Vegetation Modeling Using Satellite Imagery for Canopy Height Estimation
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Solution Overview
Problem
Current vegetation management methods for utility companies are inefficient and costly, relying on manual observations and expensive LiDAR sensors, which are time-consuming and infrequent, leading to reactive maintenance rather than proactive approaches.
Innovation Solution
A system and method utilizing satellite and aerial imagery to generate a vegetation segmentation mask, 3D point clouds, and canopy height models, integrating with digital terrain models to estimate vegetation height and encroachment, enabling need-based and risk-based trimming schedules, and reducing the reliance on expensive LiDAR data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR sensors are used to generate 3D representation of vegetation, then measurement precision is improved, but cost and processing time increase significantly
Solution Approach 1:
The patent creates a digital copy of the vegetation structure by generating a 3D point cloud from 2D aerial imagery through photogrammetric processing. This virtual model replicates the physical vegetation characteristics without requiring physical measurement devices, thereby reducing time and cost while maintaining measurement capability
Solution Approach 2:
The patent replaces the mechanical LiDAR sensing system with an optical imaging system combined with computational processing. Instead of using active light detection and ranging hardware, the system uses passive aerial photography and software-based 3D reconstruction to achieve vegetation height measurement
2Productivity
If manual observation is used to determine vegetation proximity to power lines, then cost is reduced, but productivity and measurement accuracy decrease
Solution Approach 1:
The system enables self-service vegetation monitoring by automatically processing aerial imagery to generate 3D vegetation models and proximity assessments. The technology serves itself by using readily available aerial imagery data and automated computational methods to perform what previously required manual field work
Solution Approach 2:
The patent transitions from 2D aerial imagery to 3D vegetation representation by generating point clouds and digital surface models. This dimensional transformation enables accurate height and proximity measurements that cannot be obtained from flat images alone, while maintaining the efficiency of remote sensing
3Reliability
If fixed trimming schedules are used for vegetation management, then ease of operation is improved, but reliability and resource optimization worsen
Solution Approach 1:
The system implements feedback-based vegetation management by continuously monitoring vegetation growth through repeated aerial imagery analysis. The 3D vegetation models provide feedback on actual vegetation status, enabling dynamic adjustment of trimming schedules based on real conditions rather than fixed timelines
Solution Approach 2:
The patent enables preliminary vegetation assessment by generating 3D models before trimming operations are needed. This advance knowledge of vegetation height and proximity allows utilities to plan and schedule trimming proactively, preventing power outages before they occur rather than reacting to problems
Data Source
AI summary
According to some embodiments, a system and method are provided comprising a vegetation module to receive image data from an image source; a memory for storing program instructions; a vegetation processor, coupled to the memory, and in communication with the vegetation module, and operative to execute program instructions to: receive image data; estimate a vegetation segmentation mask; generate at least one of a 3D point cloud and a 2.5D Digital Surface Model based on the received image data; estimate a relief surface using a digital terrain model; generate a vegetation masked digital surface model based on the digital terrain model, the vegetation segmentation mask and at least one of the 3D point cloud and the 2.5D DSM; generate a canopy height model based on the generated vegetation masked digital surface model; and generate at least one analysis with an analysis module, wherein the analysis module receives the generated canopy height model prior to execution of the analysis module, and wherein the analysis module uses the generated canopy height model in the generation of the at least one analysis. Numerous other aspects are provided.