The concept of topographic quality levels (QL) was originally defined in the National Enhanced Elevation Assessment report by Dewberry and further defined in the 3DEP Lidar Base Specification (LBS).
|QUALITY LEVEL||DATA SOURCE||VERTICAL ACCURACY RMSEz (cm)||NOMINAL PULSE SPACING (NPS) meters||NOMINAL PULSE SPACING (NPD) points per square meter||DIGITAL ELEVATION MODEL (DEM) cell size (meters)|
|QL0||Lidar||5 cm||<= 0.35 m||>= 8 pts/square meter||0.5 m|
|QL1||Lidar||10 cm||<= 0.35 m||>= 8 pts/square meter||0.5 m|
|QL2||Lidar||10 cm||<= 0.71 m||>= 2 pts/square meter||1 m|
|QL3||Lidar||20 cm||<= 1.41 m||>= 0.5 pts/square meter||2m|
|QL4||Imagery||139 cm||N/A||N/A||5 m|
|QL5||IfSAR||185 cm||N/A||N/A||5 m|
Lidar QLs are defined by two components:
- the nominal pulse spacing/density requirement
- the vertical positional accuracy requirement
What is QL0?
As shown in Table 1, the aggregate nominal pulse spacing (m) and pulse density (pls/m2) are the same for QL1 and QL0 lidar. However, the required absolute non-vegetated vertical accuracy (NVA) and absolute vegetated vertical accuracy (VVA) of QL0 data is two times better than that of QL1 data.
In addition, ASPRS Positional Accuracy Standards for Digital Geospatial Data (ASPRS, 2014) require that the check point survey used to verify vertical accuracy must be three times more accurate than the expected airborne lidar NVA. Table 2 shows the check point survey requirements for QL0, QL1, and QL2:
To achieve this level of accuracy, QL0 check point surveys may require static and redundant GPS surveys rather than the real-time kinematic (RTK) GPS survey techniques commonly used for QL1 or QL2 surveys. These survey requirements could substantially increase the cost of QL0 acquisition.
3DEP can work with partners who wish to acquire high density point clouds that meet or exceed the QL0 nominal pulse density requirements, but only require the QL1 accuracy requirements. These higher density point clouds would provide better definition of features and the ground, and may provide improved feature extraction capabilities. However, because the data will not meet QL0 vertical positional accuracy requirements, the project will be classified as QL1 regardless of the higher point density.