LiDAR Data Processing:

Transcription

1 LiDAR Data Processing: Concepts and Methods for LEFI Production Gordon W. Frazer GWF LiDAR Analytics

2 Outline of Presentation Data pre-processing Data quality checking and options for repair Data post-processing Summary of key points

3 Data Pre-Processing: What happens to your data before you receive it? GNSS, IMU and raw scanner data are combined to create geocoded xyz point cloud for each flight line. Source: Habib et al., 2008

4 Data Pre-Processing: Flight lines are spatially integrated and adjusted to minimize relative xyz positional error. Point cloud is adjusted to local geodetic datum to minimize absolute xyz positional error (optional).

5 Data Pre-Processing: Filter erroneous xyz points: range errors, range ambiguities (multi-pulse), above-canopy returns. Feature classification: ground, vegetation, buildings, water, etc.

6 Data Pre-Processing: Filtered and classified xyz point cloud is tiled and exported in client-defined format. Basic secondary products are extracted: DEM, CHM, contours.

7 Data Pre-Processing: What does LiDAR data look like when delivered? x y z i r n c a t

8 Data Quality Checking: Are there any issues that might impact LEFI procedures and results? Geometric: calibration and strip adjustment errors. Data voids: between flight line gaps. Classification: ground finding and other feature extraction errors. Filtering: presence of range errors, range ambiguities, sensor noise, atmospheric returns, etc. Other processing artifacts: inadequate buffering, nonuniform parameters settings for classifiers and filters.

9 Data Quality Checking: Error Type Possible Impact On LEFI Geometric Increase spatial uncertainty in xyz. Increase measurement error in area-based LiDAR metrics. Impact point classification in areas of flight line overlap. Data voids Reduce range and size of survey population. No predictions for missing population elements. Filtering Alter frequency distributions of LiDAR heights and intensities. Failure to remove above-canopy returns will alter canopy heights. Removal of above-canopy returns will alter the distribution of first returns. Classification Errors in ground will lead to errors in canopy height and all heightrelated LiDAR metrics. Inadequate tile buffers Boundary errors and artifacts in DEM, CHM and LiDAR metrics.

10 Data Quality Checking: Rapid detection of point cloud errors Digital Elevation Model (DEM): ground-finding errors, inadequate tile buffers, non-uniform parameter settings of classifier, ranging errors (pits). Canopy Height Model (CHM): above-canopy returns, range ambiguities and other sensor noise. Point densities: filtering and classification errors. Intensity distribution: filtering errors.

11 Data Quality Checking: Options for remediation Repair in-house or send back to data provider? Errors that are complex and/or frequently occurring will require support from data provider. Well specified LiDAR procurement contracts supported by third-party quality checking can alleviate many of these issues.

12 Data Post-Processing: Secondary products needed to generate a LEFI Product Digital Elevation Model (DEM) Canopy Height Model (CHM) Gridded LiDAR metrics Plot-level LiDAR metrics Other spatial support (species, age, site index, etc.) Application/Use QA/QC. Terrain variables for stratification, sample selection, and modelling. QA/QC Automated stand delineations. Stratification, sample selection, and spatial extrapolation of prediction models. Automated stand delineations. Model estimation, validation, and prediction. Stratification, sample selection, and spatial extrapolation of prediction models.

13 Data Post-Processing: Retile Point Cloud with Outside Buffer Source: Martin Isenburg, rapidlasso GmbH

14 Data Post-Processing: Digital Elevation Model

15 Data Post-Processing: Height Normalization of Point Cloud

16 Data Post-Processing: Canopy Height Model

17 Data Post-Processing: LiDAR Canopy Height and Density Metrics

18 Data Post-Processing: Gridded LiDAR Canopy Metrics

19 Data Post-Processing: Plot-Level LiDAR Canopy Metrics

20 Data Post-Processing: Create data tables for model building PLOT Y1 Y2 Y3 Y4 X1 X2 X3 X

21 Summary of Key Points: Data pre-processing includes all steps necessary to create and deliver a LiDAR point cloud. Pre-processing errors and artifacts can negatively impact a LEFI and must be corrected. Well-specified contracts and third-party quality checking can reduce the frequency and severity of preprocessing errors. Data post-processing includes the extraction of secondary products to support LEFI production.

22 Summary of Key Points: Secondary data products include gridded LiDAR canopy metrics for model spatialization, and plot-level LiDAR metrics for model development and prediction. Intensity metrics should not be used unless radiometrically corrected, calibrated or normalized.

23 Further Questions?