Gordon Stenhouse

Seismic cutlines, changing landscape metrics and grizzly bear landscape use in Alberta

Besides providing habitat to the grizzly bear (Ursus arctos) and other wildlife, the Rocky Mountain foothills of Alberta, Canada hosts considerable mining, seismic oil and gas exploration and production, and forest harvesting activities. Worldwide, such human activities influence the configuration and composition of the landscape. We assessed seismic cutline effects on landscape structure and grizzly bear use during early summer of 1999 and 2000. We studied five female and two male bears, which were GPS-collared in the spring following den emergence. The area available to this population was stratified into 49xa0km2 hexagon-shaped sub-landscapes. The scale of this stratification was determined by patterns of bear movement. Fourteen compositional and configurational landscape metrics were calculated within each landscape unit, and bear use points were pooled or ‘binned’ within each unit. Landscape use was related to landscape metrics using a Generalized Linear Model (GLM). We found that seismic cutline proportion did not explain landscape use by grizzly bears; however secondary effects of cutlines on landscape structure did. Declining use was mainly associated with increasing proportions of closed forest, and increasing variation of inter-patch distances, while use was mainly increasing with increasing mean patch size. An earlier investigation had demonstrated that adding seismic cutlines to grizzly bear habitat caused increases in the variation of inter-patch distances. Since the landscape structure of this grizzly bear population will continue to change as a function of increased levels of resource extraction activities in the near future, it is crucial to further study the detailed meaning of landscape structure at the large and small scale for effective conservation efforts.

A history of habitat dynamics: Characterizing 35 years of stand replacing disturbance

Landscape change, specifically habitat loss and modification, is thought to have an impact on the health, productivity, distribution, and survival of grizzly bears (Ursus arctos L.). Although grizzly bears may preferentially seek out areas of anthropogenic disturbances for foraging opportunities, research has found that grizzly bears experience greater mortality in these areas as a result of increased human access. Additional insights on the location and rates of anthropogenic-driven landscape change are required to better understand related impacts upon grizzly bears. In this study, a time series of 14 Landsat MSS, TM, and ETM+ images were used to retrospectively document and quantify the rate of landscape change over a 35-year period from 1973 to 2008 in a 13507km2 analysis area in western Alberta, Canada. The study area is located within a larger region that contains the highest density of grizzly bears in Alberta and has experienced increasingly intensive forest harvesting and oil and gas exploration activities during this period. To accommodate the differing spectral channels from MSS to TM/ETM+ sensors, the arctangent of the angle of the Tasseled Cap greenness-to-brightness components was computed for each image year, with sequential image pairs differenced and a threshold applied to identify stand-replacing disturbance events. Results indicated that 11% of the analysis area experienced some form of stand-replacing disturbance (e.g., cutblocks, roads, oil and gas well sites, seismic lines, power lines, pipelines, blowdown) between 1973 and 2008. The greatest proportion of this change (by area) occurred between 2004 and 2006 (24%), while the lowest proportion occurred between 2000 and 2001 (2%). Although the number of change events has fluctuated over time, with a minimum of 2888 change events between 1976 and 1978 (2%) and a maximum of 36623 change events between 2004 and 2006 (29%), the mean size of change events has decreased over time: prior to 1995, mean event size was greater than 1.5ha; after 1995, it was less than 1.5ha. The annual rate of change was greatest between 2004 and 2006 (−1.25%), and lowest between 1981 and 1990 (−0.04%). Consideration of changes within the context of units relevant to grizzly bear management (i.e., grizzly bear watershed units and core or secondary habitat areas) indicate that the amount and rate of change was not spatially or temporally uniform across the study area. While the average change event size has decreased over time, the increasing number of change events has resulted in a larger aggregate area of change in more recent years. Landsat imagery provided a large-area, synoptic, and consistent characterization of 35years of stand-replacing disturbance in our study area, providing information that enables an improved understanding of the complex interactions between grizzly bear distribution, abundance, health, survival, and habitat.

Effects of cutline density and land-cover heterogeneity on landscape metrics in western Alberta.

Forest cutlines are narrow, linear features created in geophysical surveys. In many areas of Canada, forest cutlines are not consistently detected using relatively coarse spatial resolution land-cover maps, such as those produced by classification of Landsat Thematic Mapper (TM) imagery. However, such features may be important in certain wildlife management applications, including those which require an assessment of landscape structure, or forest fragmentation, at various scales. Higher spatial resolution satellite imagery obtained from sensors on platforms such as Satellite Pour l’Observation de la Terre (SPOT) and the Indian Remote Sensing (IRS) system may be used to map forest cutlines for these applications. In this study, a TM-based land-cover map of western Alberta is analyzed with forest cutlines mapped from a TM-IRS fusion image, and the effect of increasing cutline density is quantified on five commonly used landscape metrics used to characterize landscape structure in grizzly bear habitat assessment. The accuracy of the fusion image interpretation was determined to be 88%. Simulated landscapes were tested first, and the study area was divided into 104 hexagon-shaped sample landscapes of about 6 km diameter each. Across these sample landscapes, cutline density and initial landscape heterogeneity were significant parameters in explaining change in three metrics, namely edge density, mean patch size, and patch context (expressed as mean nearest-neighbour distance). Patch size variability (expressed as the coefficient of variation of mean patch size) and patch dispersion (expressed as the coefficient of variation of mean nearest-neighbour distance) required additional information on cutline positioning. Overall, the density of the introduced cutline network and the pre-cutline metric value reliably predicted and quantified the response of landscape metrics of interest to grizzly bear biologists. This study shows the importance of mapping forest cutlines regarding their role in changing landscape structure quantification and points out the necessity of using additional remotely sensed data when the feature responsible for the landscape transformation is of too small a size to appear reliably in common TM-based classified imagery.

Object-oriented classification of multi-resolution images for the extraction of narrow linear forest disturbance

Narrow linear forest disturbances (e.g. seismic cut lines) have been found to have significant effects on wildlife habitat and biodiversity (e.g. species richness and abundance). A great deal of seismic cut lines is created in oil and gas exploration in natural forest areas every year. Accurate mapping of seismic cut lines can therefore contribute to a better understanding of wildlife habitat and biodiversity. However, previous studies have indicated that seismic cut lines were fairly difficult to detect and map even with the available high-spatial resolution imagery (e.g. Satellite Pour lObservation de la Terre, SPOT 5). Recent progress in feature segmentation and extraction software, such as Definiens Developer 7.0, has enhanced remote sensing capabilities, with the promise of being able to automate tasks. This study investigated the imagery (high resolution or very high resolution) best suited for extracting seismic cut lines using a set of rules and multi-resolution object-oriented classification methods. The data used include SPOT 5 and QuickBird multispectral images and existing Geographic Information System (GIS) databases within one bear management area (BMA) in the eastern slopes of the Rocky Mountains in Alberta. Results indicated that among the available algorithms in the Definiens Developer 7.0 package, the Lee sigma algorithm was capable of highlighting cut lines using the near-infrared (NIR) band of the SPOT 5 image and the QuickBird image. The multi-resolution segmentation was able to segment fresh cut lines when giving higher weight to the Lee sigma edge extraction layer, and the nearest-neighbour object-oriented classifier was able to classify linear features, but with noise. Classification accuracy increased following the post-classification refinement processing. The accuracy assessment indicated that, in the case of delineating fresh cut lines, the higher resolution QuickBird image performed better than SPOT 5. However, neither the QuickBird image nor the SPOT 5 image could accurately delineate relatively old cut lines.

Narrow-linear and small-area forest disturbance detection and mapping from high spatial resolution imagery

Widespread disturbance has brought a large amount of narrow-linear and small-area disturbance features (e.g., trails, seismic lines, forest roads, well sites, and cut blocks) to forest areas throughout the past decade. This issue has prompted research into finding the appropriate data and methods for mapping these narrow-linear and small-area disturbance features in order to examine their impacts on wildlife habitat. In this paper, we first described the characteristics of small forest disturbances and presented the nature of problem. We then presented a framework for detecting and extracting narrow-linear and small-area forest disturbance features. Using a SPOT 5 high spatial detail image and existing GIS databases, we applied the framework to map narrow-linear and small-area forest disturbance features in a Bear Management area (BMA) in the eastern slopes of the Rocky Mountains in Alberta, Canada. The results indicated that the proposed framework produced accurate disturbance maps for cut blocks, and forest roads & trails. The high errors of omission in the cut lines map were attributed to inconsistent geometric and radiometric patterns in the rarely-used or old cut lines. The study confirmed the feasibility of rapidly updating incomplete GIS data with linear and small-area disturbance features extracted from high spatial detail SPOT imagery. Future work will be directed towards improvement of the framework and the extraction strategy to remove a large amount of spurious features and to increase accuracy for cut lines mapping.

Remote sensing derived edge location, magnitude, and class transitions for ecological studies

Regionally intensive human activities related to resource extraction (i.e., harvesting, oil and gas extraction) are increasing the occurrence of edges found in some forested landscapes. Edges between different land cover types represent important transition zones for abiotic and biotic processes. However, boundary detection methods often identify edges solely in areas of high contrast, such as transitions between forest and non-forest areas, and are insensitive to the relative contrast and orientation of different transitions. Edge contrast and orientation can determine the magnitude and even the occurrence of ecological edge effects and should be measured to provide information on landscape condition and habitat potential. Wombling was applied to the wetness component of a tasselled cap transformation (TCT) of a Landsat scene acquired over a portion of the eastern slopes of the Rocky Mountains in Alberta, Canada. By incorporating wombled edge contrast and orientation, and edge class transition type obtained from a land cover dataset, the nature of all transitions between land cover classes within the image was characterized and quantified. The consistency between edges identified by wombling and other common methods of edge delineation (such as spatial clustering) and methods of edge quantification (such as landscape pattern indices, or LPIs) was also assessed. Land cover transitions showed a broad range of edge contrast. Comparisons of edge contrast and the LPI edge density showed a positive correlation (r2 = 0.33); however, the strength of this relationship varied with the dominant land cover type (e.g., r2 = 0.016 for broadleaf open forest to r2 = 0.48 for dense coniferous forest). Stratifying edge contrast to higher values (i.e., >1 standard deviation) increased agreement with edge density, indicating that the LPI is preferentially relating high contrast edges. This study demonstrates how unique edge characteristics may be generated from a remotely sensed continuous variable (TCT wetness). This knowledge of the location, magnitude, and class transitions found at edges provides insights into the nature of the edge effects and enables the development and testing of hypotheses informing wildlife habitat use and selection.

A medium-resolution remote sensing classification of agricultural areas in Alberta grizzly bear habitat

Habitat loss and human-caused mortality are the most serious threats facing grizzly bear (Ursus arctos L.) populations in Alberta, with conflicts between people and bears in agricultural areas being especially important. However, the agricultural land being classified as a single class in current grizzly bear habitat maps limits the understanding of the bear habitat in agriculture regions. The objectives of this research were to find the best possible classification approach from a limited selection of methods for determining multiple classes of agricultural and herbaceous land cover and to create land cover maps of agricultural and herbaceous areas which will be integrated into existing grizzly bear habitat maps for western Alberta. Three different object-based classification methods (one unsupervised method and two supervised methods) were analyzed with these data to determine the most accurate and useful method. The best method was the supervised sequential masking (SSM) technique, which gave an overall accuracy of 88% and a kappa index of agreement (KIA) of 83%. When combined with bear global positioning system (GPS) location data, it was discovered that bears in agricultural areas were found in the grass – forage crops class 77% of the time, with the small grains and bare soil - fallow fields classes making up the rest of the visited land cover. The bears were found in these areas primarily in the summer months.

Comparison of Landsat multispectral and IRS panchromatic imagery for landscape pattern analysis of grizzly bear habitat in agricultural areas of western Alberta

The grizzly bear (Ursus arctos L.) is a species that is widely recognized as an indicator of ecosystem health in west-central Alberta. Agricultural activities, oil and gas exploration and extraction, forestry, and recreation can all contribute to grizzly bear habitat fragmentation and loss. The purpose of this research was to compare two models of grizzly bear activity in agricultural areas of western Alberta, Canada, developed from landscape pattern metrics derived from Landsat and Indian Remote Sensing (IRS) based land cover classifications and assess if these models statistically converged on the same landscape metrics. Results were further explained by considering the influence of spatial, spectral, and thematic resolution, along with previous knowledge on grizzly bear habitat preference. The Landsat- and IRS-based analyses were compared using relationships between landscape metrics and both grizzly bear presence-absence data and frequency of use data. Results indicated that landscape spatial structure had at least some role in determining whether or not grizzly bears would use an area in an agricultural landscape. It was concluded that the thematic resolution represented the greatest impact on compositional metrics for both the grizzly bear presence-absence and frequency of use analyses, i.e., the Landsat-based product was more suited to revealing the function of the compositional metrics than the IRS-based product. Configurational metrics, however, were more sensitive to the higher spatial resolution map derived from the IRS data. Landscape management recommendations are suggested in the context of these geospatial results.

Assessing the impact of field of view on monitoring understory and overstory phenology using digital repeat photography.

Abstract Phenological patterns of the components within forest ecosystems, such as understory vegetation, are important indicators of climate variability, productivity, and additional ecosystem services such as food and habitat availability for wildlife. Proximal sensing systems can provide detailed phenological records at a relatively low cost. As interest in these datasets increases, we need additional information regarding the effect of different approaches on the scale of observations and camera field of view. In this research, we examine the impact of field of view on the capacity of cameras to detect changes in phenology of individual species in an image time series. We examine two co-located series of oblique images acquired using a fine and broad field of view and compare a number of phenological indicators, including the start and end of season derived for individual plant species. Our results indicate both fine and broad field of view camera systems are highly effective at detecting key markers of plant phenology with no significant differences between the two. This result supports environmental monitoring using cost-effective broad field of view cameras, or even—subject to some constraints—readily available camera stations installed for tourism or traffic monitoring. Résumé Les patrons phénologiques des composantes au sein des écosystèmes forestiers, comme la végétation de sous-bois, sont des indicateurs importants de la variabilité du climat, de la productivité et des services écosystémiques supplémentaires tels que la disponibilité de nourriture et d’habitats pour la faune. Les systèmes de détection par caméra dans l’environnement peuvent fournir des observations phénologiques détaillées à un coût relativement faible. Comme l’intérêt pour ces ensembles de données s’accroît, nous avons besoin d’informations supplémentaires concernant l’effet des différentes approches sur l’échelle des observations et sur le champ de vision de la caméra. Dans cette recherche, nous examinons l’impact du champ de vision sur la capacité des caméras à détecter des changements dans la phénologie des espèces individuelles dans une série temporelle d’images. Nous examinons deux séries colocalisées d’images obliques acquises en utilisant des champs de vision étroit et large et nous comparons quelques indicateurs phénologiques, y compris le début et la fin de la saison, dérivés pour des espèces de plante individuelle. Nos résultats indiquent que les systèmes de caméras avec des champs de vision large et étroit sont très efficaces pour la détection de marqueurs clés de la phénologie des plantes sans différence significative entre les deux. Ce résultat supporte le concept de la surveillance de l’environnement en utilisant des caméras économiques avec de larges champs de vision, ou même—avec certaines contraintes—des stations de caméras facilement accessibles installées pour le tourisme ou la surveillance du trafic.