Tree inventories derived from LiDAR and remote sensing imagery are changing forestry and utility businesses as increasing levels of accuracy and detail are achievable. Forsite bridges the worlds of traditional vegetation management methods and the cutting edge by integrating innovative data-driven forest inventories. We’re forest management specialists and we bring our deep understanding of operational and strategic vegetation management to every phase of the process. Leveraging LiDAR, satellite data, and our proprietary machine learning technology, Forsite is uniquely positioned to help clients use individual tree metrics to understand the composition, health, and geolocation of their vegetation asset base. Forsite has years of experience creating millions of acres of vegetation inventories. These analyses catalog green assets in order to plan vegetation maintenance based on risk & hazard and provide the foundation for projecting growth by species into the future; highlighting “fast-growers” that will become issues in subsequent maintenance cycles and allowing clients to proactively plan mitigation efforts.
Forsite's innovative Tree Species Identifier (TSI) technology, exclusively partnered for North American utilities with Fugro Americas, creates rich and comprehensive individual tree inventories. Using a proprietary machine-learning process, TSI provides species, tree height, and crown metrics for each stem that can be extracted from the LiDAR point cloud. With those foundation attributes derived from the LiDAR and produced by TSI, diameter at breast height (dbh) can be predicted and biomass calculated for each tree. TSI can also output environmental metrics like carbon storage, pollution removal, and replacement value.
Health and species data help to identify high-risk trees as well as enable the creation of species-specific growth projections. The identification of danger trees and branching issues with automated remote sensing allows vegetation managers to quickly identify high-priority areas; reducing costs by better leveraging field crew expertise and minimizing risk to people and property. Following the initial tree identification from LiDAR, easily and routinely update key operational areas using open source satellite information. Access and store your information on your network or utilize our secure servers.
TSI requires LiDAR with a point density of 10 pts/m2 (about 1 pt/sq ft) or higher to conduct individual tree species identification. More point density is a benefit and provides TSI with a richer analysis foundation. The species identification model uses ground truth samples selected from the project area.
TSI outputs are geospatial vector files (shapefile or *.gdb) compatible with any GIS system. Each individual tree is fully attributed with desired project metrics such as species, type (conifer/deciduous), and health (live/dead). Attributes derived directly from the LiDAR include height, elevation, canopy area, and slope. Optional attribution includes forestry metrics like board feet or volume as well as environmental metrics like carbon storage, pollution removal, and replacement value.
Standard industry requirements involve analysis of the point cloud to determine possible intrusion of vegetation with transmission and distribution lines. After automatically identifying danger trees and branching issues, field teams can rapidly identify high-priority areas and reduce costs by better leveraging human expertise and minimizing the risks to people and property.
Overhead imagery and line models shown in Figure 1. Tree overlay with Fall-In intrusion codes in Figure 2. Blue Canopy = Dead tree. Green Canopy = Live Tree. Intrusion code (None-green, P3-white, P2-yellow, P1-orange, A1-red).
A typical analysis to identify conifer from deciduous trees along the corridor as well as discern dead trees. In the images below we can see two live trees and one dead tree in the corridor. Trees #1 and #2 are live conifers and #3 is a dead conifer.
At the far left, the image taken by the field crew shows the utility lines and three conifer trees to the east. In the center image, the same three conifers are visible in the LiDAR point cloud. At the far right, we can see the TSI geospatial 3D vector segmentation overlaid on each tree. The green overlay represents trees that TSI called live conifer and the orange overlay represents trees that TSI called dead conifer.
With the species determined, we have the foundation for realistic growth projections using local area site knowledge and growth & yield curves. If LiDAR or SGM data is available from multiple time periods, growth rates in those coverage areas can be refined for greater accuracy. Using either approach, Forsite can provide vegetation managers with individual tree growth projections combined with clearance analyses to identify line sections that will have the highest concentrations of at-risk fast growers in future cycles. That information can then inform vegetation management plans and sections can be prioritized accordingly.
The following images show results from a standard TSI analysis of the vegetation in a corridor. For best results, the LiDAR comes to us with man-made structures classified in the point cloud file so we can move right to the vegetation analysis.
These images depict a sub-area of LAS section 097. According to the tree inventory information provided, the area is largely comprised of scots pine and TSI agrees. The larger, taller trees are south of the corridor and here we find the more significant biomass. The table below summarizes the TSI findings for the entire 19.6 ha area.
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