Distance Azimuth

Introduction 
The concept of distance and azimuth survey technique is a simple but very effective tool for geographers even with today's technology. This is because GPS can be prone to failure due to interference from large buildings and/or tree canopy over. Also there could be issues with the device itself or may have forgotten it before entering the field. Nevertheless, the distance and azimuth method is a very useful and foundational method in field work.

Methods 
To begin this lab the class was divided into groups of 3 and 4 people. Each group was then to complete a survey of a set of trees on the campuses southern end, on Putnam drive. The survey would consist of gathering data on each tree such tree circumference, tree type, azimuth and distance from the origin of the survey.
The Each member of the team was given a different tool to complete the survey. The tools used were two tape measures, a range finder, survey compass, GPS receiver, and field notebook. All the measurements and other data was recorded into the field notebook so that the data could be entered into an excel file to be brought into ArcMap (fig. 3).

Figure 1. GPS unit used to record the origin of the survey

Figure 2. Survey compass used collect the azimuth for each the trees in the survey

Figure 3. Excel spreadsheet that has all the data collected during the survey.

The data from the field notebook was transfered into excel. The excel spreadsheet was transfered into a newly created file geodatabase. Using the Bearing Distance to Line tool was used to give the data its azimuth and the Feature Vertixes to Points tool was used to create the individual points for each of the trees.

Results
The first step in evaluating the accuracy of the survey was to visually inspect the accuracy of the GPS location. Qualitatively the accuracy of the GPS appears to be accurate (fig. 1). The survey taken on to the west may be slightly off to the north. This was likely caused by poor GPS signals from tree cover and the adjacent hill located to the south. The map created below (fig. 1) shows the tree type for each of the trees collected within the surveys. When comparing the results of the surveys there is more homogeneity between the tree species in the western survey compared to the survey taken to the east.

Figure 4. Map created in ArcMap that displays the location and tree type for each of the trees within the survey. The locations of the trees was determined by taking the azimuth each of the trees and measuring the distance from the origin. 

Conclusion/discussion
The distance and azimuth survey method is a very effective method for data collection in the field. By taking the distance and azimuth of objects from a single point of origin, accurate spatial data can be taken in the field even without the aid of more sophisticated technology such as GPS.

Arc Collector II

Introduction 

Arc Collector

Introduction
The purpose of this lab is to use smart phones to collect geospatial data and create climate maps with the newly collected data. Smart phones have far more computing power than most GPS units and therefore it is much for convenient and cost effective to use a smart phone in the field. This lab also introduced domains and their importance for data collection.

Methods
To begin the lab the class was divided into groups and two and asked to collect weather data for the various sections on campus (fig. 1). Each was given a kestrel 3000 to record atmospheric conditions and a compass to record wind direction. The readings were imputed into the Arc Collector where the locations of each of the recordings geolocated using the smart phone's GPS. Domains were created for the different fields to insure that the data was properly imputed into the data set and to minimize normalization issues in the future.

Figure 1. Displays the different sections of campus the various groups were assigned to.

Figure 2. Kestrel 3000 used to collect surface temperature (Fahrenheit), dew point and wind speed 

Each of the groups collected between 20-30 data points in their respective zones giving the data set 154 climatic readings across the campus. Once all the readings were collected they were placed into an ArcGIS Online file (fig. 3). A new personalized map was created using this data so that it could be brought into a geodatabase so that the layers could be brought into ArcMap to create microclimate maps for the campus.

Figure 3. Displays each of groups data points collected in each of their respective zones. 

Results

Below are the four maps created from the data collected using Arc Collector. Looking at the wind speed and wind direction map below a couple patterns are evident. First, the main direction for the wind that day was from the southwest. Areas closure to the river and especially over the walking bridge had the highest wind speeds. Areas in the main campus had lower winds than surrounding areas that had more exposure to the wind. 

Figure 4. Map displaying both the wind direction as well as the wind speed for the campus

Looking at temperature maps below, areas along the river had the lowest surface temperatures and temperature taken at 2 meters. The lower surface temperature along the river is likely a result of the temperature being taken on grass. Areas of blacktop and concrete also had higher surface temperatures. The dew point map (fig. 7) followed trends that would be expected looking at the temperature at 2 meters maps (fig. 6) with areas that had high temperatures also having relatively high dew points and vice versa. 

Figure 5.  Map displaying surface temperature for the campus using graduated symbols.

Figure 6. Map displaying temperature at 2 meters for the campus using graduated symbols.

Figure 7. Map displaying surface dew point for the campus using graduated symbols.

Conclusions

Arc Collector is a very user friendly app that is extremely useful for geospatial analysists. It allows for the user to his/her smart phone to collect data in the field and develop web based applications for that data. Having the ability to have pre-created datasets with domains prior to going into field allows for the user to avoid input errors that may lead to normalization errors when processing the data. 

Survey123

Introduction
The objective of this lab was to create an online survey in ArcGIS online using the Survey123 lesson. The results from the survey could then be brought into the Survey123 Field App and an interactive map was created displaying the results of the survey.

Methods
The first step for this lab was to create a survey using the Survey123 website. The survey created was HOA Emergency Preparedness Survey. The survey consisted of 29 questions that were to designed to gauge a community's preparedness for an natural disaster. These questions consisted of questions such as surveyors name, location, housing information, and levels of preparedness (fig. 1). The questions were added using the add function. This function allowed for a variety of different types of questions to be added such as multiple choice, single choice (yes or no), and location maps.

Figure 1. An example of the questions used to complete the survey. 

After the survey was created, the survey was then published to members of my organization (UWEC). Once published, I then filled out the survey by following the provided link: https://survey123.arcgis.com/share/52932b595f0e49a98a5d1b186bff2142. After completing the survey, the Survey123 Field App was downloaded. This app allows for users to be able to download and complete surveys without being directly connected to the internet. Using the app I then completed the survey multiple times using varied answers to my previously completed survey.

After the new survey results were sent from the Survey123 app, the next step of the lab was to analyse the results and create an interactive map displaying the results under the Analyze tab. This tab allows for the results of each individual question (fig. 2) and the location for each of the survey responses (fig. 3).

Figure 2. 

Figure 3.

After analyzing the results, an interactive web map was created that allowed for each of the respondants results to be shown once they were clicked on in the form of a pop-up. The attributes that were shown in the pop-up can also be edited. For example, the surveyor's name and address were omitted from the viewable pop-up. Once the web map was finished, a web-based app created for the survey is accessible to members of the organization.

Figure 4.

Conclusion
The Survery123 allows for surveys to be created and shared with the public. The data can be easily analyzed and the locations of the respondent can be used to analyze spatial spatial patterns survey. This information can then be brought into an app where members of the same organization can have easy access to the survey information.

Sources
Learn ArcGIS: Lessons: Get Started with Survey123 ArcGIS
https://learn.arcgis.com/en/projects/get-started-with-survey123/lessons/create-a-survey.htm

BadElf GPS

Introduction
The goal of this lab was to get introducted to the BadElf GPS Pro tracking unit to track a route taken by a group of class around the university campus. The BadElf GPS technogoly directly linked to students iPhones and produced both KML and GPX files. Both of these files can be uploaded into mapping software such as ArcMap and ArcGIS Earth. The BadElf software directly connects to the user's iPhone via Bluetooth. This allows for the user to have access to a device with strong computing power without having to develope or bring another device into the field. Smartphones are also a very strong platform from which different apps can be run off of, allowing for more felxibilyu on the user end. Having the in-field computer being taken care of by the cell phone companies, it allows for GPS manufactures to focus more strongly on the developement of GPS software.

BadElf Compatible Apps
There are many different apps that are compatible with BadElf. These apps span a wide variety of categories such agriculture, aviation, fitness/health, GIS, recreation, travel, and UAS. Below are a few of the apps that I found to be interesting and how the apps are related to my interested.

Fog of World: This app provides a "fog" that covers the world that gets lifted once the user physically enters an area. By doing this, it shows areas in which the user has been. This is an app that would be fun for to use when exploring different cities this summer in Europe as it will show where I have already been and will show me areas in which I still "need" to explore.

Fulcrum: The Fulcrum app allows for users to capture a wide variety of in-field information such as GPS location, text, photos, video, and audio for specific projects. This is an app that I may find to be helpful when conducting GPR research over summer as I could link important on-site information such as GPS location, photos and field notes, to the GPR grids directly.

Mapster: This app allows for very detailed maps to be downloaded to the users cell phone and can be opened without internet connection. This could be a very helpful app for backpacking purposes.  There have been many times that I have been backpacking in areas in which I did not have a physical map or interconnection and having the ability to have an interactive map that doesn't require internet service would be very valuable.

CamerAlert: The CamerAlert app displays the location of red light camera and gives the users average speed when the user's car in within the range of a camera. The app also gives the user alerts whenever he/she is approaching a speed camera.

Cyclemeter Cycling Running GPS: The Cyclemeter Cycling Running app gives the user many different options to improve their fitness. The app can record personal statistics such as heart rate, steps taken, tracking of route, record and provide updates to the user's benchmarks. Being a person who does a lot of biking, especially in the summer months, I think that this could be a very useful app in recording my progress as the summer progresses.

Methods
To begin the lab, the class was divided into groups between and 3 and 4 people. Each group had had an iPhone and downloaded to the phones and once the app was downloaded, the BadElf unit was connected to the phone via bluetooth. Once this was done, different groups were assigned transmitters and the others were given revivever. We were then asked to find the other groups.

Figure 1. BadElf App connected to the iPhone displaying the GPS location 

Once the app was connected to the user's iPhone the app tracked the groups movements. We asked to find the other groups as part of a game of "hide-and-seek". Two groups were given the tracker and the transmitter (fig. 2). 

Figure 2. GPS transmitter for the BadElf reciever 

The tracker (fig 3.) would pick up the signal transmitted by the transmitter and give the direction to which the receiver which would display the direction of the transmitter. Each team alternated between roles of hiding the transmitter and locating it through the use of the receiver. Once the lab was completed the data was brought into ArcMap using the feature from KML tool in ArcMap where it was displayed in the form of a map (fig 4.) tracing our groups route throughout the lab. 

Figure 3. The reciver used to locate the transmiter hide by the other groups.

Figure 4. Map displaying the route our group took throughout the lab.

Conclusion
The BadElf app is a very versatile app that allows for users in the field to connect GPS technology to their their smartphone. This simplifies the amount of equipment required by the end user as the user only needs his/her phone to use the app. The data collected from this app allow for the data to transferred to other mapping software such as ArcMap and ArcGIS Earth in the form of both KML and GPX files.

Processing UAS Data

Introduction
This lab is an extension of our previous lab introduction to Pix4D, however in this lab we asked to process a set a unmaned aerial systems (UAS data. This data included ground control points (GCPs). GCPs improve the spatial accuracy of data by providing reference points for the data to be referenced to.

Methods
To begin the lab we were given a set of UAS data collected by Dr. Hupy (fig. 1). This data consisted of 69 separate UAS images. Before we began processing the data, we had to verify that the data was correct. Pix4D has integrated setting and perameters for different sensors and cameras. The software also reads the images metadata and then assigns the image a set of parameters. To begin we had to verify that the initial parameters were correct. We verified that the correct coordinate system was assigned to the data set. The data was given the WGS UTM Zone 15N coordinate system, which was correct. We also has had to change the camera type to a rolling shutter. We also set initial parameters for our data processing which involved only running the initial processing processing function. This was done to improve processing speed as we still had to make corrections to the GCPs. We also checked the google maps and kml boxes for the final product. This function allows for the data to be shared to a broader audience who may not have access to Pix4D pr ArcMap software. We also created shapefile for the data that consisted of contour lines which when brought into ArcMap would aid in mapping.

Figure 1. The image above displays the study area. The blue crosses seen within the image are the ground control points

Once the previous steps were completed, we were then asked to bring in GCPs. To do this we imported the CGPs using the Y,X,Z format. Once this was done we began the final processing of the data. Once the final processing was completed, we were given a quality report displaying the accuracy of the data (fig. 2). There was an error reported but this corrected later in lab when the GCP location was corrected (fig. 3).

Figure 2. First quality report generated after processing the data

Figure 3. GCP accuracy assessment 

The next step of lab was to correct the location of GCPs.  This was done manually insure that the GCPs were in an accurate location (fig. 4). 

Figure 4. Corrected GCP

Once the GCPs were calibrated (fig. 5), the data was then reoptimized. The Point Cloud and Mesh and DSM, Orthomosiac and Index were then checked on to finish the final processing. Once this was completed an orthomosiac and digital surface model (DSM) were created were they could later be brought into ArcMap. 

Figure 5. Data set in Ray Cloud once the GCP location was corrected

Results

After the data finished processing, it was brought into ArcMap to create a maps of the study area (fig. 6). The DSM and Orthomosiac images were displayed with hillshade, giving the images a 3D effect. The data did not show any errors in terms of the accuracy between the mosiaced image as there no distortions or gaps in the images. The DSM is pretty simple as the study area did not have a lot of variability in elevation. 

Figure 6. The maps above display the Orthomosiaced and DSM images processed in Pix4D and displayed in ArcMap/

Conclusion

This lab showed that Pix4D is a useful software program for processing UAS data and converting the data into formats that are useful in other mapping softwares such as ArcMap and Google Earth. For the case of this lab, the UAS data set was quite small, but it provide valuable expiernce in processing UAS data as this a growing field within the geospatial community.

Assignment 4: Introduction to Pix4D

Introduction
     The purpose of this assignment is introduce the Pix4D software. This software is used to process unmaned aerial systems data (UAS). This software allows for the processing of point clouds and allows for 3D volume analysis. In order to use  Pix4D, it is very important that the user has high quality data sets. This can incude the use of ground control points (GCPs), geolocation and quality imagery.
     When using using Pix4D it is very important that the UAS data has overlap. It is recommended that a there is a minimum of 75% percent of frontal and 65% of lateral overlap for most situations. If the UAS is flying over over surfaces such as snow and or sand, there needs to be increased overlap for the images. In these cases, it is recommended that the overlap should be increased to 85% frontal and 70% lateral. Pix4D is also capable of producing oblique images, if there is sufficient overlap between the images. Images that were taken over the course of multiple flights can also be processed. This is as long as the total images is below 2000 images. If using images taken from multiple flights, similar flight conditions are desired and overlap between images is very important.
     Rapid Check is also a very important function in Pix4D. This function is used to determine if there is sufficient coverage for the images in the data set. It performs this function quickly by reducing the size of the pixels in the images so that processing speed can increased.
     As mentioned above, Pix4D can use GCPs. While this is not required, it is recommended. Having accurate GCPs aids the quality and accuracy of the overlap between images. GCPs also helpful for other tasks such as processing images without geolocation as well as georeferencing.
     Once an image has been processed the software produces a quality report that contains information on the accuracy of the data collection.

Demostration 
Volume Calculations 
    Pix4D has the ability to calculate volumes of 3D surfaces. This can be done by using the volume tool found in the menu. Once the tool is selected, control points can be placed on the processed image. Once the desired control points have been placed on the image, by simply right-clicking the mouse, the volume can be calculated. This can be seen below, where the volume for 3 gravel pits were calculated using this tool (fig. 1).
    

Figure 1. Three volumes collected using the volume tool

Flyover
     Pix4D can also create animations for data sets. For this example a video was created that "flew" above the processed image. This was done by using the video tool where way points can be placed around the image. This effectively creates a flight path for the animation. Once the video's way-points are collected, the video can be rendered into video formats such mp4 for exportation.

ArcMap
    Data processed in Pix4D can also be brought into ArcMap. For this lab, two maps were created, a digital surface model (DSM) (fig.2 )and orthomosic image (fig, 3). The images for this lab were preprocessed by Dr. Hupy. The DSM and orthomosic images were brought into ArcMap as raster features. For the DSM, a hillshade was created to create better depth in the image using the hillshade tool. Once the hillshade layer was created the DSM was placed over the hillshade and was made 30% percent transparent. The DSM was also imported in ArcScene to create a 3D model. These can be seen in the upper right of two maps.

Figure 2. Displays the DSM brought into ArcMap

Figure 3. Displays the orthomosiac image

Conclusion
      Pix4D is a very effective software for processing UAS images and data sets. When processing UAS data it is important to follow the recommended overlap percentages so that the data can be processed accurately. The software provides many useful tools for calculating volumes, displaying animations, and creation of data that be used in mapping software such as ArcMap.