Forests, one of the main terrestrial ecosystems on Earth, play a vital role in climate change, conservation of biological diversity, and terrestrial ecosystems itself. 3D forest mapping at individual tree level is becoming essential for forest management and ecosystem sustainability [1
]. Traditionally, the detailed information of the individual tree is acquired through a statistical field inventory, which is labor-intensive, time-consuming, and accessibility-constrained [2
]. Therefore, accurate, efficient, and cost-effective methods for accessing the individual tree structure are of great importance [4
With the development of laser scanning in the last twenty years, most researches on forest structure metrics have focused on laser scanning point clouds from different platforms. More specifically, applications of terrestrial laser scanning (TLS) using the single-scan and multi-scan approach for forest inventory have been thoroughly investigated [2
]. To further improve the efficiency of data collection using TLS, mobile laser scanning (MLS) is used in forestry surveys because of its ability to measure complex forest areas [6
]. However, MLS is restricted by the global navigation satellite system (GNSS) shadows in forests. Complementing TLS and MLS in different observational perspectives, airborne laser scanning (ALS) has a high potential in forest applications, providing a good solution for accessing various forest characteristics, such as tree height [8
], crown diameter [9
], wood volume [10
], and biomass [11
]. Nevertheless, the spatial and temporal resolutions of the ALS system is limited because of the inflexibility and the high costs.
In recent years, improvements in the convenience and minimization of unmanned aerial vehicles (UAVs) have made it a powerful tool for 3D forest mapping, providing a distinctive combination of high spatial and temporal resolution. Jaakkola et al. [12
] provided the first investigation of forest mapping using the unmanned aerial vehicle laser scanning (ULS) and demonstrated that data collected by the ULS system was feasible for automatic measurements of forest. Liu et al. [13
] estimated the forest structure attributes using ULS in Ginkgo plantations, in which the effectiveness of plot-level metrics and individual-tree-summarized metrics derived from ULS point clouds were assessed. Furthermore, Jaakkola et al. [14
] presented a new concept, “ULS based automatic tree field reference collection”, and demonstrated the feasibility of this concept, even though the whole topic needs further research. In most reported studies of ULS based forest mapping [12
], the standard ULS systems were equipped with high-end positioning and orientation system (POS), which has high survey costs and limits the widespread use of the ULS in forest applications. The drawback of such platforms is that the size and budget are significantly larger than what could be considered useful as an operational tool in forest management [17
]. Thus, 3D forest mapping using a low-cost ULS system equipped with only low-cost sensors has a high practical meaning. However, studies focusing on 3D forest mapping using low-cost ULS, including data quality evaluation, and individual tree characteristics estimation, are still lacking and attract the attention of the academic community [17
As far as the low-cost ULS system is concerned, system integration is limited by the cost, payload, and the rapid consumption of battery power. A tradeoff must often be made among the accuracy, weight, and cost of sensors [17
]. It is difficult to obtain accurate point clouds using the direct georeferencing data estimated by the GNSS and a low-cost inertial measurement unit (IMU) because of insufficient quality control [19
]. Therefore, 3D forest mapping with the low-cost ULS system is a great challenge. To optimize the trajectory estimated by the low-cost sensors, Wallace et al. [17
] utilized a structure from motion (SfM) algorithm first, then coupled the results of SfM with GNSS/IMU using sigma point Kalman Filter. They handled the SfM algorithm and GNSS/IMU information separately and achieved good accuracy of the trajectory. However, independent SfM processing may suffer from drift, which could be effectively controlled by GNSS/IMU aided bundle adjustment [20
]. Furthermore, the performance of the 3D forest mapping (i.e., automatic individual tree segmentation, tree characteristics estimation) using the low-cost ULS system is not yet thoroughly investigated or compared with the commercial ULS system in the previous studies.
The main objectives of this paper are to (1) reconstruct point clouds accurately in mapping frame using the low-cost sensors, and (2) investigate the performance of the low-cost ULS system in 3D forest mapping by comparing it with a high-end commercial ULS system. The low-cost ULS system, named Kylin Cloud, equipped with multiple low-cost sensors (i.e., GNSS, IMU, camera, and laser scanner) is used in the experiments. To overcome the poor performance of the low-cost sensors, an automatic multisensory integration method is proposed. It reconstructs point clouds accurately in a mapping frame by integrating the GNSS data, IMU data, and image sequence utilizing GNSS and IMU aided bundle adjustment. Then individual tree segmentation and tree characteristics (i.e., tree height and crown diameter) estimation are performed using the reconstructed point clouds.
This paper is structured as follow: Section 2
illustrates the study area and the collected data. Section 3
elaborates the proposed method, which integrates the multisensory data and investigates the potential of the low-cost ULS system for 3D forest mapping. Section 4
reports the results of the experiments. Section 5
discusses the results of the experiments. Conclusions and future work are drawn at the end of this paper.
2. Study Area and Material
To investigate the feasibility of the low-cost ULS system in 3D forest mapping, two sets of data were collected by different systems (the low-cost ULS system and a commercial ULS system). In addition, both approaches, including a direct comparison of the point clouds reconstructed by two systems, and comparison of individual tree characteristics (i.e., tree height and crown diameters), were applied to validate the performance of the low-cost system. In this section, detailed information about the study area, the two ULS systems, and the collected data are provided.
2.1. Study Area
The study area, located in the Dongtai forest farm (32°52′N, 120°50′E), Yancheng City, Jiangsu Province, China. An 800 m * 100 m of nursery land was selected for data collection as shown in Figure 1
a. Fifteen sample plots were randomly selected from the study area as shown in Figure 1
b. Each sample plot is a circular area with a radius of 15 m. The main planted tree species include Dawn redwood (Metasequoia glyptostroboides
) and Poplar (Populus deltoids
). The seedlings, including Maple (Acer L.
), Weeping willow (Salix babylonica Linn.
) and Ligustrum lucidum (Ligustrum lucidum Ait.
), are planted in the nursery land.
2.2. The Low-Cost ULS System and the Collected Data
The low-cost ULS system, named Kylin Cloud, is illustrated in Figure 2
. Kylin Cloud consists of a low-cost IMU (Xsens Mti-300), a double-frequency GNSS receiver (KQ GEO), a GNSS antenna, a global shutter camera (Pointcrey Flea3), and a laser scanner (Velodyne Puck VLP-16). Kylin Cloud is mounted on a DJI M600Pro UAV with a maximum payload of 6 kg and 25 min flying time. The synchronization between the laser scanner, the camera, the GNSS receiver, and the IMU is fulfilled electronically. The raw data of the sensors is recorded by an onboard control unit based on advanced RISC machine (ARM) cortex A9, which has low power and is sufficient for the data log. Table 1
lists the specifications of the sensors. The Xsens Mti-300 is a low-cost and light IMU, which provides 200 Hz raw inertial measurements. Its gyroscope bias stability and accelerometer bias stability are 12°/h and 0.015 mg, respectively. The intrinsic parameters of the IMU were provided by the manufacturer. The Pointgrey Flea3 is a global shutter color camera with 1280 × 1024 pixels. It captures image data at 5 Hz and is not affected by rolling-shutter distortion. The initial intrinsic parameters of the camera were pre-calibrated using a camera calibration toolbox in MATLAB [21
] with an industrial checkerboard (1.5 m * 1.2 m), and then they were optimized in the proposed GNSS and IMU aided bundle adjustment. The Velodyne Puck VLP16 is a light-weight and low-cost laser scanner, which operates at a wavelength of 905 nm. It has 16 channels and supports 300,000 points per second. The measurement range of Velodyne Puck VLP16 is 100 m. The range error of the Velodyne Puck VLP16 laser scanner is about 0.03 m (1
), and it could be improved by 10 to 20% after interior calibration [22
]. The range error of the Velodyne Puck VLP16 laser scanner is about 0.03 m (1
), and it could be improved by 10% to 20% after interior calibration. As reported by Jaakkola et al. [14
], using the VLP-16 laser scanner with a range error of 3 cm is sufficient for the forest applications, so the manufacture values were used without calibration in this paper.
Data of Kylin Cloud were collected in December 2018. To ensure data quality and flight safety, the Kylin Cloud was programmed to automatically follow the pre-designed flight lines using DJI GS PRO. The flight height was set to 70 m above the ground, and the flying speed was nearly 3 m/s. It took 15 min to collect the raw data of the study area. There were 3760 images and approximately 2 GB raw laser scanning data (7,993,351 points) collected in this study area. The forward overlap of the images is over 90%, and the side overlap of the images is 70%. The density of the resulted laser point clouds (with double-echo) is 11.85 points per m2.
2.3. The High-End Commercial ULS System and the Collected Point Clouds
The commercial ULS was integrated by Green Valley International. The hardware system is composed of 6 units, including a Hexa-rotor UAV, a high-end POS (Novatel IMU-IGM-S1), a GNSS antenna, a high-end laser scanner (Riegl VUX-1), a micro-computer, a long-range Wi-Fi, as shown in Figure 3
. The survey grade laser scanner, Riegl VUX-1, has high accuracy (0.01 m) and long measurement range (300 m). Its scan speed and measurement rate are up to 200 scans per second and 550 kHz, respectively. To obtain the georeferenced point clouds according to trajectory, the Novatel IMU-IGM-S1 is mounted. The raw IMU and GNSS data are post-processed using a loosely coupled Kalman Filtering via Novatel Inertial Explorer software to generate accurate trajectory. A micro-computer is integrated to log raw data. In addition, the raw data is transmitted to the ground station through the long-range Wi-Fi system. The total price of this system is over 120,000 USD, which is much higher than the low-cost system.
The laser scanning data for the study area was collected in August 2018. The UAV system was programmed to automatically follow the pre-designed flight lines using an autopilot system of the Hexa-rotor UAV. The flying height was 140 m, the flying speed was nearly 4 m/s, and it took 8.7 min to collect the raw data of the study area. The average point density of the reconstructed point clouds is 224.33 points per m2.
The low-cost ULS system is a newly developed tool for collecting 3D information in a cost-effective way. However, 3D forest mapping with the low-cost ULS is still a great challenge because of the poor performance of the low-cost sensors. In this paper, we investigated the feasibility of the low-cost ULS for 3D forest mapping and compared the low-cost ULS system with a high-end commercial system. First, to overcome the poor performance of low-cost sensors, we proposed a multisensory integration manner for reconstructing point clouds accurately. Second, individual trees were segmented using the point clouds reconstructed by the proposed multisensory integration. Then the individual tree characteristics (e.g., tree height and crown diameter) were estimated according to the segmented trees. Results indicated that the low-cost ULS system achieved comparable accuracy of tree height and crown diameter with that of the high-end commercial system. However, for the mapping results of low and complex trees, there was still a gap between the data quality of low-cost UAV system and high-end commercial system because of insufficient point density. In general, the low-cost ULS system has shown a high potential for 3D forest mapping, even though 3D forest mapping using low-cost ULS system requires further research.
Some issues are still worthy of attention. With the development of laser technology, many low-cost laser scanners with longer measurement range (e.g., 200 m) and smaller footprint have been produced. They promote the performance of low-cost ULS systems and are more efficient for mapping in forest. What is more, the proposed trajectory estimation and individual tree segmentation have not taken advantage of the multisensory data. Thus, (1) trajectory estimation combining laser and image information; (2) individual tree segmentation by fusing images and point clouds will be explored in the future.