Register-Based Carbon Sequestration Estimation Using LiDAR and ML
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Solution Overview
Problem
Current methods for accurately measuring and monitoring carbon sequestration in forests face challenges due to limitations in remote sensing technologies, particularly in capturing year-to-year changes and achieving high accuracy in complex and diverse forest ecosystems.
Innovation Solution
The use of machine learning techniques combined with remote sensing data, specifically through a register-based carbon sequestration approach integrated with a continuous learning mechanism, allows for the estimation of carbon sequestration by classifying forest types and aggregating data at various scales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If satellite-based remote sensing is used for carbon sequestration estimation, then large-scale monitoring capability is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the monitoring system into a hierarchical structure with satellite-based coarse estimation at the top level and ground-based LiDAR fine measurement at the bottom level. This segmentation allows each component to operate at its optimal scale, with satellites providing broad coverage and ground systems providing high precision measurements for validation and calibration.
Solution Approach 2:
The patent introduces ground-based LiDAR measurements and field survey data as intermediary elements that bridge the gap between satellite remote sensing and actual carbon stocks. These intermediaries serve as ground truth data for validating and calibrating satellite-based estimates, thereby improving overall measurement precision while maintaining large-scale monitoring capability.
2Measurement precision
If ground-based survey methods are used for carbon sequestration measurement, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent divides the measurement domain into different spatial scales and complexity levels. Ground-based surveys are deployed only in representative plot locations rather than across entire forests, segmenting the high-precision measurement task to where it is most needed for calibration and validation, thereby improving productivity while maintaining precision.
Solution Approach 2:
The patent applies ground-based LiDAR and field surveys to a partial subset of the total forest area - specifically, strategically selected representative plots. This partial action provides sufficient ground truth data for calibrating and validating the satellite-based estimation model across the entire large-scale area, achieving high precision without the excessive cost and time of complete ground coverage.
3Ease of operation
If traditional remote sensing methods are used for diverse forest types, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent develops a unified multi-functional framework that integrates satellite remote sensing, ground-based LiDAR, and field survey methods into a single carbon sequestration estimation system. This universal approach can handle diverse forest types (boreal, temperate, tropical) through a common methodology, maintaining ease of operation while improving precision through the complementary strengths of each measurement technique.
Solution Approach 2:
The patent changes key parameters including spatial resolution, temporal frequency, and measurement scales to optimize performance across different forest types. By adjusting these parameters dynamically based on forest characteristics and available data, the system maintains operational simplicity while achieving high measurement precision across diverse ecosystems.
Data Source
AI summary
Techniques for registering carbon sequestration include: receiving carbon sequestration data corresponding portions of a geographic region; comparing entries to a registry; assigning entries to clusters associated with carbon sequestration values; and determining a total carbon sequestration based on an aggregating of carbon sequestration values of the entries. In another aspect, a register-based carbon sequestration may utilize continuous learning, including generating estimates of carbon sequestration based on high-resolution aerial LiDAR, multispectral imagery, or the like. Unsupervised learning and a ground-based calibration procedure are usable to delineate distinct forest types within mixed forest area. Such a calibrated carbon sequestration system demonstrates superior accuracy compared to satellite-based analysis, and is able to estimate carbon sequestration on a grid, enabling generalization across multiple forest types and scales of aggregation within a unified framework.