Distance-Azimuth Survey

Introduction

Nowadays, although technology is mainly used to spatial data collection, it’s not always available for everyone. Sometimes you can have the necessary equipment, but depending on regulations of the place the collection will be done, authorities can take it from you. In other occasions, the equipment cannot provide accuracy enough because of the natural conditions. Also, technology tends to be expensive, so not everyone is able to have access to it.

With that in mind, alternative equipment to collect data was presented to the students. The compass, distance finder or the laser device were used in the old days when the technology wasn’t as available as today. All the students were able to put the hands on the equipment and collect simple information outside, as a way to understand how the technique works. After that, the information was transferred to ArcGIS, where it was possible to analyze the points collected and to have an idea of the accuracy involved with the procedure and how it would be possible to improve it in a second collection.

Thus, each group chose one of the equipment, a site and a theme to map. This report will cover the collection of fifty trees in the Owen Park and its heights, using the laser device. The goals of this project are to acquire knowledge of how these alternative methods work and be able to understand and compare the pros and cons of each method.

Methodology

The equipment introduced give two main methods to acquire data: one is using the compass to get azimuth and a distance finder for the length between the observer and the object; in the other hand, the laser device gives both in the same equipment. Although there are technicalities that differentiate the methods, both lie on the concept of azimuth and distance. The idea is that as long as you have an accurate coordinate of your position, you can infer other positions using direction and length.

The direction is based on the azimuth, which is the clockwise angle between the north and your direction. The Earth magnetic field allows the compass to show where the magnetic north is, and by that, it’s possible to infer the azimuth. It’s important to understand that the magnetic north is different from the true north. The relation between them is called magnetic declination and it depends on the year and location you are, so adjustments have to be made. Since in Eau Claire the declination is close to zero, there are no big concerns in this matter, but it’s important to be aware of it.

Starting the first method, the compass will be corrected, if necessary, and it will be towards the object being mapped. The number observed will be the azimuth, and it will be noted in a table. Standing in the same position, one will hold the part I of the distance finder while the other will go to the object holding the other part of the device. The result will be the distance between the two parts, which will also be noted in the same table as the azimuth, but in a different field. Names and other attributes for each feature are collected at this time, using the table, to maintain organization.

The data collection in this project used the second method, which resembles the first, but it’s more convenient. The same data will be collected with the same organization in a table, however, with a different device. An internal compass provides the azimuth when directing the equipment to the target. For the distance, the laser emits infra-red energy pulses that will hit the target and return. The calculation of the time it takes, considering the speed of the pulse, will give the distance. Also, the laser device provides the option of calculating a vertical distance, which was used to acquire information about the tree heights. In this case, a tilt sensor calculates the angle between the straight line to the target, and the calculations result in its height.

Thereby, it was planned to collect fifty trees within the Owen Park between 2:30-4:00PM of February 20^th, the decision of when to collect took in consideration not only the students availability, but also the outside conditions – as temperature and precipitation – to minimize its impacts on the collection. The temperature was approximately -10°C, being reasonable for collection and there was no precipitation at the time.

Four street corners were used as origin points (Figure 1) and marked in an aerial image taken to the field. The division of tasks were settle in a way that in 25 points, one would be using the device and the other taking the notes in a table, and in the other half, the tasks would be switched. The technical modes used on the device were azimuth (AZ), slope distance (SD) and vertical distance (VD), with a precision of one decimal place. In the AZ and SD mode, the device would be hold straight to the trees, targeting its trunk. For the VD mode, the crown of the tree would be targeted, trying to reach the most thick and high branch.

Figure 1 - Andrew using street corner as a reference.

After the collection, the data should be normalized to meet the standards of the tools used in ArcGIS: Bearing Distance to Line and Feature Vertices to Points. For that, a table in Microsoft Excel was created with the essential fields (Figure 2). The data related to the origin points were collected using Google Earth. The ArcGIS help doesn’t specific in which coordinate system and units the origin points should be, so the Geographic Coordinate System in degrees was tested and worked fine in the first test, so the same standard was used at this point as well.

Figure 2 - Table creation with appropriate fields.

The “bearing” field has the information in degrees about the azimuth. The command works by creating a line staring from the origin point in direction of the azimuth and having the length of the distance field, as presented in the Figure 3, where the “0 degrees” can be considered the true north.

Figure 3 - Geometric method to locate points with azimuth (bearing) and distance.

After running this command, the Feature Vertices to Points simply create points in the vertices of any feature inputted. The result, however, includes repeated origin points, since they were vertices of the previous feature. They should be deleted because the feature class is supposed to have only tree points.

Discussion

The collection section of this exercise can be considered successful, but some problems found should be discussed. Firstly, to target an exact point is necessary to keep the device totally still, which was compromised even by the slight shake (Figure 4). This problem would be increased when the trees were far or its trunks were thin. A simple way to solve this problem is to have some sort of mobile tripod, which would guarantee the stability of the equipment. Unfortunately, this problem was only noticed already in the field, so this extra equipment was not available.

Figure 4 - Beatriz trying to using the device without slight shakes.

A similar problem was increased when dealing with the vertical distance. At this time of the year, the crowns of the trees are totally without leaves, and the branches were extremely thin and similar between all the trees. Then, it was hard to identify while targeting, if the branches observed were from the target tree or from another in front of it. It was only possible to notice the errors after targeting, by obtaining some non-logical results as three meters for an extremely tall tree. The way found to solve this problem was to move closer to the trees targeted, since the height result is not compromised by the origin point.

Next, the information collected had to be transferred into ArcGIS. For that, it’s important to emphasize the importance of editing and using the default geodatabase in the document properties. Sometimes ArcGIS run in some problems with saving in a place other than your default geodatabase. If you are not aware of which geodatabase is being defaulted, you might encounter this problem.

The use of ArcHelp was essential to find the appropriate tools that would be used and understand the details pertinent to it, especially before building the excel table, so the coherence between the commands and the information provided in the table was guaranteed.

To obtain the origin points coordinates, it’s important to think about the units and precision being used. Usually, the standard is to use one or two decimal places of precision. However, the project is dealing with a large-scale map, so displacements are easier to be noticed. Also, the input of the coordinates is in degrees, which are not easily understandable as a distance measurement. Thus, it’s necessary to calculate the meaning of 0.1° in the site to be aware of how the precision can affect the results. That involves the extent of the circumference of the Earth and the latitude of the site. In Owen Park, 0.1° represents approximately 8km. Thus, if a precision of only one decimal place was used, the trees could be placed in the other side of the city. That’s why a precision of six decimal places were used in this project, which gave reasonable results (Figure 5)

Figure 5 - Trees Locations using ArcGIS tools

However, by analyzing the results, it’s possible to notice that they are not totally accurate: the trees 1, 2, 3, 6, 8, 26, 43, 44 and 46 were placed in the middle of the street, which doesn’t represent the real-world situation. Then, some explanations for the inaccuracy were considered.

At first, since the direction of the lines seemed incorrect, the precision of the azimuth was considered as an issue. It’s true that the lack of precision has more effect when the distance is higher, so it might have caused problems with the trees collected from further distance. However, after calculating the margin of error (Figure 6) of every three based on the 0.1 level of precision of azimuth, the maximum error would be only 10 cm, so another reason for the inaccuracy had to be found.

Figure 6 - Calculation of the Margin of Error (using trigonometry formulas)

The data related to the Area of Interest that support our analysis should be collected in a scale as big or similar as the site. However, the basemap used to compare the results obtained was probably produced in smaller scales, covering a much larger area. The basemap also was simply imported from ArcMap standard database, so there’s no information about the quality or scale. It’s not possible to determine if the process of orthorectification, where the aerial image would be geometrically corrected to have an uniform scale, was applied to the photograph. With that in mind, it leads to think that the aerial image is distorted in some way, especially when noticing that if the image was slightly rotated in the clockwise direction, all the trees placed in the streets would be in their real place.

Another reason that interfere the quality and accuracy might be the presence of particles in between the collector and the target, misguiding the equipment to hit a different target. The laser is based in infrared light, so the wavelength is somewhere in between 0.7µm and 1mm. Then, objects bigger than that might affect the result obtained. This reason was though because during class, it was snowing and some weird results were found. The size of a snowflake is usually higher than 1mm, so the odd results might be caused by that. Thinking that the air might contain particles in that range of size, which cannot be easily seen, it’s not impossible to consider that collection can be compromised by that as well.

For last, it’s interesting to notice the results related to the height of the trees (Figure 7). Since there was no supporting data as the basemap for the locations, it was not possible to test for inaccuracies, however, considering the range of sizes and their distribution, it can be said that the laser device worked reasonable to acquire that information.

Figure 7 -Heights of the trees in Owen Park

Conclusion

The use of technologies for data collection, as GPS, is doubtless more convenient, practical and fast. The test for accuracy can be done more easily, by checking the PDOP of each collection. However, there’s some situations where the alternative methods can be even more precise than the GPS. For instance, in a dense forest in a cloudy day, it would be extremely hard to obtain a reasonable PDOP for the collection. However, the methods described in this project are not affected by these variables, since there’s no signal coming from a satellite. The only thing that could compromise its work would be a magnetic change, which is not considerable, or the problems discussed before, about human limitations and the laser wavelength. However, the collection with this method is time demanding and requires more knowledge from the user.

Therefore, the method used for one collection should consider all these variables to reach to the decision of what better fits its purposes. Rarely the alternative methods would be used, it’s impossible to deny the predominance of GPS. However, it’s immensely important to be aware of those methods and know how to use them in case of the unforeseen. Technology should not be neglected, of course, but we should not rely totally on it.