Assessing Marginal Shallow-Water Bathymetric Information Content of Lidar Sounding Attribute Data and Derived Seafloor Geomorphometry
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Instrumentation
2.2. Data
2.2.1. Predicted/Dependent Variable: Sounding Classification
2.2.2. Predictor/Independent Variables
Depth
Non-Geomorphometric Sounding Attribute Data (NGSAD)
Geomorphometric Sounding Attribute Data (GSAD)
Base Geomorphometry
Orthographic Geomorphometry
2.3. Modelling and Analysis
2.3.1. Model Fitting
2.3.2. Model Evaluation (Depth, NGSAD, and GSAD Assessment)
2.3.3. Individual Variable Evaluation
3. Results
3.1. Model Evaluation (Depth, NGSAD, and GSAD Assessment
- The GSAD variable suite had the lowest predictive ability for bathymetry on its own (‘GSAD only’ models) and provided little or no additional marginal benefit when incorporated into models with depth and/or the NGSAD variable suite.
- Depth was consistently the strongest stand-alone indicator of bathymetry. However, in shallow areas, the bathymetric signal strength in the NGSAD variable suite alone was comparable to the signal strength in depth alone. Nonetheless, in shallow areas, it was the combination of depth and the NGSAD variable suite that provided the best bathymetry extraction.
- The models with the greatest bathymetry predictive power and that were most efficient, i.e., fewest variables for a given accuracy, were the models that employed depth and the NGSAD variable suite.
3.2. Individual Variable Evaluation
- At least 10 variables were important in all models (Column 2, Table 3)
- Last was the second most important variable in the Shallow, Deeper, and Deepest CSs; further analyses indicated that not being the first (sounding) return from a lidar pulse increased p(Bathy).
- SBET variables (particularly stdXYZ) describing airplane/platform stability—and therefore presumably local wind and surface reflectance characteristics—were among the five most important variables in all models.
- The presence of azim2pls in models for two CSs (Deep and Deepest) may also have captured a bathymetric signal related to momentary surface or ocean conditions since there was no instrumental or other reason that a particular azimuth would be better or worse for identifying bathymetry.
- Inciangle (the 20° instrument angle corrected for yaw, pitch, and roll) was important for all CSs except the Deepest CS; this may also relate to how the dynamics of light reflection and/or penetration relate to bathymetry in lidar data.
- Surprisingly, intensity was only among the five most important variables for two CSs, suggesting that other NGSAD related more to the bathymetric signal of soundings.
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
Acronym | Definition |
ADASYN | Adaptive Synthetic Sampling Approach |
CHRT | CUBE with Hierarchical Resolution Techniques |
CS | Case Study |
EN | Estimation Node |
FNR/FPR | False Negative/Positive Rate |
GSAD | Geomorphometric Sounding Attribute Data |
LAS | Not an acronym; a file containing lidar data |
MBES | Multi-Beam Echo Sounder |
ML | Machine Learning |
MSL | Mean Sea Level |
NOAA | National Oceanic and Atmospheric Administration (United States) |
NGSAD | Non-Geomorphometric Sounding Attribute Data |
ODT | Optimal Decision Threshold |
P(Bathy) | Probability of an individual sounding being Bathy |
PDT | Probability Decision Threshold |
RMSE | Root mean square error |
SAD | Sounding Attribute Data |
SMOTE | Synthetic Minority Oversampling Technique |
TIN | Triangulated Irregular Network |
TNR/TPR | True Negative/Positive Rate |
UTM | Universal Transverse Mercator |
XGB | Extreme Gradient Boosting |
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Identifier/Rel. Depth | Shallow | Deep | Deeper | Deepest |
---|---|---|---|---|
Latitude/Longitude | 24°35′08″/81°42′31″ | 24°28′39″/81°41′18″ | 24°40′00″/81°47′00″ | 24°40′00″/81°47′00″ |
Description | Shallow—only the southwest is below mean sea level (MSL) | Gradual slope with a few ‘dimples’ approximately 1 m tall throughout. | Gradually sloped and cut by relatively shallow channels with bathymetry sparse in the northwest | Long fairly flat trapezoid with small lower depth “mound’ in northeast. Bathymetry extremely sparse and clustered. |
Area covered (m2) | 30,000 | 250,000 | 250,000 | 75,000 |
Approx. depth range below MSL (m) | From −2 to 0 | From −6 to −3 | From −11 to −8 | From −16 to −13 |
Total Returns (million) | 0.6 | 7.6 | 3.7 | 1.0 |
Return density (pts/m2) | 27.6 | 30.4 | 14.8 | 13.3 |
% Bathymetry | 78 | 76 | 21 | 0.4 |
Number of flight paths | 5 | 7 | 7 | 2 |
Type | Nature | Variable (Name) |
---|---|---|
Depth | Depth |
|
NGSAD: Sounding-based | Sounding-specific |
|
NGSAD: Airplane stability | SBET |
|
Edge-based |
|
CS | Number of Variables with Importance > 0.00 1 | Variables with Importance = 0.0 | Five Most Important Contributing Variables 2 | Cumulative Importance of 5 Most Important |
---|---|---|---|---|
Shallow | 12 | Num_returns, first_of_many (2) | Depth, intensity, last, stdXYZ, inciangle | 0.83 |
Deep | 11 | Return_no, first_of_many, last_of_many (3) | Depth, intensity, stdXYZ, inciangle, azim2pls | 0.97 |
Deeper | 11 | Return_no, first_of_many, scandirect (3) | Depth, last, stdYwPtRl, stdXYZ, inciangle | 0.80 |
Deepest | 10 | Num_returns, first_of_many, last_of_many, inciangle (4) | Depth, last, stdXYZ, pls_frm_hdng, azim2pls | 0.89 |
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Lowell, K.; Calder, B. Assessing Marginal Shallow-Water Bathymetric Information Content of Lidar Sounding Attribute Data and Derived Seafloor Geomorphometry. Remote Sens. 2021, 13, 1604. https://doi.org/10.3390/rs13091604
Lowell K, Calder B. Assessing Marginal Shallow-Water Bathymetric Information Content of Lidar Sounding Attribute Data and Derived Seafloor Geomorphometry. Remote Sensing. 2021; 13(9):1604. https://doi.org/10.3390/rs13091604
Chicago/Turabian StyleLowell, Kim, and Brian Calder. 2021. "Assessing Marginal Shallow-Water Bathymetric Information Content of Lidar Sounding Attribute Data and Derived Seafloor Geomorphometry" Remote Sensing 13, no. 9: 1604. https://doi.org/10.3390/rs13091604