## 4 ALS Data Visualization, Analysis and Quality/Accuracy Assessment
### Quality and Accuracy Assessment
When multiple flight lines are combined into tiles the overall precision might be decreased, however there are algorithms and techniques that attempt to improve the \"match\" between points from different flight lines. The accuracy of the point cloud is usually assessed by comparing ALS elevation against elevation values obtained with kinematic GNSS measurements made over a flat uniform unobstructed surface (airport runway, road, sports field, _etc._). Of course this is a measure of only the vertical accuracy and it is done under ideal conditions, the vertical accuracy of ALS return of sloped, irregular surfaces under vegetation cover is generally significantly poorer [70]. Assessing the horizontal accuracy of the point cloud data is more complicated and it requires, for example the use of special ground targets [71], or obtaining reference 3-dimensional data of sloped surfaces and computing normal vectors to those surfaces and doing the same with the ALS data, by comparing the normal vectors it is then possible to compute the horizontal accuracies. The overall accuracy of an elevation raster will depend on first instance on the accuracy of the underlying ALS returns, on the accuracy of the classifying algorithm, the interpolation method used to produce the raster and the relationship between return density and raster resolution (spacing). In areaswithout vegetation cover, if the return density is high enough and there are multiple returns per raster cell then the elevation value for the cell will be an averaged and/or smoothed value based on the multiple returns in and around the cell, thus increasing the vertical accuracy of the elevation raster. On the other hand if the distribution of the returns is sparse with respect to the raster cell size then the vertical accuracy of the model might be lower than the accuracy of the returns that it is based upon. In terms of horizontal accuracy, even when the horizontal accuracy of the point cloud might be on the order of 30 cm, the horizontal accuracy of a raster cannot be better than half the raster resolution. So if the raster has a resolution of 1 meter the best estimate of the horizontal position of a feature is 50 cm. Another concept that can be confused with precision and accuracy is fidelity. In terms of ALS data, fidelity can be defined as the degree of exactness with which a feature is represented by the point cloud or elevation raster. Let us for example assume that we are digitizing a rectangular platform structure from a DEM. How well will the length, width and height of the mound derived from the ALS data match the dimension as taken with a tape measure or total station in the field? The answer to this question is related to the fidelity of the data, which in the case of the DEM will depend on the precision and the resolution of the DEM, but perhaps more importantly it will be related to how well the surface was sampled. For instance if the surface illumination was not high enough to ensure that laser shots intercept the exact corners of the platform, then these corners will probably not be represented in the DEM and hence the fidelity of the DEM will not be good enough to ensure a good match between the ALS derived measurements and the ground measurements. When comparing GNSS and total station data to ALS data it is important to keep in mind the precision and accuracies of each of the data products. Total station measurements are extremely precise; however their absolute accuracy will depend on the accuracy of the geodetic control points used as a reference and the rigor of the field procedures. GNSS receivers are also very precise but their performance varies greatly depending on their accuracy grade (geodetic \(\sim\)mm, survey \(\sim\)cm, mapping \(\sim\)dm [72]), and the procedures use to collect and reduce the GNSS observations. It also depends on the configuration of the satellite constellation at the time of the measurements, and the strength and quality of the signals.