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

Abstract

An airborne Ku-band frequency-modulated continuous waveform (FM-CW) profiling radar, Tomoradar, records the backscatter signal from the canopy surface and the underlying ground in the southern boreal forest zone of Finland. The recorded waveforms are transformed into canopy height profiles (CHP) with a similar methodology utilized in large-footprint light detection and ranging (LiDAR). The point cloud data simultaneously collected by a Velodyne ® 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.

Simulation of Ku-Band Profile Radar Waveform by Extending Radiosity Applicable to Porous Individual Objects (RAPID2) Model

“…For lidar, it is hard to detect the detailed information in complex canopy and understory vegetation, due to the mutual shielding of branches and leaves. Zhou et al compared the canopy height profiles extracted from the Tomoradar waveforms and lidar data; both data were used in this paper [14]. They found that the locations of the canopy height profiles acquired from the lidar were obviously higher than those from the Tomoradar.…”

Using Microwave Profile Radar to Estimate Forest Canopy Leaf Area Index: Linking 3D Radiative Transfer Model and Forest Gap Model

“…We compared each Tomoradar average waveform with the simulated profile radar waveforms using the waveform matching method [39]. The ground points of both Tomoradar waveforms and simulated waveforms were identified firstly [28] and the range from the ground point to 2 m above the ground point was treated as ground scattering contribution [21]. In order to correspond to the difference in ground scattering intensity caused by possible ground features (such as different ground cover, moisture, or micro-topography) in the actual scenes, we performed differential amplification on the ground scattering contribution of the simulated waveform, that is, the ground contribution was multiplied by four coefficients of 0.1, 0.4, 0.7, and 1.3.…”

The Determination of Effective Beamwidth of Ku Band Profiling Radar Based on Waveform Matching Method in the Boreal Forest of Finland

“…The Tomoradar and lidar measurements were time-tagged with GPS time and coincided in a centimeter-level manner. The suites of all instruments on the helicopter are presented in Figure 1b [10]. The field test was carried in the autumn of 2016 under partly cloudy conditions.…”

“…Tomoradar can provide four linear polarization measuring capabilities in Ku band based bistatic configuration with 15 cm range solution, to improve the understanding of the radar backscatter response for forestry mapping and inventories. Subsequent studies on forest inventory, especially for vertical forest structure using FMCW radar data have been conducted in the past few years [10][11][12][13][14].…”