Explore

Volumetrics 2

Introduction

In this lab, I conducted raster data analysis using UAS-derived datasets collected over multiple dates during an active mine dredging operation. Building on prior experience with ESRI tutorials, this project focused on applying geospatial tools in ArcGIS Pro without step-by-step guidance. The goal was to analyze terrain changes, identify potential hazards such as flooding and slope failure, and evaluate how raster processing techniques influence data quality and interpretation.

Importance to the UAS Industry

Raster data analysis is a critical component of UAS applications, especially in industries like mining, construction, and environmental management. UAS platforms provide high-resolution datasets such as Digital Surface Models and orthomosaics, which allow for monitoring of dynamic environments over time.

Steps and Process

1. Data Preparation and Project Setup

Transferred the Litchfield dredging dataset into my temp folder.

Created a new ArcGIS Pro project and established a folder connection to the geodatabase.

Loaded multiple DSM and orthomosaic datasets from different dates to observe changes.

2. Initial Data Exploration

Reviewed raster properties including projection, cell size, and elevation ranges.

Compared datasets across dates using visualization tools to understand how the mining operation evolved.

3. Raster Clipping (Extract by Mask)

Used a predefined polygon to isolate the area of interest.

Applied the Extract by Mask tool to remove unnecessary data and focus analysis on the dredge pile.

4. Resampling

Reduced raster resolution to a 50 cm cell size using bilinear interpolation.

Standardized datasets for consistent analysis and improved processing efficiency.

Evaluated tradeoffs between resolution and computational performance.

5. Data Cleaning (Fill Tool)

Applied the Fill tool to remove surface noise and irregularities in the DSMs.

Improved data quality for more accurate terrain analysis, particularly for slope and aspect calculations.

6. Terrain Analysis

Generated hillshades to visualize terrain features.

Conducted aspect analysis to identify flat areas and potential water collection zones.

Used map algebra to locate areas within specific elevation ranges.

7. Slope and Hazard Assessment

Identified slopes exceeding 30 degrees, particularly south-facing slopes prone to failure.

Evaluated safety risks associated with steep and unstable terrain.

8. Elevation and Change Detection

Identified areas above 245 meters elevation to assess pile growth.

Performed raster differencing between dates to analyze changes over time.

Recognized limitations in this process due to inconsistencies in datasets.

9. Visualization and Mapping

Created map layouts for each analysis with proper cartographic elements.

Combined hillshades, DSMs, and classified outputs for clear interpretation.

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Conclusion and Lessons Learned

This lab reinforced the importance of preprocessing and data quality in raster analysis. One major takeaway is that raw UAS data often contains noise that must be corrected before meaningful analysis can occur. Tools like resampling and fill play a critical role in improving usability, but they also introduce tradeoffs between accuracy and efficiency.

I also learned that consistent datasets are essential for change detection. Differences in resolution, alignment, or surface conditions can lead to misleading results when comparing rasters over time. Proper standardization and possibly more advanced processing techniques are necessary for reliable analysis.

Additionally, this project highlighted how UAS data can be used to identify real-world hazards such as flooding. These insights are directly applicable to industry operations, where safety and efficiency depend on accurate geospatial analysis. Overall, this lab strengthened my ability to independently apply raster analysis tools and interpret complex geospatial data in a practical context.

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