Localized Contour Tree for Surface Depression Characterization

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Previous studies have relied on coarse resolution topographical data, which are unable to reliably resolve small surface depressions and often create artifact depressions, making it difficult to distinguish real surface depression features and ignore their geometric and topological properties, thus underestimating their hydrological and ecological impacts.

Innovation Solution

A novel model using a localized contour tree method based on a pour contour concept, which generates a digital elevation model (DEM) to identify and characterize surface depressions by constructing a contour tree representation, allowing for accurate delineation and quantification of surface depressions across scales.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If coarse resolution topographical data are used, then data processing is simpler and faster, but surface depressions cannot be reliably resolved and artifact depressions are created

Engineering Contradiction:

Improvedata processing efficiencyVSAvoiddepression detection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent changes the resolution parameter of topographical data from coarse to high resolution, enabling reliable detection of small surface depressions while maintaining computational feasibility through efficient algorithms that process the detailed data

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If high resolution topographical data are used, then small surface depressions can be reliably resolved, but data processing becomes more complex and time-consuming

Engineering Contradiction:

Improvedepression detection accuracyVSAvoiddata processing efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent segments the high resolution topographical data processing into systematic steps: generating contour lines at multiple elevation levels, constructing contour trees to represent depression hierarchies, and extracting depression features. This segmentation makes the complex processing manageable and efficient

Inventive Principle:

Principle #1Segmentation

3Ease of operation

If depression-filling algorithms are used to remove surface depressions, then continuous water flow is ensured, but geometric and topological properties of surface depressions are lost

Engineering Contradiction:

Improvehydrological analysis simplicityVSAvoiddepression geometric and topological properties

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

Instead of filling or removing depressions, the patent extracts and preserves their geometric and topological properties by constructing contour trees that represent depression hierarchies, boundaries, and relationships, maintaining this information for hydrological and ecological analysis

Inventive Principle:

Principle #2Taking out (Extraction)

4Ease of manufacture

If traditional raster-based methods are used for depression analysis, then implementation is straightforward, but accurate delineation and quantification of complex surface depressions is difficult

Engineering Contradiction:

Improvemethod implementation simplicityVSAvoiddepression delineation accuracy

Core Design Contradiction:

Ease of manufactureVSManufacturing precision

Solution Approach 1:

The patent transitions from traditional two-dimensional raster grid analysis to a tree-based hierarchical representation, adding a dimensional aspect that captures the nested structure of contours and depressions. This enables accurate delineation of complex depression boundaries and relationships while maintaining computational tractability

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS10096154B2Localized contour tree method for deriving geometric and topological properties of complex surface depressions based on high resolution topographical data

Publication Date: 2018.10.09 UNIVERSITY OF CINCINNATI

AI summary

Computer-implemented methods for detecting and characterizing surface depressions in a topographical landscape based on processing of high resolution digital elevation model data according to a local tree contour algorithm applied to an elevation contour representation of the landscape, and characterizing the detected surface depressions according to morphometric threshold values derived from data relevant to surface depressions of the topographical area. Non-transitory computer readable media comprising computer-executable instructions for carrying out the methods are also provided.