LiDAR Tree Detection Using Planting Data Filtering
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Solution Overview
Problem
Existing remote sensing technologies, such as LiDAR, struggle to accurately identify young trees amidst surrounding vegetation due to their similar height, making it difficult to assess tree growth and require additional management techniques.
Innovation Solution
The use of planting data, obtained through GPS or aerial imagery, is employed to filter LiDAR return data, creating a buffer zone around known tree planting locations to isolate and analyze only relevant data points, thereby simplifying the detection of planted trees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If LiDAR data is used to detect trees, then coverage area is improved, but detection precision for young trees deteriorates
Solution Approach 1:
The patent segments the LiDAR data analysis process into multiple stages: first filtering data based on planting location information to create a preliminary set, then applying tree detection algorithms only to this filtered data. This segmentation allows the system to maintain broad coverage while improving detection precision by focusing computational resources on relevant areas where trees are expected to be located.
Solution Approach 2:
The patent applies preliminary filtering action using planting location data before the main tree detection process. By pre-identifying areas where trees were planted based on GPS or aerial imagery information, the system prepares the data in advance, removing irrelevant points and creating a focused dataset that improves detection precision without sacrificing coverage area.
2Area of stationary object
If tree detection algorithms are applied to all LiDAR data, then detection coverage is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and removes irrelevant LiDAR data points based on planting location information before applying tree detection algorithms. By taking out data points that do not correspond to expected tree locations, the system reduces the volume of data that requires complex processing, thereby lowering computational complexity while maintaining detection coverage in areas where trees are actually located.
Solution Approach 2:
The patent applies tree detection algorithms partially, only to the subset of LiDAR data that passes the initial filtering based on planting locations. This partial application of the complex detection algorithm to a reduced dataset maintains adequate detection coverage for actual trees while significantly reducing the overall computational complexity compared to applying the algorithm to all LiDAR data points.
3Speed
If young trees are detected based on height alone, then detection speed is improved, but detection accuracy deteriorates
Solution Approach 1:
The patent adds another dimension to tree detection by incorporating planting location information as a third factor alongside height and spatial distribution. Instead of relying solely on height (one dimension), the system uses the combination of LiDAR measurements and known planting locations to improve accuracy. This dimensional expansion allows the system to maintain detection speed while significantly improving accuracy for young trees that are similar in height to surrounding vegetation.
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
This approach enhances the accuracy of tree detection and growth assessment, allowing for more effective forest management decisions, such as fertilization and vegetation control, by isolating LiDAR data points within defined buffer zones around planted trees.
Implementation Method 1
With LiDAR data, an aircraft, such an airplane or helicopter, is flown over the land while laser pulses are fired at the ground in a repetitive sweeping pattern. A detector on the aircraft measures the time and intensity of the pulses that are reflected from the underlying vegetation, ground etc.
Implementation Method 2
A detector on the aircraft measures the time and intensity of the pulses that are reflected from the underlying vegetation, ground etc. Because the altitude and speed of the aircraft are accurately known from GPS information recorded by the aircraft, each returned pulse can be assigned an accurate three-dimensional geographic coordinate.
Data Source
AI summary
Disclosed is a system and method for processing LiDAR return data prior to analyzing the data to detect planted trees. LiDAR return data for an area in question is filtered to remove data that are not within a predetermined area of where trees have been planted. Planting data such as GPS data that is collected by tractors or other equipment records the location of where trees are planted. The planting data is used to filter the LiDAR return data by eliminating or ignoring LiDAR return data that are not within a buffer zone around the location where the trees have been planted. Once filtered, the LiDAR return data can be analyzed to detect trees or other items of interest in the LiDAR return data.