Timber Species Identifier (TSI) is a system designed to readily incorporate any tree species. The TSI code was written to enable an almost limitless species catalog.
The combination of innovative spatial analysis and Object Raku's years of experience working with remote sensing data enabled the team to achieve the TSI breakthrough. TSI's species identification capability represents a true innovation and a tremendous advantage to the forestry industry, allowing companies to extract the maximum return and operational benefit from their LiDAR investment.
TSI provides stem-based information for planning, inventory, and operations. To date, TSI has been tested and validated on over 2,000,000 hectares of timber area and has segmented over 650 million trees. TSI outputs adhere to standard GIS specifications and so can be easily integrated into existing GIS networks and applications. The first step in a TSI project is to determine the scope of your organization's species identification requirements. In a nutshell, we want to determine how many different species need to be identified over how much area.
From there we will look at any existing LiDAR data and its suitability for the TSI process. The key factors at this stage are the point density of the data along with the completeness of the LiDAR attributes. Minimum point densities of 10 pts per square meter are usually suitable for analysis and more is better. TSI does not require correlated imagery or other remote sensing data.
The Timber Species Identifier (TSI) attempts to segment and identify each tree in a target area and perform a discrete analysis of the LiDAR points to determine tree species. Overall accuracy is typically measured against cruise & scale reports or individual stem tests. Each of these measures are useful but not perfect.
Cruise estimates carry their own error bars and the latter tend to get larger for smaller areas. Cruise estimates are also extremely sensitive to plot placement. Scale or harvest information can be skewed by timber left on site or simply not recorded accurately to the correct source block.
Individual stem tests would seem a better solution but it is difficult & expensive to get a good mix of testable stems across large areas of interest. Nature is messy and subtle differences in canopy shape and reflectivity are part of what makes species identification so challenging. As a result, accuracies are heavily influenced by the trees selected for testing. In small sample sizes, say under 50 samples per species, accuracies in one area can prove above 90% while another zone might record 60%.
A probability score was implemented in TSI version 1.6.15 to help client's identify high accuracy areas and to better leverage operational harvest requirements. The probability scores represent the strength of signal; how well does a particular tree match up to species characteristics in the project species database.
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