Paul Treitz

Lidar remote sensing of biophysical properties of tolerant northern hardwood forests

Previous forest research using time-of-flight lidar data has primarily focused on forest ecosystems with conifers as the predominant tree type. In this study, small-footprint time-of-flight lidar data were used to estimate biophysical properties of tolerant hardwood forests composed predominantly of mature sugar maple (Acer saccharum Marsh.) and yellow birch (Betula alleghaniensis Britton) in the Turkey Lakes Watershed (TLW) near Sault Ste. Marie, Ontario. Ground reference data were collected during the first two weeks of July 2000 for 49 circular sample plots, each 0.04 ha (or 400 m2) in area. Lidar data were acquired on 24 August 2000 using an Optech ALTM 1225 (Optech Incorporated, Toronto, Ont.). Ten biophysical forest metrics were derived for each plot: (1) maximum tree height (hmax), (2) Loreys mean tree height (hLorey), (3) mean diameter at breast height (DBH), (4) total basal area (BA), (5) percent canopy openness (CO%), (6) leaf area index (LAI), (7) ellipsoidal crown closure (CC), (8) total aboveground biomass (BIO), (9) total wood volume (VOL), and (10) stem density (SD). Likewise, three laser height metrics were derived for each plot: (1) maximum laser height (Lhmax), (2) mean laser height (Lhmean), and (3) mean laser height calculated from lidar returns filtered based on a threshold applied to the intensity return values (LhIR). The results demonstrate that for each forest with a given stand structure, there exists one or more laser height metrics derived from lidar data that are capable of providing an estimate of various biophysical properties. Lhmax was the best estimator of hmax (r2 = 0.79) and hLorey (r2 = 0.87); LhIR was the best estimator of BA (r2 = 0.85), BIO (r2 = 0.85), and VOL (r2 = 0.87); and Lhmean was the best estimator of CC (r2 = 0.89), DBH (r2 = 0.63), CO% (r2 = 0.76), LAI (r2 = 0.80), and SD (r2 = 0.86). The results illustrate the potential for laser height metrics to estimate (i) plot heights and stem densities, (ii) aboveground biomass and volume, and (iii) canopy-related measures.

Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

A conceptual model describing why laser height metrics derived from airborne discrete return laser scanner data are highly correlated with above ground biomass is proposed. Following from this conceptual model, the concept of canopy-based quantile estimators of above ground forest biomass is introduced and applied to an uneven-aged, mature to overmature, tolerant hardwood forest. Results from using the 0th, 25th, 50th, 75th and 100th percentiles of the distributions of laser canopy heights to estimate above ground biomass are reported. A comparison of the five models for each dependent variable group did not reveal any overt differences between models with respect to their predictive capabilities. The coefficient of determination (r 2 ) for each model is greater than 0.80 and any two models may differ at most by up to 9%. Differences in root-mean-square error (RMSE) between models for above ground total, stem wood, stem bark, live branch and foliage biomass were 8.1, 5.1, 2.9, 2.1 and 1.1 Mg ha−1, respectively.

Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems

Remote sensing has demonstrated wide applicability in the area of estimating and mapping forest physical and structural features. Focus in recent years has been directed towards measuring the biophysical/physiological character of forest ecosystems in order to estimate and predict forest ecosystem health and sustainability. The following reviews the relationship between forest condition and reflectance; remote-sensing measurements (and derivatives) that provide biophysical/physiological information; and the potential of hyperspectral sensors in the measurement of these parameters.

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.

High Spatial Resolution Remote Sensing Data for Forest Ecosystem Classification: An Examination of Spatial Scale

Detailed forest ecosystem classifications have been developed for large regions of northern Ontario, Canada. These ecosystem classifications provide tools for ecosystem management that constitute part of a larger goal of integrated management of forest ecosystems for long-term sustainability. These classification systems provide detailed stand-level characterization of forest ecosystems at a local level. However, for ecological approaches to forest management to become widely accepted by forest managers, and for these tools to be widely used, methods must be developed to characterize and map or model ecosystem classes at landscape scales for large regions. In this study, the site-specific Northwestern Ontario Forest Ecosystem Classification (NWO FEC) was adapted to provide a landscape-scale (1:20 000) forest ecosystem classification for the Rinker Lake Study Area located in the boreal forest north of Thunder Bay, Ontario. High spatial resolution remote sensing data were collected using the Compact Airborne Spectrographic Imager (CASI) and analyzed using geostatistical techniques to obtain an understanding of the nature of the spatial dependence of spectral reflectance for selected forest ecosystems at high spatial resolutions. Based on these analyses it was determined that an optimal size of support for characterizing forest ecosystems (i.e., optimal spatial resolution), as estimated by the mean ranges of a series of experimental variograms, differs based on (i) wavelength, (ii) forest ecosystem class, and (iii) mean maximum canopy diameter (MMCD). In addition, maximum semivariance as estimated from the sills of the experimental variograms increased with density of understory.

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.

Image classification of a northern peatland complex using spectral and plant community data

Abstract Ordination and cluster analysis are two common methods used by plant ecologists to organize species abundance data into discrete “associations”. When applied together, they offer useful information about the relationships among species and the ecological processes occurring within a community. Remote sensing provides surrogate data for characterizing the spatial distribution of ecological classes based on the assumption of characteristic reflectance of species and species associations. Currently, there exists a need to establish and clarify the link between theories and practices of classification by ecologists and remote sensing scientists. In this study, high spatial resolution Compact Airborne Spectrographic Imager (CASI) reflectance data were examined and compared to plant community data for a peatland complex in northern Manitoba, Canada. The goal of this research was to explore the relationship between classification of species cover and community data and reflectance values. Ordination and cluster analysis techniques were used in conjunction with spectral separability measures to organize clusters of community-based data that were suitable for classification of CASI reflectance data, while still maintaining their ecological significance. Results demonstrated that two-way indicator species analysis (TWINSPAN) clusters did not correspond well to spectral reflectance and gave the lowest classification results of the methods investigated. The highest classification accuracies were achieved with ecological classes defined by combining the information obtained from a suite of analysis techniques (i.e., TWINSPAN, correspondence analysis (CA), and signature separability analysis), albeit not statistically superior to the classification obtained from the signature separability analysis alone.

LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

Over the past two decades there has been an abundance of research demonstrating the utility of airborne light detection and ranging (LiDAR) for predicting forest biophysical/inventory variables at the plot and stand levels. However, to date there has been little effort to develop a set of protocols for data acquisition and processing that would move governments or the forest industry towards cost-effective implementation of this technology for strategic and tactical (i.e., operational) forest resource inventories. The goal of this paper is to initiate this process by examining the significance of LiDAR data acquisition (i.e., point density) for modeling forest inventory variables for the range of species and stand conditions representing much of Ontario, Canada. Field data for approximately 200 plots, sampling a broad range of forest types and conditions across Ontario, were collected for three study sites. Airborne LiDAR data, characterized by a mean density of 3.2 pulses m−2 were systematically decimated to produce additional datasets with densities of approximately 1.6 and 0.5 pulses m−2. Stepwise regression models, incorporating LiDAR height and density metrics, were developed for each of the three LiDAR datasets across a range of forest types to estimate the following forest inventory variables: (1) average height (R2(adj) = 0.75–0.95); (2) top height (R2(adj) = 0.74–0.98); (3) quadratic mean diameter (R2(adj) = 0.55–0.85); (4) basal area (R2(adj) = 0.22–0.93); (5) gross total volume (R2(adj) = 0.42–0.94); (6) gross merchantable volume (R2(adj) = 0.35–0.93); (7) total aboveground biomass (R2(adj) = 0.23–0.93); and (8) stem density (R2(adj) = 0.17–0.86). Aside from a few cases (i.e., average height and density for some stand types), no decimation effect was observed with respect to the precision of the prediction of the majority of forest variables, which suggests that a mean density of 0.5 pulses m−2 is sufficient for plot and stand level modeling under these diverse forest conditions across Ontario.

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.