Getting Acquainted with ArcGIS Pro

For this lab, I was following along with this ArcGIS tutorial and recapping what I learned.

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Introduction to GIS

Geospatial data is data that has a reference to Earth. It must have a defined scale and a projection. Geographic information systems (GIS) differ from a standalone digital maps because a map is simply an image that portrays the earth. Often times, maps do not specify the type of projection portrayed, and sometimes they do not even have a scale. Understanding the importance of geospatial concepts and data is fundamental to working with UAS because in order to produce coherent and accurate data with UAS requires the user to be aware of the projection and scale of the images. Without these, little confidence can be put into the accuracy of the acquired data. This ArcGIS Pro both covered the basics of navigating ArcGIS Pro while also reinforcing some key geospatial concepts and fundamentals. Creating a map with multiple layers illustrated how datasets can be related to one another based on their projections. The workflow involved many datasets with different types of data that illustrated the full picture that GIS helps portray.

Methodology

Section 1: Explore the Study Area

    The tutorial starts out with the basics. I created the project, imported the data, and set the basemap. A basemap is quite literally, “the base of the map” or the background. This gives context for the data that will eventually be overlaid on top. For this scenario, since we will be looking at both roads and forestry, satellite imagery makes the most sense. There are many other options such as street maps and different color maps for maximal contrast with the data. From there, the locate tool can be used to locate the Brazilian state of Rondônia since all of the data will be based around this area. The datasets are then added over the basemap: Brazilian states, an outline of the Amazon ecoregion, and cities in the state of Rondônia. The symbology tab of the catalog pane is used to edit the visibility and appearance of each dataset. For example, the color and thickness of the roads and cities have been edited for improved visibility. All of these changes were then added to a customized Geodatabase for easy storage and reference.
    The next part took a deeper look at the projected road data. First, the Roads layer is added to the map. From there, the “Official roads” are selected via a logical expression built with the “Select Layer By Attribute” tool in the “Geoprocessing” pane. This creates a SQL expression which is then run under the hood to differentiate the official roads from the unofficial. The result of the search creates its own feature class which can then be colored to stand out. The result is shown in Figure 2.1.

Figure 2.1: Official Roads (shown in white) have been 

differentiated from unofficial roads (shown in purple).

Section 2: Compare Roads and Deforestation

    In order to analyze the impact the roads have on deforestation, another layer is added from the Rondônia database to illustrate the deforestation. To see the basemap through this layer of vector data, the opacity is edited under the “Effects” group of the “Appearance” tab. By turning the Roads layer’s visibility on and off, it is clear that the deforestation appears most frequently where the roads are built. In order to visualize the protected forests in the area, another layer is added and colored green to represent the protected forests of Rondônia which have very little overlap of deforestation (Figure 2.2).

Figure 2.2: Deforestation in Rondônia (shown in white)

versus protected forest (shown in green).

 Figure 2.3: Deforestation highlighted near existing roads

(shown in blue).

Figure 2.4: Percentage of deforested area near roads calculated

(shown highlighted).

Section 3: Predict the Impact of the Proposed Road

The proposed road raster dataset was located in the Rondonia geodatabase. In order to make vector data from the portrayed raster data, a “Feature” class is created using the same South America Albers Equal Area Conic system as the other data in this GIS. Once the feature class has been created, it was necessary to digitize the road via the “Snapping” tool and “Trace”. The symbology of this feature was then modified and the fields Name and Status were added to the “Attribute Table”. 
To find a total area of potential deforestation around the proposed road, the Planned Road layer had to be buffered to the same 5.5-kilometer distance and multiply the buffer area by the percentage of deforestation observed around existing roads. In this part, however, previously deforested areas were erased via the “Erase” tool so they were not included in the total. The result of this buffering is illustrated in Figure 2.5.

Figure 2.5: Proposed Road with buffer and predicted

deforestation (shown in Purple).

Section 4: Finish and Print the Map

    In order to present the data clearly and concisely on one map, the proposed road was edited to stand out and the basemap was muted, among other minor tweaks to the visuals. Once the visuals were pleasing, an inset map is created to give global context to the location of Rondônia. Figure 2.6 illustrates the final printed map.

Figure 2.6: Final result map.

Conclusion

Based off of my experience using ArcGIS Pro in this tutorial, it is a powerful tool for visualizing GIS. As with many complex editing softwares, the learning curve is steep but well worth the effort. The final result of the 4 hours of work I put into the tutorial was concise, clean, and easy to read. I could see ArcGIS Pro being a powerful tool for the UAS and even broader aviation world. There are certain applications that would benefit greatly from using it, given the right dataset and user. On the other hand, it could make some simple problems too complex. For example, if accuracy is not that important or if the sample area is too small, this software may take more time than is necessary. In the scenario with Rondônia, UAS could assist in making more accurate predictions of deforestation. When zooming in on the deforestation layer that was generated with satellite imagery, it did not look very accurate. UAS could produce clearer images in a shorter time frame.