Interpolation - applications Interpolating a temperature surface Problem SOLUTION

Interpolation - applications Interpolating a rainfall surface Problem SOLUTION

Interpolation - applications Interpolating a landscape surface Problem SOLUTION

Interpolation - applications Interpolating a forest canopy surface Problem SOLUTION

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.”

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

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

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

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

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.

Voronoi / Thiessen Polygons

Voronoi / Thiessen Polygons

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

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

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

NATURAL NEIGHBOR SPLINE KRIGING IDW TOPO to RASTER

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.

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)!

LiDAR Light detection and ranging

LiDAR Light detection and ranging

LiDAR – Terrestrial 3D laser scanning

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

SfM – Structure from Motion PhotoScan

SfM – Structure from Motion