LiDAR Vegetation Point Allocation and Species Identification
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Solution Overview
Problem
Existing LiDAR systems fail to accurately differentiate between individual trees, bushes, and other vegetation, and identify specific attributes from raw LiDAR data, leading to inaccurate characterization of vegetation attributes.
Innovation Solution
A method that processes LiDAR data by allocating points to individual items of vegetation, using a crown identification routine to create digital representations and estimate attributes such as height and crown parameters, and a species identification routine to differentiate between species based on intensity and branching patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR data is collected at high sampling intensity to capture detailed vegetation information, then measurement precision is improved, but the ability to differentiate between individual vegetation items deteriorates due to data complexity
Solution Approach 1:
The patent segments the continuous LiDAR point cloud data into discrete individual vegetation items by detecting crown boundaries and separating points that belong to different vegetation entities. This segmentation transforms the complex undifferentiated data into manageable individual units that can be independently analyzed for precise attribute measurement.
Solution Approach 2:
The patent introduces intermediate processing steps including crown identification routines and geographic area allocation that act as mediators between raw LiDAR data and final vegetation attributes. These intermediaries organize the complex data structure by assigning points to specific vegetation items based on spatial relationships and crown boundaries.
2Device complexity
If manual sampling methods are used to characterize vegetation attributes, then device complexity is reduced, but measurement precision and accuracy deteriorate due to extrapolation errors
Solution Approach 1:
The patent replaces manual field sampling methods with automated LiDAR-based optical scanning systems. This substitution eliminates the need for physical measurement and manual data collection, allowing for comprehensive coverage of all vegetation items rather than relying on limited samples that require extrapolation to larger populations.
Solution Approach 2:
The patent changes the fundamental measurement parameter from manual physical measurement of limited samples to automated optical detection of complete vegetation populations. By using LiDAR technology to capture three-dimensional spatial information and reflective properties of all vegetation items, the system achieves superior measurement precision without the extrapolation errors inherent in manual sampling.
3Productivity
If LiDAR technology is used to obtain comprehensive vegetation information, then productivity is improved, but the ability to identify specific vegetation attributes deteriorates due to inability to differentiate individual items
Solution Approach 1:
The patent utilizes the vertical dimension by detecting crown boundaries at different heights and using elevation data to separate overlapping vegetation items. By analyzing the three-dimensional spatial distribution of LiDAR points, the system can differentiate between individual vegetation items even when they overlap in the horizontal plane, preserving differentiation information while maintaining high productivity.
Solution Approach 2:
The patent employs intensity variations in LiDAR return signals as an analog to color differentiation. Different vegetation items exhibit distinct intensity patterns based on their reflective properties, leaf density, and canopy structure. By analyzing these intensity variations, the system can differentiate between individual vegetation items and identify specific attributes while processing large datasets efficiently.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate allocation of LiDAR points to individual vegetation items and identification of species, providing precise characterization of vegetation attributes, improving the accuracy of vegetation assessment and inventory management.
Implementation Method 1
The distance to the target location may be quantified by measuring the time delay between transmission of the pulse and receipt of one or more reflected return signals
Implementation Method 2
Light Detection and Ranging ('LiDAR') is an optical remote scanning technology used to identify distances to remote targets
Implementation Method 3
a target on the ground will reflect return signals in response to a laser pulse with varying amounts of intensity
Data Source
AI summary
Aspects of the present invention are directed at using LiDAR data to identify attributes of vegetation. In this regard, a method is provided that allocates points to individual items of vegetation from raw LiDAR data. In one embodiment, the method includes selecting a coordinate position represented in the LiDAR data that generated a return signal. Then, a determination is made regarding whether the selected coordinate position is inside a geographic area allocated to a previously identified item of vegetation. If the selected coordinate position is not within a geographic area allocated to a previously identified item of vegetation, the method determines that the selected coordinate position is associated with a new item of vegetation. In this instance, a digital representation of the new item of vegetation is generated.