# The Comparison of Canopy Height Profiles Extracted from Ku-band Profile Radar Waveforms and LiDAR Data

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## Abstract

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^{®}VLP-16 LiDAR on-board the same platform represent the frequency of discrete returns, which are also applied to the extraction of the CHP by calculating the gap probability and incremental distribution. To thoroughly explore the relationships of the CHP derived from Tomoradar waveforms and LiDAR data we utilized the effective waveforms of one-stripe field measurements and comparison them with four indicators, including the correlation coefficient, the root-mean-square error (RMSE) of the difference, and the coefficient of determination and the RMSE of residuals of linear regression. By setting the Tomoradar footprint as 20 degrees to contain over 95% of the transmitting energy of the main lobe, the results show that 88.17% of the CHPs derived from Tomoradar waveforms correlated well with those from the LiDAR data; 98% of the RMSEs of the difference ranged between 0.002 and 0.01; 79.89% of the coefficients of determination were larger than 0.5; and 98.89% of the RMSEs of the residuals ranged from 0.001 to 0.01. Based on the investigations, we discovered that the locations of the greatest CHP derived from the Tomoradar were obviously deeper than those from the LiDAR, which indicated that the Tomoradar microwave signal had a stronger penetration capability than the LiDAR signal. Meanwhile, there are smaller differences (the average RMSEs of differences is only 0.0042 when the total canopy closure is less than 0.5) and better linear regression results in an area with a relatively open canopy than with a denser canopy.

## 1. Introduction

^{®}VLP-16 LiDAR installed on the same platform was employed to offer a coincident laser point cloud. Considerable amount of research has been carried out in the field of retrieving forest biomass from satellite radar data. There have been two main research lines, using either (1) SAR backscattering information [22] or (2) elevation models extracted from the SAR data [23], to retrieve forest biomass estimation models. Without going too deep into the details of these techniques, one of the main challenges today is to better understand the interaction of radar waves with forest vertical structures under variable environmental conditions. By comparing the Tomoradar waveforms and high-density ALS data, it may be possible to advance the understanding of spaceborne radar responses from forests as well and, accordingly, contribute to the current SAR satellite-based biomass retrieval models. Thus, in this paper, the estimations of the CHP, which is a typical characterization parameter, are investigated using Tomoradar waveforms and LiDAR discrete returns.

## 2. Materials and Methods

#### 2.1. Site Description

^{®}LiDAR—Tomoradar) can be overlapped at the centimeter-level. The configurations of all instruments on the helicopter are illustrated in Figure 1b.

#### 2.2. Tomoradar Waveforms

#### 2.3. LiDAR Data

^{®}VLP-16 laser scanner operating with 300,000 points/second was installed strictly on the same platform as the Tomoradar to offer a laser point cloud of the study area. The Velodyne

^{®}LiDAR records the first and strongest return for every transmitted laser pulse with a beam size of 12.7 mm (horizontal) × 9.5 mm (vertical) at the exit with a beam divergence of approximately three milliradians [25], and instantaneously provides 16 laser footprints with an interval of two degrees on the ground along the flying trajectory. With a rotation of 360 degrees of the laser scanner, 16 parallel scan lines are simultaneously generated with uniform distributions across the flying track. The average point density was approximately 36 points per square meter for the selected stripe. For the purpose of determining the centres of the laser point cloud within the Tomoradar footprint on the ground, higher-precision LiDAR data collected by a Leica

^{®}ALS70-HA laser scanner at Evo in 2015 were also utilized. However, the data is mainly used as a DEM model, and all the comparisons were based on data collected by VLP-16 laser scanner and Tomoradar.

#### 2.4. Methods

#### 2.4.1. Derivation of Canopy Height Profile from Tomoradar Waveforms

#### (1) Filtering Noise for Raw Power Waveform

#### (2) Identifying Canopy and Ground

#### (3) Canopy Closure Profile

_{i}and d

_{k}denote the distance from the helicopter to the i-th and k-th canopy layer; d

_{c}and d

_{2}are the distances from the helicopter to the canopy top and the 2 m boundary above ground, respectively. The scaling factor γ, corresponding to scattering coefficient ratio between canopy and ground, is supposed to be one in the paper, since the reflectance ratio is also ignored when calculating the canopy closure with discrete LiDAR returns in the following section.

#### (4) Cumulative Distribution of the Plant Area

#### (5) The CHP Extracted from Tomoradar Waveforms

#### 2.4.2. Derivation of Canopy Height Profile from LiDAR Data

_{p}(d

_{i}) can be estimated by accumulating the numbers of laser hits from the canopy top to a certain distance d and normalizing it by the total numbers of LiDAR point clouds (N) within the Tomoradar footprint cone:

_{k}represents the numbers of laser hits down to a preset boundary d

_{i}above the ground (2 m in this research).

#### 2.4.3. Comparison Method

^{2}) and a RMSE of the differences (δ

_{d}) were computed by the following expressions:

^{2}) and the RMSE of residuals (δ

_{R}) are given by:

## 3. Results and Discussion

#### 3.1. Analysis of the Differences of the CHPs

_{d}) for all 6766 measurements. Moreover, we presented the illustrations of δ

_{d}and total canopy closures in Figure 11 to reveal the inner connections between them.

_{d}) varied from 0.002 to 0.01, and the average of the difference (μ

_{d}) was less than 0.012. Furthermore, the RMSEs and total canopy closures fluctuated with completely opposite tendencies for all 6766 measurements. This suggested that CHP derived from Tomoradar waveforms were approaching those from the LiDAR data, when the microwave or laser transmits into a relatively open canopy. However, the differences increased if the microwave or laser projected into a denser canopy due to the different penetration capabilities. In Figure 11, the average of δ

_{d}for a canopy with smaller total closure (less than 0.5) is 0.0042, and yet which is 0.0058 for a canopy with larger total closure (larger than 0.5).

#### 3.2. Linear Regression Results

^{2}) and RMSEs of residual (δ

_{R}) present similar distributions with correlation coefficients (R

^{2}) and RMSE of the differences (δ

_{d}), individually. However, both R

^{2}and δ

_{d}were greater than r

^{2}and δ

_{R}, respectively. The comparisons discovered that regression results should be better when the CHPs derived from the Tomoradar waveforms correlate well with those from the LiDAR data. Meanwhile, in Figure 12a, 79.89% of the coefficients of determination were larger than 0.5, which denoted that most of the relationships between the CHPs from Tomoradar waveforms and those from the LiDAR data can be accurately explained by using the linear regression models. Furthermore, 98.89% of the RMSEs of residuals (δ

_{R}) ranged from 0.001 to 0.01, and the averages of the residuals were approximately zero in Figure 12b.

## 4. Conclusions

_{d}) were less than 0.012; (2) 79.89% of the coefficients of determination were larger than 0.5; and (3) 98.89% of the RMSEs of residuals (δ

_{R}) ranged from 0.001 to 0.01.

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**(

**a**) Trajectory diagram in the west section of study area (black line), and a route of investigated data in one stripe (red line); and (

**b**) the configurations of the instruments on the Bell 206 helicopter: Tomoradar, LiDAR, and GNSS-IMU.

**Figure 2.**(

**a**) Raw Tomoradar waveform profiles in one stripe in HH mode; (

**b**) normalized power waveforms converted from raw Tomoradar waveforms; (

**c**) a single power waveform at the green line location of the power waveforms. All raw Tomoradar waveforms are scaled from 20 m to 90 m for better illustration, even though they were recorded from 10 m to 150 m. The power waveform is normalized by its corresponding maximum of each measurement.

**Figure 4.**The processing steps of converting Tomoradar waveforms into the CHP: (

**a**) raw power waveform with 15 cm range resolution; (

**b**) processed waveforms by filtering noise, and the identified canopy top, 2 m above ground, center, and end of the ground; (

**c**) the canopy closure profile and cumulative plant area; and (

**d**) the incremental distribution of the plant area (CHP) at 2 m above ground.

**Figure 5.**(

**a**) The Velodyne

^{®}LiDAR point cloud within one Tomoradar footprint cone, the ground return and canopy return are derived by calculating the number of point cloud in the specified region; and (

**b**) the Tomoradar transmitted antenna pattern, and its divergence angle is inconstant with the variability of strength.

**Figure 6.**The distributions of CHP for 6676 measurements: (

**a**) the CHPs from the LiDAR data; (

**b**) the CHPs from Tomoradar waveforms; (

**c**) the CHPs from Tomoradar and LiDAR for the 6000th measurement; and (

**d**) the locations of greatest CHP.

**Figure 7.**(

**a**) The correlation coefficients of CHPs derived from Tomoradar and LiDAR; and (

**b**) the proportions of correlation coefficients to 6766 measurements based on the strength of correlation. If a correlation coefficient is greater than zero, it is a positive correlation. On the contrary, it is a negative relationship.

**Figure 8.**The Tomoradar waveform and the LiDAR data at the 5270th measurement: (

**a**) Tomoradar normalized power waveform and processed results; and (

**b**) the LiDAR point cloud within the Tomoradar cone (red color) and neighboring ones (blue color).

**Figure 9.**The CHP extracted from the 5270th Tomoradar waveform, and the LiDAR data within the Tomoradar footprint with beam width settings of six degrees and 12 degrees.

**Figure 10.**(

**a**) The updated correlation coefficients of CHP derived from Tomoradar and LiDAR data; and (

**b**) the updated proportions of correlation coefficient, when Tomoradar footprint cone is 20 degrees.

**Figure 11.**The RMSEs of the difference of CHP derived from Tomoradar waveforms and the LiDAR data (blue color) and the total canopy closure (red color) for all reserved measurements.

**Figure 12.**The scatterplot of canopy height profile from Tomoradar data versus that from the LiDAR data canopy height profile (red spots) and best linear fit (blue line).

**Figure 13.**The distributions of linear regression results for all 6766 measurements: (

**a**) coefficient of determination; and (

**b**) the RMSE of residuals.

Weak and Negative | Very Weak and Negative | Very Weak and Positive | Weak and Positive | Moderate and Positive | Strong and Positive | Very Strong and Positive |
---|---|---|---|---|---|---|

1.29% | 1.74% | 6.01% | 16.37% | 30.53% | 34.67% | 9.39% |

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## Share and Cite

**MDPI and ACS Style**

Zhou, H.; Chen, Y.; Feng, Z.; Li, F.; Hyyppä, J.; Hakala, T.; Karjalainen, M.; Jiang, C.; Pei, L. The Comparison of Canopy Height Profiles Extracted from Ku-band Profile Radar Waveforms and LiDAR Data. *Remote Sens.* **2018**, *10*, 701.
https://doi.org/10.3390/rs10050701

**AMA Style**

Zhou H, Chen Y, Feng Z, Li F, Hyyppä J, Hakala T, Karjalainen M, Jiang C, Pei L. The Comparison of Canopy Height Profiles Extracted from Ku-band Profile Radar Waveforms and LiDAR Data. *Remote Sensing*. 2018; 10(5):701.
https://doi.org/10.3390/rs10050701

**Chicago/Turabian Style**

Zhou, Hui, Yuwei Chen, Ziyi Feng, Fashuai Li, Juha Hyyppä, Teemu Hakala, Mika Karjalainen, Changhui Jiang, and Ling Pei. 2018. "The Comparison of Canopy Height Profiles Extracted from Ku-band Profile Radar Waveforms and LiDAR Data" *Remote Sensing* 10, no. 5: 701.
https://doi.org/10.3390/rs10050701