Lab 9 Using Arc Collector

Introduction-

Lab 9 was a centered around data collection with a mobile device. The ArcGIS Collector app, for iPhone, was used to collect this data, and ArcGIS Online was used to create the map. ArcGIS Collector is used to collect mobile data without a UAS. The data collected is on a predesigned map from ArcGIS Online, with the specific layers and points desired to be mapped. ArcGIS Collector relates to UAS by the collection of data for mapping. Although UAS data collection is more dynamic and accurate, the mobile app still allows you to map an area and its features.

Methods-

To start the lab, once the app was installed, a tutorial on ArcGIS's Collector details the basics the app can deliver. The tutorial has the reader try the free version of the mobile app, and using the Parks template provided. The template already has the layers and feature designations preset, so the tutorial requires no preparation. The task of this tutorial is to go to a park and find a picnic table to map the tables location, then take a picture of it. Once the picnic table's position is defined, the next and final step is to stream map the closest path to the picnic table. I preformed the tutorial in the park in Bloomfield, IN. Figure 1 represent the picnic table location, figure 2 and 3 represent the streaming and finalized path to the picnic table.

Figure 1: Picnic table location

Figure 2: Streaming picnic table path

Figure 3: Mapped path

 After the finishing the tutorial, the lab continues diving into the app by having the user signing in and completing another tutorial. When signed into the Collector app there are no maps provided, so the user has to create one on ArcGIS Online. When signed into the website, the tutorial has the user prepare the map by building and defining layers for the soon to be created map. The defining features of the map include: picnic tables, restrooms, and water fountains. Once all of the details are finished, the map has to be created and shared with your account for mobile data collection. Then the tutorial has the reader go back to the park and map the picnic tables, restrooms, and water fountains. I went back to the Bloomfield park, and finished mapping it. The following figures represent the finalized mapping of the park, and some of the features for which the area was surveyed.

Figure 4: Bloomfield Park map

Figure 5: Water fountain example

Figure 6: Bathroom example

Figure 7: Picnic table example

Discussion:

This lab showed me that there are more ways of quickly surveying and mapping an area's surroundings. Although the app does not collect as much data as UAS, the app can be used to map features in the mission planning process. The data I collected was limited, due to the park being small, but having data like this for mission planning will allow operators to have a map of the surveying area to avoid. This could be because of large trees, densely populated areas and telephone poles that could be a physical risks or EMI risks. The URL below provides a link to the map I created.
https://purdueuniversity.maps.arcgis.com/home/item.html?id=efdbe26314e54a47b5902eb473c8c29c#overview

Lab 8: Calculate Impervious Surfaces from Spectral Imagery

Introduction-

This weeks lab consisted of following an online lesson, Calculate Impervious Surfaces from Spectral Imagery, from Learn ArcGIS Online Lesson Gallery. The lesson consists of three sections: Segment the imagery, Classify the imagery and Calculate impervious surface area, but the first two sections were the only assigned portions of the lesson. The goal of the lesson is to understand the process of how to take UAS data and be able to identify and calculate the surface area of impervious areas.

Segment the imagery-

Before we can classify the impervious and pervious areas, once the data is downloaded, we have to change the bands combination to allow the features to clearly show. This step was done with the Extract bands tool in the raster function pane. Extract bands tool makes a new image that uses different colors to display the difference between the pervious and impervious areas in the map, shown in figure 1.

Figure 1: Extracted bands

Next the Classification Wizard was used for image segmentation and classification. Segmentation of the image was to group some of the same looking pixels together to generalize the image, making it easier to classify. There are three parameters of the Classification Wizard to control the segmentation of the map: spectral detail, minimum segment size in pixels and show segment lines only. Changing these three from default values to the given values, defines the important parts to easier distinguish between pervious and impervious areas. The final segmented map should look like figure 2.

Figure 2: Segmented classification

Classify the imagery

After getting segmenting for easier classification, the second section begins by creating training samples. Training samples are polygons created in general areas of either pervious and impervious areas to distinguish the difference, and categorize what each color represents. So two training samples were created to classify pervious versus impervious areas, and given their own distinct color. Then subclasses were made within each training sample to classify the what the exact area represents, like for impervious there are gray roofs, driveways and roads, whereas pervious has bare earth, grass, water and shadows. These samples will later be used to distinguish what area each pixel represents. 

The next step finally classifies the image, representing pervious versus impervious. After clicking run the map should change colors representing the areas classified as pervious or impervious like figure 3.

Figure 3: Classified areas

After running the classification, it is important to reclassify small errors within the map. So for the final page of the wizard the Reclassification page allows you to find these mistakes and correct them. My run did not experience any errors in the preview. Then finally the final classification is to be ran for the final map. After selecting finish the final map should look like figure 4, with green representing pervious areas and grey areas.

Figure 4: Pervious versus Impervious areas

Lab 7: Volumetrics

Introduction:

In this two part lab assignment, the first job was to create a DSM in Pix 4D and find the volume values within an isolated area. In part two, the job was to make an elevation progression map between three sets of raster data of an isolated hill in Litchfield, WI.

Part 1:

In part one of this project ArcGIS Pro was used to isolate a portion of the Wolf Creek data set, and determine the volumetrics (Figure 1-2). The Volume tool was used to outline the desired survey area. Within this area we can determine the terrain's 3D volume, cut volume, fill volume, and total volume (Figure 3). After the volume values are found, ArcGIS Pro was used to map the survey area along with calculating the area's elevations. The tools used to accomplish this part's goal were: Cookie Cutter to extract the desired area to calculate the raster data set, clip to create the polygon, Extract by Mask was used to extract the polygon to separate this from the original raster data set, and Surface Volume for to determine the area's volume and area (Figure 4-5). Then of course the area has to mapped to display the survey area, along with a comparison between the survey area and the original raster set.

Figure 1: Pix4D custom average output

Figure 2: Pix4D volume area

Figure 3: Pix4D default volume

Figure 4: Arc Pro volume part 1

Figure 5: Arc Pro average elevation

Figure 6

Figure 7

Part 2:

Part two's focus was on the new Litchfield data set. The data included three different raster data sets representing three consecutive months of the area. After inputting the data, they all needed to be resampled to get the same pixel size. for all three. The Resampling tool changes the spatial resolution of the raster data. This was used to increase the accuracy of the comparison by changing the three raster datas to 10cm pixel size. Surface volume was then found for each raster data set, Figure 6 represents the comparison over time. Cut Fill was then used to visually compare the surface volume of the three raster sets, represented in Figure 7. Mapping of the area's three rasters was created in a side by side represented in Figure 8.

	July	August	September
Coordinate System	WGS 1984 UTM Zone 15N	WGS 1984 UTM Zone 15N	WGS 1984 UTM Zone 15N
Pixel Size	10cm	10cm	10cm
Minimum/Maximum Height	226.286m 254.15m	218.943m 271.194m	221.116m 267.257m

Figure 6

Figure 7

Figure 8