lecture 10 pt 2

Digital Terrain Modeling

Digital Terrain Modeling (DTM), also known as elevation modeling, involves creating digital representations of terrain surfaces. The process typically includes data acquisition, processing, analysis, and visualization, with feedback loops to refine the model.

Data Sources for Terrain Analysis

Several data sources can be used for terrain analysis:

  • Elevation Data:

    • Field Survey: Direct measurements taken on-site.

    • Photogrammetry: Using cameras to capture angles and derive measurements.

    • Remote Sensing (LiDAR): Commonly used method involving laser scanning from aircraft or drones to measure distances to the ground.

    • Topographic Maps: Traditional contour maps that provide elevation data at specific locations.

LiDAR Data

LiDAR (Light Detection and Ranging) is an active remote sensing technique used to measure distances with high spatial and vertical resolution accuracy (around 15 cm).

  • How LiDAR Works: LiDAR involves emitting laser pulses and measuring the time it takes for the pulses to return, thus determining the distance to the target.

  • Active vs. Passive Remote Sensing: LiDAR is an active form of remote sensing because it emits its own energy source (laser).

  • Multiple Returns: LiDAR can record up to five returns, capturing different features:

    • First Return: Represents the top of the landscape features such as treetops or building tops (Digital Surface Model - DSM).

    • Last Return: Represents the bare ground (Digital Terrain Model - DTM or DEM).

Digital Surface Model (DSM) vs. Digital Terrain Model (DTM/DEM)

  • Digital Surface Model (DSM): Represents the Earth's surface, including all objects on it (buildings, trees, etc.).

  • Digital Terrain Model (DTM) / Digital Elevation Model (DEM): Represents the bare Earth surface without any objects.

  • Normalized Digital Surface Model (nDSM): Calculated by subtracting the DTM from the DSM (nDSM=DSM−DTMnDSM = DSM - DTM). The nDSM provides the heights of objects on the surface, such as building heights or tree canopy heights.

Digital Elevation Model (DEM)

A DEM is a raster data model that represents the topographic surface using elevation values. Key aspects of DEMs include:

  • Data Model: Raster-based, with elevation as the primary value.

  • Cell Size: Common cell sizes include 30 meters, often provided by the USGS (U.S. Geological Survey) as a global model.

  • Vertical Accuracy: Can be around 1 meter.

  • Data Sources: Geoscience Australia manages DEM data for Australia.

  • Resolution Trade-off: Higher resolution DEMs require more computational power.

Triangulated Irregular Network (TIN)

A TIN is a vector data model that represents terrain using a network of non-overlapping triangles. Key aspects of TINs include:

  • Data Model: Vector-based, using triangles to connect data points with elevation values.

  • Structure: Based on irregularly distributed elevation points connected by triangles.

  • Knots: Each vertex of the triangle has an elevation value and x, y coordinates.

  • 3D Visualization: TINs are suitable for 3D draping and visualization.

  • Data Input Flexibility: TIN models are able to incorporate new data easily without redoing the entire process.

Advantages and Disadvantages of TIN vs. DEM

  • TIN:

    • Advantage: Flexible with input data sources (contour lines, GPS data, LiDAR data). Easy to add new data.

    • Disadvantage: More complex data format.

  • DEM:

    • Advantage: Simple raster data format, good for analysis.

    • Disadvantage: Fixed cell size, less flexible for updates; requires redoing the entire process for revisions.

Both TIN and DEM can be converted to each other and there are ready to use tools in ArcGIS Pro.

Terrain Variables

Terrain variables are derived from elevation models to provide information about the landscape. Key terrain variables include:

  • Height/Elevation: The absolute elevation value, important for temperature, vegetation, and visibility analyses.

  • Slope: The rate of change in elevation (rise over run), indicating the steepness of the terrain.

  • Aspect: The direction a slope faces, influencing temperature, sunlight exposure, and suitability for various land uses (e.g., residential development, ski resorts).

  • Upslope Area: The watershed area above a point, relevant for hydrological analysis.

  • Flow Length: The longest upstream flow path to a point, used in hydrological modeling.

  • Upslope Length: The total upstream flow path length from a point.

  • Visibility: Whether one point is visible from another, considering terrain obstructions.

Slope Calculation

  • Definition: Slope is the change in elevation (rise) with respect to the horizontal distance (run).

  • Units: Measured in degrees or as a percentage (rise/run).

  • Relationship: The relationship between angle and percentage is given by: riserun=tan(θ)\frac{rise}{run} = tan(\theta)

Slope Calculation in GIS

GIS software approximates slope using neighboring cells in a DEM. ArcGIS uses an algorithm that considers eight neighboring cells, weighting immediate cells more heavily.

  • East-West Gradient (B): Represents the change in elevation in the east-west direction.

  • North-South Gradient (C): Represents the change in elevation in the north-south direction.

  • Slope Calculation Formula: The slope is approximated using the formula: B2+C2\sqrt{B^2 + C^2}

  • Weighted Average: This method calculates a weighted average of the elevation changes, giving more weight to closer cells.

  • Gradient Calculation: The east-west gradient is calculated as the difference in elevation between the east and west cells, divided by eight times the cell size. Similarly, the north-south gradient is calculated.

It is important to consider the limitations of the GIS environment because GIS do slope calculation as approximations.

Handling No Data Cells

When no data cells are located in the neighborhood, the Z value of the central cell is assigned to the no data cell. This can lead to a flattening of the surface around the edge cells.

Applications of Slope Analysis

Slope analysis can be applied to various types of continuous data, in addition to elevation models. For example, it can be used to analyze species abundance or population density.

  • A high slope in species abundance indicates a sharp drop in the number of species in the landscape.

This section provided an overview of how slope is calculated and used in GIS, emphasizing the importance of understanding the approximation methods and potential limitations.