulation by comparing tree canopy heights derived from LiDAR data between two municipalities with and without a regulation. The primary research question of this study is whether trees are taller in the municipality with a tree removal permit regulation than one without such a regulation. For comparison to the previous studies (e.g., Conway & Urbani, 2007; Heynen & Lindsey, 2003; Landry & Pu, 2010), this study also questions whether canopy cover estimated from high resolution aerial photography is higher in the municipality with a permit regulation than one without such a regulation. Fig. 1. Illustrations of (a) 2008 aerial photography and (b) triangulated irregular network (TIN) created from the 2007 LiDAR data.
2. Materials and methods 2.1. Study area The study area is two neighboring municipalities (the City of Lakeway, and the Village of The Hills) located near the city of Austin, TX, USA. The two municipalities are typical North American suburban communities predominantly used for single family residences. In both municipalities, more than 90% of populations were white and more than 70% of households were family households. Income and education levels were higher than US averages (Table 1). In 2002, Lakeway amended a land development ordinance and adopted a tree removal permit regulation. This ordinance requires a landowner to obtain a permit before removing trees larger than 41 cm in diameter at breast height (DBH) from a private land. Tree species that are either very common or invasive in this region are exempted from this permit. Those trees were tree of heaven (Ailanthus altissima), mimosa (Albizia julibrissin), Bois d’Arc (Maclura pomifera), Chinaberry (Melia azedarach), black willow (Salix nigra), hackberry (Celtis spp.), and Ashe’s juniper (Juniperus ashei). Once a landowner applies for a permit, a city arborist visits the site and reviews if tree removal is necessary for site development. Even if the permit is issued, the removed trees must be replaced by trees larger than 2.5 cm in DBH. This ordinance also requires installing fences to prevent damage to existing trees from construction activities (City of Lakeway, 2011). Hills is a small municipality that has its own ordinances. Hills was selected as a control site because it is similar in many socioeconomic characteristics to Lakeway (Table 1). Hills does not have any regulation to protect trees in private lands (Village of The Hills, 2011). Climate is semi-arid and subtropical with hot and dry summer. Mean annual temperature is 21 ◦ C and mean annual precipitation is 882 mm. During the hottest month (August), the mean temperature reaches 35 ◦ C with precipitation of 64 mm (US National Climatic Data Center, 2003). Because of hot and dry summer, tree growth in this region is limited by water (Sung, Li, Rogers, Volder, & Wang,
2011). As a result, the study area was covered by patches of grasslands and forests depending on local water availability. Dominant tree species are Ashe’s juniper (Juniperus ashei) and live oak (Quercus virginiana), both of which can grow up to 10–15 m (Sung and Li, in press). 2.2. Tree canopy cover Tree canopy cover was classified from aerial photography taken on June 1, 2008, as a part of the US national agricultural imagery program (NAIP) (available from http://tnris.state.tx.us) (Fig. 1). The NAIP images were acquired using a Leica ADS40 SHH52 digital camera sensor that can detect solar radiation reflected from the earth surface in four spectral bands (blue, green, red, and near infrared). The NAIP images had a pixel size of 0.5 m. A two-step classification procedure was used for land cover classification. First, the images were pre-processed by an object-based image segmentation (OBIS), an image processing algorithm that extracts geographic objects by merging contiguous pixels based on the spectral similarity (Benz, Hofmann, Willhauck, Lingenfelder, & Heynen, 2004). OBIS has been widely used to classify land covers from fine-resolution remote sensing images because, unlike a per-pixel based algorithm, it does not suffer from a salt and pepper noise problem (Blaschke, 2010; Carleer, Debeir, & Wolff, 2005). Second, the OBIS objects were assigned into one of five land cover types (impervious surface, tree canopy, grass, bareground, and water) using an iterative self-organizing data analysis (ISODATA), an unsupervised classification algorithm that automatically identifies spectrally distinct clusters (Jensen, 2005). BerkeleyImageSeg (Environmental Software Developers, 2010) and ENVI 4.7 (ITT VIS, 2009) were used for OBIS and ISODATA, respectively. User-specific parameters of the OBIS (threshold = 20 pixels, shape = 0.5, compactness = 0.5) and ISODATA (the maximum number of classes = 20, the minimum number of pixels in each class = 1, the maximum class standard deviation = 1, the minimum class distance = 5, the maximum number of merge
C.Y. Sung / Landscape and Urban Planning 104 (2012) 19–25
21
Table 1 Socioeconomic characteristics of the city of Lakeway and the village of the Hills.a Characteristics
Hills
Lakeway
US
Total population White population Family households (families) With own children under 18 years Average household size Total housing unit Occupied housing units Owner-occupied housing units Bachelor’s degree or higher (percent of the population older than 25) Per capita income (in 2009 inflation-adjusted US dollars)
2485 90.7% 86.6% 36.3% 2.69 1028 89.9% 91.3% 73.7% 62,811
10,367 94.8% 73.5% 31.2% 2.46 4,979 84.5% 84.3% 62.0% 51,648
– 74.5% 66.7% 31.0% 2.60 – 88.2% 66.9% 73.7% 27,041
a
2005–2009 estimates by US Census Bureau (2011).
pairs = 2) were determined by trials and errors so that the final land cover map had the highest classification accuracy. The classification accuracy was calculated using 300 reference pixels randomly selected from the NAIP images. The overall classification accuracy of the final land cover map was 84.0% and kappa coefficient was 0.78 (Table 2). Tree canopy was the most accurately classified land cover type. Of 127 pixels, 117 were correctly classified as tree canopies (both producer and user accuracies were 92.1%).
2.3. Canopy heights Canopy heights were derived from LiDAR data obtained in 2007 by the US Federal Emergency Management Agency (FEMA) (available from http://www.capcog.org) (Fig. 1). The LiDAR data were collected, on average, at 1.4 m ground sampling distance (GSD) by three systems (Optech 2050, ALS49, and ALS50 LH). Previous studies that were conducted in natural forests derived canopy heights by subtracting a digital elevation model (DEM) from a digital surface model (DSM) (Means et al., 2000; Næsset, 1997). DEM and DSM are grid models whose cell values represented the elevations of the ground and the uppermost surface that were estimated by taking the minimum and maximum LiDAR elevations within a cell, respectively. However, this approach cannot be directly applied to urban areas where the uppermost surface is not always tree canopy but other artificial materials (e.g., building roofs). To avoid an error that mistakenly identifies the height of an artificial material as the height of a tree canopy, DSM was calculated using LiDAR data falling only on tree canopy cover classified in Section 2.2. Because the LiDAR did not collect ground samples uniformly but in a zigzag fashion, the final DEM and DSM were created to have 5 m cell size, which is about four times larger than the average GSD. Canopy height model (CHM) was calculated by taking the difference between DEM and DSM. To exclude pixels that may represent the heights of other materials underneath tree canopy, CHM cells whose values were less than 1 m were excluded from the analysis (Næsset, 1997).
2.4. Comparing canopy height and canopy cover between two study municipalities The effect of Lakeway’s tree removal permit regulation was evaluated by comparing mean canopy heights (MCHs) and PCCs of parcels where houses were constructed for four years after the adoption of the regulation (2003–2006) in Lakeway with those parcels in Hills. Parcel boundaries were obtained from Travis County Appraisal District. The 2007 FEMA LiDAR data did not covered about one fifth of property parcels located in the southwest corner of the study area. Thus, only parcels that were completely covered by the LiDAR data were used in this analysis. This study focused on parcels used for single family residences. Parcels along the shoreline of the Lake Travis were also excluded because many of those parcels extended into lake water where tree cannot grow. Remaining parcels were used to develop a general linear model (GLM) with MCH and PCC as dependent variables, municipality (0 for Hills and 1 for Lakeway) as a factor, and percent canopy cover in 1997 (PCC97) and parcel size as covariates. PCC97 were included to estimate vegetation conditions before houses were constructed. Because 1997 aerial photography was a black-and-white image (available from http://www.capcog.org), canopy cover was manually digitized on screen. Parcel size was included to control the luxury effect, which refers to the phenomenon that a wealthy resident tends to spend more money to maintain gardens with denser and more diverse plants (Grove et al., 2006; Hope et al., 2003; Larsen & Harlan, 2006; Luck, Smallbone, & O’Brien, 2009; Martin, Warren, & Kinzig, 2004). Initially, appraised property value was considered as a separate covariate but was excluded in the final analysis due to its severe multicollinearity with parcel size. Pearson’s correlation coefficient between parcel size and appraised property value was 0.441, which indicates a strong positive relationship between the two variables. Parcel size was selected as a covariate because it is a better predictor for the level of tree protection than appraised property value. The higher predictability of the parcel size may be attributed to space availability, i.e., in a
Table 2 Accuracy assessment of land cover classification. Classified
Tree canopy Grass Impervious surface Bareground Water Column total Producer’s accuracy (%)
Reference Tree canopy
Grass
Impervious surface
Bareground
Water
Row total
User’s accuracy (%)
117 10 0 0 0 127 92.1
1 56 1 3 0 61 91.8
7 4 39 7 3 60 65.0
1 0 10 30 0 41 73.2
1 0 0 0 10 11 90.9
127 70 50 40 13 300 –
92.1% 80.0% 78.0% 75.0% 76.9% – –
Overall accuracy = 84.0%; kappa coefficient = 0.778.
22
C.Y. Sung / Landscape and Urban Planning 104 (2012) 19–25
large parcel, more space is not occupied by houses and other builtup structures, and existing trees were less likely damaged during construction. Property value poorly explains the variation in the level of tree protection in the study area (correlation coefficients of appraised value were 0.021 with MCH and −0.067 with PCC). All statistical inferences were based on a bootstrapping procedure, a computationally expensive statistical technique that constructs a confidence interval using an empirical distribution of a statistic of interest derived from a number of datasets resampled with replacement from an original dataset (Efron, 1993). The bootstrapping procedure was used because residuals of the GLMs were not normally distributed. Confidence intervals for the parameters of the GLMs were constructed by bias corrected and accelerated (BCa) method using 999 resampled datasets generated by ‘boot’ package in R (Canty & Ripley, 2009). To explore if the difference in MCH and PCC had existed between the two municipalities even before Lakeway adopted the tree removal permit regulation, the same analyses were also conducted with parcels where houses were constructed for four years before the amendment of the ordinance (1998–2001). Because canopy heights were estimated from the 2007 LiDAR data, MCHs of the parcels developed in 1998–2001 and in 2003–2006 represented vegetation conditions six to nine years and one to four years since housing constructions, respectively (seven to ten years and two to five years for PCCs).
3. Results and discussion 3.1. Descriptive statistics There were 419 and 246 single family residential parcels where houses were constructed for four years before and after Lakeway adopted the tree removal permit regulation in 2002, respectively (Table 3). For the parcels developed in 2003–2006, Lakeway (5.1 m) had, on average, higher MCH than Hills (4.2 m). The difference in MCHs between the two municipalities was not observed in the parcels developed in 1998–2001. Average MCHs of the parcels developed in 1998–2001 were similar between Lakeway (5.2 m) and Hills (5.3 m). PCC had a similar trend to MCH. Average PCCs were similar between Lakeway (40.6%) and Hills (42.4%) for the parcels developed in 1998–2001, while higher in Lakeway (33.2%) than Hills (28.6%) for the parcels developed in 2003–2006. Vegetation conditions before housing construction and parcel size were different between the two municipalities. For the parcels developed in 2003–2006, Lakeway had, on average, higher PCC97 and smaller parcel size than Hills. Average PCC97s were 71.1% for Lakeway and 59.2% for Hills, and average parcel sizes were 0.18 ha for Lakeway and 0.10 ha for Hills.
can be attributed to the effect of the tree removal permit regulation adopted by Lakeway in 2002. This argument can be further supported by comparison between the parcels developed before and after the adoption of the regulation within same municipality. Previous studies reported that tree canopy cover continuously increases for the first decades since the initial site development (Grove et al., 2006; Hope et al., 2003; Martin et al., 2004; Troy, Grove, O’Neil-Dunne, Pickett, & Cadenasso, 2007). A similar result was observed in Hills in that the parcels developed in 1998–2001 had higher MCHs than those developed in 2003–2006. The growth of nursery trees that were planted after housing construction may result in the higher MCHs in the parcels developed in 1998–2001. Tree heights in Lakeway did not follow this trend. In Lakeway, trees in the parcels developed in 2003–2006 were as tall as those developed in 1998–2001, which suggests that landowners in Lakeway protected more existing trees after Lakeway adopted the tree removal permit regulation. Parcel size and vegetation condition before housing construction had effects on MCHs in the two municipalities. PCC97 was positively related to MCHs (statistically significant at ˛ = 0.01 with the parcels developed in each of the two study periods). As PCC97 increased by 1%, MCH increased by 0.011 m in a parcel developed in 1998–2001 and 0.016 m in a parcel developed in 2003–2006, respectively (Table 4). Parcel size was also positively related to MCH in the parcels developed in 2003–2006 (statistically significant at ˛ = 0.05). As parcel size increased by 1 ha, MCH increased by 4.57 m in a parcel developed in 2003–2006. To control the unwanted variations in parcel size and PCC97, this study selected the control site that has similar to Lakeway in natural and socioeconomic characteristics and included the two covariates in the GLMs. Unfortunately, GLM can only partially control the effects of covariates. To further eliminate the potential effects of the two covariates and examine the net effect of the tree removal permit, additional analyses were conducted only with parcels whose parcel size and PCC97 were within one standard deviation from the means of the control site (Hills). The results with the subset of the dataset lead to the similar conclusion with those with the full dataset. After excluding parcels that had exceptionally high or low values from the mean parcel size and PCC97 of the control site and then controlling the remaining variations in the two covariates, Lakeway had, on average, 1.61 m higher MCH than Hills in the parcels developed in 2003–2006 (statistically significant at ˛ = 0.01), while two municipalities had similar MCHs in the parcels developed in 1998–2001 (Table 5). Considering that mature heights of the two dominant tree species, Ashe’s juniper and live oak, were 10–15 m in this region, the increase in canopy height by 1.61 m must be a substantial benefit to Lakeway. 3.3. The effect of Lakeway’s tree removal permit on canopy cover
3.2. The effect of the tree removal permit on canopy height The results of the GLMs illustrate that Lakeway’s tree removal permit regulation had a positive effect on tree protection. Holding the two covariates constant, MCHs of the parcels developed in 2003–2006 in Lakeway were, on average, 0.58 m higher than those parcels in Hills (statistically significant at ˛ = 0.05), while MCHs of the parcels developed in 1998–2001 was not statistically significantly different between Lakeway and Hills (Table 4). Because trees with similar ages are likely to have similar canopy heights, the lack of difference in MCHs of the parcels developed in 1998–2001 suggests that landowners in the two municipalities made similar decisions on tree protection before Lakeway adopted the tree removal permit regulation. Because there was no other change that may affect tree protection in Hills between the two study periods, the higher MCHs of the parcel developed in 2003–2006 in Lakeway
Contrary to MCH, PCC was not statistically significantly different between the two municipalities for the parcels developed in each of the two study periods (Table 4). This result may suggest the weakness of using PCC in the analysis of urban trees. As mentioned earlier, aerial photography cannot distinguish mature and young trees. Thus, if a landowner removed existing trees and planted nursery ones, PCC of this parcel was similar to that of other parcels where existing trees were protected. Urban planners should be cautious when making a decision on urban tree management based on canopy cover derived from aerial photography. 3.4. Limitations of this study Finally, it is worth noting the limitation of this study. The GLMs only moderately explain the variations in MCHs (R2 of the GLMs with the full dataset ranged from 0.114 to 0.461) (Table 4), which
C.Y. Sung / Landscape and Urban Planning 104 (2012) 19–25
23
Table 3 Descriptive statistics of single family residential parcels developed before (1998–2001) and after (2003–2006) the adoption of the tree removal permit regulation by Lakeway. The number of parcels
Mean canopy height in 2007 (m)
sd
Mean canopy cover in 2008 (%)
sd
Mean canopy cover in 1997 (%)
sd
Mean parcel size (ha)
sd
1998–2001 Hills Lakeway
112 307
5.3 5.2
0.9 1.2
40.6 42.4
11.3 16.5
71.0 66.7
23.8 27.7
0.129 0.153
0.074 0.068
2003–2006 Hills Lakeway
51 195
4.2 5.1
1.2 1.9
28.6 33.2
17.3 11.6
59.2 71.1
25.5 26.5
0.104 0.181
0.044 0.092
Table 4 Parameter estimates and bootstrapping confidence intervals (CI) of general linear models (GLMs) with the full data. Variables
Parameter estimates
95% CI Lower bound (2.5%)
99% CI Upper bound (97.5%)
Lower bound (0.5%)
Upper bound (99.5%)
1998–2001 mean canopy height (m) (R2 = 0.282) Intercept 4.21 Municipality 0.15 Parcel size 1.34 PCC97** 0.011
−0.11 −0.21 0.007
0.37 3.22 0.016
−0.17 −0.56 0.005
0.44 3.83 0.017
2003–2006 mean canopy height (m) (R2 = 0.143) Intercept 3.16 0.58 Municipality* 4.57 Parcel size* PCC97** 0.016
0.01 0.14 0.001
0.88 6.82 0.024
−0.14 −1.37 0.006
1.03 7.58 0.027
1998–2001% canopy cover (R2 = 0.461) Intercept 19.6 Municipality 1.99 Parcel size** 33.0 0.24 PCC97**
−0.52 16.9 0.18
4.83 49.3 0.29
−1.53 9.7 0.16
5.95 54.5 0.31
2003–2006% canopy cover (R2 = 0.115) 16.8 Intercept 0.01 Municipality 37.4 Parcel size** PCC97** 0.13
−0.32 1.29 0.06
0.19 5.22 0.20
−0.42 0.76 0.03
0.25 6.34 0.22
* **
Statistically significant at ˛ = 0.05. Statistically significant at ˛ = 0.01.
indicates the presence of other factors that affected MCHs in the study area. For instance, landscape maintenance after construction may influence MCHs. If, for some reason, residents in Lakeway irrigate more often than those in Hills, this may lead to the higher MCHs in Lakeway. Water conservation policy was similar (irrigation is restricted twice a week for Lakeway and three times a week for Hills), and therefore, unlikely to make the difference in irrigation practices between the two municipalities. This study also
controlled the wealth of a resident, which is known to affect landscape maintenance, by selecting the control site that is similar in many socioeconomic characteristics to Lakeway and including the parcel size as a covariate (Grove et al., 2006; Larsen & Harlan, 2006; Larson, Casagrande, Harlan, & Yabiku, 2009; Luck et al., 2009; Troy et al., 2007). However, the possibility that there was disparity in landscape maintenance between the two municipalities cannot be completely ruled out without detailed data on water usage
Table 5 Parameter estimates and bootstrapping confidence intervals (CI) of general linear models (GLMs) with the subset of data whose parcel size and percent canopy cover in 1997 (PCC97) were one standard deviation from the means of parcels in Hills (N = 196 for parcels developed in 1998–2001 and N = 72 for parcels developed in 2003–2006). Variables
Parameter estimates
95% CI Lower bound (2.5%)
99% CI Upper bound (97.5%)
Lower bound (0.5%)
Upper bound (99.5%)
2
1998–2001 mean canopy height (m) (R = 0.059) 3.94 Intercept 0.35 Municipality −1.88 Parcel size PCC97** 0.019
−0.02 −6.33 0.007
0.72 2.88 0.029
−0.13 −7.61 0.004
0.84 4.09 0.032
2003–2006 mean canopy height (m) (R2 = 0.299) 1.58 Intercept 1.61 Municipality** −4.62 Parcel size PCC97** 0.040
0.72 −27.50 0.015
2.53 17.85 0.066
0.40 −37.94 0.004
2.94 24.98 0.073
**
Statistically significant at ˛ = 0.01.
24
C.Y. Sung / Landscape and Urban Planning 104 (2012) 19–25
for irrigation in the parcel level. Readers should be aware of this limitation when interpreting the results of this study. 4. Conclusion Lakeway’s tree removal permit regulation has been successful to protect trees in private lands. Using LiDAR-derived tree canopy heights, this study found that trees in the parcels developed after Lakeway adopted the tree removal permit regulation in 2002 were, on average, taller in Lakeway than a neighboring municipality without such a regulation. In contrast, tree heights in the parcels developed before the regulation were similar between the two municipalities. Combining these two results, it can be concluded that the tree removal permit regulation had a positive effect on tree protection from housing development in Lakeway. This result provides a strong evidence of the efficacy of a tree removal permit regulation, but there are still many questions to be answered before urban planners and policy makers adopt a similar tree protection policy to protect urban trees in their municipality. For instance, Lakeway’s tree removal permit regulation does not specify criteria for issuing a permit but relies on the arborist’s subjective decision. Without criteria, the success of a tree removal permit regulation will largely depend on the arborist’s judgment on the need for tree removal. Further studies are required to elaborate implementation mechanisms of a tree removal permit regulation. The result of this study also shows that LiDAR technology can provide an unprecedented opportunity to investigate the three dimensional structures of urban trees. It is recommended that future studies on urban forest management utilize more LiDAR technology. References Akbari, H., Kurn, D. M., Bretz, S. E. & Hanford, J. W. (1997). Peak power and cooling energy savings of shade trees. Energy and Buildings, 25, 139–148. Angold, P. G., Sadler, J. P., Hill, M. O., Pullin, A., Rushton, S., Austin, K., Small, E., Wood, B. & Wadsworth, R. (2006). Biodiversity in urban habitat patches. Science of The Total Environment, 360, 196–204. Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I. & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GISready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58, 239–258. Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 2–16. Booth, D. B., Hartley, D. & Jackson, R. (2002). Forest cover, impervious-surface area, and the mitigation of stormwater impacts. Journal of the American Water Resources Association, 38, 835–845. Canty, A. & Ripley, B. (2009). R package ‘boot’ (Version 1.2-41) [computer software]. Available from:. http://cran.r-project.org/web/packages/boot/boot.pdf Carleer, A. P., Debeir, O. & Wolff, E. (2005). Assessment of very high spatial resolution satellite image segmentation. Photogrammetric Engineering & Remote Sensing, 71, 1285–1294. Churkina, G., Brown, D. G. & Keoleian, G. (2010). Carbon stored in human settlements: The conterminous United States. Global Change Biology, 16, 135–143. City of Lakeway. (2011). City of Lakeway Ordinances. Available from:. http://www.cityoflakeway.com/index.aspx?nid=108 (retrieved 13.07.11) Conway, T. M. & Urbani, L. (2007). Variations in municipal urban forestry policies, a case study of Toronto, Canada. Urban Forestry & Urban Greening, 6, 181–192. Cooper, J. C. (1996). Legislation to protect and replace trees on private land: Ordinances in Westchester County, New York. Journal of Arboriculture, 22, 270–278. Coughlin, R. E., Mendes, D. C. & Strong, A. L. (1988). Local programs in the United States for preventing the destruction of trees on private land. Landscape and Urban Planning, 15, 165–171. Efron, B. (1993). An Introduction to the Bootstrap. New York: Chapman & Hall. Environmental Software Developers. (2010). BerkeleyImageSeg (Version 1.0rc8) [computer software]. (Berkeley, CA). Escobedo, F., Varela, S., Zhao, M., Wagner, J. E. & Zipperer, W. (2010). Analyzing the efficacy of subtropical urban forests in offsetting carbon emissions from cities. Environmental Science & Policy, 13, 362–372. Goodwin, N. R., Coops, N. C., Tooke, T. R., Christen, A. & Voogt, J. A. (2009). Characterizing urban surface cover and structure with airborne lidar technology. Canadian Journal of Remote Sensing, 35, 297–309.
Grove, J. M., Troy, A. R., O’Neil-Dunne, J. P. M., Burch, W. R., Jr., Cadenasso, M. L. & Pickett, S. T. A. (2006). Characterization of households and its implications for the vegetation of urban ecosystems. Ecosystems, 9, 578–597. Heynen, N. C. & Lindsey, G. (2003). Correlates of urban forest canopy cover: Implications for local public works. Public Works Management & Policy, 8, 33–47. Hill, E., Dorfman, J. H. & Kramer, E. (2010). Evaluating the impact of government land use policies on tree canopy coverage. Land Use Policy, 27, 407–414. Hodgson, M. E., Jensen, J. R., Tullis, J. A., Riordan, K. D. & Archer, C. M. (2003). Synergistic use of Lidar and color aerial photography for mapping urban parcel imperviousness. Photogrammetric Engineering & Remote Sensing, 69, 973–980. Hope, D., Gries, C., Zhu, W., Fagan, W. F., Redman, C. L., Grimm, N. B., Nelson, A. L., Martin, C. & Kinzig, A. (2003). Socioeconomics drive urban plant diversity. Proceedings of the National Academy of Sciences of the United States of America, 100, 8788–8792. ITT VIS. (2009). ENVI (Version 4.7) [computer software]. (Boulder, CO). Jensen, J. R. (2005). Introductory digital image processing (third ed.). Upper Saddle River, NJ: Prentice Hall. Kim, J. & Ellis, C. D. (2009). Determining the effects of local development regulations on landscape structure: Comparison of The Woodlands and North Houston, TX. Landscape and Urban Planning, 92, 293–303. Landry, S. & 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, 94–104. Larsen, L. & Harlan, S. L. (2006). Desert dreamscapes: Residential landscape preference and behavior. Landscape and Urban Planning, 78, 85–100. Larson, K. L., Casagrande, D., Harlan, S. L. & Yabiku, S. T. (2009). Residents’ yard choices and rationales in a desert city: Social proirities, ecological impacts, and decision tradeoffs. Environmental Management, 44, 921–937. Lefsky, M. A., Cohen, W. B., Parker, G. G. & Harding, D. J. (2002). Lidar remote sensing for ecosystem studies. BioScience, 52, 19–30. Lim, K., Treitz, P., Wulder, M., St-Onge, B. & Flood, M. (2003). LiDAR remote sensing of forest structure. Progress in Physical Geography, 27, 88–106. Luck, G. W., Smallbone, L. T. & O’Brien, R. (2009). Socio-economics and vegetation change in urban ecosystems: Pattern in space and time. Ecosystems, 12, 604–620. Martin, C. A., Warren, P. S. & Kinzig, A. P. (2004). Neighborhood socioeconomic status is a useful predictor of perennial landscape vegetation in residential neighborhoods and embedded small parks of Phoenix, AZ. Landscape and Urban Planning, 69, 355–368. McPherson, E. G., Simpson, J. R., Xiao, Q. & Wu, C. (2011). Million tree Los Angeles canopy cover and benefit assessment. Landscape and Urban Planning, 99, 40–50. Means, J. E., Acker, S. A., Fitt, B. J., Renslow, M., Emerson, L. & Hendrix, C. J. (2000). Predicting forest stand characteristics with airborne scanning lidar. Photogrammetric Engineering & Remote Sensing, 66, 1367–1371. Næsset, E. (1997). Determination of mean tree height of forest stands using airborne laser scanner data ISPRS. Journal of Photogrammetry and Remote Sensing, 52, 49–56. Nowak, D. J. & Crane, D. E. (2002). Carbon storage and sequestration by urban trees in the USA. Environmental Pollution, 116, 381–389. Nowak, D. J., Crane, D. E. & Stevens, J. C. (2006). Air pollution removal by urban trees and shrubs in the United States. Urban Forestry & Urban Greening, 4, 115–123. Pataki, D. E., Alig, R. J., Fung, A. S., Golubiewski, N. E., Kennedy, C. A., McPherson, E. G., Nowak, D. J., Pouyat, R. V. & Romero Lankao, P. (2006). Urban ecosystems and the North American carbon cycle. Global Change Biology, 12, 2092–2102. Sander, H., Polasky, S. & Haight, R. G. (2010). The value of urban tree cover: A hedonic property price model in Ramsey and Dakota counties, MN, USA. Ecological Economics, 69, 1646–1656. Schmied, A. & Pillmann, W. (2003). Tree protection legislation in European cities. Urban Forestry & Urban Greening, 2, 115–124. Secord, J. & Zakhor, A. (2007). Tree detection in urban regions using aerial lidar and image data. IEEE Geoscience and Remote Sensing Letters, 4, 196–200. Simpson, J. R. & McPherson, E. G. (1998). Simulation of tree shade impacts on residential energy use for space conditioning in Sacramento. Atmospheric Environment, 32, 69–74. Smith, M. L., Zhou, W., Cadenasso, M., Grove, M. & Band, L. E. (2010). Evaluation of the national land cover database for hydrologic applications in urban and suburban Baltimore, Maryland. Journal of the American Water Resources Association, 46, 429–442. Sung, C. Y. & Li, M.-H. (2010). The effect of urbanization on stream hydrology in hillslope watersheds in central Texas. Hydrological Processes, 24, 3706-2717. Sung, C. Y., Li, M. H. Considering plant phenology for improving the accuracy of urban impervious surface mapping in subtropical climate regions. International Journal of Remote Sensing, doi:10.1080/01431161.2011.591445, in press. Sung, C. Y., Li, M.-H., Rogers, G. O., Volder, A. & Wang, Z. (2011). Investigating alien plant invasion in urban riparian forests in a hot and semi-arid region. Landscape and Urban Planning, 100, 278–286. Taylor, J. J., Brown, D. G. & Larsen, L. (2007). Preserving natural features: A GIS-based evaluation of a local open-space ordinance. Landscape and Urban Planning, 82, 1–16. Troy, A. R., Grove, J. M., O’Neil-Dunne, J. P. M., Pickett, S. T. A. & Cadenasso, M. L. (2007). Predicting opportunities for greening and patterns of vegetation on private urban lands. Environmental Management, 40, 394–412.
C.Y. Sung / Landscape and Urban Planning 104 (2012) 19–25 Ulrich, R. S. (1984). View through a window may influence recovery from surgery. Science, 224, 420–421. US Census Bureau. (2011). American FactFinder. Available from:. http:// factfinder.census.gov/home/saff/main.html? lang=en (retrieved 03.07.11) US National Climatic Data Center. (2003). US Climatic Normals 1971–2000. Available from:. http://cdo.ncdc.noaa.gov/climatenormals/clim81/TXnorm.txt (retrieved 10.07.11)
25
Village of The Hills. (2011). Rules and regulations. Available from: http://www. thehillspoa.com/thehills/sub category list.asp?category=17&title=Rules+and+ Regulations (retrieved 18.08.11) Zhou, W., Troy, A. & Grove, M. (2008). Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors, 8, 1613–1636.