In this lab, we're diving into raster data analysis using data collected from a mine dredging operation. We've already covered some ESRI tutorials on this topic, so now it's time to put that knowledge to use. First off, we need to get our hands on the data. We'll be working with the Litchfield folder, which contains a geodatabase with all the datasets we need. Since this dataset is pretty hefty, it'll take some time to transfer over.

Once we've got the data, it's time to start working on it. We'll create a project folder and connect it to the database within the Litchfield folder. Next, we'll familiarize ourselves with some key operations we'll be using, like Extract by Mask, Resample, and Fill. These tools will help us manipulate and analyze the data effectively.

Now, onto preparing our data for analysis. We're dealing with real-world data from a mine dredging project, so there's a lot to consider. We'll start with the dataset from July 4th, establishing a base dataset to track material added during dredging. We'll clip out the area we're interested in and standardize the cell size to make analysis easier.

After getting familiar with the datasets, we'll perform some specific analyses. For example, we'll conduct aspect analysis on August 27th to assess flooding extent, use map algebra to identify flat areas, and locate slopes exceeding 30 degrees to prevent slope failure. We'll also compare datasets from different dates to see how the pile has changed over time.

Throughout the lab, we'll be creating maps to visualize our analysis results. Each analysis should have a map layout with proper cartographic elements, including hillshades and locator maps. By the end of the lab, we'll have gained valuable experience in raster data analysis and its application in real-world scenarios like mine dredging operations.