Generating Pit-free Canopy Height Models from Airborne Lidar

Khosravipour, Anahita; Skidmore, Andrew K.; Isenburg, Martin; Wang, Zhihui; Hussin, Y.A.

doi:10.14358/pers.80.9.863

Cited by 270 publications

(210 citation statements)

References 41 publications

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“…Thus, the CHM was ready for field validation (Figure 7). Detailed information about how to generate error-free CHMs can be found in Khosravipour et al (2014).…”

Section: Creating the Canopy Height Modelmentioning

confidence: 99%

Estimating tree heights with images from an unmanned aerial vehicle

Birdal

Avdan

Türk

2017

Geomatics, Natural Hazards and Risk

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Unmanned aerial vehicles (UAV) have been used in a variety of fields in the last decade. In forestry, they have been used to estimate tree heights and crowns with different sensors. This approach, with a consumer-grade onboard system camera, is becoming popular because it is cheaper and faster than traditional photogrammetric methods and UAV-light detecting and ranging systems (UAV-LiDAR). In this study, UAV-based imagery reconstruction, processing, and local maximum filter methods are used to obtain individual tree heights from a coniferous urban forest. A low-cost onboard camera and a UAV with a 96-cm wingspan made it possible to acquire high resolution aerial images (6.41 cm average ground sampling distance), ortho-images, digital elevation models, and point clouds in one flight. Canopy height model, obtained by extracting the digital surface model from the digital terrain model, was filtered locally based on the pixel-based window size using the provided algorithm. For accuracy assessment, ground-based tree height measurements were made. There was a high 94% correlation and a root-mean-square error of 28 cm. This study highlights the accuracy of the method and compares favourably to more expensive methods.

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“…Thus, the CHM was ready for field validation (Figure 7). Detailed information about how to generate error-free CHMs can be found in Khosravipour et al (2014).…”

Section: Creating the Canopy Height Modelmentioning

confidence: 99%

Estimating tree heights with images from an unmanned aerial vehicle

Birdal

Avdan

Türk

2017

Geomatics, Natural Hazards and Risk

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“…The point clouds were an octree data structure with a mean point density and spacing of 286.27 points/m 2 and 0.05 m, respectively. To produce the pit-free CHM, the point clouds were processed using LAStools rapidlasso GmbH [56], following methodology used by [57] and substituting the platform from airborne to non-airborne to adapt the process to TLS data. For the ease of manageability, we divided the point clouds into 250 MB large tiles with a 25 m buffer.…”

Section: Terrestrial Laser Scanner (Tls) Canopy Height Model (Chm)mentioning

confidence: 99%

“…This would ensure that only triangles three times larger than the resolution were used for CHM generation. following methodology used by [57] and substituting the platform from airborne to non-airborne to adapt the process to TLS data. For the ease of manageability, we divided the point clouds into 250 MB large tiles with a 25 m buffer.…”

Section: Terrestrial Laser Scanner (Tls) Canopy Height Model (Chm)mentioning

confidence: 99%

“…To replicate each point eight times in a discrete circle, we used a radius of 0.075 m around every input point, and then increased the spatial resolution of the point clouds two-fold to 0.03 m. Noise and unclassified points were filtered from ground points. Whereas we would expect pits within TLS data, similar to Lidar data, we still have height variations within forest stands and so we account for these by computing CHM at different height thresholds [57,59]. We therefore adopted the American Society of Photogrammetry and Remote Sensing (ASPRS) classification of Lidar point clouds by computing CHM for every class [60] as <0.5 m (bare + understory), 0.5-<2 m (low vegetation), 2-<5 m (medium vegetation), and ≥5 m (high vegetation).…”

Section: Terrestrial Laser Scanner (Tls) Canopy Height Model (Chm)mentioning

confidence: 99%

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Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Odipo

Nickless

Berger

et al. 2016

Forests

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Abstract:The use of optical remote sensing data for savanna vegetation structure mapping is hindered by sparse and heterogeneous distribution of vegetation canopy, leading to near-similar spectral signatures among lifeforms. An additional challenge to optical sensors is the high cloud cover and unpredictable weather conditions. Longwave microwave data, with its low sensitivity to clouds addresses some of these problems, but many space borne studies are still limited by low quality structural reference data. Terrestrial laser scanning (TLS) derived canopy cover and height metrics can improve aboveground biomass (AGB) prediction at both plot and landscape level. To date, few studies have explored the strength of TLS for vegetation structural mapping, and particularly few focusing on savannas. In this study, we evaluate the potential of high resolution TLS-derived canopy cover and height metrics to estimate plot-level aboveground biomass, and to extrapolate to a landscape-wide biomass estimation using multi-temporal L-band Synthetic Aperture Radar (SAR) within a 9 km 2 area savanna in Kruger National Park (KNP). We inventoried 42 field plots in the wet season and computed AGB for each plot using site-specific allometry. Canopy cover, canopy height, and their product were regressed with plot-level AGB over the TLS-footprint, while SAR backscatter was used to model dry season biomass for the years 2007, 2008, 2009, and 2010 for the study area. The results from model validation showed a significant linear relationship between TLS-derived predictors with field biomass, p < 0.05 and adjusted R 2 ranging between 0.56 for SAR to 0.93 for the TLS-derived canopy cover and height. Log-transformed AGB yielded lower errors with TLS metrics compared with non-transformed AGB. An assessment of the backscatter based on root mean square error (RMSE) showed better AGB prediction with cross-polarized (RMSE = 6.6 t/ha) as opposed to co-polarized data (RMSE = 6.7 t/ha), attributed to volume scattering of woody vegetation along river valleys and streams. The AGB change analysis showed 32 ha (3.5%) of the 900 ha experienced AGB loses above an average of 5 t/ha per annum, which can mainly be attributed to the falling of trees by mega herbivores such as elephants. The study concludes that SAR data, especially L-band SAR, can be used in the detection of small changes in savanna vegetation over time.

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“…They used a high point density of 50 points/m 2 . Khosravipour et al (2014) also presented an algorithm which is able to create a pit-free CHM raster using full waveform ALS data with 160 points/m 2 density. The algorithm significantly improves the accuracy of tree detection compared to local maxima based methods.…”

Section: Introductionmentioning

confidence: 99%

Detection of Single Tree Stems in Forested Areas From High Density Als Point Clouds Using 3d Shape Descriptors

Amiri¹,

Polewski²,

Yao³

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Airborne Laser Scanning (ALS) is a widespread method for forest mapping and management purposes. While common ALS techniques provide valuable information about the forest canopy and intermediate layers, the point density near the ground may be poor due to dense overstory conditions. The current study highlights a new method for detecting stems of single trees in 3D point clouds obtained from high density ALS with a density of 300 points/m 2 . Compared to standard ALS data, due to lower flight height (150-200 m) this elevated point density leads to more laser reflections from tree stems. In this work, we propose a three-tiered method which works on the point, segment and object levels. First, for each point we calculate the likelihood that it belongs to a tree stem, derived from the radiometric and geometric features of its neighboring points. In the next step, we construct short stem segments based on high-probability stem points, and classify the segments by considering the distribution of points around them as well as their spatial orientation, which encodes the prior knowledge that trees are mainly vertically aligned due to gravity. Finally, we apply hierarchical clustering on the positively classified segments to obtain point sets corresponding to single stems, and perform 1-based orthogonal distance regression to robustly fit lines through each stem point set. The 1-based method is less sensitive to outliers compared to the least square approaches. From the fitted lines, the planimetric tree positions can then be derived. Experiments were performed on two plots from the Hochficht forest in Oberösterreich region located in Austria. We marked a total of 196 reference stems in the point clouds of both plots by visual interpretation. The evaluation of the automatically detected stems showed a classification precision of 0.86 and 0.85, respectively for Plot 1 and 2, with recall values of 0.7 and 0.67.

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Generating Pit-free Canopy Height Models from Airborne Lidar

Cited by 270 publications

References 41 publications

Estimating tree heights with images from an unmanned aerial vehicle

Estimating tree heights with images from an unmanned aerial vehicle

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Detection of Single Tree Stems in Forested Areas From High Density Als Point Clouds Using 3d Shape Descriptors

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