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

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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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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Edge effect on carabid assemblages along forest-grass transects

Web Ecology ◽

10.5194/we-2-7-2001 ◽

2001 ◽

Vol 2 (1) ◽

pp. 7-13 ◽

Cited By ~ 14

Author(s):

T. Magura ◽

B. Tóthmérész ◽

T. Molnár

Keyword(s):

Edge Effect ◽

Abiotic Factors ◽

Canopy Cover ◽

Distinct Species ◽

Forest Edge ◽

Principal Coordinates Analysis ◽

Forest Interior ◽

Principal Coordinates ◽

Significant Edge ◽

Characteristic Species

Abstract. During 1997 and 1998, we have tested the edge-effect for carabids along oak-hornbeam forest-grass transects using pitfall traps in Hungary. Our hypothesis was that the diversity of carabids will be higher in the forest edge than in the forest interior. We also focused on the characteristic species of the habitats along the transects and the relationships between their distribution and the biotic and abiotic factors. Our results proved that there was a significant edge effect on the studied carabid communities: the Shannon diversity increased significantly along the transects from the forest towards the grass. The diversity of the carabids were significantly higher in the forest edge and in the grass than in the forest interior. The carabids of the forest, the forest edge and the grass are separated from each other by principal coordinates analysis and by indicator species analysis (IndVal), suggesting that each of the three habitats has a distinct species assemblages. There were 5 distinctive groups of carabids: 1) habitat generalists, 2) forest generalists, 3) species of the open area, 4) forest edge species, and 5) forest specialists. It was demonstrated by multiple regression analyses, that the relative air moisture, temperature of the ground, the cover of leaf litter, herbs, shrubs and canopy cover, abundance of the carabids’ preys are the most important factors determining the diversity and spatial pattern of carabids along the studied transects.

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Assessing the Ability of Image Based Point Clouds Captured from a UAV to Measure the Terrain in the Presence of Canopy Cover

Forests ◽

10.3390/f10030284 ◽

2019 ◽

Vol 10 (3) ◽

pp. 284 ◽

Cited By ~ 8

Author(s):

Luke Wallace ◽

Chris Bellman ◽

Bryan Hally ◽

Jaime Hernandez ◽

Simon Jones ◽

...

Keyword(s):

Ground Surface ◽

Canopy Cover ◽

Weather Conditions ◽

Point Clouds ◽

Unmanned Aerial Systems ◽

Image Capture ◽

Terrain Model ◽

Processing Strategies ◽

Aerial Systems

Point clouds captured from Unmanned Aerial Systems are increasingly relied upon to provide information describing the structure of forests. The quality of the information derived from these point clouds is dependent on a range of variables, including the type and structure of the forest, weather conditions and flying parameters. A key requirement to achieve accurate estimates of height based metrics describing forest structure is a source of ground information. This study explores the availability and reliability of ground surface points available within point clouds captured in six forests of different structure (canopy cover and height), using three image capture and processing strategies, consisting of nadir, oblique and composite nadir/oblique image networks. The ground information was extracted through manual segmentation of the point clouds as well as through the use of two commonly used ground filters, LAStools lasground and the Cloth Simulation Filter. The outcomes of these strategies were assessed against ground control captured with a Total Station. Results indicate that a small increase in the number of ground points captured (between 0 and 5% of a 10 m radius plot) can be achieved through the use of a composite image network. In the case of manually identified ground points, this reduced the root mean square error (RMSE) error of the terrain model by between 1 and 11 cm, with greater reductions seen in plots with high canopy cover. The ground filters trialled were not able to exploit the extra information in the point clouds and inconsistent results in terrain RMSE were obtained across the various plots and imaging network configurations. The use of a composite network also provided greater penetration into the canopy, which is likely to improve the representation of mid-canopy elements.

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Evaluation of Ground Surface Models Derived from Unmanned Aerial Systems with Digital Aerial Photogrammetry in a Disturbed Conifer Forest

Remote Sensing ◽

10.3390/rs11010084 ◽

2019 ◽

Vol 11 (1) ◽

pp. 84 ◽

Cited By ~ 10

Author(s):

Alexander Graham ◽

Nicholas Coops ◽

Michael Wilcox ◽

Andrew Plowright

Keyword(s):

Canopy Cover ◽

Point Clouds ◽

Unmanned Aerial Systems ◽

Optimal Parameters ◽

Conifer Forest ◽

Terrain Modeling ◽

Ground Truth Data ◽

Aerial Photogrammetry ◽

Terrain Slope ◽

Aerial Systems

Detailed vertical forest structure information can be remotely sensed by combining technologies of unmanned aerial systems (UAS) and digital aerial photogrammetry (DAP). A key limitation in the application of DAP methods, however, is the inability to produce accurate digital elevation models (DEM) in areas of dense vegetation. This study investigates the terrain modeling potential of UAS-DAP methods within a temperate conifer forest in British Columbia, Canada. UAS-acquired images were photogrammetrically processed to produce high-resolution DAP point clouds. To evaluate the terrain modeling ability of DAP, first, a sensitivity analysis was conducted to estimate optimal parameters of three ground-point classification algorithms designed for airborne laser scanning (ALS). Algorithms tested include progressive triangulated irregular network (TIN) densification (PTD), hierarchical robust interpolation (HRI) and simple progressive morphological filtering (SMRF). Points were classified as ground from the ALS and served as ground-truth data to which UAS-DAP derived DEMs were compared. The proportion of area with root mean square error (RMSE) <1.5 m were 56.5%, 51.6% and 52.3% for the PTD, HRI and SMRF methods respectively. To assess the influence of terrain slope and canopy cover, error values of DAP-DEMs produced using optimal parameters were compared to stratified classes of canopy cover and slope generated from ALS point clouds. Results indicate that canopy cover was approximately three times more influential on RMSE than terrain slope.

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Analyzing the edge effects in a Brazilian seasonally dry tropical forest

Brazilian Journal of Biology ◽

10.1590/1519-6984.16014 ◽

2016 ◽

Vol 76 (1) ◽

pp. 169-175 ◽

Cited By ~ 1

Author(s):

D. M. Arruda ◽

P. V. Eisenlohr

Keyword(s):

Edge Effects ◽

Tree Species ◽

Forest Canopy ◽

Canopy Cover ◽

Dry Forest ◽

Forest Edges ◽

Dry Forests ◽

Seasonally Dry ◽

Dry Tropical Forests ◽

Microclimatic Conditions

Abstract Due to the deciduous nature of dry forests (widely known as seasonally dry tropical forests) they are subject to microclimatic conditions not experienced in other forest formations. Close examinations of the theory of edge effects in dry forests are still rare and a number of questions arise in terms of this topic. In light of this situation we examined a fragment of the dry forest to respond to the following questions: (I) Are there differences in canopy cover along the edge-interior gradient during the dry season? (II) How does the microclimate (air temperature, soil temperature, and relative humidity) vary along that gradient? (III) How does the microclimate influence tree species richness, evenness and abundance along that gradient? (IV) Are certain tree species more dominant closer to the forest edges? Regressions were performed to address these questions. Their coefficients did not significantly vary from zero. Apparently, the uniform openness of the forest canopy caused a homogeneous internal microclimate, without significant differentiation in habitats that would allow modifications in biotic variables tested. We conclude that the processes of edge effect commonly seen in humid forests, not was shared with the dry forest assessed.

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

Remote Sensing ◽

10.3390/rs12101652 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1652

Author(s):

Sean Krisanski ◽

Mohammad Sadegh Taskhiri ◽

Paul Turner

Keyword(s):

Forest Canopy ◽

Measurement Techniques ◽

Digital Terrain Model ◽

Canopy Cover ◽

Field Measurements ◽

Point Clouds ◽

3D Models ◽

Unmanned Aircraft ◽

Global Navigation Satellite Systems ◽

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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Leveraging TLS as a Calibration and Validation Tool for MLS and ULS Mapping of Savanna Structure and Biomass at Landscape-Scales

Remote Sensing ◽

10.3390/rs13020257 ◽

2021 ◽

Vol 13 (2) ◽

pp. 257 ◽

Cited By ~ 1

Author(s):

Shaun R. Levick ◽

Tim Whiteside ◽

David A. Loewensteiner ◽

Mitchel Rudge ◽

Renee Bartolo

Keyword(s):

Remote Sensing ◽

Vegetation Structure ◽

Laser Scanning ◽

Canopy Cover ◽

Structural Diversity ◽

Point Clouds ◽

Large Area ◽

Calibration And Validation ◽

Savanna Vegetation ◽

Landscape Scales

Savanna ecosystems are challenging to map and monitor as their vegetation is highly dynamic in space and time. Understanding the structural diversity and biomass distribution of savanna vegetation requires high-resolution measurements over large areas and at regular time intervals. These requirements cannot currently be met through field-based inventories nor spaceborne satellite remote sensing alone. UAV-based remote sensing offers potential as an intermediate scaling tool, providing acquisition flexibility and cost-effectiveness. Yet despite the increased availability of lightweight LiDAR payloads, the suitability of UAV-based LiDAR for mapping and monitoring savanna 3D vegetation structure is not well established. We mapped a 1 ha savanna plot with terrestrial-, mobile- and UAV-based laser scanning (TLS, MLS, and ULS), in conjunction with a traditional field-based inventory (n = 572 stems > 0.03 m). We treated the TLS dataset as the gold standard against which we evaluated the degree of complementarity and divergence of structural metrics from MLS and ULS. Sensitivity analysis showed that MLS and ULS canopy height models (CHMs) did not differ significantly from TLS-derived models at spatial resolutions greater than 2 m and 4 m respectively. Statistical comparison of the resulting point clouds showed minor over- and under-estimation of woody canopy cover by MLS and ULS, respectively. Individual stem locations and DBH measurements from the field inventory were well replicated by the TLS survey (R2 = 0.89, RMSE = 0.024 m), which estimated above-ground woody biomass to be 7% greater than field-inventory estimates (44.21 Mg ha−1 vs 41.08 Mg ha−1). Stem DBH could not be reliably estimated directly from the MLS or ULS, nor indirectly through allometric scaling with crown attributes (R2 = 0.36, RMSE = 0.075 m). MLS and ULS show strong potential for providing rapid and larger area capture of savanna vegetation structure at resolutions suitable for many ecological investigations; however, our results underscore the necessity of nesting TLS sampling within these surveys to quantify uncertainty. Complementing large area MLS and ULS surveys with TLS sampling will expand our options for the calibration and validation of multiple spaceborne LiDAR, SAR, and optical missions.

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Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽

10.3390/land10040433 ◽

2021 ◽

Vol 10 (4) ◽

pp. 433

Author(s):

Xiaolan Huang ◽

Weicheng Wu ◽

Tingting Shen ◽

Lifeng Xie ◽

Yaozu Qin ◽

...

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Forest Canopy ◽

Canopy Cover ◽

Google Earth ◽

Tree Canopy ◽

Landsat Data ◽

High Resolution Data ◽

Modis Ndvi ◽

Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

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