Interpolation Routines Assessment in ALS-Derived Digital Elevation Models for Forestry Applications
Abstract
:1. Introduction
2. Methods
2.1. Study Area
2.2. ALS Data Acquisition
Property | Value |
---|---|
Sensor | ALS60 |
Scanning method | Oscillating plane mirror (saw-tooth pattern) |
Date | January and February 2011 |
Mean flying height above ground (m) | 3012 |
Mean flying speed (km/h) | 241 |
Nominal point density (point/m2) | 1.5 |
Field of view (degrees) | 40 |
Beam divergence angle (mrad) | 0.22 |
Scan angle (degrees) | ±22° |
Total extension of LAS files (km2) | 12 |
Point count | 18,495,618 |
Altimetric accuracy (RMSE in m) | 0.20 |
Planimetric accuracy (RMSE in m) | 0.30 |
2.3. Data Processing
2.3.1. Surface Interpolation Methods
Method | Advantages/Disadvantages | Parameterization |
---|---|---|
Natural neighbor | It is simple because it has no adjustable parameters. It is extremely computationally efficient. It can create artifacts when points are sparse. | Not applicable. |
Triangulated Irregular Network (TIN) to raster | It is simple and computationally efficient. If point density is lower than the output cell size, the triangle of the intermediate TIN will be transferred to the output DEM. | Linear and natural neighbor methods were tested to create the raster surface from the TIN. |
Inverse Distance Weighted (IDW) | It requires a moderate decision-making and can also be computationally intensive. | Power of 0.5, 1, 1.5, 2, 2.5 and 3, and a variable search radius with 6, 12 and 24 minimum points were tested. |
ANUDEM | It allows the incorporation of spatial restrictions in the interpolation process, such as contours, streams, etc. Its primary purpose is to create a surface suitable for hydrologic modeling. It is extremely computationally intensive. | Surfaces were created with drainage enforcement both on and off. |
Kriging | It requires a lot of decision-making and it is very computationally intensive. | The fitted model of the semivariogram was “Gaussian”. Sector types of 1, 4, 4 with an offset of 45° and 8, with 2 to 5 neighbors were tested. |
Point to raster | It is the simplest method and it is very computationally efficient. Mean is sensitive to extreme values/outliers, especially when the sample size is small. | Not applicable. |
2.3.2. DEM Accuracy Assessment
2.3.3. Error Analysis
2.3.4. Error Prediction
3. Results
3.1. Error Analysis
Interpolation Method | Parameterization | Resolution (m) | Min Error (m) | Max Error (m) | Range (m) | SD (m) | ME (cm) | RMSE (cm) | MAE (cm) |
---|---|---|---|---|---|---|---|---|---|
TIN to raster | Applying natural neighbor interpolation to TIN triangles to obtain cell values. | 1 | −3.14 | 3.50 | 6.64 | 0.16 | 0.59 | 2.68 | 11.73 |
Natural neighbor | Not applicable. | 1 | −4.96 | 3.61 | 8.57 | 0.17 | 0.03 | 2.95 | 12.14 |
ANUDEM | Surface created with no drainage enforcement. | 1 | −3.06 | 3.31 | 6.37 | 0.17 | −0.1 | 2.99 | 12.14 |
IDW | Power of 2 and variable search radius with 12 minimum points. | 1 | −2.77 | 3.81 | 6.58 | 0.19 | −0.32 | 3.64 | 12.9 |
Kriging | Parameters determined fitting the semivariogram model. 4 sectors with an offset of 45° for the search neighborhood. | 1 | −4.42 | 4.44 | 8.86 | 0.20 | −0.9 | 3.91 | 14.08 |
Point to raster | Not applicable. | 1 | −4.45 | 4.24 | 8.69 | 0.26 | −1.29 | 6.64 | 18.25 |
Kriging | Parameters determined fitting the semivariogram model. 1 sector for the search neighborhood. | 2 | −2.71 | 4.23 | 6.94 | 0.23 | 0.48 | 5.25 | 16.25 |
ANUDEM | Surface created with no drainage enforcement. | 2 | −2.75 | 3.71 | 6.46 | 0.23 | 0.16 | 5.42 | 16.73 |
TIN to raster | Applying natural neighbor interpolation to TIN triangles to obtain cell values. | 2 | −4.14 | 3.29 | 7.43 | 0.23 | 0.86 | 5.48 | 16.94 |
Natural neighbor | Not applicable. | 2 | −3.26 | 3.70 | 6.96 | 0.23 | 0.86 | 5.52 | 16.94 |
IDW | Power of 2 and variable search radius with 12 minimum points. | 2 | −2.76 | 3.81 | 6.57 | 0.24 | 0.32 | 5.74 | 16.74 |
Point to raster | Not applicable. | 2 | −4.40 | 4.08 | 8.48 | 0.42 | −2.73 | 17.67 | 29.54 |
Interpolation Method | Parameterization | Resolution (m) | Min Error (m) | Max Error (m) | Range (m) | SD (m) | ME (cm) | RMSE (cm) | MAE (cm) |
---|---|---|---|---|---|---|---|---|---|
IDW | Power of 1 and variable search radius with 24 minimum points. | 1 | −0.05 | 1.24 | 1.29 | 0.22 | 29.80 | 37.10 | 30.01 |
Kriging | Parameters determined fitting the semivariogram model. 1 sector for the search neighborhood. | 1 | −0.03 | 1.23 | 1.26 | 0.24 | 30.10 | 38.10 | 30.19 |
Natural neighbor | Not applicable. | 1 | −0.02 | 1.28 | 1.31 | 0.24 | 32.80 | 40.40 | 32.88 |
TIN to raster | Applying linear interpolation to TIN triangles to obtain cell values. | 1 | 0.03 | 1.44 | 1.40 | 0.28 | 32.70 | 42.80 | 32.67 |
ANUDEM | Surface created with drainage enforcement. | 1 | −0.04 | 1.49 | 1.53 | 0.31 | 33.90 | 45.40 | 34.16 |
Point to raster | Not applicable. | 1 | −0.13 | 1.77 | 1.90 | 0.36 | 36.80 | 50.90 | 37.29 |
IDW | Power of 0.5 and variable search radius with 6 minimum points. | 2 | −0.29 | 1.25 | 1.55 | 0.28 | 29.70 | 40.60 | 31.11 |
ANUDEM | Surface created with drainage enforcement. | 2 | −0.12 | 1.27 | 1.39 | 0.28 | 32.40 | 42.70 | 33.79 |
Kriging | Parameters determined fitting the semivariogram model. 1 sector for the search neighborhood. | 2 | −0.03 | 1.58 | 1.61 | 0.30 | 32.50 | 44.10 | 32.67 |
Natural neighbor | Not applicable. | 2 | −0.05 | 1.86 | 1.90 | 0.32 | 33.20 | 46.00 | 33.37 |
TIN to raster | Applying linear interpolation to TIN triangles to obtain cell values. | 2 | −0.04 | 1.45 | 1.48 | 0.33 | 34.00 | 47.10 | 34.13 |
Point to raster | Not applicable. | 2 | −0.15 | 2.53 | 2.68 | 0.49 | 40.00 | 63.00 | 41.10 |
3.3. Error Prediction
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Share and Cite
Montealegre, A.L.; Lamelas, M.T.; Riva, J.D.l. Interpolation Routines Assessment in ALS-Derived Digital Elevation Models for Forestry Applications. Remote Sens. 2015, 7, 8631-8654. https://doi.org/10.3390/rs70708631
Montealegre AL, Lamelas MT, Riva JDl. Interpolation Routines Assessment in ALS-Derived Digital Elevation Models for Forestry Applications. Remote Sensing. 2015; 7(7):8631-8654. https://doi.org/10.3390/rs70708631
Chicago/Turabian StyleMontealegre, Antonio Luis, María Teresa Lamelas, and Juan De la Riva. 2015. "Interpolation Routines Assessment in ALS-Derived Digital Elevation Models for Forestry Applications" Remote Sensing 7, no. 7: 8631-8654. https://doi.org/10.3390/rs70708631