Longcliffe Golf Course

Dr John Strodachs MCInstCES, Director, Applications in CADD 

Golf course survey

THE purpose of this survey case study of Longcliffe Golf Club, Loughborough was to identify highs and lows for drainage and to determine surface areas of fairways, greens and tee boxes for the accurate application of surface treatments, such as chemical sprays. As an extra exercise, the slopes on greens were mapped.

The project used a combination of different data types and sources and as with any golf course changes are constantly being made and the course updated. Many of the bunkers were reshaped and refilled to give them both consistency and visual impact and some tee boxes replaced.

Fairways are tree-lined making traditional total station surveys not the most appropriate option, therefore we relied on GNSS. In the early stages, signal strength and tree shadowing caused problems.

At one stage we stood in the middle of the 18th fairway, clear of any trees for over 30 minutes unable to get a signal. Thankfully, matters later improved.

GNSS survey

In the early stages, signal strength and tree shadowing caused problems. At one stage we stood in the middle of the 18th fairway, clear of any trees for over 30 minutes unable to get a signal.

The first GNSS survey was carried out in 2009 which included surveying the fairway centre lines and identifying the highest and lowest points on the course for drainage purposes.

The course elevation changes from 132m at its highest to 75m at its lowest; a total height change of 57m.

The tee to green centreline of each hole was surveyed, also identifying elevation changes.

Tee to green centrelines

The greens, bunkers and ditches were surveyed together with other obstacles, like bunds. The sprinkler heads around each green were identified so that we could come back later and carry out a total station survey of each green for slopes.

Since it wasn’t practical to do a full survey of tree and fairway outlines, ther appropriate data sources wre explored.

While it is possible to get imagery from Google Maps, these are neither georeferenced nor orthorectified. But there are sources that provide this information.

The photography was sourced from GetMapping, however, there are other sites such as Emapsite. Be careful though as there may be images flown at different times.

Early images are cheaper but may be missing detail.

Selecting the coverage and flown date

Since it wasn’t practical to do a full survey of tree and fairway outlines, we looked at other appropriate data sources. While it is possible to get imagery from Google Maps, these are neither georeferenced nor orthorectified.The first image was dated 2006-2013 but details had changed, so we subsequently updated the imagery and used an image dated from 2019. From this image the tree canopy could be identified, along with the fairway outline. These were digitised in n4ce providing useful information for the green staff but in 2D.

Since our ground survey was restrictive and didn’t provide levels everywhere, we filled out using lidar data from the Environment Agency. The latest data was flown in 2017 and updated in 2018, with various other data formats available. We selected 1m grid intervals and downloaded both the digital surface model (DSM) and digital terrain model (DTM). The DTM contained filtered data removing vegetation, so it is smoothed out.

The data supplied contains a number of 1km sub-cells. Sub-cell SK4917_DSM_1m covered our site with the dark areas in the contour mapping identifying trees.

Contours from SK4917_DSM_1m closely matched the ground survey when we compared levels obtained by GNSS with those found in the lidar grid. It was a match within 5cm, which was more than acceptable for our needs. 

Contours from SK4917_DSM_1m closely matched the ground survey.

GNSS levels and lidar grid interpolation

Since our ground survey was restrictive and didn’t provide levels everywhere, we filled out using lidar data from the Environment Agency. At this point we were in a position to tidy up our survey, replacing old data with updated data. We then used this updated survey to calculate surface areas of tees, greens and bunkers, digitising outlines of fairways and trees, and finally determining slopes on greens.

Alongside the data from the original surveys, the updated lidar grid file, and the orthorectified georeferenced aerial photography for the site, the coded features were given a string number that represented the hole they belong to.

Confirming new bunker positions using imagery.

For example, BNK3 would be a bunker belonging to hole three.

We could have simply added the updated survey to the original, but then we would have had two or more overlapping features, making it difficult to identify the latest.New 11th green bunkers

We could have simply added the updated survey to the original, but then we would have had two or more overlapping features, making it difficult to identify the latest.

One solution would be to backcloth the updated survey to the original but in highlight colour. This is exactly what we did here on the 11th green.

The original bunker was replaced with two new bunkers, shown in magenta in a backcloth. The original bunker outline could be deleted and the two magenta outlines copied into the foreground original model, adding the imagery to the backcloth.

Since there was both registered imagery and lidar data, if some detail was missed it can be added through digitisation.

Contour mapping greens.

Digitising the revised 5th tee box

The old bunker outline can be seen, together with the ‘new’ outline. This was digitised using the points and insert option, setting the code to TE5, with model interpolation using reference model sk4917_DSM_1m.

The top and bottoms of the banking were also added. To see if the string levels were smooth as expected, the trick is to query the string option.

What we saw was a spike because there was a tree overhanging the bank. This could be clearly seen from the contours of the lidar file. Two options were available to us – firstly remove the offending point or secondly smooth the string using points on either side of the spike.

Since there was both registered imagery and lidar data, if some detail was missed it can be added through digitisation.With the lidar file being used to provide levels, we had to make sure spurious levels were not being picked up and ensure sharp changes in slopes or ditches were surveyed directly as the lidar file provides a 1m square grid of points and may ignore these features and the head greenkeeper wanted to know the plan areas of greens and tee boxes.

Query string

Firstly, we needed to make sure each bounding string was complete by joining any breaks and removing overlaps. We then used the query string option.

Finally, we could see representative grades on the greens, using the lidar data already identified as being accurate to +/- 5cm. In a perfect world, we’d need to carry out a total station survey on each green, for greater accuracy.

Contour mapping greens

We set the contour intervals for the lidar backcloth to major 1m (blue) and minor 0.05m (cyan). The contours should be smooth, however, it could have indicated trends that may be useful to golfers. It was important to remember the contour maps were justified northerly and not in the direction of the fairway.

Summary

This case study investigated how a survey of a golf course could be carried out using different data sources. It also looked at how the survey could be edited when changes were made to the course using the backcloth facility in n4ce. 

Dr John Strodachs MCInstCES, Director, Applications in CADD

www.appsincadd.co.uk

@appsincadd

All images courtesy of Applications in CADD.