damage to in ground assets in a particular study area, and the correlation of tree risk ratings to known wastewater chokes.

Transcription

1 TREE ROOT DAMAGE RISK TO IN GROUND ASSETS SPATIAL ANALYSIS USING TREE CANOPY MAPPING Richard Lemon 1, Neil Fraser 1, Laura Kelly 1, Mary-Ellen Feeney 1, Kate O Loan 1 Rod Kerr 2, Padmi Pinidiya 2, Craig Mitchell 2 1. Jacobs Group Australia ANZ Infrastructure & Environment, Sydney, NSW, Australia 2. Sydney Water Corporation, Sydney, NSW, Australia Abstract This project involves using satellite and aerial imagery, together with image analysis techniques, to develop tree canopy mapping for Sydney Water s area of operation and undertaking preliminary spatial correlation analysis between the tree canopy and in ground asset data. A method for assessing potential tree root damage risk to in ground assets has been developed using tree canopy mapping. This paper will discuss the method of producing the data and the use of the data to predict the risk of tree root damage to in ground assets. Introduction Wastewater discharges due to sewer blockage (choke) are an issue for Sydney Water as they can impact on public health and the environment. About 75% of chokes can be attributed to tree roots. A study conducted in 2005 for a small area of the inner west of Sydney showed a high correlation exists between chokes and the surrounding tree canopy. A similar correlation is likely to exist for watermain breaks. By better understanding how trees interact with the sewer, Sydney Water aims to improve the management of trees and the wastewater network and reduce the risk to public health and the environment. This project uses satellite and aerial imagery, together with image analysis techniques, to develop tree canopy mapping for Sydney Water s entire area of operation. It also undertakes a preliminary spatial correlation analysis between the tree canopy and in ground asset attributes and failure data. The outcome of the project will be a better understanding of how the blockage rate varies with tree proximity to sewers. The project supports Sydney Water s desire to provide better services at a lower cost. The information will be used to refine minimum planting distances in tree planting guides that assist property owners and Councils in their management of trees. It will also be used to improve proactive sewer inspection and repair by improving the targetting of assets for investigation (high risk assets). This paper will discuss the method of producing the data, the use of the data in charting risk of tree root damage to in ground assets in a particular study area, and the correlation of tree risk ratings to known wastewater chokes. Tree canopy mapping Previous work by Jacobs for Sydney Water in (2005) had demonstrated that, using multispectral imagery and image processing techniques, it was possible to extract reliable tree canopy polygons over a cluttered urban environment. Although it was recognised that tree species play a significant role in defining tree damage risk to assets, the same study found that it was not possible to reliably classify individual tree species. So in March 2014 Sydney Water Corporation (SWC) requested Jacobs (through the ENSure joint venture) to extend the tree canopy mapping to its entire area of operation (4,457sqkm, shown in Figure 1). The tree canopy mapping was derived from high resolution aerial and satellite imagery. It was important that the tree canopy mapping reflect the current tree canopy cover for Sydney. It was therefore essential to use the most recent imagery available. To maximise the reliability of the image processing the following requirements applied in the selection of source imagery: Recent, ideally acquired within the previous 12 months; Multispectral image resolution of 2.0m or better; Ortho-rectified, to ensure spatial accuracy; Minimum of three visible bands plus near infrared to differentiate between vegetation and other objects; Acquired in summer (dryer) months to maximise the spectral difference between trees and grasses; Acquired at a high sun angle to minimise shadows; Near vertical view angle to minimise offset of above ground features and maintain spatial accuracy; and Cloud, smoke and haze free to eliminate areas of no data.

2 The 10cm resolution AUSIMAGE ortho-rectified aerial imagery, flown in January 2014, was available for most of the area (about 80%, see Figure 2). This imagery met all of the above criteria and was resampled to 1.5m resolution and re-cut into 15km x 15km prior to processing. With a spatial resolution of 2.0m it was decided to use WorldView 2 (WV2) satellite imagery from the DigitalGlobe image archive for the remaining areas. All selected scenes were captured during the summer months, within 10deg of nadir (one scene at 15deg), were cloud free and were typically less than 2 years old. To ensure spatial accuracy each WV2 scene was ortho-rectified prior to undergoing further image processing. An object based classification method was used to extract the tree canopy data. This is a method that performs classification on groups of like pixels (or objects ). It works by creating image objects through image segmentation and stablishing a set of classification rules for each image or scene. These rules classify objects by using a number of approaches including: Spectral band values Pixel geometry (e.g. extent and shape) Texture (e.g. contrast, homogeneity) Arithmetic (differences between spectral band values for each pixel e.g. NDVI). Once a rule set has been established for each scene it is tested and, if necessary, modified until the optimal results are achieved. Rule sets can be used again, sometimes with slight modification, to classify other similar image scenes. Figure 3 shows part of a typical 1.5m resolution image and Figure 4 shows, for the same area, typical tree canopy objects derived through image segmentation. Once the optimal tree canopy objects have been derived they are merged together and visually inspected by plotting over the best available imagery. Small, single pixel polygons are deleted and significant classification errors are manually edited. Figure 5 shows final tree canopy objects plotted over best available imagery. A qualitative estimate, based on a visual inspection of the completed data set, is that >98% of all tree canopies have been reliably mapped. Asset age and condition Asset material Pipe junction type Asset depth Tree species Size/age of tree Number/volume of trees nearby the asset Proximity (closeness) of trees to the asset The development of a tree risk grid, initially for a 15km x 15km pilot tile near Prospect (Figure 7), is an attempt to model the risk to wastewater assets from trees, irrespective of the other risk factors. Once quantified, the risk grid can be used to allocate a level of tree risk to each wastewater asset. Later the derived tree risk factor can be combined with other risk factors to enhance the risk assessment for each asset. A pilot tree risk grid was developed using two primary data sets, a 15km x 15km tile of tree canopy data and wastewater choke data supplied by Sydney Water. The wastewater choke data was supplied as a GIS layer and consisted of attributed points representing the location of wastewater blockages across the Sydney Water network between 1999 and Of the recorded wastewater blockages over the last 16 years 75% were caused by tree roots. (Table 1). Further analysis showed that the number and percentage of chokes caused by roots varied slightly from year to year in both number and as a percentage of total chokes (presumably as a result of fluctuations in rainfall), but always remained significant with around 60% to 80% of all chokes caused by trees (Table 2). Table 1: Wastewater choke causes across the network. Percentage to total Cause number of chokes DEBRIS and silt 11 GREASE 3 ROOTS 75 SOFT CHOKE 6 Other 5 Development of a tree risk grid The tree canopy map provides information about the presence or absence of trees at a particular location; it does not however provide a direct representation of the risk of tree damage to underground assets at a particular location. For example, both points highlighted in the diagram at Figure 6 are under tree canopy, but the risk of tree damage to assets at each of these points is likely to be quite different. The Sydney Water planning team think the risk of tree damage to a wastewater asset is a function of many factors including:

3 Table 2: Wastewater chokes per year due to roots across the network and pilot area Fiscal year Percentage (across network) Percentage (across pilot area) Table 3: Wastewater choke causes across the pilot area Cause Percentage DEBRIS and silt 11 GREASE 8 ROOTS 71 SOFT CHOKE 7 Other 3 An analysis of the wastewater choke data over the pilot area was also carried out to ensure that the pilot area was typical of the broader network (Tables 2 and 3). Sydney s tree cover is in a constant state of change as new trees are planted, existing trees grow and mature and trees are cut down or die. Since the tree canopy dataset is based upon recent imagery it was expected that it would be highly correlated with recent wastewater chokes. Table 4: Analysis of distance from tree canopy to root choke since 1999 Total Records ( ) Average 2.24m Standard Deviation 4.03m Minimum Maximum 0m 105m Table 5: Analysis of distance from tree canopy, root chokes in and Total Records ( ) 1133 Average 1.43m Standard Deviation 2.60m Minimum 0m Maximum 18.72m As expected, the average distances from tree canopy to choke for the pilot area (Table 4) is significantly reduced when the tree canopy dataset is compared with only recent chokes (Table 5). It was therefore decided to develop a risk grid that would be highly correlated to the wastewater chokes caused by roots in only the 18 months surrounding the imagery capture (January 2014), that is the fiscal years , From the analysis above, and assuming a normal distribution, a distance of 6.63m from the tree canopy would capture 95% of recent recorded wastewater chokes (mean of 1.43m x 2 = 6.63m). Since the resolution of the tree canopy dataset and the resulting risk grid is 1.5m, the distance over which trees might be expected to affect the network was extended to 7.5m (5 pixels). The following process was used to develop the Tree Risk Grid. R1 9 R2 16 R3 24 R4 32 R5 40 Figure 8: Generation of Tree Risk Grid Centre Pixel Tree pixel Tree canopy The numbers of Tree pixels in each band (R1 to R5, as illustrated in Figure 8) are summed together. Note that the original (centre) pixel value is included in the R1 calculation. The sum value for each R1, R2, R3, R4 & R5 is converted into a percentage of the total number of pixels in each band. This initially makes the weighting for each band equal. Then a lineal drop off in the weighting for each band is applied to ensure that pixels close to the centre pixel effect the weighting more than pixels further away.

4 Band Table 6: Relative band weighting applied Number of Tree pixels Total number of pixels % weight Risk number R R R R R Risk number for the centre pixel Table 7b: Classification Approach B Class Risk Class Divisions Wastewater Choke % of Chokes Chokes / 100km of pipe Low 0 to Medium 0.2 to High 0.8 to This weighted sum results in a risk number between 0 3 assigned to the central pixel. This risk assessment was calculated for each pixel in the pilot area (Table 6). Determination of Tree Risk Classification The risk values caluculated for each pixel were subsequently divided into broad risk classes of High, Medium and Low. The class divisions were empirically determined using three different approaches (A, B & C) to ensure that the majority of the wastewater chokes from , were in the high risk class, with progressively less in the medium and low classes. To test results the choke rate per 100km of pipe was calculated for each risk class. Tables 7 (a-c) show the three classification approaches tested over the pilot area. Figures 9 (ac) show, for each approach, the resulting chokes per 100km of pipe in each risk class. Figure 9b: Classification Approach B: Chokes / 100km by Risk Class Table 7c: Classification Approach C Class Risk Class Divisions Wastewa ter Choke % of Chok es Chokes / 100km of pipe Low 0 to Medium 0.2 to High 1.0 to Table 7a: Classification Approach A Class Risk Class Divisions Wastewater Chokes % of Chokes Chokes / 100km of pipe Rare Low 0 to Medium 0.2 to High 0.6 to Figure 9c: Classification Approach C: Chokes / 100km by Risk Class Figure 9a: Classification Approach A: Chokes / 100km by Risk Class To better inform the selection of the best risk classification approach, each approach was also tested over three additional areas with varying chararcteristics (in terms of land use, tree cover, terrain, etc). Classification approach C was determined to provide the most consistent spread of chokes per 100km across the three classes and all four tiles (areas) tested.

5 Figures 10 and 11 show the Tree Root Risk grid plotted over the tree canopy polygons and recorded wastewater choke locations. Determination of wastewater pipe asset risk We are trialing a number of approaches to allocate tree root risk to wastewater assets. Approaches being trialled include the following: Determine the risk class for each cell intersected by the asset, allocate this to the intersecting segment and classify as per the divisions in Table 7c. (See the result illustrated in Figure 14). For each asset, sum the risk numbers for all cells intersected by the asset, divide by the total number of cells intersected to determine an average risk value and classify as per the divisions in Table 7c. (See the result illustrated in Figure 12). Determine the highest risk number for the cells intersected by the assets, allocate this to the asset and classify as per the divisions in Table 7c. (See the result illustrated in Figure 13). Tables 8 and 9 show the additional information that would be avilable for each asset as a result of the above analysis. Some results of this initial assessment, plotted over the risk grid and recorded chokes, are shown in Figure 12 - Risk class of average value of risk grid calculated across full wasewater main segment; Figure 13 - Highest risk class encountered along the wastewater main segment and Figure 14 - Risk class of wastewater main segment as a result of intersection with risk grid. Figure 1: Tree canopy data extent required by Sydney Water Conclusion A cost effective and reliable method of mapping tree canopies across large, cluttered urban and semi urban environments has been developed and verified using high resultion imagery. An approach to derive a tree risk grid, initially applicable to wastewater networks, has been developed using tree canopy mapping and wastewater choke history data. The resulting risk grid is highly correlated to recent wastewater choke data demonstrating its potential for predicting wastewater chokes and improving the management of wastewater assets. Some preliminary work is currently underway to use the tree risk grid in conjunction with Sydney Water s wastewater assest database to investigate options for the allocation of tree risk values to indiviual wastewater assets. A number of approaches are being tested and the results are looking promising. This approach of relating chokes to surrounding tree canopy, will deliver a better outcome for Sydney Water s customers and the environment and assist Sydney Water to achieve world class performance. Figure 2: Total area covered by 2014 AUSIMAGE Figure 3: Part of a typical 1.5m resolution image

6 Figure 4: Typical tree canopy objects derived through image segmentation Figure 7: Pilot tile (15km x 15km) in the Prospect study area showing recent wastewater choke locations Figure 5: Final tree canopy objects plotted over best available imagery Figure 6: Both these locations are under tree canopy but clearly the level of risk from trees is different

7 Table 8: Original Wastewater Main Polyline Dataset - added Risk Analysis fields EXTRA FIELD NAME DESCRIPTION length_j Total length of Wastewater Main segment Risk class of average value of risk grid calculated across full Wastewater Main MEAN_CLASS segment H_TOT_LEN Length of Wastewater Main segment which is in the HIGH risk class M_TOT_LEN Length of Wastewater Main segment which is in the MEDIUM risk class L_TOT_LEN Length of Wastewater Main segment which is in the LOW risk class MAX_CLASS Highest risk class encountered along the Wastewater Main segment H_PERCENT Percentage of Wastewater Main segment which is in the HIGH risk class M_PERCENT Percentage of Wastewater Main segment which is in the MEDIUM risk class L_PERCENT Percentage of Wastewater Main segment which is in the LOW risk class Table 9: Wastewater Main Polyline Dataset segmented per Wastewater Main Id and per Choke Class added Risk Analysis fields EXTRA FIELD NAME length_j GRIDCODE Seg_length CLASS DESCRIPTION Total length of Original unsegmented Wastewater Main segment Original Risk ID from Reclassified Risk grid Length of Original unsegmented Wastewater Main segment Risk class of Wastewater Main segment Figure 10: Tree Canopy and Tree Root Risk Class Figure 11: Tree Root Risk Class and recorded Root wastewater chokes

8 Figure 12: Risk class of average value of risk calculated across full Wastewater Main segment Figure 13: Highest risk class encountered along the Wastewater Main segment Figure 14 Risk class of Wastewater Main segment as a result of intersection with risk map