- Improved algorithms
Process UAV point cloud data from aerial LiDAR and point clouds generated from oblique photographs.
Subsample point clouds by resolution.
Calculate canopy cover, gap fraction or LAI even when the data has no return information.
Improved filtering algorithm for the following, 1) morphological operations to identify ground points; 2) ground point simulation along the buffer zone participating in the initial DTM construction; 3) and downward densification is performed before upward densification. For more details, refer to Zhao et al (2016), “Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas”.
Individual tree segmentation
Export segmentation results using “Extract by TreeID”;
Adaptive distance threshold; better handling of point clouds from different forest types;
Improved mass data processing.
- Batch processing
Batch processing functions for stat, segmentation and regression; automated workflows for performing multiple functions with multiple files.
- Custom data format
Custom data format *.LiData; accelerates point data access for cloud visualization and processing. Export LiData to common formats (i.e., *.las, *.txt, *.csv).
- Bug fixes in LiForest 2.0
Improved algorithms for extracting point cloud boundaries.
Fix LAS I/O issues.
Fix data merging issues.
Please read our “What’s New in LiForest 2.1″ information sheet for more details. As always, we appreciate all of your feedback and suggestions. Please keep the great ideas coming!Download