Estimating Tree Diameters from an Autonomous Below-Canopy UAV with Mounted LiDAR

Chisholm, Ryan A.; Rodríguez‐Ronderos, M. Elizabeth; Lin, Feng

doi:10.3390/rs13132576

Cited by 8 publications

(5 citation statements)

References 33 publications

(64 reference statements)

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“…However, in the context of CCF, and owing to occlusion, the precision drops off with smaller trees such as those from regeneration [50]. Below-canopy remote sensing techniques-such as TLS, MLS, and photogrammetry-are better suited to the accurate mapping of regeneration [59,72,[82][83][84], and it has been shown in irregular tropical forests that MLS can identify small-diameter understory trees with far greater geospatial positioning accuracy, 6 cm, than methods using aerial data, which had 6-m positioning error [91]. The development of tree detection algorithms for use with below-canopy point clouds is happening rapidly, and there now are several solutions available which can accurately locate, identify, and measure trees and saplings from point cloud data [95][96][97][98].…”

Section: Remote Sensing For Ccf Inventory Measurement and Stock Mappingmentioning

confidence: 99%

Continuous Cover Forestry and Remote Sensing: A Review of Knowledge Gaps, Challenges, and Potential Directions

Stoddart,

Suarez,

Mason

et al. 2023

Curr. For. Rep.

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Purpose of Review Continuous cover forestry (CCF) is a sustainable management approach for forestry in which forest stands are manipulated to create irregular stand structures with varied species composition. This approach differs greatly from the traditional approaches of plantation-based forestry, in which uniform monocultures are maintained, and thus, traditional methods of assessment, such as productivity (yield class) calculations, are less applicable. This creates a need to identify new methods to succeed the old and be of use in operational forestry and research. By applying remote sensing techniques to CCF, it may be possible to identify novel solutions to the challenges introduced through the adoption of CCF. Recent Findings There has been a limited amount of work published on the applications of remote sensing to CCF in the last decade. Research can primarily be characterised as explorations of different methods to quantify the target state of CCF and monitor indices of stand structural complexity during transformation to CCF, using terrestrial and aerial data collection techniques. Summary We identify a range of challenges associated with CCF and outline the outstanding gaps within the current body of research in need of further investigation, including a need for the development of new inventory methods using remote sensing techniques. We identify methods, such as individual tree models, that could be applied to CCF from other complex, heterogenous forest systems and propose the wider adoption of remote sensing including information for interested parties to get started.

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Section: Remote Sensing For Ccf Inventory Measurement and Stock Mappingmentioning

confidence: 99%

Continuous Cover Forestry and Remote Sensing: A Review of Knowledge Gaps, Challenges, and Potential Directions

Stoddart,

Suarez,

Mason

et al. 2023

Curr. For. Rep.

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“…Liu et al (2022) calculated the number of discovered trees during the flight path, but typically the works concentrate solely on autonomous flying. On the other side, the forest research utilizing under-canopy drones has been mainly based on manual flying by human pilots (Kuželka and Surovỳ, 2018;Krisanski et al, 2020;Hyyppä et al, 2020aChisholm et al, 2013Chisholm et al, , 2021Wang et al, 2021;Tavi, 2023). Shimabuku et al (2023) used semi-automated commercial drone that was able to avoid collisions with tree-trunks but the flight path had to be manually corrected after the the avoidance.…”

Section: Introductionmentioning

confidence: 99%

Autonomous robotic drone system for mapping forest interiors

Karjalainen,

Koivumäki,

Hakala

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. During the last decade, the use of drones in forest monitoring and remote sensing has become highly popular. While most of the monitoring tasks take place in high altitudes and open air, in the last few years drones have also gained interest in under-canopy data collection. However, flying under the forest canopy is a complex task since the drone can not use Global Navigation Satellite Systems (GNSS) for positioning and it has to continually avoid obstacles, such as trees, branches, and rocks, on its path. For that reason, drone-based data collection under the forest canopy is still mainly based on manual control by human pilots. Autonomous flying in GNSS-denied obstacle-rich environment has been an actively researched topic in the field of robotics during the last years and various open-sourced methods have been published in the literature. However, most of the research is done purely from the point-of-view of robotics and only a few studies have been published in the boundary of forest sciences and robotics aiming to take steps towards autonomous forest data collection. In this study, a prototype of an autonomous under-canopy drone is developed and implemented utilizing state-of-the-art open-source methods. The prototype is utilizing the EGO-Planner-v2 trajectory planner for autonomous obstacle avoidance and VINS-Fusion for Visual-inertial-odometry based GNSS-free pose estimation. The flying performance of the prototype is evaluated by performing multiple test flights with real hardware in two different boreal forest test plots with medium and difficult densities. Furthermore, the first results of the forest data collecting performance are obtained by post-processing the data collected with a low-cost stereo camera during one test flight to a 3D point cloud and by performing diameter breast at height (DBH) estimation. In the medium-density forest, all seven test flights were successful, but in the difficult test forest, one of eight test flights failed. The RMSE of the DBH estimation was 3.86 cm (12.98 %).

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“…Remote sensing (RS) methods such as terrestrial laser scanning (TLS), mobile laser scanning (MLS), airborne laser scanning (ALS), unmanned aerial vehicle (UAV) LiDAR, and digital aerial photogrammetry (DAP) have proved to be efficient and accurate for estimating tree parameters [12][13][14][15][16][17][18][19]. In recent years, UAV LiDAR has become increasingly popular [20][21][22][23][24][25][26] as it provides 3D information with a higher spatial resolution due to its low flying altitude [27][28][29]. It also accommodates a higher data acquisition frequency for continuous forest monitoring and offers high temporal resolution [30].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Individual Tree Height Estimated from LiDAR and Digital Aerial Photogrammetry in Young Forests

et al. 2022

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Biomass stored in young forests has enormous potential for the reduction of fossil fuel consumption. However, to ensure long-term sustainability, the measurement accuracy of tree height is crucial for forest biomass and carbon stock monitoring, particularly in young forests. Precise height measurement using traditional field measurements is challenging and time consuming. Remote sensing (RS) methods can, however, replace traditional field-based forest inventory. In our study, we compare individual tree height estimation from Light Detection and Ranging (LiDAR) and Digital Aerial Photogrammetry (DAP) with field measurements. It should be noted, however, that there was a one-year temporal difference between the field measurement and LiDAR/DAP scanning. A total of 130 trees (32 Scots Pine, 29 Norway Spruce, 67 Silver Birch, and 2 Eurasian Aspen) were selected for height measurement in a young private forest in south-east Finland. Statistical correlation based on paired t-tests and analysis of variance (ANOVA, one way) was used to compare the tree height measured with the different methods. Comparative results between the remote sensing methods and field measurements showed that LiDAR measurements had a stronger correlation with the field measurements and higher accuracy for pine (R2 = 0.86, bias = 0.70, RMSE = 1.44) and birch (R2 = 0.81, bias = 0.86, RMSE = 1.56) than DAP, which had correlation values of (R2 = 0.71, bias = 0.82, RMSE = 2.13) for pine and (R2 = 0.69, bias = 1.19, RMSE = 2.08) for birch. The correlation of the two remote sensing methods with the field measurements was very similar for spruce: LiDAR (R2 = 0.83, bias = 0.30, RMSE = 1.17) and DAP (R2 = 0.83, bias = 0.44, RMSE = 1.26). Moreover, the correlation was highly significant, with minimum error and mean difference (R2 = 0.79–0.98, MD = 0.12–0.33, RMSD = 0.45–1.67) between LiDAR and DAP for all species. However, the paired t-test suggested that there is a significant difference (p < 0.05) in height observation between the field measurements and remote sensing for pine and birch. The test showed that LiDAR and DAP output are not significantly different for pine and spruce. Presumably, the time difference in field campaign between the methods was the reason for these significant results. Additionally, the ANOVA test indicated that the overall means of estimated height from LiDAR and DAP were not significantly different from field measurements in all species. We concluded that utilization of LiDAR and DAP for estimating individual tree height in young forests is possible with acceptable error and comparable accuracy to field measurement. Hence, forest inventory in young forests can be carried out using LiDAR or DAP for height estimation at the individual tree level as an alternative to traditional field measurement approaches.

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Estimating Tree Diameters from an Autonomous Below-Canopy UAV with Mounted LiDAR

Cited by 8 publications

References 33 publications

Continuous Cover Forestry and Remote Sensing: A Review of Knowledge Gaps, Challenges, and Potential Directions

Continuous Cover Forestry and Remote Sensing: A Review of Knowledge Gaps, Challenges, and Potential Directions

Autonomous robotic drone system for mapping forest interiors

Comparison of Individual Tree Height Estimated from LiDAR and Digital Aerial Photogrammetry in Young Forests

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