Enhancing Methods for Under-Canopy Unmanned Aircraft System Based Photogrammetry in Complex Forests for Tree Diameter Measurement

The application of Unmanned Aircraft Systems (UAS) beneath the forest canopy provides a potentially valuable alternative to ground-based measurement techniques in areas of dense canopy cover and undergrowth. This research presents results from a study of a consumer-grade UAS flown under the forest canopy in challenging forest and terrain conditions. This UAS was deployed to assess under-canopy UAS photogrammetry as an alternative to field measurements for obtaining stem diameters as well as ultra-high-resolution (~400,000 points/m2) 3D models of forest study sites. There were 378 tape-based diameter measurements collected from 99 stems in a native, unmanaged eucalyptus pulchella forest with mixed understory conditions and steep terrain. These measurements were used as a baseline to evaluate the accuracy of diameter measurements from under-canopy UAS-based photogrammetric point clouds. The diameter measurement accuracy was evaluated without the influence of a digital terrain model using an innovative tape-based method. A practical and detailed methodology is presented for the creation of these point clouds. Lastly, a metric called the Circumferential Completeness Index (CCI) was defined to address the absence of a clearly defined measure of point coverage when measuring stem diameters from forest point clouds. The measurement of the mean CCI is suggested for use in future studies to enable a consistent comparison of the coverage of forest point clouds using different sensors, point densities, trajectories, and methodologies. It was found that root-mean-squared-errors of diameter measurements were 0.011 m in Site 1 and 0.021 m in the more challenging Site 2. The point clouds in this study had a mean validated CCI of 0.78 for Site 1 and 0.7 for Site 2, with a mean unvalidated CCI of 0.86 for Site 1 and 0.89 for Site 2. The results in this study demonstrate that under-canopy UAS photogrammetry shows promise in becoming a practical alternative to traditional field measurements, however, these results are currently reliant upon the operator’s knowledge of photogrammetry and his/her ability to fly manually in object-rich environments. Future work should pursue solutions to autonomous operation, more complete point clouds, and a method for providing scale to point clouds when global navigation satellite systems are unavailable.

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A Comparative Study to Evaluate Accuracy on Canopy Height and Density Using UAV, ALS, and Fieldwork

Forests ◽

10.3390/f11020241 ◽

2020 ◽

Vol 11 (2) ◽

pp. 241 ◽

Cited By ~ 3

Author(s):

Cheonggil Jin ◽

Che-young Oh ◽

Sanghyun Shin ◽

Nkwain Wilfred Njungwi ◽

Chuluong Choi

Keyword(s):

Forest Canopy ◽

Forest Type ◽

Canopy Cover ◽

Tree Height ◽

Field Measurements ◽

Point Clouds ◽

Aerial Photographs ◽

Measurement Unit ◽

Maximum Height ◽

Cover Density

Accurate measurement of the tree height and canopy cover density is important for forest biomass and management. Recently, Light Detection and Ranging (LIDAR) and Unmanned Aerial Vehicle (UAV) images have been used to estimate the tree height and canopy cover density for a forest stands. More so, UAV systems with autopilot functions, affordable Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) have created new possibilities, aided by available photogrammetric programs. In this study, we investigated the possibility of data collection methods using an Aerial LIDAR Scanner (ALS) and an UAV together with a fieldworks to evaluate accurate the tree standard metrics in Singyeri, Gyeongjusi, and Gyeongsangbukdo province. The derived metrics via statistical analyses of the ALS and UAV data and validated by field measurements were compared to a published forest type map (scale 1:5000) by the Korea Forest Service; geared towards improving the forest attributes. We collected data and analyzed and compared them with existent the forest type map produced from an aerial photographs and a digital stereo plotter. The ALS data of around 19.5 points·m–2 were collected by an airplane, then processed and classified using the LAStools; while about 362 images of the UAV were processed via Structure from Motion algorithm in the Agisoft Metashape Pro. Thus, we calculated the metrics using the point clouds of both an ALS and an UAV, and then verified their similarity. The fieldwork was manually done on 110 sampled trees. Calculated heights of the UAV were 3.8~5.8 m greater than those for the ALS; and when correlated with the fieldwork, the UAV data overestimated, while the maximum height of the ALS data was more accurate. For the canopy cover, the ALS computed canopy cover was 10%~30% less than that of the UAV. However, the canopy cover above 2 m by an UAV was the best measurement for a forest canopy. Therefore, these results assert that the examined techniques are robust and can significantly complement methods of the conventional data acquisition for the forest type map.

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Estimation of forest canopy cover: a comparison of field measurement techniques

Silva Fennica ◽

10.14214/sf.315 ◽

2006 ◽

Vol 40 (4) ◽

Cited By ~ 141

Author(s):

Lauri Korhonen ◽

Kari Korhonen ◽

Miina Rautiainen ◽

Pauline Stenberg

Keyword(s):

Field Measurement ◽

Forest Canopy ◽

Measurement Techniques ◽

Canopy Cover

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Identification of Micro-Scale Landforms of Landslides Using Precise Digital Elevation Models

Geosciences ◽

10.3390/geosciences9030117 ◽

2019 ◽

Vol 9 (3) ◽

pp. 117 ◽

Cited By ~ 5

Author(s):

František Chudý ◽

Martina Slámová ◽

Julián Tomaštík ◽

Roberta Prokešová ◽

Martin Mokroš

Keyword(s):

Large Scale ◽

Forest Canopy ◽

Digital Elevation Models ◽

Canopy Cover ◽

Point Clouds ◽

Tree Canopy ◽

Automatic Identification ◽

Optical Parameters ◽

Micro Scale ◽

Digital Elevation

An active gully-related landslide system is located in a deep valley under forest canopy cover. Generally, point clouds from forested areas have a lack of data connectivity, and optical parameters of scanning cameras lead to different densities of point clouds. Data noise or systematic errors (missing data) make the automatic identification of landforms under tree canopy problematic or impossible. We processed, analyzed, and interpreted data from a large-scale landslide survey, which were acquired by the light detection and ranging (LiDAR) technology, remotely piloted aircraft system (RPAS), and close-range photogrammetry (CRP) using the ‘Structure-from-Motion’ (SfM) method. LAStools is a highly efficient Geographic Information System (GIS) tool for point clouds pre-processing and creating precise digital elevation models (DEMs). The main landslide body and its landforms indicating the landslide activity were detected and delineated in DEM-derivatives. Identification of micro-scale landforms in precise DEMs at large scales allow the monitoring and the assessment of these active parts of landslides that are invisible in digital terrain models at smaller scales (obtained from aerial LiDAR or from RPAS) due to insufficient data density or the presence of many data gaps.

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Evaluating the Capability of Unmanned Aerial System (UAS) Imagery to Detect and Measure the Effects of Edge Influence on Forest Canopy Cover in New England

Forests ◽

10.3390/f12091252 ◽

2021 ◽

Vol 12 (9) ◽

pp. 1252

Author(s):

Heather Grybas ◽

Russell G. Congalton

Keyword(s):

New England ◽

Forest Canopy ◽

Canopy Cover ◽

Point Clouds ◽

Forest Edge ◽

P Value ◽

Unmanned Aerial Systems ◽

Forest Edges ◽

Significant Edge ◽

The Face

Characterizing and measuring the extent of change at forest edges is important for making management decisions, especially in the face of climate change, but is difficult due to the large number of factors that can modify the response. Unmanned aerial systems (UAS) imagery may serve as a tool to detect and measure the forest response at the edge quickly and repeatedly, thus allowing a larger amount of area to be covered with less work. This study is a preliminary attempt to utilize UAS imagery to detect changes in canopy cover, known to exhibit changes due to edge influences, across forest edges in a New England forest. Changes in canopy cover with increasing distance from the forest edge were measured on the ground using digital cover photography and from photogrammetric point clouds and imagery-based maps of canopy gaps produced with UAS imagery. The imagery-based canopy gap products were significantly more similar to ground estimates for canopy cover (p value > 0.05) than the photogrammetric point clouds, but still suffered overestimation (RMSE of 0.088) due to the inability to detect small canopy openings. Both the ground and UAS data were able to detect a decrease in canopy cover to between 45–50 m from the edge, followed by an increase to 100 m. The UAS data had the advantage of a greater sampling intensity and was thus better able to detect a significant edge effect of minimal magnitude effect in the presence of heavy variability.

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COMPARISON ASSESSMENT OF DIGITAL 3D MODELS OBTAINED BY DRONE-BASED LIDAR AND DRONE IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlvi-4-w5-2021-113-2021 ◽

2021 ◽

Vol XLVI-4/W5-2021 ◽

pp. 113-118

Author(s):

M. Bouziani ◽

M. Amraoui ◽

S. Kellouch

Keyword(s):

Laser Scanner ◽

Digital Terrain Model ◽

Point Clouds ◽

3D Models ◽

Pulse Frequency ◽

Airborne Lidar ◽

Measurement Unit ◽

Surface Model ◽

Digital Products

Abstract. The purpose of this study is to assess the potential of drone airborne LiDAR technology in Morocco in comparison with drone photogrammetry. The cost and complexity of the equipment which includes a laser scanner, an inertial measurement unit, a positioning system and a platform are among the causes limiting its use. Furthermore, this study was motivated by the following reasons: (1) Limited number of studies in Morocco on drone-based LiDAR technology applications, (2) Lack of study on the parameters that influence the quality of drone-based LiDAR surveys as well as on the evaluation of the accuracy of derived products. In this study, the evaluation of LiDAR technology was carried out by an analysis of the geometric accuracy of the 3D products generated: Digital Terrain Model (DTM), Digital Surface Model (DSM) and Digital Canopy Model (DCM). We conduct a comparison with the products generated by drone photogrammetry and GNSS surveys. Several tests were carried out to analyse the parameters that influence the mission results namely height, overlap, drone speed and laser pulse frequency. After data collection, the processing phase was carried out. It includes: the cleaning, the consolidation then the classification of point clouds and the generation of the various digital models. This project also made it possible to propose and validate a workflow for the processing, the classification of point clouds and the generation of 3D digital products derived from the processing of LiDAR data acquired by drone.

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Workflow for 3D geovisualization of the data obtained with the use of Unmanned Aerial Vehicle in Augmented Reality

Abstracts of the ICA ◽

10.5194/ica-abs-1-105-2019 ◽

2019 ◽

Vol 1 ◽

pp. 1-1

Author(s):

Łukasz Halik ◽

Maciej Smaczyński ◽

Beata Medyńska-Gulij

Keyword(s):

Augmented Reality ◽

Large Scale ◽

3D Model ◽

Digital Terrain Model ◽

Point Clouds ◽

3D Models ◽

Irregular Shape ◽

Terrain Model ◽

Natural Aggregate ◽

Digital Terrain

Abstract. The attempt to work out the geomatic workflow of transforming low-level aerial imagery obtained with unmanned aerial vehicles (UAV) into a digital terrain model (DTM) and implementing the 3D model into the augmented reality (AR) system constitutes the main problem discussed in this article. The authors suggest the following workflow demonstrated in Fig. 1.The series of pictures obtained by means of UAV equipped with a HD camera was the source of data to be worked out in the final stage of the geovisualization. The series was then processed and a few point clouds were isolated from it, being later used for generating test 3D models.The practical aim of the research conducted was to work out, on the basis of the UAV pictures, the 3D geovisualization in the AR system that would depict the heap of the natural aggregate of irregular shape. The subsequent aim was to verify the accuracy of the produced 3D model. The object of the study was a natural aggregate heap of irregular shape and denivelations up to 11 meters.Based on the obtained photos, three point clouds (varying in the level of detail) were generated for the 20&thinsp;000-meter-square area. The several-centimeter differences observed between the control points in the field and the ones from the model might corroborate the usefulness of the described algorithm for creating large-scale DTMs for engineering purposes. The method of transformation of pictures into the point cloud that was subsequently transformed into 3D models was employed in the research, resulting in the scheme depicting the technological sequence of the creation of 3D geovisualization worked out in the AR system. The geovisualization can be viewed thanks to a specially worked out mobile application for smartphones.

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BARE-EARTH EXTRACTION AND DTM GENERATION FROM PHOTOGRAMMETRIC POINT CLOUDS WITH A PARTIAL USE OF AN EXISTING LOWER RESOLUTION DTM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b3-201-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 201-206

Author(s):

M. Debella-Gilo

Keyword(s):

Laser Scanning ◽

Forest Canopy ◽

Interpolation Method ◽

Digital Terrain Model ◽

Ground Cover ◽

Point Clouds ◽

Local Slope ◽

Terrain Model ◽

National University ◽

Forest Canopy Density

A method of extracting bare-earth points from photogrammetric point clouds by partially using an existing lower resolution digital terrain model (DTM) is presented. The bare-earth points are extracted based on a threshold defined by local slope. The local slope is estimated from the lower resolution DTM. A gridded DTM is then interpolated from the extracted bare-earth points. Five different interpolation algorithms are implemented and evaluated to identify the most suitable interpolation method for such non-uniformly scattered data. The algorithm is tested on four test sites with varying topographic and ground cover characteristics. The results are evaluated against a reference DTM created using aerial laser scanning. The deviations of the extracted bare-earth points, and the interpolated DTM, from the reference DTM increases with increasing forest canopy density and terrain roughness. The DTM created by the method is significantly closer to the reference DTM than the lower resolution national DTM. The ANUDEM (Australian National University Digital Elevation Modelling) interpolation method is found to be the best performing interpolation method in terms of reducing the deviations and in terms of modelling the terrain realistically with minimum artefacts, although the differences among the interpolation methods are not considerably large.

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BARE-EARTH EXTRACTION AND DTM GENERATION FROM PHOTOGRAMMETRIC POINT CLOUDS WITH A PARTIAL USE OF AN EXISTING LOWER RESOLUTION DTM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-201-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 201-206 ◽

Cited By ~ 3

Author(s):

M. Debella-Gilo

Keyword(s):

Laser Scanning ◽

Forest Canopy ◽

Interpolation Method ◽

Digital Terrain Model ◽

Ground Cover ◽

Point Clouds ◽

Local Slope ◽

Terrain Model ◽

National University ◽

Forest Canopy Density

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Regional Sampling of Forest Canopy Covers Using UAV Visible Stereoscopic Imagery for Assessment of Satellite-Based Products in Northeast China

Journal of Remote Sensing ◽

10.34133/2022/9806802 ◽

2022 ◽

Vol 2022 ◽

pp. 1-14

Author(s):

Tianyu Yu ◽

Wenjian Ni ◽

Zhiyu Zhang ◽

Qinhuo Liu ◽

Guoqing Sun

Keyword(s):

Forest Canopy ◽

Structural Information ◽

Rapid Development ◽

Canopy Cover ◽

Point Clouds ◽

Spectral Information ◽

Surface Model ◽

Simple Method ◽

Tree Crowns ◽

Global Land

Canopy cover is an important parameter affecting forest succession, carbon fluxes, and wildlife habitats. Several global maps with different spatial resolutions have been produced based on satellite images, but facing the deficiency of reliable references for accuracy assessments. The rapid development of unmanned aerial vehicle (UAV) equipped with consumer-grade camera enables the acquisition of high-resolution images at low cost, which provides the research community a promising tool to collect reference data. However, it is still a challenge to distinguish tree crowns and understory green vegetation based on the UAV-based true color images (RGB) due to the limited spectral information. In addition, the canopy height model (CHM) derived from photogrammetric point clouds has also been used to identify tree crowns but limited by the unavailability of understory terrain elevations. This study proposed a simple method to distinguish tree crowns and understories based on UAV visible images, which was referred to as BAMOS for convenience. The central idea of the BAMOS was the synergy of spectral information from digital orthophoto map (DOM) and structural information from digital surface model (DSM). Samples of canopy covers were produced by applying the BAMOS method on the UAV images collected at 77 sites with a size of about 1.0 km2 across Daxing’anling forested area in northeast of China. Results showed that canopy cover extracted by the BAMOS method was highly correlated to visually interpreted ones with correlation coefficient (r) of 0.96 and root mean square error (RMSE) of 5.7%. Then, the UAV-based canopy covers served as references for assessment of satellite-based maps, including MOD44B Version 6 Vegetation Continuous Fields (MODIS VCF), maps developed by the Global Land Cover Facility (GLCF) and by the Global Land Analysis and Discovery laboratory (GLAD). Results showed that both GLAD and GLCF canopy covers could capture the dominant spatial patterns, but GLAD canopy cover tended to miss scattered trees in highly heterogeneous areas, and GLCF failed to capture non-tree areas. Most important of all, obvious underestimations with RMSE about 20% were easily observed in all satellite-based maps, although the temporal inconsistency with references might have some contributions.

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Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand

Remote Sensing ◽

10.3390/rs10081266 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1266 ◽

Cited By ~ 26

Author(s):

Patrick Shin ◽

Temuulen Sankey ◽

Margaret Moore ◽

Andrea Thode

Keyword(s):

Unmanned Aerial Vehicle ◽

Ponderosa Pine ◽

Forest Canopy ◽

Canopy Cover ◽

Field Measurements ◽

Tree Density ◽

Density Estimates ◽

Canopy Fuels ◽

Aerial Vehicle ◽

Canopy Bulk Density

Forests in the Southwestern United States are becoming increasingly susceptible to large wildfires. As a result, forest managers are conducting forest fuel reduction treatments for which spatial fuels and structure information are necessary. However, this information currently has coarse spatial resolution and variable accuracy. This study tested the feasibility of using unmanned aerial vehicle (UAV) imagery to estimate forest canopy fuels and structure in a southwestern ponderosa pine stand. UAV-based multispectral images and Structure-from-Motion point clouds were used to estimate canopy cover, canopy height, tree density, canopy base height, and canopy bulk density. Estimates were validated with field data from 57 plots and aerial photography from the U.S. Department of Agriculture National Agriculture Imaging Program. Results indicate that UAV imagery can be used to accurately estimate forest canopy cover (correlation coefficient (R2) = 0.82, root mean square error (RMSE) = 8.9%). Tree density estimates correctly detected 74% of field-mapped trees with a 16% commission error rate. Individual tree height estimates were strongly correlated with field measurements (R2 = 0.71, RMSE = 1.83 m), whereas canopy base height estimates had a weaker correlation (R2 = 0.34, RMSE = 2.52 m). Estimates of canopy bulk density were not correlated to field measurements. UAV-derived inputs resulted in drastically different estimates of potential crown fire behavior when compared with coarse resolution LANDFIRE data. Methods from this study provide additional data to supplement, or potentially substitute, traditional estimates of canopy fuel.

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