Tree Canopy Modeling with Kabachnik Ellipses for Ground Truthing
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Solution Overview
Problem
Current methods for modeling forests, particularly in urban areas, are inadequate for accurately determining total canopy cover and canopy volume using remotely sensed data, as they rely on outdated tree census parameters that fail to provide in-situ derived estimates, and are often too costly or require advanced technologies like LiDAR, which are expensive and limited in accessibility.
Innovation Solution
The development of systems and methods that utilize custom geometric shapes, such as Kabachnik ellipses and quadrilaterals, to model tree canopies based on in-situ measurements, allowing for the generation of both 2D and 3D models that can be used to ground truth remotely sensed data, harmonize different data sets, and estimate metrics like biomass and carbon storage, using less computing power and accessible data sources like Google Earth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional tree census parameters (DBH, height, species) are used for forest modeling, then the method is simple and widely applicable, but the accuracy of canopy cover and biomass estimation is insufficient
Solution Approach 1:
The patent segments the tree canopy into standardized geometric shapes (cylinders, cones, spheres, ellipsoids) based on crown classification. This segmentation allows accurate calculation of canopy volume and biomass by summing the volumes of individual geometric segments, resolving the contradiction between measurement precision and model complexity.
Solution Approach 2:
The patent introduces new parameters (crown width, crown length, crown classification type) alongside traditional parameters (DBH, height). By changing the parameter set to include geometric descriptors, the model achieves higher accuracy in canopy cover estimation while maintaining practical applicability through standardized measurement protocols.
2Measurement precision
If LiDAR technology is used for accurate forest measurement, then measurement precision is high, but cost and accessibility are limited
Solution Approach 1:
The patent creates simplified 3D geometric models (copies) of tree canopies using readily available 2D imagery and basic measurements. Instead of requiring expensive LiDAR point cloud data, the method generates accurate enough volumetric models using affordable inputs like Google Earth images and field measurements of DBH and height, achieving cost-effectiveness while maintaining measurement precision.
Solution Approach 2:
The patent uses inexpensive, easily obtainable data sources (satellite imagery, basic field measurements) as disposable inputs to generate accurate forest metrics. These cheap input data replace expensive LiDAR technology, making accurate forest inventory accessible to governments and organizations with limited resources.
3Adaptability or versatility
If custom geometric models (Kabachnik shapes) are used for tree modeling, then adaptability to various tree forms is improved, but device complexity increases
Solution Approach 1:
The patent employs standardized curved geometric shapes (spheres, ellipsoids, cones, cylinders) to model tree canopies. These geometric forms naturally adapt to various tree forms through parameter adjustment (width, length, height, orientation) while maintaining mathematical simplicity. The Kabachnik geometric models use these standard shapes with defined orientation axes, providing versatility across different tree species and forms without excessive complexity.
Data Source
AI summary
Systems and methods for tree census collection are provided. Many embodiments provide improvements to tree modeling, including dimensions of tree crowns, which provides greater accuracy in tree modeling. Furthermore, the improvements to tree modeling provide in-situ datasets to ground truth high resolution satellite imagery, LiDAR, and other remotely sensed products and models. The method may also be used to model and ground truth other remotely sensed phenomena having irregular shapes, such as nebula, vapor plumes, volcanic eruptions, cloud cover, sea cover, on Earth, other planetary bodies, or elsewhere in space, and for improved modeling of remotely sensed physical phenomena from data collected from satellites, embedded sensors, telescopes and other astrophotography systems.