1. Interpolation - applications
Interpolating a temperature surface
Problem
SOLUTION

2. Interpolation - applications
Interpolating a rainfall surface
Problem
SOLUTION

3. Interpolation - applications
Interpolating a landscape surface
Problem
SOLUTION

4. Interpolation - applications
Interpolating a forest canopy surface
Problem
SOLUTION

5. Interpolation – as defined by ESRI
Definition: “Interpolation predicts values for cells in a raster from a limited number of sample data points. It can be used to predict unknown values for any geographic point data, such as elevation, rainfall, chemical concentrations, and noise levels.”

6. MA: Michael Madsen (2017) On Landsurface Change Detection Using Lidar Datasets of Different Spatial Resolutions: A Case Study Examining Colorado's Waldo Canyon Fire Burn Scar
ESPL: Fire then Flood – Practical Procedures and Warnings when Detecting Topographic Change between Airborne LiDAR Datasets Collected at Different Spatial Resolutions Michael Madsen, Brandon Vogt, Diep Dao, and Steve Jennings

7. Interpolation BEFORE interpolating – remember GIGO: make a histogram
sort data
remove or correct outliers

8. Interpolation Some common interpolation techniques: TINs
Voronoi / Thiessen polygons
non-linear interpolation
trend surface analysis
kriging

9. TIN
Non-overlapping triangles
with x,y,z coordinates
TINs

10. Voronoi / Thiessen Polygons
Pronounced “vo-ro-noy”
Any point within a Voronoi polygon is closer to the polygon’s known point than any other known points.

11. Voronoi / Thiessen Polygons

12. Voronoi / Thiessen Polygons

13. What about unpredictable, complex, wavy, or hilly surfaces?

14. Interpolation trend surface analysis
A curve fitting process fits equations of typical curves to the curves found in the known data.
Considers distance of points from curve
Commonly used with precipitation data
New surface usually does not pass directly through known data points

15. Interpolation kriging
- Unlike other methods, kriging involves an interactive investigation of the spatial behavior of the data
- Can consider “like” areas on far away parts of surface
Can consider randomness in a surface
Not suited for data with known spikes or abrupt changes
Processor intensive
- Weights assigned based on spatial autocorrelation of sample points

16. NATURAL NEIGHBOR
SPLINE
KRIGING
IDW
TOPO to RASTER

18. Which technique to use? Each technique gives different results
None is more accurate than the others for all situations
ESRI: The many interpolation methods each make different assumptions of the data, and certain methods are more applicable for specific data; for example, one method may account for local variation better than another. Each method produces predictions using different calculations.

19. How do you test the accuracy of your new surface?
Two ways
Remove a sample
Create new surface (interpolate)
Check the sample against the surface (Did the technique predict the missing sample?)
Put the sample back, repeat with another sample
Try different techniques
and / or…
Michael used GPS to validate estimated points (determined which worked better)!

20. LiDAR
Light detection and ranging

21. LiDAR
Light detection and ranging

22. LiDAR – Terrestrial 3D laser scanning

23. LiDAR – Terrestrial 3D laser scanning
Hardware
Scanner head
Targets
Software
Cyclone

24. SfM – Structure from Motion
PhotoScan

25. SfM – Structure from Motion