Overview

WSG Inventory Management System supports lidar-based inventories in a number of ways. MobileMap supports lidar cruises to help augment or validate lidar-derived inventory data and can display lidar-derived tree and raster data (e.g., Canopy Height Models) as base maps. InventoryManager can be used to load and update lidar-derived inventory data, can display lidar-derived tree and raster data and can compile lidar-derived tree data based on geographic location.

MobileMap

Data model for lidar inventory

The main difference between a lidar inventory data model and a standard inventory model is the inclusion of Point geometry for lidar trees. During the modeling process MobileMap: Data Model / Data Modeling Process the Trees layer is set to Point geometry (instead of the default of Table). If the data model will store lidar-derived trees, attributes should be added to the data model to store the incoming data from the lidar dataset provider. If the lidar data model will be used for field data collection (e.g., collection of lidar training) then detailed structure attributes may need to be added (e.g., crown width, height to base of live crown, etc.) to aid lidar modelling.

Cruise types

Lidar Analysis Training Data

When collecting field data for lidar analysis (e.g., training data for machine learning analysis of lidar data) it is critical that the field data can be linked to the lidar data. This requires using relatively large fixed area plots rather than traditional variable radius plots. It is important to map all trees in the field which can be done by recording the distance and azimuth to a precise plot center.

MobileMap supports integration with laser rangefinders, such as the Hagloff Vertex Geo to streamline the process of recording tree height, distance and azimuth. MobileMap: Laser Rangefinder Integration.

When collecting lidar training data it can be helpful to have lidar-derived rasters (e.g., CHM) to ensure spatial alignment of plot center and reference trees, but this is not absolutely necessary.

Augmentation

Lidar augmentation cruises are designed to improve lidar-derived inventories by collecting attributes that can pose challenges for lidar modelling, such as species, dbh, defect, tree health.

In this scenario, lidar trees are assigned to randomly located sample plots and downloaded into MobileMap. the plots are taken into the field and each tree is updated to add missing data (e.g., product, grade, defect) and correct errors (e.g., species, DBH). Additionally, errors of commission and omission are addressed by adding trees that were missed by the lidar model or removing trees that were added (e.g., when a forked top appears as two trees in the lidar tree data).

Lidar augmentation cruises can use standard or check cruise mode. Standard mode is more common, where cruisers update values directly on the original lidar tree records. As an alternative, check cruise mode can be used and combined with check cruise scoring rules to calculate an objective and quantitative score of lidar tree accuracy. MobileMap: Check Cruising InventoryManager: Check Cruise Rules Inventory Management System: Check Cruising

Validation

Lidar validation cruises are intended to assess the quality of internal or vendor-provided lidar inventories. They differ from augmentation cruises in that they do not seek to add or improve the lidar inventory, but merely assess the quality so that users know how reliable the data are and what uses they are suitable for.

Lidar validation cruises can use standard or check cruise mode. When used in standard mode, they typically are conducted as a traditional cruise. Random sample points are collected and cruised in a typical fashion, although greater attention is placed on measurements that can be directly compared to lidar trees, including tree height and width. Stand level analysis results are then compared to stand level lidar-derived results and used to assess the ability of the lidar to predict field measured stand metrics.

As an alternative, Check cruise mode can be used to directly compare lidar-trees to field measured trees. In this scenario, the cruise is set up like a lidar augmentation cruise, with trees assigned to random plots and downloaded to MobileMap for direct premeasurement in the field. This approach is typically preferred as it can be combined with check cruise scoring rules to calculate an objective and quantitative score of lidar tree accuracy. See the following articles for more information on using check cruise scoring MobileMap: Check Cruising InventoryManager: Check Cruise Rules Inventory Management System: Check Cruising

Data Visualization

Tree data

Once the data model has been developed that has Point geometry and attribute fields that match both the lidar analysis outputs AND additional fields that might be collected in the field, the lidar data can be loaded into ArcGIS using ArcGIS Pro or other tools. Given the large data volume that is typical of lidar-derived tree lists, data loading can be fairly slow. In our experience, loading data into ArcGIS from ArcGIS Pro is very slow. Speed can be improved when using batch processing with Python. When using ArcGIS Online (rather than ArcGIS Enterprise) the fastest approach to data loading is add data from a Shapefile using ArcGIS Online’s ‘Update Data’ tool:

1. Project lidar-derived tree Point data into Web Mercator using ArcGIS Pro

2. Export projected Lidar-derived tree Point data in Shapefile format

3. Create a Zip file with all of the files in the Shapefile

4. Sign in to ArcGIS Online and navigate to your lidar data model Feature Service

5. Use the ‘Update Data’ tool

   1. Select Add Data

   2. Select the Trees layer

   3. Drag the zipped Shapefile into the tool

   4. Map the attribute fields between the Shapefile and the destination tree data model

This approach has been tested with up to a million trees and found to be an effective way to load lidar-derived tree data.

Lidar-derived Raster data

Lidar-derived Raster, including Canopy Height Models (CHM) can be displayed as base maps in MobileMap just like any other base map data. Follow the instructions for creating base map TPKs at MobileMap: Base Map Creation for MobileMap.

Open

InventoryManager

InventoryManager supports the visualization, editing and analysis of lidar-derived inventory, just like it does for traditional inventory data. The main differences are the data volume of tree data, the spatial coordinates for trees, and the lack of plot level information.

Data Visualization

Since lidar-derived tree data have explicit spatial coordinates, they can be displayed on a map just like any point data. Given the large data volume of lidar-derived tree datasets, however, care must be taken when adding lidar trees to the map, as it can severely impact performance of the map display. Typically lidar trees are added to the map but default to not displaying, and to only display when enabled at a high zoom level (e.g., zoom level 17+).

Lidar-derived raster datasets display much faster and are often more useful for displaying the spatial patter of tree characteristics like height, density, basal area. The image below shows a lidar-derived maximum tree height raster displayed in InventoryManager at zoom level 17 with the lidar trees displayed as red “X” symbols. The lidar trees can be toggled off by clicking the eye icon next to the Trees layer on the map legend. Similarly, the max tree height raster can be toggled off by clicking the eye icon on the map legend to show the original imagery base map.

Open

As with any other layer in the InventoryManager map, lidar trees can be queried by selecting the Trees layer in the Search tab, then clicking on any Tree feature. Similarly, they can be searched using a combination of spatial and attribute filters.

Compilation

When InventoryManager is used for compilation, it is configured for the specific merch specs, volume and/or taper equations and reporting requirements. In a traditional inventory, 1 or more stands are selected to start the process, the stands are used to retrieve the cruise plots, and the plots are used to retrieve and merchandize the trees.

With lidar-based inventory data the process is similar, although there are typically no plots. Instead, 1 or more stands are selected to start the process, the stands are used directly retrieve the trees based on their spatial location. The trees are then run through the merchandizer to calculate the volumes and generate reports. Since plots are not typically used, some reporting metrics, such as average number of trees per plot, or plot variability, will not be available when using lidar data.

Given the large data volume of lidar inventories, some compilation tasks may be limited in total area that can can be processed and may take longer to complete. Our testing is currently showing a processing time of ~1,000 trees per second. The example report below shows a compilation of 28 stands with a combined total of over 200 acres and over 26 thousand trees.