Laura Chasmer

Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment

An airborne scanning light detection and ranging (lidar) survey using a discrete pulse return airborne laser terrain mapper (ALTM) was conducted over the Utikuma boreal wetland area of northern Alberta in August 2002. These data were analysed to quantify vegetation class dependent errors in lidar ground surface elevation and vegetation canopy surface height. The sensitivity of lidar-derived land-cover frictional parameters to these height errors was also investigated. Aquatic vegetation was associated with the largest error in lidar ground surface definition (+0.15 m, SD = 0.22, probability of no difference in height P < 0.01), likely a result of saturated ground conditions. The largest absolute errors in lidar canopy surface height were associated with tall vegetation classes; however, the largest relative errors were associated with low shrub (63%, –0.52 m, P < 0.01) and aquatic vegetation (54%, –0.24 m, P < 0.01) classes. The openness and orientation of vegetation foliage (i.e., minimal projection of horizontal area) were thought to enhance laser pulse canopy surface penetration in these two classes. Raster canopy height models (CHMs) underestimated field heights by between 3% (aspens and black spruce) and 64% (aquatic vegetation). Lidar canopy surface height errors led to hydraulic Darcy–Weisbach friction factor underestimates of 10%–49% for short (<2 m) vegetation classes and overestimates of 12%–41% for taller vegetation classes.

Examining the Influence of Changing Laser Pulse Repetition Frequencies on Conifer Forest Canopy Returns

The distribution of laser pulses within conifer forest trees and canopies are examined by varying the rate of laser pulse emission and the inherent laser pulse properties (laser pulse energy, pulse width, pulse length, and roll-over or trigger time). In this study, an Optech, Inc. ALTM 3100 airborne lidar is used, emitting pulses at 50 kHz and 100 kHz, allowing for changes in laser pulse characteristics while also keeping all other survey parameters equal. We found that: 1. Pulses and associated characteristics emitted at 50 kHz penetrated further into the canopy than 100 kHz for a significant number of individual trees. 2. At tall tree plots with no understory, pulses emitted at 50 kHz penetrated further into the canopy than 100 kHz for a significant number of plots. 3. For plots with significant understory and shorter trees, pulses emitted at 100 kHz penetrated further into the canopy than 50 kHz. We suspect that this may be due, in part, to canopy openness. Laser pulse energy and character differences associated with different laser pulse emission frequencies are likely a contributing factor in laser pulse penetration through the canopy to the ground surface. Efforts to understand laser pulse character influences on canopy returns are important as biomass and vegetation structure models derived from lidar are increasingly adopted.

Towards a universal lidar canopy height indicator

A light detection and ranging (lidar) canopy height study was conducted with 13 datasets collected using four different models of airborne laser terrain mapper (ALTM) sensors over 13 widely variable vegetation types ranging in average height from <1 m to 24 m at five sites across Canada between 2000 and 2005. The study demonstrates that the vertical standard deviation of all topographically detrended first and last laser pulse returns (LSD) is a robust estimator of canopy height (Ht) for a wide variety of vegetation types and heights and lidar survey configurations. After regressing Ht against LSD for 77 plots and transects, it was found that Ht could be predicted as a simple multiplication (M) of LSD (M = 2.5, coefficient of determination (r2) = 0.95, root mean square error (RMSE) = 1.8 m, tail probability (p) < 0.01). For forest plots only, LSD was found to better predict average tree height (r2 = 0.80, RMSE = 2.1 m, p < 0.01) than Loreys height (r2 = 0.59, RMSE = 3.0 m, p < 0.01). A test of the LSD canopy height model was performed using stand heights (HtFRI) from an independent forest resource inventory (FRI) for four vegetation classes. Results from the raw FRI and modelled stand height comparison displayed close to a 1:1 relationship (HtFRI = 0.97HtLSD, r2 = 0.73, RMSE = 4.7 m, p < 0.01, n = 38). All plot and transect canopy heights were also compared with the localized maxima of laser pulse returns (Lmax). For individual surveys over homogeneous vegetation types, Lmax generally provides a better canopy height indicator. Across all surveys and site types, however, LSD was almost always shown to have a more consistent relationship with actual canopy height. The only observed exception was in the case of forest plot level Loreys mean tree height. The advantages of using a multiplier of LSD to estimate canopy height are its apparent insensitivity to survey configuration and its demonstrated applicability to a range of vegetation types and height classes.

Mapping Snowpack Depth beneath Forest Canopies Using Airborne Lidar

An evaluation of airborne lidar (Light Detection And Ranging) technology for snow depth mapping beneath different forest canopy covers (deciduous, coniferous, and mixed) is presented. Airborne lidar data were collected for a forested study site both prior to and during peak snowpack accumulation. Manual field measurements of snow depth were collected coincident with the peak snowpack lidar survey, and a comparison between field and lidar depth estimates was made. It was found that (1) snow depth distribution patterns can be mapped by subtracting a “bare-earth” DEM from a “peak snowpack” DEM, (2) snow depth estimates derived from lidar data are strongly related to manual field measures of snow depth, and (3) snow depth estimates are most accurate in areas of minimal understory. It has been demonstrated that airborne lidar data provide accurate snow depth data for the purpose of mapping spatial snowpack distribution for volume estimations, even under forest canopy conditions.

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

Estimates of canopy height (H) and fractional canopy cover (FC) derived from lidar data collected during leaf-on and leaf-off conditions are compared with field measurements from 80 forested riparian buffer plots. The purpose is to determine if existing lidar data flown in leaf-off conditions for applications such as terrain mapping can effectively estimate forested riparian buffer H and FC within a range of riparian vegetation types. Results illustrate that: 1) leaf-off and leaf-on lidar percentile estimates are similar to measured heights in all plots except those dominated by deciduous compound-leaved trees where lidar underestimates H during leaf off periods; 2) canopy height models (CHMs) underestimate H by a larger margin compared to percentile methods and are influenced by vegetation type (conifer needle, deciduous simple leaf or deciduous compound leaf) and canopy height variability, 3) lidar estimates of FC are within 10% of plot measurements during leaf-on periods, but are underestimated during leaf-off periods except in mixed and conifer plots; and 4) depth of laser pulse penetration lower in the canopy is more variable compared to top of the canopy penetration which may influence within canopy vegetation structure estimates. This study demonstrates that leaf-off lidar data can be used to estimate forested riparian buffer canopy height within diverse vegetation conditions and fractional canopy cover within mixed and conifer forests when leaf-on lidar data are not available.

Peatland Hydrology of Discontinuous Permafrost in the Northwest Territories: Overview and Synthesis

Field studies were initiated in 1999 at Scotty Creek in the lower Liard River basin, NWT, Canada, to improve understanding of and ability to predict the major water fluxes and storage processes within a wetland-dominated zone of the discontinuous permafrost region. This paper synthesises a decade of published and unpublished research at Scotty Creek for the purpose of presenting the major factors that should be considered by water scientists and managers as a basis for modelling and management strategies. Five main topics are covered: (1) peatlands of lower Liard River valley; (2) hydrological characteristics of permafrost plateaus, flat bogs, and channel fens; (3) runoff generation on permafrost plateaus; (4) conceptual model of peatland hydrology; and (5) climate warming and implications for basin runoff. This synthesis offers a practical understanding of the hydrology of wetland-dominated basins with discontinuous permafrost. It also offers insight into how landscape changes resulting from climate or human disturbances may influence the basin hydrograph.

Quantifying errors in discontinuous permafrost plateau change from optical data, Northwest Territories, Canada: 1947-2008.

The discontinuous permafrost zone has been subject to increased air temperatures over recent decades. Permafrost thaw can cause changes to topography, hydrology, vegetation, and trace gas fluxes, and thus it is important to monitor changes in permafrost area through time. Optical imagery can be used to generate time-series databases of near-surface spectra that may be related to permafrost area. This provides a spatial perspective on area permafrost change that is not easily obtained from field data alone. This study examines the cumulative maximum and minimum errors of aerial and satellite imagery used for change detection within the Scotty Creek watershed, Fort Simpson, NWT, Canada. The results illustrate that, unless unchanging linear features are found throughout every image used (e.g., to be used as multitemporal tie points) and radiometric normalization can be applied (problematic for film images), direct image to image comparisons (e.g., subtraction) are not appropriate. Further, measureable cumulative errors are often produced by misclassification of edges, resolution limitations, and increased landscape fragmentation. At Scotty Creek, increased fragmentation of permafrost plateaus occurred from 1947 to 2008. Cumulative maximum and minimum errors result in an approximate 8%–26% error in permafrost area when compared with the total area of the site. Rates of permafrost area reduction within the study area were approximately 0.5% every year, determined from linear correlation (r2 = 0.91, n = 5). Therefore, based on the maximum cumulative error (a worst-case scenario), approximately 21–32 years (for resolutions of 0.18–1.10 m) is required between images to approximate change within this particular site. Increased (decreased) rates of change at other sites will decrease (increase) the timing required to identify change between images beyond error bounds.

Slope Estimation from ICESat/GLAS

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Moving Toward Consistent ALS Monitoring of Forest Attributes across Canada

As airborne laser scanning (ALS) gains wider adoption to support forest operations in Canada, the consistency and quality of derivative products that support long-term monitoring and planning are becoming a key issues for managers. The Canadian Consortium for Lidar Environmental Applications Research (C-CLEAR) has supported almost 200 projects across Canada since 2000, with forest-related studies being a dominant theme. In 2010 and 2011, field operations were mobilized to support 13 ALS projects spanning almost the full longitudinal gradient of Canada’s forests. This paper presents case studies for seven plus an overview of some best practices and data processing workflow tools that have resulted from these consortium activities. Although the projects and research teams are spread across Canada, the coordination and decade of experience provided through C-CLEAR have brought common methodological elements to all. It is clear that operational, analytical and reporting guidelines that adhere to community accepted standards are required if the benefits promised by ALS forestry are to be realized. A national Lidar Institute that builds upon the C-CLEAR model and focuses on developing standards, guidelines, and certified training would address this need.

Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment

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.