Influence of Vegetation Structure on Lidar-derived Canopy Height and Fractional Cover in Forested Riparian Buffers During Leaf-Off and Leaf-On Conditions

Wasser, Leah; Day, R. L.; Chasmer, L.; Taylor, Alan H.

doi:10.1371/journal.pone.0054776

Cited by 110 publications

(90 citation statements)

References 63 publications

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“…Similar to prior research in the use of LIDAR for measuring forests [35,65,66] this study found a strong relationship between canopy height metrics, 3D point cloud density (PD), and canopy penetration (CP). Both PD and CP were strongly related to forward photographic overlap (Figure 3, Figure 4).…”

Section: The Importance Of Image Overlapsupporting

confidence: 82%

Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

2015

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Ecological remote sensing is being transformed by three-dimensional (3D), multispectral measurements of forest canopies by unmanned aerial vehicles (UAV) and computer vision structure from motion (SFM) algorithms. Yet applications of this technology have out-paced understanding of the relationship between collection method and data quality. Here, UAV-SFM remote sensing was used to produce 3D multispectral point clouds of Temperate Deciduous forests at different levels of UAV altitude, image overlap, weather, and image processing. Error in canopy height estimates was explained by the alignment of the canopy height model to the digital terrain model (R 2 = 0.81) due to differences in lighting and image overlap. Accounting for this, no significant differences were observed in height error at different levels of lighting, altitude, and side overlap. Overall, accurate estimates of canopy height compared to field measurements (R 2 = 0.86, RMSE = 3.6 m) and LIDAR (R 2 = 0.99, RMSE = 3.0 m) were obtained under optimal conditions of clear lighting and high image overlap (>80%). Variation in point cloud quality appeared related to the behavior of SFM 'image features'. Future research should consider the role of image features as the fundamental unit of SFM remote sensing, akin to the pixel of optical imaging and the laser pulse of LIDAR.

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Section: The Importance Of Image Overlapsupporting

confidence: 82%

Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

2015

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“…Biome Vertical Accuracy (m) Metric [4] Old growth tropical forest 1.95 RMSE [4] Secondary tropical forest 1.44 RMSE [4] Selectively logged tropical forest 1.62 RMSE [5] Steep Mediterranean shrubland 0.13-0.41 RMSE [6] Temperate conifer 0.21 RMSE [7] Temperate conifer −0.05/0.12 Mean/SD [8] Temperate conifer 0.31/0.29 Mean/SD [9] Temperate conifer 0.59 RMSE [10] Temperate conifer 0.24 RMSE [11] Temperate deciduous and conifer 1.22 RMSE [11] Temperate grass 0.37 RMSE [12] Temperate mixed 0.38 N/A [11] Temperate pine 0.45 RMSE [11] Temperate shrub 1.53 RMSE [13] Tropical forest 1.8 Mean [14] Tropical forest 0.43 RMSE [15] Tropical forest 0.37 RMSE [4] Tropical swamp forest 1.64 RMSE [16] Tropical swamp forest 0.16 and 0.41 RMSE [17] Tropical swamp forest 0.12 RMSE [17] Tropical swamp forest burn scar 0.19 RMSE DTM accuracy can be affected by horizontal and vertical accuracy of survey instruments (e.g., Global Navigation Satellite Systems (GNSS), total stations (TS), inertial measurement units (IMU) aboard aircraft [18], data collection parameters, site characteristics, data processing methods and algorithms. Typically, the highest quality GNSS equipment can give point coordinates accurate to better than 1 cm in open-sky conditions (i.e., no canopy cover, which may block satellite signals [19]), whereas total station measurements are not affected by canopy cover, and point accuracy can be 2-3 mm.…”

Section: Sourcementioning

confidence: 99%

Assessment of Errors Caused by Forest Vegetation Structure in Airborne LiDAR-Derived DTMs

2017

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Airborne Light Detection and Ranging (LiDAR) is a survey tool with many applications in forestry and forest research. It can capture the 3D structure of vegetation and topography quickly and accurately over thousands of hectares of forest. However, very few studies have assessed how accurately LiDAR can measure surface topography under forest canopies, which may be important, for example, in relation to analysis of pre-and post-burn surface height maps used to quantify the combustion of organic soils. Here, we use ground survey equipment to assess digital terrain model (DTM) accuracy in a deciduous broadleaf forest, during both leaf-on and leaf-off conditions. Using the leaf-on LiDAR dataset we quantitatively assess vertical vegetation structure, and use this as a categorical explanatory variable for DTM accuracy. In the presence of leaf-on vegetation, DTM accuracy is severely reduced, with low-stature undergrowth vegetation (such as ferns) causing the greatest errors (RMSE > 1 m). Errors are lower under leaf-off conditions (RMSE = 0.22 m), but still of a magnitude similar to that reported for mean depths of burn in fires involving organic soils. We highlight the need for adequate ground control schemes to accompany any forest-based airborne LiDAR survey which require highly accurate DTMs.

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“…This suggests that leaf-off LiDAR 445 may be superior to leaf-on LiDAR in describing habitat attributes related to the vertical structure in 446 deciduous forests. As shown by Wasser et al (2013), this is most likely related to the increased laser 447 pulse penetration through the canopy during leaf-off conditions, which enhances the detection of 448 subcanopy vegetation elements affecting the manoeuvrability of bats. As illustrated in Figure 2, the 449 increased canopy penetration during leaf-off conditions leads to an increased detection of vegetation 450 elements in the lowest forest strata, which includes tree regeneration and shrubs, both being essential 451 elements of vertical forest structure.…”

Section: Lidar Provides Unique Habitat Information 417mentioning

confidence: 99%

From field surveys to LiDAR: Shining a light on how bats respond to forest structure

Froidevaux

Zellweger

Bollmann

et al. 2016

Remote Sensing of Environment

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. From field surveys to LiDAR: Shining a light on how bats respond to forest structure. Remote Sensing of Environment, 175, 242-250. DOI: 10.1016/j.rse.2015 Peer reviewed version Link to published version (if available): 10.1016/j.rse. 2015.12.038 Link to publication record in Explore Bristol Research PDF-document This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at http://www.sciencedirect.com/science/article/pii/S0034425715302601. University of Bristol -Explore Bristol Research General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Detailed information about three-dimensional vegetation structure proves increasingly useful for 25 studying species-habitat relationships in forest ecosystems. This particularly applies to species that 26 make extensive use of the three-dimensional habitat space in forests, such as bats. Bats show 27 considerable variation in flight morphology and echolocation traits that are shaped by habitat features, 28 and are excellent model taxa for investigating relationships between vegetation structure and animal 29 occurrence and movement. The aims of this study were (1) to investigate the relationship between the 30 activity of bats and forest structure and (2) to compare the performance of airborne Light Detection 31 and Ranging (LiDAR) and terrestrial field surveys for measuring habitat features in a representative 32 sample of mixed and deciduous forests in the Swiss lowlands. Leaf-on and leaf-off LiDAR data were 33 used separately, as well as in combination, to evaluate the relative strength of these datasets to 34 describe 3D canopy architecture and vertical forest structure. Field measurements included structural 35 variables such as leaf area index (LAI), vertical layering, snags, as well as shrub and ground 36 vegetation cover. We recorded 145,433 echolocation call sequences from bats and assigned them into 37 three echolocation guilds (short-, mid-and long-range echolocators; SRE, MRE, LRE, respectively) 38 treating separately the dominant species (Pipistrellus pipistrellus; Pp). We used Generalized Mixed 39Effects Models (GLMMs) and applied an information-theoretic approach to assess relationships 40 between guild-specific activity patterns of bats and forest structure in the forest interior, as well as in 41 forest gaps. Standardized coefficients were used to evaluate variable effect sizes and relative 42 importance. We found that guild-specific bat activity was clearly related to three-dimensional forest 43 structure. The activity of SRE, MRE and Pp bats was negatively affected by foliage height diversity, 44indicating that a large scatter of vegetation elements along 3D forest profiles may restrict accessibility 45 for manoeuvrable bats. Outer canopy surface ruggedness was significantly and positively related to 46 the activity of MRE and LRE bats, as well as Pp, all of which may profit from increased...

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Influence of Vegetation Structure on Lidar-derived Canopy Height and Fractional Cover in Forested Riparian Buffers During Leaf-Off and Leaf-On Conditions

Cited by 110 publications

References 63 publications

Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

Assessment of Errors Caused by Forest Vegetation Structure in Airborne LiDAR-Derived DTMs

From field surveys to LiDAR: Shining a light on how bats respond to forest structure

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