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Contemporary forest-health initiatives require technologies and workflows that can monitor forest degradation and recovery simply and efficiently over large areas. Spectral recovery analysis—the examination of spectral trajectories in satellite time series—can help democratize this process, particularly when performed with cloud computing and open-access satellite archives. We used the Landsat archive and Google Earth Engine (GEE) to track spectral recovery across more than 57,000 forest harvest areas in the Canadian province of Alberta. We analyzed changes in the normalized burn ratio (NBR) to document a variety of recovery metrics, including year of harvest, percent recovery after five years, number of years required to achieve 80% of pre-disturbance NBR, and % recovery the end of our monitoring window (2018). We found harvest areas in Alberta to recover an average of 59.9% of their pre-harvest NBR after five years. The mean number of years required to achieve 80% recovery in the province was 8.7 years. We observed significant variability in pre- and post-harvest spectral recovery both regionally and locally, demonstrating the importance of climate, elevation, and complex local factors on rates of spectral recovery. These findings are comparable to those reported in other studies and demonstrate the potential for our workflow to support broad-scale management and research objectives in a manner that is complimentary to existing information sources. Measures of spectral recovery for all 57,979 harvest areas in our analysis are freely available and browseable via a custom GEE visualization tool, further demonstrating the accessibility of this information to stakeholders and interested members of the public.

Shadows from buildings, terrain, and other elevated features represent lost and (or) impaired data values that hinder the quality of optical images acquired under all but the most diffuse illumination conditions. This is particularly problematic in high-spatial-resolution imagery acquired from unmanned aerial vehicles (UAVs), which generally operate very close to the ground. However, the flexibility and low cost of re-deployment of the platform also presents opportunities, which we capitalize on in a new workflow designed to eliminate shadows from UAV-based orthomosaics. Our straightforward, three-step procedure relies on images acquired from two different UAV flights, where illumination conditions produce diverging shadow orientations: one before solar noon and another after. From this multi-temporal image stack, we first identify and then eliminate shadows from individual orthophoto components, then construct the final orthomosaic using a feature-matching strategy with the commercial software package Photoscan. The utility of our strategy is demonstrated over a treed-wetland study site in northwestern Alberta, Canada; a complex scene containing a wide variety of shadows, which our workflow effectively eliminated. While shadow-reduced orthomosaics are generally less useful for feature-identification tasks that rely on the shadow element of image interpretation, they create a superior foundation for most other image-processing routines, including classification and change-detection.

Research Highlights: This is the most comprehensive analysis to date of the accuracy of height estimates for individual conifer seedlings derived from drone-based image point clouds (DIPCs). We provide insights into the effects on accuracy of ground sampling distance (GSD), phenology, ground determination method, seedling size, and more. Background and Objectives: Regeneration success in disturbed forests involves costly ground surveys of tree seedlings exceeding a minimum height. Here we assess the accuracy with which conifer seedling height can be estimated using drones, and how height errors translate into counting errors in stocking surveys. Materials and Methods: We compared height estimates derived from DIPCs of different GSD (0.35 cm, 0.75 cm, and 3 cm), phenological state (leaf-on and leaf-off), and ground determination method (based on either the DIPC itself or an ancillary digital terrain model). Each set of height estimates came from data acquired in up to three linear disturbances in the boreal forest of Alberta, Canada, and included 22 to 189 surveyed seedlings, which were split into two height strata to assess two survey scenarios. Results: The best result (root mean square error (RMSE) = 24 cm, bias = &minus, 11 cm, R2 = 0.63, n = 48) was achieved for seedlings &gt, 30 cm with 0.35 cm GSD in leaf-off conditions and ground elevation from the DIPC. The second-best result had the same GSD and ground method but was leaf-on and not significantly different from the first. Results for seedlings &le, 30 cm were unreliable (nil R2). Height estimates derived from manual softcopy interpretation were similar to the corresponding DIPC results. Height estimation errors hardly affected seedling counting errors (best balance was 8% omission and 6% commission). Accuracy and correlation were stronger at finer GSDs and improved with seedling size. Conclusions: Millimetric (GSD &lt, 1 cm) DIPC can be used for estimating the height of individual conifer seedlings taller than 30 cm.

While much is known about the relationship between Clark’s nutcracker and whitebark pine, information on nutcracker energetic behavioural strategies − the driving factors behind nutcracker emigration − and the impact of nutcracker behaviour on whitebark pine communities remain uncertain. To investigate nutcracker energetic behaviour, we developed a spatially explicit agent-based model (ABM) to simulate the underlying behavioural mechanisms nutcrackers are most likely to employ during foraging in the South Cascades near Mt. Rainier, Washington. The ABM is comprised of cognitive nutcracker agents possessing memory and decision-making heuristics that act to optimize energy acquisition and loss. Environmental data layers for elevation and basal area of tree species were used to represent the landscape in terms of habitat and energy resources. We employed the evaludation approach for an organized sequence of model development and analysis, including: data evaluation, conceptual model evaluation, implementation, verification, model output verification (calibration consisting of comparison of parameters informed by nutcracker ecology to real-world empirical values; pattern-oriented modeling − POM), model analysis (sensitivity of model to changes in parameters and processes), and model output corroboration (use of POM to compare model output to real-world patterns from empirical investigations of nutcracker ecology, independent of calibration). Simulations were conducted on alternative nutcracker behavioural-energetic mechanism strategies by assigning different fitness-maximizing goals to agents. We found that an integrated energetic requirement (IER) mechanism, which includes both the short-term and long-term energetic needs of nutcracker agents to be the best-fit scenario. Our results affirm previous research that nutcrackers are responsive to changes in their energetic environment, and that they are capable of projecting energy budgets well into the future. The development of this ABM provides a basis for future research, such as a means to assess the driving conditions necessary for nutcrackers when choosing between a resident and emigrant strategy and as a planning tool to model nutcracker responses to potential landscape changes, which may facilitate long-term WBP conservation.

Coarse woody debris (CWD, parts of dead trees) is an important factor in forest management, given its roles in promoting local biodiversity and unique microhabitats, as well as providing carbon storage and fire fuel. However, parties interested in monitoring CWD abundance lack accurate methods to measure CWD accurately and extensively. Here, we demonstrate a novel strategy for mapping CWD volume (m3) across a 4300-hectare study area in the boreal forest of Alberta, Canada using optical imagery and an infra-canopy vegetation-index layer derived from multispectral aerial LiDAR. Our models predicted CWD volume with a coefficient of determination (R2) value of 0.62 compared to field data, and a root-mean square error (RMSE) of 0.224 m3/100 m2. Models using multispectral LiDAR data in addition to image-analysis data performed with up to 12% lower RMSE than models using exclusively image-analysis layers. Site managers and researchers requiring reliable and comprehensive maps of CWD volume may benefit from the presented workflow, which aims to streamline the process of CWD measurement. As multispectral LiDAR radiometric calibration routines are developed and standardized, we expect future studies to benefit increasingly more from such products for CWD detection underneath canopy cover.

Coarse woody debris (CWD, large parts of dead trees) is a vital element of forest ecosystems, playing an important role in nutrient cycling, carbon storage, fire fuel, microhabitats, and overall forest structure. However, there is a lack of effective tools for identifying and mapping both standing (snags) and downed (logs) CWD in complex natural settings. We applied a random forest machine learning classifier to detect CWD in centimetric aerial imagery acquired over a 270-hectare study area in the boreal forest of Alberta, Canada. We used a geographic object-based image analysis (GEOBIA) approach in the classification with spectral, spatial, and LiDAR (light detection and ranging)-derived height predictor variables. We found CWD to be detected with great accuracy (93.4 &plusmn, 4.2% completeness and 94.5 &plusmn, 3.2% correctness) when training samples were located within the application area, and with very good accuracy (84.2 &plusmn, 5.2% completeness and 92.2 &plusmn, 3.2% correctness) when training samples were located outside the application area. The addition of LiDAR-derived variables did not increase the accuracy of CWD detection overall (&lt, 2%), but aided significantly (p &lt, 0.001) in the distinction between logs and snags. Foresters and researchers interested in CWD can take advantage of these novel methods to produce accurate maps of logs and snags, which will contribute to the understanding and management of forest ecosystems.

Surface air temperature (Tair) is a critical driver of ecosystem processes and phenological dynamics, and can be estimated in near-real time with satellite remote sensing. However, persistent cloud cover often creates large spatial and temporal gaps in our observation records. Previous studies have successfully mapped Tair; however, the challenges of mapping forest understory temperatures (Tust) are relatively unexplored. This study describes a methodology for constructing cloud-free composites of Tust at 250 m spatial resolution. We used generalized linear models to correlate daily average Tust with ground-surveyed forest structural characteristics and land surface temperature (LST) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Models were applied to all four daily MODIS overpasses and combined in to a single image to maximize cloud-free spatial coverage. Pixel temperatures within the remaining cloud gaps were estimated using a temporal averaging algorithm that incorpor...

Resource-access road crossings are expected to alter peatland hydrological properties by obstructing surface and sub-surface water flows. We conducted a multi-year study at two boreal peatlands – a forested bog and a shrubby rich fen near Peace River, Alberta – to study the impacts of resource access roads on the hydrology of adjacent peatland. Field measurements (bi-weekly depth to water table and hydraulic head, one-time hydraulic conductivity) during the growing seasons (May-August) of 2016 and 2017 were taken from sampling plots representing: 1) sides of the road (upstream and downstream); 2) distance from the road (obstruction); and 3) distance from culverts. Compared to the growing season average precipitation for the region of 1.8 mm d−1, the study period had very wet conditions in 2016 (3.7 mm d−1) and dry conditions in 2017 (1.1 mm d−1). In contrast to our assumptions, resource access road disturbed the surface and sub-surface water flow at the bog, but the effect was minimal at the fen as the road orientation was nearly parallel to the flow direction at the latter. At the bog, the shallowest depth to water table position was observed at upstream areas closer to the road, when culverts were located >20 m distance from transects. In contrast, when culverts were present

Allometric equations for estimating aboveground biomass (AGB) from easily measured plant attributes are unavailable for most species common to mid-continental boreal peatlands, where shrubs comprise a large component of the vegetation community. Our study develops allometric equations for three dominant genera found in boreal fens: Alnus spp. (alder), Salix spp. (willow) and Betula pumila (bog birch). Two different types of local equations were developed: (1) individual equations based on genus/phylogeny, and (2) a general equation that pooled all individuals regardless of genera. The general equation had a R2 = 0.97 (n = 82), and was not significantly different (p &gt, 0.05) than any of the phylogenetic equations. This indicated that a single generalized equation is sufficient in estimating AGB for all three genera occurring in our study area. A closer look at the performance of the general equation revealed that smaller stems were predicted less accurately than larger stems because of the higher variability of leafy biomass found in small individuals. Previously published equations developed in other ecoregions did not perform as well as our local equations.

Rapid assessment of forest regeneration using unmanned aerial vehicles (UAVs) is likely to decrease the cost of establishment surveys in a variety of resource industries. This research tests the feasibility of using UAVs to rapidly identify coniferous seedlings in replanted forest-harvest areas in Alberta, Canada. In developing our protocols, we gave special consideration to creating a workflow that could perform in an operational context, avoiding comprehensive wall-to-wall surveys and complex photogrammetric processing in favor of an efficient sampling-based approach, consumer-grade cameras, and straightforward image handling. Using simple spectral decision rules from a red, green, and blue (RGB) camera, we documented a seedling detection rate of 75.8 % (n = 149), on the basis of independent test data. While moderate imbalances between the omission and commission errors suggest that our workflow has a tendency to underestimate the seedling density in a harvest block, the plot-level associations with ground surveys were very high (Pearson&rsquo, s r = 0.98, n = 14). Our results were promising enough to suggest that UAVs can be used to detect coniferous seedlings in an operational capacity with standard RGB cameras alone, although our workflow relies on seasonal leaf-off windows where seedlings are visible and spectrally distinct from their surroundings. In addition, the differential errors between the pine seedlings and spruce seedlings suggest that operational workflows could benefit from multiple decision rules designed to handle diversity in species and other sources of spectral variability.

Separating native grasses from tame pastures is an important mapping exercise that assists in the assessment of biodiversity, delineation of species’ habitat, and appraisal of rangeland health. However, native grasslands (primarily naturally occurring species) and tame pastures (primarily nonnative grasses planted for hay, pasture, or seed) are spectrally similar and therefore difficult to differentiate with traditional remote sensing techniques and with air-photo interpretation. We used seasonal profiles of the normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments to examine the separability of native grasslands and tame pastures (where both types employ the C3 photosynthetic pathway) in the Dry Mixedgrass natural subregion of Alberta, Canada. The two classes were found to have different rates of spring green up at the pixel level that allowed for separation with a simple linear discriminant function. We achieved an overall accuracy of 73% ( $\mathrm{n} = 100$ independent test cases) with the MODIS time series—a statistically significant improvement of the photo-interpretation-based Grassland Vegetation Inventory (52%): the current standard for vegetation information in Alberta’s agricultural zone. We also found that the multitemporal method was able to select dates for single-date classifications that provided relatively high classification accuracies (up to 71% overall). In addition to achieving higher levels of overall accuracy than more conventional methods, the MODIS time series produced much more reliable identification of abandoned pastures: formerly planted rangelands that lack many of the visual cues used by photo interpreters.

Modern advances in cloud computing and machine-leaning algorithms are shifting the manner in which Earth-observation (EO) data are used for environmental monitoring, particularly as we settle into the era of free, open-access satellite data streams. Wetland delineation represents a particularly worthy application of this emerging research trend, since wetlands are an ecologically important yet chronically under-represented component of contemporary mapping and monitoring programs, particularly at the regional and national levels. Exploiting Google Earth Engine and R Statistical software, we developed a workflow for predicting the probability of wetland occurrence using a boosted regression tree machine-learning framework applied to digital topographic and EO data. Working in a 13,700 km2 study area in northern Alberta, our best models produced excellent results, with AUC (area under the receiver-operator characteristic curve) values of 0.898 and explained-deviance values of 0.708. Our results demonstrate the central role of high-quality topographic variables for modeling wetland distribution at regional scales. Including optical and/or radar variables into the workflow substantially improved model performance, though optical data performed slightly better. Converting our wetland probability-of-occurrence model into a binary Wet-Dry classification yielded an overall accuracy of 85%, which is virtually identical to that derived from the Alberta Merged Wetland Inventory (AMWI): the contemporary inventory used by the Government of Alberta. However, our workflow contains several key advantages over that used to produce the AMWI, and provides a scalable foundation for province-wide monitoring initiatives.

Groundwater level (GWL) and depth to water (DTW) are related metrics aimed at characterizing groundwater-table positions in peatlands, and two of the most common variables collected by researchers working in these ecosystems. While well-established field techniques exist for measuring GWL and DTW, they are generally difficult to scale. In this study, we present a novel workflow for mapping groundwater using orthophotography and photogrammetric point clouds acquired from unmanned aerial vehicles. Our approach takes advantage of the fact that pockets of surface water are normally abundant in peatlands, which we assume to be reflective of GWL in these porous, gently sloping environments. By first classifying surface water and then extracting a sample of water elevations, we can generate continuous models of GWL through interpolation. Estimates of DTW can then be obtained through additional efforts to characterize terrain. We demonstrate our methodology across a complex, 61-ha treed bog in northern Alberta, Canada. An independent accuracy assessment using 31 temporally coincident water-well measurements revealed accuracies (root mean square error) in the 20-cm range, though errors were concentrated in small upland pockets in the study area, and areas of dense tree covers. Model estimates in the open peatland areas were considerably better.

Photogrammetric point clouds (PPCs) provide a source of three-dimensional (3-D) remote sensing data that is well-suited to use over small areas that are within the scope of observation by unmanned aerial vehicles (UAVs). We compared PPC-based structural metrics to traditional ground surveys conducted by field personnel in order to assess the capacity of PPC data to contribute to vegetation-reclamation surveys. We found good statistical agreement between key structural vegetation parameters, such as mean and maximum vegetation height, with PPC metrics successfully predicting most height and tree-diameter metrics using multivariate linear regression. However, PPC metrics were not as useful for estimating ground-measured vegetation cover. We believe that part of the issue lies in the mismatch between PPC- and ground-based measurement approaches, including subjective judgement on behalf of ground crews: a topic that requires more investigation. Our work highlights the emerging value of UAV-based PPCs to complement, and in some cases supplement, traditional ground-based sources of measured vegetation structure.

Fire is an important disturbance process historically present across the northern Great Plains. Previous research from northern dry mixed-grass prairie suggests that C4 (warm season) grasses replace C3 (cool season) climax species with increasing fire, particularly in the spring. This hypothesis was tested at a landscape scale at Canadian Forces Base Suffield Alberta, by exploring the relationship between ecosystem states (C3 dominant, C3/C4 codominant, C4 dominant) inferred from a MODIS multitemporal plant functional type classification (pseudo R 2 : 0.598, overall accuracy: 0.74) and interyear fire history digitized from the Landsat archive (1972–2007). Probit regression showed that succession processes were different between range sites, where C4-dominant pixels were positively related to fire ( P R 2 = 1) and completely replaced C3-dominant pixels on loamy range sites after 14 fires in 36 yr. In contrast, C3- and C3/C4-codominant pixels were related with fire on Blowouts range sites ( P R 2 = 1), where C3/C4-codominant pixels replaced C3-dominant pixels with increasing fire. Finally, there were no statistically significant relationships between ecosystem states and fire for Sands range sites. Analysis of recovery showed that after loamy pixels experienced three to six fires in 18 yr followed by 18 yr of rest, C3-dominant pixels were reduced by over 30% compared to unburned pixels. Finally, intrayear fire timing (2001–2009) was explored with the use of Wilcoxon signed-rank tests between the spatial extent of spring and summer fires, with the use of MODIS burned-area data. Results indicate that fires are not limited to the spring season ( P

We recently completed the accuracy assessment of a Landsat-derived landcover polygon layer covering the entire province of Alberta 660,000 km2, Canada, for which we gathered reference information for nearly 5000 randomly selected polygons ranging from two hectares to thousands of hectares in size. This gave us the unique opportunity to quantify, for the first time, how the probability of correctly classifying a landcover object varies with its size. Irrespective of whether they are represented as polygons or as sets of connected pixels with the same label, the classification accuracy of landcover objects decreases as their size decreases, steadily for large and medium sizes, and more dramatically when they are within two orders of magnitude of the pixel size of the input image. We show that this size-dependency is bound to occur whenever the size distribution of landcover objects follows an inverse power law. Our results are consistent with previous studies on related issues, confirm the need to account for size when assessing the accuracy of object-based landcover maps, and cast doubts on the validity of 1 recently proposed object-based accuracy estimators, and 2 landscape pattern analyses where the minimum patch size is close to the pixel size.

Grizzly bear (Ursus arctos) populations across their range are being threatened by anthropogenic development and associated increases in human-caused mortality. However, details surrounding the impact of cumulative human effects are not yet fully understood, as prior research has focused primarily on habitat selection of individual disturbance features, rather than the spatio-temporal dynamics of aggregated disturbance processes. We used grizzly bear relative-abundance information from a DNA population inventory alongside a GIS database of human footprint dynamics to gain insight into the relationships between human disturbance features and the spatial distribution of grizzly bears in west-central Alberta, Canada: a landscape experiencing heavy resource development. We used candidate model-selection techniques and zero-inflated Poisson regression models to test competing hypotheses about disturbance processes, neighborhood effect and landscape characteristics. The best model explained about 57% of the overall variation in relative grizzly bear abundance. Areas with lower ‘disturbance exposure’ (i.e. high mean distance to new disturbances over time), lower ‘neighborhood disturbance’ (i.e. disturbance density around those areas), and higher ‘availability of regenerating forest’, were associated with higher bear abundance. In addition, areas located further away from an adjacent protected area exhibited a higher probability of ‘excess absences’, accounting indirectly for the cumulative effects of disturbance and the history of human-caused mortality. Our results suggest that managing the spatio-temporal exposure of grizzly bears to new disturbance features may be an important consideration for conserving this species in rapidly changing landscapes.

Human disturbances are a major driver of biodiversity declines world-wide, and the intensely used Alberta forest landscape is no exception to this trend. Monitoring of such large areas is typically conducted via multi-temporal land-cover maps from remote sensing, but automated and efficient procedures for reliable, operational applications have yet to be fully developed. In an effort to contribute to this need, we developed an innovative approach to landscape monitoring: the disturbance-inventory framework, which is applied for the first time as described here to monitor annual changes in an 8800-km2 multi-use landscape in west-central Alberta, Canada. Using this framework, we (1) report on the spatio-temporal distribution of industrial disturbances such as harvesting cutblocks, oil and gas wells, coal mines, and road/pipelines; and (2) track the associated annual changes in land-cover composition and configuration between 1998 and 2005. To enable spatially explicit analyses within the study area, we divided it into 178, 49 km2-square landscape cells. The overall area-based annual rate of change of 0.62% for this multi-use may be considered moderate compared to other regions, where change was mainly shaped by a single use, i.e., forestry. However, the spatially explicit nature of our analysis revealed that the eastern half of the study area is subject to considerably higher rates of change, mainly due to the concurrent appearance of disturbances from forestry and the oil and gas industry. The western half, by contrast, is more restricted by rugged terrain and fewer roads. The average distance to disturbance features across the entire study area decreased from 1500 m to 1200 m over the seven years. Total forest area, mean and largest patch size, and mean shape index all decreased consistently over the same period. The detected rapid change and associated fragmentation call for ongoing monitoring of this and other multi-use landscapes, which could be undertaken using this framework.

Geographically weighted regression (GWR) extends the conventional ordinary least squares (OLS) regression technique by considering spatial nonstationarity in variable relationships and allowing the use of spatially varying coefficients in linear models. Previous forest studies have demonstrated the better performance of GWR compared to OLS when calibrated and validated at sampled locations where field measurements are collected. However, the use of GWR for remote-sensing applications requires generating estimates and evaluating the model performance for the large image scene, not just for sampled locations. In this study, we introduce GWR to estimate forest canopy height using high spatial resolution Quickbird (QB) imagery and evaluate the influence of sampling density on GWR. We also examine four commonly used spatial analysis techniques – OLS, inverse distance weighting (IDW), ordinary kriging (OK) and cokriging (COK) – and compare their performance with that using GWR. Results show that (i) GWR outperf...

Modern ecological models often account for the influence of the surrounding environment by using landscape pattern indices (LPIs) as measures of landscape structure. Ideally, the landscape samples from which these LPIs are ex- tracted should be centered on the locations where the response variable was measured. However, in situations where this is not possible due to a lack of adequate full-coverage landcover data, the question arises as to what degree this circumstance creates a bias in the value of the LPIs, thereby obscuring their relation with the response variable. To address this question, we extracted four representative LPIs from 30 rectangular (3 × 6 km) landscape samples evenly distributed across a 10 000 km 2 boreal forest study area. These rectangles were subjected to systematic displacements across a range of distances (0.5 to 2.5 km) and directions, after which we recomputed the LPIs. We found that a 1 km spatial offset led to an average of 15% deviation of original LPI values. Unfortunately, as the offset increased, the range of resulting deviations also widened, making it difficult to predict this effect. Our findings fill a gap in the literature on landscape pattern analysis and suggest that researchers should avoid LPIs extracted from spatially offset landscape samples. Resume : Les modeles ecologiques modernes tiennent souvent compte de l'influence de l'environnement avoisinant en utili- sant des indices paysagers (IP) comme mesure de la structure du paysage. Les regions pour lesquelles ces IP sont extraits devraient idealement provenir du centre des endroits ou la variable etudiee a ete mesuree. Cependant, dans les cas ou cela n'est pas possible du au manque de donnees qui couvrent adequatement toute la couverture terrestre, la question surgit a sa- voir jusqu'a quel point cette situation cree un biais dans la valeur des IP, obscurcissant ainsi leur relation avec la variable etudiee. Pour aborder cette question, nous avons extrait quatre IP representatifs de 30 echantillons rectangulaires (3 × 6 km) de paysage egalement repartis dans une zone d'etude de 10 000 km 2 de foret boreale. Ces rectangles ont ete soumis a des deplacements systematiques sur une serie de distances (0,5 a 2,5 km) et dans differentes directions apres quoi les IP ont ete recalcules. Nous avons trouve qu'un decalage spatial d'un kilometre entrainait une deviation moyenne de 15 % par rapport aux valeurs d'IP originales. Malheureusement, a mesure que le decalage augmente, la dispersion des deviations qui en re- sulte s'accroit, ce qui rend difficile la prediction de cet effet. Nos resultats comblent une lacune dans la litterature et indi- quent que les chercheurs devraient eviter d'extraire des IP a partir d'echantillons decales de paysage. (Traduit par la Redaction)

Remote sensing plays a critical role in contemporary monitoring programs, but our strategies for processing these data using automated procedures are not always reliable. In particular, the task of separating real from spurious changes remains problematic, especially in an object-based environment where differential errors in classification quality, spatial registration, scene illumination, resolution, and object delineation have forced some operators to adopt labor-intensive visual-interpretation strategies, or employ manual interaction on an object-by-object basis. In this paper, we present an updated summary of our new disturbance-inventory approach to land-cover monitoring that combines object-based classification and change-detection strategies with boundary-conditioning routines designed to maximize the spatial and thematic integrity of the finished products. With this approach, the final maps are only altered in regions of confirmed change, and spurious gaps, slivers, stretches, and encroachments are avoided. The approach constitutes an innovative, efficient, and transparent framework that can handle all the basic landscape dynamics, including feature appearance, disappearance, succession, expansion, and shrinkage, without the need for manual editing.

The long-term record of global Landsat data is an important resource for studying Earth's system. Given the identified gaps in Landsat data and the undetermined future status of Landsat data availability, alternatives to Landsat imagery need to be tested in an operational environment. In this study, forest land cover and crown closure maps generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and System Pour 1'Observation de la Terre (SPOT) data were compared to Landsat-based map products currently in use by the grizzly bear habitat-mapping program. Overall accuracies greater than 85% were obtained for both ASTER-and SPOT-based land cover maps. The ASTER and SPOT classification accuracies were higher than that achieved by Landsat. Crown closure maps derived from ASTER and SPOT data show a small increase in accuracy when compared to the Landsat products. Overall, these results demonstrate that ASTER and SPOT could provide alternative data sources for producing maps in the event of a gap in the Landsat data.

This paper describes the conceptualization and implementation of an agent-based model to investigate how varying levels of human presence could affect elements of wolf behavior, including highway crossings; use of areas in proximity to roads and trails; size of home ranges; activities, such as hunting, patrolling, resting, and feeding pups; and survival of individuals in Banff and Kootenay National Parks, Canada. The model consists of a wolf module as the primary component with five packs represented as cognitive agents, and grizzly bear, elk, and human modules that represent dynamic components of the environment. A set of environmental data layers was used to develop a friction model that serves as a base map representing the landscape over which wolves moved. A decision model was built to simulate the sequence of wolf activities. The model was implemented in a Java Programming Language using RePast, an agent-based modeling library. Six months of wolf activities were simulated from April 16 to October 15 (i.e., a season coherent with regard to known wolf behaviors), and calibrated with GPS data from wolf radiocollars (n = 15) deployed from 2002 to 2004. Results showed that the simulated trajectories of wolf movements were correlated with the observed trajectories (Spearman's rho 0.566, P

A primary activity required to support sustainable forest management is the detection and mitigation of forest disturbances. These disturbances can be planned, through urbanization and harvesting, or unplanned, through insect infestations or fire. Detection and characterization of disturbance types are important, as different disturbances have different ecological effects and may require unique managerial responses. As such, it is necessary for forest managers to have as complete and current information as possible to support decision making. In this study, we developed a framework to automatically detect and label disturbances derived from remotely sensed images. Disturbances were detected through traditional image differencing of medium-resolution imagery (Landsat-7 Enhanced Thematic Mapper Plus (ETM+), resampled to 30 m) but were refined and augmented through comparison with edge features extracted from high spatial resolution satellite imagery (Indian Remote Sensing (IRS) satellite 1C panchromatic imagery, resampled to 5 m). By incorporating spectral information, derived composite band values (tasselled cap transformations), spatial and contextual information, and secondary datasets, we were able to capture and label disturbance features with a high level of overall agreement (91%). Areal features, such as harvest areas, are captured and labelled more reliably than linear features such as roads, with 92% and 72% agreement when compared with control data, respectively. By incorporating rule-based disturbance attribution with remote sensing change detection, we envision the update of land cover databases with reduced human intervention, aiding more rapid data integration and opportunities for timely managerial responses. Resume´. La detection et l'attenuation des perturbations dans une foret constituent une etape essentielle pour assurer la gestion durable de la foret. Ces perturbations peuvent e planifiees, comme dans le cas de l'urbanisation et de l'exploitation forestiere ou encore, non planifiees suite a des infestations par les insectes ou des incendies. La detection et la caracterisation des divers types de perturbations sont importantes etant donneque ces differentes perturbations entraoˆnent des effets e differents et peuvent exiger des reponses qui leur sont propres en termes de gestion. Ainsi, il est necessaire pour les gestionnaires de la foret de disposer de l'information la plus complete et la plus ajour possible pour soutenir le processus de prise de decision. Dans cette e on developpe une methodologie pour detecter et e automatiquement les perturbations derivees des images de teledetection. Les perturbations ont ed etectees en utilisant la methode traditionnelle de differenciation d'images a moyenne resolution (images ETM+ (« Enhanced Thematic Mapper Plus ») de Landsat-7, re ´-echantillonnees a ` 30 m), quoique celles-ci aient eteraffinees et ameliorees par le biais d'une comparaison avec les caracteristiques de contours extraites d'images satellite ahaute resolution spatiale (images panchromatiques du satellite IRS-1C (« Indian Remote Sensing satellite 1C »), re ´-echantillonnees a 5 m). En integrant l'information spectrale, les valeurs des bandes composites derivees (espace indiciel transforme ´-TCT), l'information spatiale et contextuelle ainsi que des ensembles secondaires de donnees, il a epossible de capturer et d'etiqueter les caracteristiques des perturbations avec un niveau global de concordance e ´ (91 %). Les caracteristiques spatiales, comme la surface des zones de coupe, sont capturees et ees de facon plus fiable que les caracteristiques lineaires telles que les routes, avec une concordance de 92 % et de 72 % respectivement comparativement aux donnees de controle. En integrant l'attribution des perturbations obtenue al'aide d'un systeme a base de regles avec les resultats de la detection des changements par teledetection, on envisage de realiser la mise ajour des bases de donnees du couvert avec une intervention humaine minimale facilitant ainsi l'integration plus rapide des donnees tout en accroissant les capacites de reponse des gestionnaires acourt terme. (Traduit par la Redaction)

Wildlife habitat mapping strongly supports applications in natural resource management, environmental conservation, impacts of anthropogenic activity, perturbed ecosystem restoration, species-at-risk recovery and species inventory. Remote sensing has long been identified as a feasible and effective technology for large-area habitat mapping. However, existing and future uncertainties in remote sensing will definitely have a significant effect on the relevant scientific research. This article attempts to identify the current challenges and opportunities in remote sensing for large-area wildlife habitat mapping, and accordingly provide possible solutions and directions for further research.

Remote sensing plays a key role in landscape monitoring, but our handling of these data in a multi-temporal time series is not yet fully developed. Of particular concern is the presence of spatial and thematic errors in independently created maps that distort measures of landscape pattern and constrain the reliability of change analysis. In addition, there is a need to incorporate continuous attributes of cover gradients for flexible map representations that support a variety of applications. In this paper, we present a framework for generating temporally and categorically dynamic land-cover maps that provide such a reliable and adaptable foundation. The centerpiece is a spatio-temporal disturbance-inventory database, created through semi-automated change detection and conditioned with boundary-matching procedures, which can be used to backdate and update both continuous and categorical reference maps. We demonstrate our approach using multi-annual Landsat imagery from a forested region in west-central Alberta, Canada, between the years 1998 and 2005.

Researchers and managers undertaking wildlife habitat assessments commonly require spatially explicit environmental map layers such as those derived from forest inventory and remote sensing. However, end users of geospatial products must often make choices regarding the source and level of detail required for characterizing habitat elements, with few published resources available for guidance. We appraised three environmental data sources that represent options often available to researchers and managers in wildlife ecological studies: (i) a pre-existing forest inventory; (ii) a general-purpose, singleattribute remote sensing land cover map; and (iii) a specific-purpose, multi-attribute remote sensing database. The three information sources were evaluated with two complementary analyses: the first designed to appraise levels of map quality (assessed on the basis of accuracy, vagueness, completion, consistency, level of measurement, and detail) and the second designed to assess their relative capacity to explain patterns of grizzly bear (Ursus arctos) telemetry locations across a 100,000-km 2 study area in west-central Alberta, Canada. We found the forest inventory database to be reasonably functional in its ability to support resource selection analysis in regions where coverage was available, but overall, the data suffered from quality issues related to completeness accuracy, and consistency. The generalpurpose remote sensing land cover product ranked higher in terms of overall map quality, but demonstrated a lower capacity for explaining observed patterns of grizzly bear habitat use. We found the best results using the specific-purpose, multi-attribute remote sensing database, and recommend that similar information sources be used as the foundation for wildlife habitat studies whenever possible, particularly those involving large areas that span jurisdictional boundaries.