Christopher W. Bater

Evaluating error associated with lidar-derived DEM interpolation

Light detection and ranging (lidar) technology is capable of precisely measuring a variety of vegetation metrics, the estimates of which are usually based on relative heights above a digital elevation model (DEM). As a result, the development of these elevation models is a critical step when processing lidar observations. A number of different algorithms exist to interpolate lidar ground hits into a terrain surface. We tested seven interpolation routines, using small footprint lidar data, collected over a range of vegetation classes on Vancouver Island, British Columbia, Canada. The lidar data were randomly subsetted into a prediction dataset and a validation dataset. A suite of DEMs were then generated using linear, quintic, natural neighbour, regularized spline, spline with tension, a finite difference approach (ANUDEM), and inverse distance weighted interpolation routines, at spatial resolutions of 0.5, 1.0 and 1.5m. In order to examine the effects of terrain and ground cover on interpolation accuracies, the study area was stratified by terrain slope, vegetation structural class, lidar ground return density, and normalized difference vegetation indices (NDVI) derived from Quickbird and Landsat7 ETM+ imagery. The root mean square (RMS) and mean absolute errors of the residuals between the surfaces and the validation points indicated that the 0.5m DEMs were the most accurate. Of the tested approaches, the regularized spline and IDW algorithms produced the most extreme outliers, sometimes in excess of +/-6m in sloping terrain. Overall, the natural neighbour algorithm provided the best results with a minimum of effort. Finally, a method to create prediction uncertainty maps using classification and regression tree (CART) analysis is proposed.

Lidar plots * a new large-area data collection option: context, concepts, and case study

Forests are an important global resource, playing key roles in both the environment and the economy. The implementation of quality national monitoring programs is required for the generation of robust national statistics, which in turn support global reporting. Conventional monitoring initiatives based on samples of field plots have proven robust but are difficult and costly to implement and maintain, especially for large jurisdictions or where access is difficult. To address this problem, air photo- and satellite-based large area mapping and monitoring programs have been developed; however, these programs also require ground measurements for calibration and validation. To mitigate this need for ground plot data we propose the collection and integration of light detection and ranging (lidar) based plot data. Lidar enables accurate measures of vertical forest structure, including canopy height, volume, and biomass. Rather than acquiring wall-to-wall lidar coverage, we propose the acquisition of a sample of scanned lidar transects to estimate conditions over large areas. Given an appropriate sampling framework, statistics can be generated from the lidar plots extracted from the transects. In other instances, the lidar plots may be treated similar to ground plots, providing locally relevant information that can be used independently or integrated with other data sources, including optical remotely sensed data. In this study we introduce the concept of “lidar plots” to support forest inventory and scientific applications, particularly for large areas. Many elements must be considered when planning a transect-based lidar survey, including survey design, flight and sensor parameters, acquisition considerations, mass data processing, and database development. We present a case study describing the acquisition of over 25 000 km of lidar data in Canadas boreal forests in the summer of 2010. The survey, which included areas of managed and unmanaged forests, resulted in the production of more than 17 million 25 × 25 m lidar plots with first returns greater than 2 m in height. We conclude with insights gained from the case study and recommendations for future surveys.

Linking ground-based to satellite-derived phenological metrics in support of habitat assessment

Changes in the timing of plant phenology are important indicators of inter-annual climatic variations and are a critical driver of food availability and habitat use for a range of species. A number of remote sensing techniques have recently been developed to observe vegetation cycles throughout the year, including the use of inexpensive visible spectrum digital cameras at the stand level and the use of high temporal frequency Advanced Very High Resolution Radiometer National Oceanic and Atmospheric Administration (AVHRR NOAA) and MODerate resolution Imaging Spectroradiometer (MODIS) imagery at a satellite scale. A fundamental challenge with using satellite data to track plant phenology, however, is the trade-off between the level of spatial detail and the revisit time provided by the sensor, and the ability to verify the interpretation of phenological activity. One way to address this challenge is to integrate remotely sensed observations obtained at different spatial and temporal scales to provide information that contains both high temporal density and fine spatial resolution observations. In this article, we compare measures of vegetation phenology observed from a network of ground-based cameras with satellite-derived measures of greenness derived from a fused broad (MODIS) and fine spatial (Landsat) scale satellite data set. We derive and compare three key indicators of phenological activity including the start date of green-up, start date of senescence and length of growing season from both a ground-based camera network and 30 m spatial resolution synthetic Landsat scenes. Results indicate that although field-based estimates, generally, predicted an earlier start and end of the vegetation season than the fused satellite observations, highly significant relationships were found for the prediction of the start (R 2 = 0.65), end (R 2 = 0.72) and length (R 2 = 0.70) of the growing season across all sites. We conclude that some predictable bias exists however unlike visual field measures of the collected data represent both a spectral and a visual archive for later use.

Stability of Sample-Based Scanning-LiDAR-Derived Vegetation Metrics for Forest Monitoring

The objective of this paper is to gain insights into the reproducibility of light detection and ranging (LiDAR)-derived vegetation metrics for multiple acquisitions carried out on the same day, where we can assume that forest and terrain conditions at a given location have not changed. Four overlapping lines were flown over a forested area in Vancouver Island, British Columbia, Canada. Forty-six 0.04-ha plots were systematically established, and commonly derived variables were extracted from first and last returns, including height-related metrics, cover estimates, return intensities, and absolute scan angles. Plot-level metrics from each LiDAR pass were then compared using multivariate repeated-measures analysis-of-variance tests. Results indicate that, while the number of returns was significantly different between the four overlapping flight lines, most LiDAR-derived first return vegetation height metrics were not. First return maximum height and overstory cover, however, were significantly different and varied between flight lines by an average of approximately 2% and 4%, respectively. First return intensities differed significantly between overpasses where sudden changes in the metric occurred without any apparent explanation; intensity should only be used following calibration. With the exception of the standard deviation of height, all second return metrics were significantly different between flight lines. Despite these minor differences, the study demonstrates that, when the LiDAR sensor, settings, and data acquisition flight parameters remain constant, and time-related forest dynamics are not factors, LiDAR-derived metrics of the same location provide stable and repeatable measures of the forest structure, confirming the suitability of LiDAR for forest monitoring.

The influence of ground- and lidar-derived forest structure metrics on snow accumulation and ablation in disturbed forests

The current mountain pine beetle infestation in British Columbias lodgepole pine forests has raised concerns about potential impacts on water resources. Changes in forest structure resulting from defoliation, windthrow, and salvage harvesting may increase snow accumulation and ablation (i.e., spring runoff and flooding risk) below the forest canopy be- cause of reduced snow interception and higher levels of radiation reaching the surface. Quantifying these effects requires a better understanding of the link between forest structure and snow processes. Light detection and ranging (lidar) is an in- novative technology capable of estimating forest structure metrics in a detailed, three-dimensional approach not easily ob- tained from manual measurements. While a number of previous studies have shown that increased snow accumulation and ablation occur as forest cover decreases, the potential improvement of these relationships based on lidar metrics has not been quantified. We investigated the correlation between lidar-derived and ground-based traditional canopy metrics with snow accumulation and ablation indicators, demonstrating that a lidar-derived forest cover parameter was the strongest pre- dictor of peak snow accumulation (r 2 = 0.70, p < 0.001) and maximum snow ablation rate (r 2 = 0.59, p < 0.01). Improving our ability to quantify changes in forest structure in extensive areas will assist in developing more robust models of water- shed processes.

A simple technique for co-registration of terrestrial LiDAR observations for forestry applications

Light detection and ranging (LiDAR) from terrestrial platforms provides unprecedented detail about the three-dimensional structure of forest canopies. Although airborne laser scanning is designed to yield a relatively homogeneous distribution of returns, the radial perspective of terrestrial laser scanning (TLS) results in a rapid decrease of number of returns with increasing distance from the instrument. Additionally, when used in forested environments, significant parts of the area under investigation may be obscured by tree trunks and understorey. A possible approach to mitigate this effect is to combine TLS observations acquired at different locations to obtain multiple perspectives of an area under investigation. The denser and more evenly distributed observations then allow a spatially explicit and more comprehensive study of forest characteristics. This study demonstrates a simple approach to combine TLS observations made at multiple locations using bright reference targets as tie-points. Results show this technique was able to accurately combine the different TLS data sets (root mean square error (RMSE): 0.04–0.7 m, coefficient of determination (R 2): 0.70–0.99). Terrain elevations from TLS system were highly correlated with field-measured terrain heights (R 2: 0.70–0.98).

Assessing differences in tree and stand structure following beetle infestation using lidar data

The current mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation in British Columbia is the largest in recorded history and has caused unprecedented damage to the lodgepole pine (Pinus contorta Dougl. var. latifolia Engelm.) forests of the interior of the province. During the early years after attack, changes to overall crown structure are relatively minor due to low needle loss; within several years, however, needle loss can be substantial, even at the stand level. Needle loss can affect snow hydrology due to the role of the canopy in interception and accumulation and its impacts on radiation transmission, wind speed, and the overall snowmelt energy balance. In addition, the infestation is impacting other forest attributes such as wildlife habitat, forest fire risk and behaviour, and biogeochemistry. In this paper we investigate variations in light detection and ranging (lidar) return hit densities and distributions, analyzed with high spatial resolution digital camera imagery, in response to changes in forest cover and structure due to beetle infestation at both the individual tree level and the stand level. Results indicate that the density of lidar returns from tree crowns is impacted by the later health status of the tree, with a larger number of returns from green and early attack phases and a significantly smaller number of returns from grey-attack crowns. At the stand level, there are a number of significant relationships between plot-level indicators of infestation and lidar-derived structural metrics, in particular with vegetation cover (r2  = 0.76, p < 0.001). The total number and vertical distribution of returns from vegetation in green, red-attacked, and grey-attacked pine stands were distinct. We conclude that the potential to combine the structure information derived from lidar technology with assessment of heath status from aerial imagery provides unique quantitative data that may be used to map lodgepole pine stands according to structural attributes relevant to both silviculturalists and hydrologists.

Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with lidar plots

Many areas of forest across northern Canada are challenging to monitor on a regular basis as a result of their large extent and remoteness. Although no forest inventory data typically exist for these northern areas, detailed and timely forest information for these areas is required to support national and international reporting obligations. We developed and tested a sample-based approach that could be used to estimate forest stand height in these remote forests using panchromatic Very High Spatial Resolution (VHSR, < 1 m) optical imagery and light detection and ranging (lidar) data. Using a study area in central British Columbia, Canada, to test our approach, we compared four different methods for estimating stand height using stand-level and crown-level metrics generated from the VHSR imagery. ‘Lidar plots’ (voxel-based samples of lidar data) are used for calibration and validation of the VHSR-based stand height estimates, similar to the way that field plots are used to calibrate photogrammetric estimates of stand height in a conventional forest inventory or to make empirical attribute estimates from multispectral digital remotely sensed data. A k-nearest neighbours (k-NN) method provided the best estimate of mean stand height (R 2 = 0.69; RMSE = 2.3 m, RMSE normalized by the mean value of the estimates (RMSE-%) = 21) compared with linear regression, random forests, and regression tree methods. The approach presented herein demonstrates the potential of VHSR panchromatic imagery and lidar to provide robust and representative estimates of stand height in remote forest areas where conventional forest inventory approaches are either too costly or are not logistically feasible. While further evaluation of the methods is required to generalize these results over Canada to provide robust and representative estimation, VHSR and lidar data provide an opportunity for monitoring in areas for which there is no detailed forest inventory information available.

DESIGN AND INSTALLATION OF A CAMERA NETWORK ACROSS AN ELEVATION GRADIENT FOR HABITAT ASSESSMENT

Developments in distributed sensing, web camera image databases, and automated data visualization and analysis, among other emerging opportunities, have resulted in a suite of new techniques for monitoring habitat at many different scales. Data from these networks can provide important information on the timing of plant phenology with implications for habitat status and condition. In this article, we describe the design and deployment of a small network of cameras established along an elevation gradient in western Alberta, Canada, with the purpose of developing a more comprehensive understanding of seasonal phenophases and the reproductive timing of understory forest vegetation. During an eight-month period in 2009, over 6,700 images were acquired across seven sites throughout the growing season, providing a rich dataset documenting phenological activity of both the under- and overstory forest components. Strong elevation and climate responses were observed. A mathematical function was fitted to the data to demonstrate the capacity to capture phenological trends. This article demonstrates the utility of these types of relatively inexpensive, portable systems for monitoring seasonal vegetation development and change at high temporal resolutions across landscapes.

An early warning system to forecast the close of the spring burning window from satellite-observed greenness

Spring represents the peak of human-caused wildfire events in populated boreal forests, resulting in catastrophic loss of property and human life. Human-caused wildfire risk is anticipated to increase in northern forests as fuels become drier, on average, under warming climate scenarios and as population density increases within formerly remote regions. We investigated springtime human-caused wildfire risk derived from satellite-observed vegetation greenness in the early part of the growing season, a period of increased ignition and wildfire spread potential from snow melt to vegetation green-up with the aim of developing an early warning wildfire risk system. The initial system was developed for 392,856 km2 of forested lands with satellite observations available prior to the start of the official wildfire season and predicted peak human-caused wildfire activity with 10-day accuracy for 76% of wildfire-protected lands by March 22. The early warning system could have significant utility as a cost-effective solution for wildfire managers to prioritize the deployment of wildfire protection resources in wildfire-prone landscapes across boreal-dominated ecosystems of North America, Europe, and Russia using open access Earth observations.