Lidar Processing for Forestry Applications
LiForest provides a platform that enables users to freely manipulate the LiDAR point clouds and create useful spatial products. LiForest’s visualizer makes it easy to view data and consists of several modules for LiDAR point cloud processing that include LiDAR-based forest metric calculation, digital model generation, regression models and individual tree segmentation.
Create the most useful lidar products including digital elevation models (DEMs), digital surface models (DSMs) and canopy height models (CHMs). Create regression models using training data (ground truth data) for forest parameter estimation using 4 available regression models.
Includes tools for converting formats and data management for filtering, normalizing, extracting and organizing LiDAR datasets.
Statistical Modules for creating statistical metrics including elevation and intensity metrics, as well as canopy cover, leaf area index (LAI) and gap fraction metrics.
Isolate individual trees from lidar using CHM-based tree segmentation algorithms or a direct point cloud segmentation to extract individual tree location, crown size, and tree height.
Visualize high density point clouds using different display modes; by elevation, intensity, classification, RGB, return, Tree ID and more.
Optimized Data Format
Accelerate data visualization and processing using the LiData format that can be exported to common data types. Supported file formats include:
Import file formats: LiData, las, laz, txt, asc, neu, xyz, pts, csv, tif, jpg, shp, LiModel, csv.
Export file formats: las, laz, txt, asc, neu, xyz, pts, csv.
Automatic point cloud classification
Integrated algorithms for point cloud filtering; classify by attribution, low points, below surface, isolate points, air points, height above ground and minimum elevation.
Forest inventory & Batch processing wizard
Forestry applications; generate forest parameters, including surface models (i.e.,DEM, DSM and CHM), lidar quantile matrix, canopy cover, LAI and Gap Fraction. Derived parameters are useful for forest resources investigation and management.
Variety of statistical parameters related to point cloud and forest metrics, including elevation and intensity metrics, canopy cover, LAI and gap fraction.
Ground point classification, classify by attribution, low points, below surface, isolate points, air points, height above ground and min elevation.
Generate surface models including Digital Elevation Models (DEMs), Digital Surface Models (DSMs) and Canopy Height Models (CHMs).
CHM-based tree segmentation and point cloud-based segmentation to extract tree location, tree height and crown size. The latter allows for individual trees visualization as point clouds (ALS data, UAV data and TLS data). Export segmentation results using “Extract by TreeID”.
Forest metric estimations including: above Ground Biomass (AGB), tree height and by using standardized regression models (i.e., linear regression, support vector machine and fast artificial neural networks).