Measuring individual tree height using a combination of stereophotogrammetry and lidar

St-Onge, Benoît; Jumelet, Julien; Cobello, Mario; Vega, Cédric

doi:10.1139/x04-093

Cited by 81 publications

(45 citation statements)

References 10 publications

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“…To utilise the advantages of each method, the option to combine photogrammetry and LiDAR for forest inventory purposes has attracted the attention of many researchers (Popescu et al 2004, StOnge et al 2004, 2008, Holmgren et al 2008, Bohlin et al 2012, Nurminen et al 2013. For example, St-Onge et al (2004) proposed the photogrammetric-LiDAR ('photo-LiDAR') approach to estimate tree height by calculating the difference between the photogrammetrically detected tree tops and tree bottoms derived from LiDAR DTM. They underestimated the photo-LiDAR tree heights on average by 0.59 m compared to the fi eld measured heights.…”

Section: Discussionmentioning

confidence: 99%

Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution

et al. 2015

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. Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution. Ann. For. Res. 58(1): 125-143.Abstract. Tree height is one of the more fundamental measurements in forest inventories. In addition to classical field measurements, tree height may be estimated by remote sensing methods, such as by photogrammetric measurements of aerial images. Since it has been found and generally accepted that the extraction of forest and tree data from classical analogue aerial photographs has certain limitations, especially in the densely canopied forests, the usefulness of photogrammetric-based forest inventory in many countries remains a controversial issue. Therefore, this paper focuses on investigating the possibility of applying digital photogrammetric method to estimate mean stand height. Photogrammetric stereo-measurements of tree height were conducted on colour infrared images of high spatial resolution (ground sample distance -GSD -of 30 cm and 10 cm) using a digital photogrammetric workstation. The height of each tree within 183 sample plots (14 subcompartments) were calculated as the difference between the tree top elevations determined with the aerial images and the corresponding tree bottom elevations determined from the digital terrain model. To compare the photogrammetric-and field-estimated mean stand heights, the mean plot heights were calculated for both photogrammetric and field estimates of tree heights. Repeated measurements using ANOVA testing did not reveal a statistically significant difference (p > 0.05) between the field-estimated and photogrammetric-estimated mean stand heights using the 30 cm and 10 cm GSD digital aerial images. Deviations of the mean stand heights estimated using the images of both spatial resolutions were similar to the field-estimated heights. Using the 30 cm images the deviations of the photogrammetrically estimated mean stand height amounted to 0.35 m (1.59%) on average, whereas using the 10 cm images the deviations amounted to 0.31 m (1.41%) compared to the field estimation. Therefore, it can be concluded that the 30 cm GSD aerial images allow for the photogrammetric measurement of mean stand heights with accuracy similar to 10 cm GSD aerial images. In addition, 30 cm GSD aerial images are more favourable financially since the same area of interest could be covered with a considerably smaller number of images than of the 10 cm GSD aerial images.

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Section: Discussionmentioning

confidence: 99%

Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution

et al. 2015

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“…Thus, site productivity is not static (Skovsgaard and Vanclay 2007) and in a site with stand replacing disturbance regimes (fire, harvesting), the original site index estimate may no longer reflect true site productivity (Stearns-Smith 2001). Third, photogrammetric methods for estimating tree height are prone to error under certain stand conditions, as it is often impossible to measure ground elevation near trees growing in dense forest (St-Onge et al 2004;Véga and St-Onge 2008). Any error in the original photogrammetric estimate of height is propagated in the original estimation of site index, thereby impacting height projections when the inventory is grown forward.…”

Section: Forest Inventorymentioning

confidence: 99%

Implications of differing input data sources and approaches upon forest carbon stock estimation

Wulder

White

Stinson

et al. 2009

Environ Monit Assess

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Site index is an important forest inventory attribute that relates productivity and growth expectation of forests over time. In forest inventory programs, site index is used in conjunction with other forest inventory attributes (i.e., height, age) for the estimation of stand volume. In turn, stand volumes are used to estimate biomass (and biomass components) and enable conversion to carbon. In this research, we explore the implications and consequences of different estimates of site index on carbon stock characterization for a 2,500-ha Douglas-fir-dominated landscape located on Eastern Vancouver Island, British Columbia, Canada. We compared site index estimates from an existing forest inventory to estimates generated from a combination of forest inventory and light detection and ranging (LIDAR)-derived attributes and then examined the resultant differences in biomass estimates generated from a carbon budget model (Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3)). Significant differences were found between the original and LIDAR-derived site indices for all species types and for the resulting 5-m site classes (p < 0.001). The LIDAR-derived site class was greater than the original site class for 42% of stands; however, 77% of stands were within ±1 site class of the original class. Differences in biomass estimates between the model scenarios were significant for both total stand biomass and biomass per hectare ( p < 0.001); differences for Douglas-fir-dominated stands (representing 85% of all stands) were not significant ( p = 0.288). Overall, the relationship between the two biomass estimates was strong (R 2 = 0.92, p < 0.001), suggesting that in certain circumstances, LIDAR may have a role to play in site index estimation and biomass mapping.

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“…Recent efforts are moving toward the operationalization of LiDAR forest applications. For example, baseline forest resource inventory (FRI) (Woods et al 2011), sample-based calibration of regional forest biomass products (Hopkinson et al 2011;Wulder et al 2012) and growth and yield monitoring, using LiDAR data, (Yu et al 2004;Hopkinson et al 2008;McRoberts et al 2015) and possible extension from aerial photograph/LiDAR combinations (St-Onge et al 2004). However, during the last 2 decades of LiDAR hardware development, there have been many technical advances, which have resulted in increased system accuracy, performance, capability, and specialization toward niche applications.…”

Section: Introductionmentioning

confidence: 99%

Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment

Hopkinson

Chasmer

Gynan³

et al. 2016

Canadian Journal of Remote Sensing

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Abstract. Airborne LiDAR is increasingly used in forest carbon, ecosystem, and resource monitoring. For practical design and manufacture reasons, the 1064 nm near-infrared (NIR) wavelength has been the most commonly adopted, and most literature in this field represents sampling characteristics in this wavelength. However, due to eye-safety and application-specific needs, other common wavelengths are 1550 nm and 532 nm. All provide canopy structure reconstructions that can be integrated or compared through space and time but the consistency or complementarity of 3D airborne LiDAR data sampled at multiple wavelengths is poorly understood. Here, we report on multispectral LiDAR missions carried out in 2013 and 2015 over a managed forest research site. The 1st used 3 independent sensors, and the 2nd used a single sensor carrying 3 lasers. The experiment revealed differences in proportions of returns at ground level, vertical foliage distributions, and gap probability across wavelengths. Canopy attenuation was greatest at 532 nm, presumably due to leaf tissue absorption. Relative to 1064 nm, foliage was undersampled at midheight percentiles at 1550 nm and 532 nm. Multisensor data demonstrated differences in foliage characterization due to combined influences of wavelength and acquisition configuration. Single-sensor multispectral data were more stable but demonstrated clear wavelength-dependent variation that could be exploited in intensity-based land cover classification without the aid of 3D derivatives. This work sets the stage for improvements in land surface classification and vertical foliage partitioning through the integration of active spectral and structural laser return information.

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Measuring individual tree height using a combination of stereophotogrammetry and lidar

Cited by 81 publications

References 10 publications

Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution

Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution

Implications of differing input data sources and approaches upon forest carbon stock estimation

Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment

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