## 4 ALS Data Visualization, Analysis and Quality/Accuracy Assessment
### Quality and Accuracy Assessment  
Probably one of the most common questions is: How good and how accurate are ALS observations? One of the most common verification techniques is to overlay field survey data collected using GNSS and/or total stations on the ALS derived DEM as shown in Figure 25. Immediately it will be evident that certain features match up fairly well while others do not. To understand the geolocation quality of ALS data it is important to understand the terminology used. The two key concepts are precision and accuracy. Precision is defined as the repeatability of a set of measurements (how well measurements match to each other) also called uncertainty; accuracy is defined as how close the measurements are to the true or reference values. For instance if we used the profiles in Figure 16, the laser shots from the different flight lines on the left profile are precise as they match well with each other. The data used for the right profiles are not as they do not match well with each other. Data might be precise but not necessarily accurate, let us go  
Figure 25: Overlay of field survey data over an ALS data derived DEM for the LWD site close to San Ignacio in central Belize. (**a**) Shaded relief image of DSM; (**b**) field data overlaid on shaded relief image of DEM. back to the data used to generate the left profile. It is clear that the data agree well with each other, but what if the data elevations were biased by a meter with respect to the real elevation, or what if the horizontal coordinates are slightly offset to the north or any other direction? In this case the data are precise but not accurate. Accuracy refers to how well the ALS data spatially matches a specific reference. In order to assess the accuracy of a dataset it is necessary to have a reference dataset or standard that was collected with equipment or procedures that are considered superior. For instance geodetic grade GNSS procedures and equipment are considered superior to mapping grade GNSS procedures and equipment, and thus geodetic grade data can be used as an accuracy reference for mapping grade data. The only thing that can be said regarding the accuracy and precision of ALS data with a high degree of certainty is that it can vary by a wide range despite the existence of industry standards on how to assess, compute and report it. The accuracy and precision will be different for each data product and it is affected by every single aspect in the ALS data processing workflow from data collection to the interpolation method that was used to generate the elevation rasters. It will also be different from survey to survey even when the observations may have been collected with the same equipment by the same crew. If the sensor is well calibrated the precision of a single flight line point cloud is generally very high, usually less than 2 cm. The accuracy of the single strip point cloud will depend mainly on the accuracy of the derived aircraft trajectory, which depends on the length of the baseline between the aircraft and the GNSS reference station, the geometry of the GNSS constellation, the quality of the GNSS observations and finally the quality of the processing algorithm [47]. Other factors that affect the accuracy of the point cloud are the random errors in the IMU measurements, the aircraft flying height above ground (higher altitude cause higher horizontal uncertainty), uncertainty in the determination of the scan angle and the size of the laser beam footprint. Reasonable values for the accuracy of single line point cloud data are about 1 decimeter in the vertical and between 2 and 3 decimeters in the horizontal [24]. Also as a basic rule the horizontal precision is usually lower than the vertical precision and the horizontal uncertainty is usually higher for larger beam divergences, higher angle of incidence [47, 68, 69].