Field Methods: ArcPad Data Collection Part 1

ArcPad Data Collection Part 1

Introduction

In a previous exercise entitled "Geodatabases, Attributes, and Domains," from March 1, 2015, I walked through the proper steps for creating and preparing a geodatabase for field research (click HERE). In this instructional blog, I highlight just how important going through the proper methods of geodatabase construction are to easy, concise, and accurate field measurements. This blog will instruct as to the proper methods of deploying the newly created geodatabase into the field. In this case we are using a Trimble Juno 3 Series Handheld global positioning system (GPS) device (Figure 1).

Figure 1. This image shows the Trimble Juno 3 Series. This unit was used in the field for data collection.

This device is equipped with two different types of software for data collection. One of these data collection programs is called TerraSync, the native GIS platform for Trimble units (Trimble, 2015). The second program is called ArcPad, one of the many programs in the ArcGIS suite from Esri (Esri, 2015). As ArcPad is part of the ArcGIS suite, it offers seamless integration with ArcGIS for Desktop, ArcGIS for Server, and ArcGIS for Online (Esri, 2015). We will be using ArcPad to record our data while in the field, as we will be able to utilize the power of our already created geodatabase, created in ArcGIS, with all its inherent parameters to quickly and accurately collect data.

The procedures to properly prepare our geodatabase for deployment onto the Trimble Juno units is very brief, though if done incorrectly can lead to issues in the field. For this exercise we were collecting data on the University of Wisconsin-Eau Claire campus (Figure 2).

Figure 2. This image shows the projected study area for the ArcPad Data Collection exercise.

Methods
The first step to preparing a geodatabase for deployment is to start a new ArcMap session. Open ArcMap and navigate to the folder that the geodatabase has been stored in. The first thing to be done is to add in the point feature class created prior that data points will be created for (Figure 3). NOTE: Failure to do this will result in the basemap image being displayed first and will slow down the map in the field.

Figure 3. This figure shows what the table of contents for a newly opened ArcMap file, after the point file for data has been dropped in first (Trimble, 2015).

In this case we will only be using one feature class for data collection, however, if more were being used then this would be the time to add them. For example, one project I worked on looked at the distribution and amount of invasive strain of reed canary grass on the banks of the Lower Chippewa River. Data collection for this project came in the form of points, lines, and polygons. Points were for very small patches of reed canary grass. Lines were for thin lines of reed canary grass along the banks. Polygons were for larger patches of reed canary grass that required us to dock our canoes and walk around the extent of the polygon. In this case, it was crucial that the proper feature be easily laid out within the ArcPad menu for quick data collection.

The next step is to add the backdrop into the map. For this step, it is necessary to select an image that will not be a large blur when zoomed into the proper level. Personally, I believe that it is also necessary to select an image that allows for some field checking. If in an urban setting, chose an image that allows you to see the study area and pick an area of reference that is discernible on the map. This will allow you to check to see if your data points are being projected properly in the field by seeing where the point is placed on the ArcPad interface in reference to your actual location. Finally, ensure that the image used is zoomed into an area that encompasses the study area, as this level of zoom will be cached and preserved for when the file is opened on ArcPad.

Following this, save the ArcMap file containing the feature class and properly zoomed image. Now we need to check out the data for use on ArcPad. The first step is to go to the Customize menu on ArcMap and select "Extensions" (Figure 4).

Figure 4. This image shows where the Extensions option can be found within the ArcMap toolbar. 

Select ArcPad Data Manager in order to check out the extension for use in ArcMap. Then go to Customize again and hover over the Toolbars dropdown menu and select ArcPad Data Manager Toolbar. This will allow for the data to be checked out and made ready for ArcPad. Select the first icon to the right of the "ArcPad Data Manager" text icon. This is the "Get Data for ArcPad" icon (Figure 5).

Figure 5. This shows the ArcPad Data Manager Toolbar and all of its options (Esri, 2015).

Click "Next" on the initial page. Select Action menu and choose "Checkout all Geodatabase Layers" (Figures 6-8). Unfortunately, when creating this blog, I did not have access to a version of ArcMap with the ArcPad Data Manager as I was using a remotely sourced desktop version of ArcMap. However, I will be using images from a previous student, Lee Fox, who took this course in spring 2014.

Figure 6. This image shows the second window where the data is initially checked in (Fox, 2014). Select the Action menu and select "Checkout all Geodatabase Layers."

The next step is to specify a name for the folder that will be created for the data. Under the "Specify a name for the folder that will be created to store the data" list the name of the folder, paying attention to not include spaces in the folder name (Figure 7). Make sure that under "Where do you want this folder to be stored?:" that the ArcMap file that you have saved is the one selected.

Figure 7. This image shows the Select Output Options menu within the Get Data From ArcPad menu (Fox, 2014).

After clicking next to get to the next window the final option is to ensure that the "Create the ArcPad data on this computer now" option is selected and then select "Finish" (Figure 8). If the process is successful a screen will appear after the process has finished stating that the operation was successful.

Figure 8. This figure shows the final screen that needs to be input in the the Get Data From ArcPad wizard (Fox, 2014). Selecting finish will start the process of making the data ready for deployment.

Once the folder containing the deployable data has been created make sure to copy it in case the data is compromised in any way. This will ensure that the original data is unaltered and will be immediately redeployable in need be. Make sure the Trimble Juno unit is connected to the computer, copy the folder again, and paste it onto the SD drive of the Trimble Juno.

Once you return from the field all that needs to be done is the data needs to be copied from its location on the SD and back into the folder created in the previous process as the checkout folder. Make sure the proper extension (ArcPad Data Manager) is turned on as before and use the tool "Get Data from ArcPad" (Figure 5).

Results
Using the Trimble Juno 3 Series in the field we were able to gather results for Surface Temperature, 2 Meter Temperature, Wind Chill, Wind Speed, Humidity, Dew Point, and Ground Cover. This data was collected over 16 points. The results were used to create two sample maps (Figure 9 and Figure 10).

Figure 9. This image shows the map created from the temperature taken at the surface. Darker blue represents colder areas while darker green represents warmer areas. The warmest areas are located located over blacktop.

Figure 10. This image shows the wind speed over areas of campus. Areas of dark green represent lower wind speeds while dark red represents higher wind speeds.

Conclusion
Proper movement of a geodatabase to ArcPad for deployment can make or break a data collection outing. If the geodatabase is not moved over properly it can slow down the entire process and make field collection of data very problematic and slow. Properly moving it over, however, will usually result in a streamlined method of data collection that allows for quick response time from the software and easy recording practices. This will allow the next step of the exercise, collecting significantly more data points, to be done in an easy and streamlined manner. The geodatabase used in this exercise was selected for deployment for the entire class in order to provide a standardized method of data collection. This will allow all the data to be merged in a quick and easy manner after the data collection process has been conducted.

References Cited
Esri. (2015). ArcPad - Mobile Data Collection & Field Mapping Software. Retrieved March 7, 2015, from http://www.esri.com/software/arcgis/arcpad

Esri. (2015). ArcPad User Guide. Retrieved March 7, 2015, from http://webhelp.esri.com/arcpad/8.0/userguide/index.htm#arcpad_data_manager/concept_datamanager.htm

Fox, L. (2014, March 3). Field Activity #7: ArcPad Data Collection. Retrieved March 8, 2015 from http://uwecleefoxmethods.blogspot.com/2014/03/field-activity-8-arcpad-data-collection.html

Trimble. (2015). Trimble – Juno 3 Series Handheld | Trimble Agriculture. Retrieved March 7, 2015, from http://www.trimble.com/Agriculture/juno-3.aspx

Trimble. (2015). TerraSync. Retrieved March 7, 2015, from http://www.trimble.com/mappingGIS/TerraSync.aspx

Field Methods: Unmanned Aerial Systems Mission Planning

Unmanned Aerial Systems Mission Planning

Introduction

Unmanned Aerial Systems (UAS) constitute a wide variety of remotely controlled aerial systems, ranging from fixed-wing, single-rotor, and multi-rotor aerial vehicles (Colomina and Molina, 2014; Military Factory, 2015). The versatility of these systems allow for a plethora of uses from helping with precision agriculture, to 3D mapping, to helping with search and rescue efforts (Handwerk, 2013). UAS's have been around for quite some time now and were originally developed for use as military reconnaissance. In recent years, focus has shifted from only using unmanned aerial systems for use in military operations to recreational and commercial use. A multitude of technology exists to outfit these UAS systems for the multitude of tasks they will be used for. Certain cameras and sensors that can be put on the aircraft have different capabilities (Colomina and Molina, 2014). These different sensors can see in different spectral ranges, spectral bands, thermal sensitivities and visible band resolutions (Colomina and Molina, 2014). Armed with this knowledge, it will be possible to assess a number of different scenarios and determine the best way to use unmanned aerial systems to solve the issues presented.

Scenario 1

A power line company spends lots of money on a helicopter company monitoring and fixing problems on their line. One of the biggest costs is the helicopter having to fly up to these things just to see if there is a problem with the tower. Another issue is the cost of just figuring how to get to the things from the closest airport.

In the situation provided above, a rotary wing UAS, such as the Draganflyer S6, would be the most fitting to complete this task (Figure 1). The S6 retails for $8995 per unit and comes equipped with a Sony QX100 digital camera with HD live stream video capabilities.

Figure 1. This shows an example of a rotary wing UAS. There are four rotors on this aerial system, improving maneuverability, stability, allowing for direct vertical or horizontal movements, and allowing for the ability to hover (Draganfly.com).

Rotary wing systems have a number of strategic advantages over fixed wing systems that make rotary wing systems better for this type of a job. The rotary wing system is able to move vertically and horizontally as need be and are also able to maneuver with greater agility than the fixed wing counterparts (UAV Insider, 2013). However, there are a number of questions that would need to be asked before a decision was made.

• What is the necessary flight range?
• What is the necessary flight duration?
• What weather conditions can be expected?

Rotary wing systems are more mechanically advanced than fixed wings, leading to shorter flight ranges and lower speeds (UAV Insider, 2013). If the distance of flight is too great, a rotary wing system will possibly not be able to accomplish the task because it will be out of range or the amount of battery will not be great enough to finish the mission. Weather is also a constant factor to be wary off with UAS's, as they are small and are susceptible to being pushed around by wind higher in the sky.

If all necessary conditions are met, the next steps will be to decide on the proper equipment for the job. In this case, sensors will not be a necessary piece of equipment, because only items within the visible spectrum need to be examined. A good camera that will be able to relay live feed video back to field headquarters station will be a necessary piece of equipment. This will allow the fliers to see where the downed lines are and record information that will allow others to know where to go and what the issues may be to fix the lines.

A number of live feed cameras exist that are mountable on UAS's. One particular camera, the Sony QX100, retails for $1595 (Figure 2). The Sony QX100 is gyro stabilized, easily mountable to a UAS, and transmits a live feed that can be synced to a smart phone with a Sony app, allows the user to control the zoom, and allows the user to trigger the shutter. The Sony QX100 has a 20 Megapixel lens, effective resolution of 5472 x 3649, and video resolution of 1440 x 1080 (Draganfly.com).

Figure 2. This camera is mountable on a UAS and provides live feed to a smart phone and the ability for the smart phone user to control the zoom and shutter (Draganfly.com).

This equipment should provide the necessary equipment to properly monitor the power lines for damage and provide a visual avenue to remotely assess the damage.

Scenario 2

A pineapple plantation has about 8000 acres, and they want you to give them an idea of where they have vegetation that is not healthy, as well as help them out with when might be a good time to harvest.

Given the study area in the situation provided above, a fixed wing UAS would be the most fitting to complete this task (Figure 3).

Figure 3. This shows a fixed wing system being launched. The Falcon system can carry a payload of 2 lbs, making it perfect for the equipment that will be explained. It also is able to travel long distances and has over a 3 mile range, making it a prime candidate for this type of mission (Falconunmanned.com).

A fixed wing provides longer flight durations and higher speeds that make this system better equipped for large areas, however it does require a runway for takeoff and landing, unlike the rotary wing system. The fixed wing in Figure 3 is the Falcon, retailing for $12000.

A number of questions should be addressed to better determine if unmanned aerial systems provide a viable option to assist the needs of the pineapple plantation.

• What is the potential cause of the unhealthy vegetation?
• What time of year is it?
• What have current weather conditions been like?

The factor that will be examined is the Normalized Difference Vegetation Index (NDVI), which essentially determines the density of green in a certain area (NASA EO). Healthy green plants absorb wavelengths in the visible spectrum and reflect wavelengths in the near infrared spectrum. Unhealthy plants with less chlorophyll are not as able to absorb the visible spectrum wavelengths and instead take in more of the near infrared spectrum. (Figure 4 and Figure 5).

Figure 4. This shows the electromagnetic spectrum from ultraviolet wavelengths to far infrared wavelengths (Vividlight.com). The two wavelength ranges necessary from this study are the visible light range and the near infrared rage. The visible range will be used to monitor the color of pineapples as they grow. The near infrared spectrum will be used to calculate a NDVI index.

Figure 5. This shows the amount of absorption of the visible and near infrared light spectrums (NASA EO). The health vegetation on the left absorbs mostly visible light, reflecting roughly 8% of it, while reflecting 50% of the near infrared light. The unhealthy vegetation on the right reflects only 40% of the near infrared spectrum and reflects over three times the amount of visible light as the healthy vegetation.

A few pieces of equipment will be necessary for this assessment. The Tetracam Lightweight Agricultural Digital Camera (ADC-Lite) provides imaging in the 450-1050 wavelengths, perfect for capturing visible and near infrared imagery, and retails at $3795 (Figure 6). This can be used to create a NDVI image, using a program such as Erdas Imagine.

Figure 6. This shows the Lightweight Agricultural Digital Camera (ADC-Lite) (Tetracam.com). This camera acquires imagery in the wavelengths between 450-1050 nm (Colomina and Molina, 2014).

A higher end visible light spectrum camera, such as the Sony Nex-7 would be able to show vegetation colors and potentially help to show when the pineapples are ripe and have changed colors. This unit retails for $1099 and a multitude of lenses can be purchased to enhance the zoom capabilities of the camera (Figure 7).

Figure 7. The Sony Nex 7 is a favorite visible light spectrum camera for UAS purposes (Colomina and Molina, 2014).

Conclusion

The use of unmanned aerial systems is a growing industry. Commercial and recreational uses continue to expand as low altitude airspace continues to fill up with more and more users. This ability of a company or business to utilize UAS to assess issues that may arise offers an interesting alternative to issues previously solved using conventional aerial methods or by pricey ground reconnaissance. As the industry continues to grow, UAS will continue to grow in demand and everything from precision agriculture, to monitoring protected herds of animals, to monitoring chemicals in the atmosphere will provide us with a never ending well of data to analyze. Being informed and understanding the equipment necessary to complete a mission are essential concepts to making the use of a UAS a cost-saving venture.

References Cited

Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79-97. Retrieved March 1, 2015, from http://www.sciencedirect.com/science/article/pii/S0924271614000501

Draganfly.com. (n.d.). Sony QX100 Camera System with Single Axis Stabilized Camera Mount. Retrieved March 1, 2015, from http://www.draganfly.com/sku/DF-QX100I-1B.php5#Zoom

Falconunmanned.com. (n.d.). Retrieved March 2, 2015, from http://www.falconunmanned.com/

Handwerk, B. (2013, December 2). 5 Surprising Drone Uses (Besides Amazon Delivery). Retrieved March 1, 2015, from http://news.nationalgeographic.com/news/2013/12/131202-drone-uav-uas-amazon-octocopter-bezos-science-aircraft-unmanned-robot/

Krock, L. (2002). Spies that fly. Retrieved March 1, 2015, from http://www.pbs.org/wgbh/nova/spiesfly/uavs.html

Military Factory. (2015, January 4). UAV and Drone Aircraft. Retrieved March 1, 2015, from http://www.militaryfactory.com/aircraft/unmanned-aerial-vehicle-uav.asp

NASA EO. (n.d.). Measuring Vegetation (NDVI & EVI). Retrieved March 1, 2015, from http://earthobservatory.nasa.gov/Features/MeasuringVegetation/measuring_vegetation_2.php

Tetracam.com. (n.d.). Tetracam Products. Retrieved March 1, 2015, from http://www.tetracam.com/Products1.htm

UAV Insider. (2013, September 8). Rotary Wing vs Fixed Wing UAVs. Retrieved March 1, 2015, from http://www.uavinsider.com/rotary-wing-vs-fixed-wing-uavs/

Vividlight.com (n.d.). Spectrum of Light. Retrieved March 1, 2015, from http://www.vividlight.com/29/images/Spectrum%20of%20Light.jpg

Field Methods: Geodatabases, Attributes and Domains

Geodatabases, Attributes, and Domains

Introduction

Proper recording techniques and field reconnaissance are not achievable without first determining a game plan and then properly laying out tools with which to accomplish organized recording. This statement is no different when comparing the act of scribbling notes into a Rite-in-the-Rain or logging notes into a Trimble Juno handheld GPS unit, equipped with ArcPad to record precise location and a multitude of attributes. For a researcher and collector of data to acquire the best possible notes in a given situation, preparation and organization are key. I have a specific way that I record my notes into my field notebook. I start by filling out the lines on the top of the Rite-in-the-Rain pages; location, date, project/client. Then, on the first few lines, I will record the time, current weather conditions, and predicted weather conditions. This will help me to recall conditions after I have returned from the field. I will make changes to this depending on my task. If I am conducting a soil pit profile I will likely record if there has been significant rainfall or a lack of rainfall in the recent past. This also is pertinent in I am on a river and the stage is higher/lower than average because of recent precipitation. Within my notes I will record things a certain way. If I traverse a number of sites while at one location I will indent all of my notes after my site number and location. I will do the same if I am describing a soil profile. This allows me to see clear breaks between different locations and different attributes.

The same type of preparation and methodical style exists with the Trimble Juno 3 Series handheld GPS devices that we deploy into the field. If the feature classes and geodatabases deployed with the unit are not properly set up, proper data collection becomes a nightmare. On the other hand, if proper organization and setup methods are observed, the ability and ease of use for the units increases significantly. Proper geodatabase setup essentially encapsulates the entire issue of proper organization and data collection. A geodatabase is "the native data structure for the storage and analysis of geographic information. Just like maps contain a collection of many thematic layers, the geodatabase is a collection of thematic datasets" (Zeiler and Murphy, 2010, 7). Geodatabases allow for advanced functionality and setup of many different components such as feature classes and datasets, raster datasets, toolboxes, and a plethora of other components. Within the Properties of a geodatabase, a highly powerful setup tool known as Domain is available for use. Domains are basically a set of rules that describe the acceptable values for feature classes within a geodatabase (ArcGIS Help, 2012). Domains help to reduce the potential for inaccurate data recording and also help to automate and standardize recordings. For example, this geodatabase is being created to aid in an exercise where accurate temperature recordings will be key to recording accurate data to develop a microclimate of the University of Wisconsin-Eau Claire campus. A domain allows me to set the range of acceptable values between -30°F and 60°F. If, while in the field, I was attempting to record a temperature of 21°F, but accidentally entered 221°F, a properly setup domain would not allow that recording to be input. 

As a class, we discussed the necessary components to make this geodatabase functional in the field, for our purposes. We determined that the following domains were necessary (ranges and units listed in parentheses): Wind Speed (0 to 50 mph), Wind Direction (0 to 360°), Humidity (%), Dew Point (-30°F to 60°F), Temperature at the surface (-30°F to 60°F), Temperature at 2 meters above the surface (-30°F to 60°F), and Wind Chill (-30°F to 60°F). Ground Cover was another domain we set up that dealt with coded values, rather than ranges. Coded values use a list of different codes to correspond to a unique value. The coded values with their corresponding values listed in parentheses are: Grass (Grass), Snow (Snow), Con (Concrete), BT (Blacktop), OW (Open Water), Grav (Gravel), Sand (Sand), and Other (Other). When inputting values, these codes allow for quick and easy recording of the different types of ground cover we encounter. A final field for Notes was added to help explain anything that we need to in the field that is not covered already by the domains and fields created.

Methods

Proper setup of a geodatabase, domains, and a feature class are quite easy and save a lot of issues in the future. 

• Open ArcCatalog through the stand-alone program or through ArcMap.
• Navigate to and select a folder for storage.
• Right click the folder and select New --> File Geodatabase (Figure 1).

Figure 1. This image shows where to find the option to create a new File Geodatabase within the ArcCatalog sidebar in ArcMap.

• Name the geodatabase.
• Right click the geodatabase and select New --> Feature Class.
• Right click the geodatabase and open Properties, found at the bottom of the menu.
• The right tab will be called Domain. Click the tab (Figure 2).

Figure 2. This shows what will open up when the Properties menu of a File Geodatabase is selected. Domains can be created and assigned here.

• Create the desired domains.
• Assign ranges or coded values as necessary (Figure 3 and Figure 4).

Figure 3. This shows the File Geodatabase Properties menu after it has been populated with domains. The selected domain is an example of a range domain, with a minimum value of -30 (degrees Fahrenheit) and a maximum value of 60 (degrees Fahrenheit).

Figure 4. This shows the File Geodatabase Properties menu after it has been populated with domains. The selected domain is an example of a coded value domain. This lists specific codes that correspond to other values. This example is for ground cover types. The list on the bottom of the figure is where the coded values are input and the descriptions are listed. 

• After domains are created, assigned ranges/coded values, and described, close the Properties menu.
• Right click the geodatabase and select New --> Feature Class (Figure 5)

Figure 5. This image shows where to find the option to create a new Feature Class within the ArcCatalog sidebar in ArcMap.

• Give a name and create a point feature class.
• Assign a projection relative to your location.
• Create fields to correspond to the domains created previously (Figure 6).
• Select the proper domain from the bottom of the window where it says "Domain."

Figure 6. This shows the Feature Class Properties' Fields tab after it has been populated with domains.

• Make sure to select the proper Data Type for the proper fields (Figure 7). 

Figure 7. This figure shows the different types of numeric data types available. Short and Long integer are not able to contain decimal places, while Float and Double are (ArcGIS, 2013).

• When recording data, all of these fields will be able to be populated. If the range is exceeded the record will not be input.
• Close out of the Feature Class Properties window.
• Close ArcMap/ArcCatalog.

Conclusion

Proper planning and organizational skills are key to accurate, easy to read, standardized field data collection. In this exercise, we developed a geodatabase with domains and a feature class. The domains assign allow for a range or coded set of values to be input into the corresponding field within the point feature class. This reduces the potential error and standardizes responses for the field. If these steps are not undertaken, the method of data collection and recording has the potential to contain errors and differences that make the data not similar when analyses begin later.

References Cited

ArcGIS Help. (2012, February 10). A quick tour of attribute domains. Retrieved March 1, 2015, from http://resources.arcgis.com/en/help/main/10.1/index.html#//001s00000001000000

ArcGIS Help. (2013, July 30). ArcGIS field data types. Retrieved March 1, 2015, from http://resources.arcgis.com/en/help/main/10.1/index.html#//003n0000001m000000

Zeiler, M., & Murphy, J. (2010). Modeling our world: The ESRI guide to geodatabase concepts (2nd ed., p. 7). Redlands, California: ESRI Press.