## 4 ALS Data Visualization, Analysis and Quality/Accuracy Assessment
### Confidence of Features Visible on ALS Data  
As a general procedure ground validation of ALS data products needs to be performed. How about areas that are so remote that ground validation cannot be performed right away? How confident can a researcher be about the features that are evident on ALS data products? To answer this question we can take a systematic approach by reducing (decimating) the number of classified ground returns of the point cloud to generate elevation rasters simulating the collection of fewer ALS observations; and then processing the decimated observations using different interpolation algorithms to see if the features of interests can still be readily detected and look much the same (size and shape) as in the original dataset. The decimation can be done at random, just eliminating ground returns, or it can also be done systematically by using a model key point algorithm. The model key point algorithm is based on a TIN generated from the original returns, and then individual points and triangular facets are analyzed to determine if the information that they provide is redundant in defining triangular facets of a given size. This allows determining the minimum number of points that are needed to define a TIN surface model with a given resolution. Figure 26 presents results from one such point cloud decimation test based on data from a section of the Honduran Mosquitia dataset that shows the rectangular plaza surrounded by mounds. The first row of images are shaded relief maps generated using different interpolation methods based on the original number of classified ground returns. The images in the column on the extreme left present the distribution of the ground returns that are depicted as red dots over a DEM produced using kriging. There were a total of 47,556 returns for a surface area of 69,836 m\({}^{2}\), which corresponds to an average 0.68 ground returns/m\({}^{2}\). The number of ground points appears to be sufficient to create a 1-m elevation raster. However, the issue with ground points under vegetation cover is that their distribution is not uniform as can be observed in Figure 26.  
Figure 26: Results from a decimation and interpolation test to evaluate the degree of confidence of features in ALS DEMs. For each image row the same dataset is gridded using kriging, inverse distance weighting to a power and triangulation with linear interpolation based on: **(top row)** the original returns classified as ground; **(second row)** identification of the model key points (MKP) that allow to retain integrity of features \(\sim\) 20 m long; **(third row)** 70% of the MKP; **(bottom row)** 50% of the MKP. To further illustrate the distribution of ground points Figure 27 presents two histograms, the first histogram shows the number of ground points located within the raster cells, the second histogram represents the distribution of the area of influence of each ground point. The area of influence of each point is defined using Voronoi diagrams (Thiessen polygons), which create \"exclusive\" cells surrounding each ground return in such a way that any point within the cell is closer to the return it encloses than any other return. These two histograms show that there are certain grid nodes from which there is no return within the grid cell (\(>\)85%), there are other cells that have up to 25 returns (not included in the histogram ), and that the distance between a grid node and the nearest ground returns may be up to 7 m.  
**Figure 27.** Histograms showing the distribution of the original ground points. (**a**) Number of ground returns located within a raster cell; (**b**) Distribution of ground returns based on its area of influence. However, despite this non-uniform distribution within the model, the key point algorithm was applied with a resolution of 20 m. The result was that only 17,116 \"key\" points are needed to define the surface model, which is just 36 percent of the points contained in the original data set.