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Background Assessing cumulative effects of anthropogenic and natural disturbances on forest carbon (C) stocks and fluxes, because of their relevance to climate change, is a requirement of environmental impact assessments (EIAs) in Canada. However, tools have not been developed specifically for these purposes, and in particular for the boreal forest of Canada, so current forest C assessments in EIAs take relatively simple approaches. Here, we demonstrate how an existing tool, the Generic Carbon Budget Model (GCBM), developed for national and international forest C reporting, was used for an assessment of the cumulative effects of anthropogenic and natural disturbances to support EIA requirements. We applied the GCBM to approximately 1.3 million ha of upland forest in a pilot study area of the oil sands region of Alberta that has experienced a large number of anthropogenic (forestry, energy sector) and natural (wildfire, insect) disturbances. Results Over the 28 years, 25% of the pilot study area was disturbed. Increasing disturbance emissions, combined with declining net primary productivity and reductions in forest area, changed the study area from a net C sink to a net C source. Forest C stocks changed from 332.2 Mt to 327.5 Mt, declining by 4.7 Mt at an average rate of 0.128 tC ha−1 yr−1. The largest cumulative areas of disturbance were caused by wildfire (139,000 ha), followed by the energy sector (110,000 ha), insects (33,000 ha) and harvesting (31,000 ha) but the largest cumulative disturbance emissions were caused by the energy sector (9.5 Mt C), followed by wildfire (5.5 Mt C), and then harvesting (1.3 Mt C). Conclusion An existing forest C model was used successfully to provide a rigorous regional cumulative assessment of anthropogenic and natural disturbances on forest C, which meets requirements of EIAs in Canada. The assessment showed the relative importance of disturbances on C emissions in the pilot study area, but their relative importance is expected to change in other parts of the oil sands region because of its diversity in disturbance types, patterns and intensity. Future assessments should include peatland C stocks and fluxes, which could be addressed by using the Canadian Model for Peatlands.

Background Assessing cumulative effects of anthropogenic and natural disturbances on forest carbon (C) stocks and fluxes, because of their relevance to climate change, is a requirement of environmental impact assessments (EIAs) in Canada. However, tools have not been developed specifically for these purposes, and in particular for the boreal forest of Canada, so current forest C assessments in EIAs take relatively simple approaches. Here, we demonstrate how an existing tool, the Generic Carbon Budget Model (GCBM), developed for national and international forest C reporting, was used for a relatively rigorous assessment of the cumulative effects of anthropogenic and natural disturbances to support EIA requirements. We applied the GCBM to approximately 1.3 million ha of upland forest in a pilot study area of the oil sands region of Alberta that has experienced a large number of anthropogenic (forestry, energy sector) and natural (wildfire, insect) disturbances.Results 24% of the pilot study area was disturbed. Increasing disturbance emissions combined with declining net primary productivity over the 29 years of the pilot study changed the area from a net C sink to a net C source. Forest C stocks changed from 332 Mt to 327.5 Mt, declining at an average rate of 0.155 tC ha− 1 yr− 1. The largest cumulative areas of disturbance were caused by wildfire (139,000 ha), followed by the energy sector (110,000 ha), insects (33,000 ha) and harvesting (31,000 ha) but the largest cumulative disturbance emissions were caused by the energy sector (9.5 Mt C), followed by wildfire (5.5 Mt C), and then harvesting (1.3 Mt C).Conclusion An existing forest C model was used successfully to provide a rigorous regional cumulative assessment of anthropogenic and natural disturbances on forest C, which meets requirements of EIAs in Canada. The assessment showed the relative importance of disturbances on C emissions in the pilot study area but their relative importance is expected to change in other parts of the oil sands region because of its diversity in disturbance, types, patterns and intensity. Future assessments should include peatland C stocks and fluxes, which could be addressed by using the Canadian Model for Peatlands.

The impact of agriculture on biodiversity depends on the extent and types of agriculture and the degree to which agricultural land contrasts with the natural ecosystem. Most research on the latter comes from studies on the influence of different agricultural types within a single ecosystem with far less study on how the natural ecosystem context shapes the response of biodiversity to agricultural production. We used citizen science data from agricultural areas in Canada’s Eastern Hardwood-Boreal (forest ecosystem, n=108 landscapes) and Prairie Pothole (prairie ecosystem, n=99) regions to examine how ecosystem context shapes the response of avian species diversity, functional diversity and abundance to the amount of arable crop and pastoral agriculture at landscape scales. Avian surveys were conducted along 8km transects of Breeding Bird Survey routes with land cover assembled within a 20km2landscape around each transect. The amount of agriculture at which species diversity peaked differed between the forest (15%) and prairie (51%) ecosystems, indicating that fewer species tolerated the expansion of agriculture in the former. In both ecosystems, functional diversity initially increased with agriculture and peaked at higher amounts (forest: 42%, prairie: 77%) than species diversity suggesting that functional redundancy was lost first as agriculture increased. Species turnover with increasing agriculture was primarily among functional groups in forest where a shift from a low to a high agriculture landscape led to a decline in the percent of the community represented by Neotropical migrants, insectivores, upper foliage gleaners and bark foragers, and an increase in the percent of the community represented by short-distance migrants, granivores, omnivores and ground gleaners. There were few distinct shifts in the percent of the community represented by different functional groups in the prairie ecosystem. Total abundance was the least sensitive measure examined in both ecosystems and indicated that species losses with agriculture are likely followed by numerical compensation from agriculture tolerant species. Our results highlight the importance of ecosystem context for understanding how biodiversity is affected by agricultural production with declines in diversity occurring at lower agricultural extents in ecosystems with lower similarity between natural and agricultural land covers. These findings allow for more specific conservation recommendations including managing for species intolerant to agriculture in prairie ecosystems and limiting the expansion of high contrast agriculture and the loss of semi-natural habitat, such as hedge rows, in historically forested ecosystems.

Time series analysis of Landsat is limited by sparse and irregular sampling of clear-sky observations due to acquisition limitations, clouds, shadows, atmosphere, and sensor artifacts. Many remote sensing applications utilizing coarse spatial resolution time series methods are not suitable for Landsat due to observation sparsity. In this research we develop an imputation based approach to constrain the harmonic modeling method of Zhu et al., 2012 , Zhu et al., 2015 and Zhu and Woodcock (2014b) to reconstruct Landsat time series at a regular temporal interval. The approach was assessed for a boreal forest region in central Canada for different sparsity conditions. The imputed Landsat estimates for a specific pixel were predicted from climate or AVHRR data. These estimates were given a small weight relative to available Landsat observations in fitting the final harmonic model essentially constraining it to a more plausible range. In addition we implemented the model in a piecewise manner to handle non-linear temporal drift related to factors such as climate change, drought, or the allometric nature of vegetation regrowth. Results show that the inclusion of imputed estimates improved model predictions in the presence of observation sparsity. Where there were less than 3 observations within ± 20 days the imputation approach performed better, with a reduction in average reflectance error of 0.001 to 2.5. Error assessment with hold out observations, comparison to MODIS time series, and example predicted images are presented.

Land cover maps are the basic data layer required for understanding and modeling ecological patterns and processes. The Circumpolar Arctic Vegetation Map (CAVM), produced in 2003, has been widely used as a base map for studies in the arctic tundra biome. However, the relatively coarse resolution and vector format of the map were not compatible with many other data sets. We present a new version of the CAVM, building on the strengths of the original map, while providing a finer spatial resolution, raster format, and improved mapping. The Raster CAVM uses the legend, extent and projection of the original CAVM. The legend has 16 vegetation types, glacier, saline water, freshwater, and non-arctic land. The Raster CAVM divides the original rock-water-vegetation complex map unit that mapped the Canadian Shield into two map units, distinguishing between areas with lichen- and shrub-dominated vegetation. In contrast to the original hand-drawn CAVM, the new map is based on unsupervised classifications of seventeen geographic/floristic sub-sections of the Arctic, using AVHRR and MODIS data (reflectance and NDVI) and elevation data. The units resulting from the classification were modeled to the CAVM types using a wide variety of ancillary data. The map was reviewed by experts familiar with their particular region, including many of the original authors of the CAVM from Canada, Greenland (Denmark), Iceland, Norway (including Svalbard), Russia, and the U.S. The analysis presented here summarizes the area, geographical distribution, elevation, summer temperatures, and NDVI of the map units. The greater spatial resolution of the Raster CAVM allowed more detailed mapping of water-bodies and mountainous areas. It portrays coastal-inland gradients, and better reflects the heterogeneity of vegetation type distribution than the original CAVM. Accuracy assessment of random 1-km pixels interpreted from 6 Landsat scenes showed an average of 70% accuracy, up from 39% for the original CAVM. The distribution of shrub-dominated types changed the most, with more prostrate shrub tundra mapped in mountainous areas, and less low shrub tundra in lowland areas. This improved mapping is important for quantifying existing and potential changes to land cover, a key environmental indicator for modeling and monitoring ecosystems. The final product is publicly available at www.geobotany.uaf.edu and at Mendeley Data, DOI: 10.17632/c4xj5rv6kv.1 .

Random Forest variable importance measures such as mean decrease in accuracy or Gini index are often used to reduce the number of predictor variables for a given classification problem. However, previous studies suggest that Random Forest variable ranking measures are biased, particularly in the presence of highly correlated variables. As a result, variables selected after ranking might not achieve the highest possible classification accuracy. Here, we introduce a new variable importance metric based on a simple statistical measure of association, which can be interpreted as a (statistically) low order measure of class separability. The method is applied to a multi-temporal/multi-sensor data set used to classify wetland types. Results show that variable reduction using the suggested measure results in higher accuracies compared to variable reduction based on the mean decrease in accuracy or Gini index, which fail to rank variables by true importance due to the effects of correlation.

The Alberta Oil Sands (AOS) is a unique area in Canada undergoing significant disturbance and recovery due to a variety of anthropogenic and natural factors. Accurately quantifying these changes in space and time is important for assessing ecosystem status and trends. In this research, we implemented an approach to combine Landsat time series for the period 1984–2012 with ancillary change datasets to derive detailed change attribution in the AOS. Detected changes were attributed to causes including fire, forest harvest, surface mining, insect damage, flooding, regeneration, and several generic change classes (abrupt/gradual, with/without regeneration) with accuracies ranging from 74% to 100% for classes that occurred frequently. Lower accuracies were found for the generic gradual change classes which accounted for less than 3% of the affected area. Timing of abrupt change events were generally well captured to within ±1 year. For gradual changes timing was less accurate and variable by change type. A land...

The impact of agriculture on biodiversity depends on the extent and types of agriculture and the degree to which agricultural land contrasts with the natural ecosystem. Most research on the latter comes from studies on the influence of different agricultural types within a single ecosystem with far less study on how the natural ecosystem context shapes the response of biodiversity to agricultural production. We used citizen science data from agricultural areas in Canada’s Eastern Hardwood-Boreal (forest ecosystem, n=108 landscapes) and Prairie Pothole (prairie ecosystem, n=99) regions to examine how ecosystem context shapes the response of avian species diversity, functional diversity and abundance to the amount of arable crop and pastoral agriculture at landscape scales. Avian surveys were conducted along 8km transects of Breeding Bird Survey routes with land cover assembled within a 20km2 landscape around each transect. The amount of agriculture at which species diversity peaked differed between the forest (15%) and prairie (51%) ecosystems, indicating that fewer species tolerated the expansion of agriculture in the former. In both ecosystems, functional diversity initially increased with agriculture and peaked at higher amounts (forest: 42%, prairie: 77%) than species diversity suggesting that functional redundancy was lost first as agriculture increased. Species turnover with increasing agriculture was primarily among functional groups in forest where a shift from a low to a high agriculture landscape led to a decline in the percent of the community represented by Neotropical migrants, insectivores, upper foliage gleaners and bark foragers, and an increase in the percent of the community represented by short-distance migrants, granivores, omnivores and ground gleaners. There were few distinct shifts in the percent of the community represented by different functional groups in the prairie ecosystem. Total abundance was the least sensitive measure examined in both ecosystems and indicated that species losses with agriculture are likely followed by numerical compensation from agriculture tolerant species. Our results highlight the importance of ecosystem context for understanding how biodiversity is affected by agricultural production with declines in diversity occurring at lower agricultural extents in ecosystems with lower similarity between natural and agricultural land covers. These findings allow for more specific conservation recommendations including managing for species intolerant to agriculture in prairie ecosystems and limiting the expansion of high contrast agriculture and the loss of semi-natural habitat, such as hedge rows, in historically forested ecosystems.

Terricolous macrolichens are an important food source for caribou (Rangifer tarandus) and can greatly influence their movement, distribution and demography over time. Mapping the spatial distribution and cover of macrolichens with remote sensing can serve as an important approach for assessing the impact of disturbances (e.g. fire, grazing, trampling) on lichen cover at the landscape scale and for monitoring post-disturbance rates of recovery. Previous remote sensing-based efforts to retrieve the distribution and abundance of lichen have been restricted to particular regions and thus are not indicative of the potential for large extent mapping and monitoring. In this study, we assessed the effectiveness of machine learning methods for retrieving lichen cover and change across different regions in northern Canada and Alaska using Landsat-5 images, topographic and climate data. Global and regional-scale models were evaluated to assess whether regionally specific analyses would improve performance. Of the models tested, the deep neural network was the most accurate for predicting lichen cover (model efficiency (ME) = 0.58, mean absolute error (MAE)

The rates and causes of juvenile mortality are central features of the dynamics and conservation of large mammals, like woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)), but intrinsic and extrinsic factors may be modified by variations in animal abundance. We tested the influences of population size, climate, calf weight and sex on survival to 6 months of age of 1241 radio-collared caribou calves over three decades, spanning periods of population growth (1979–1997) and decline (2003–2012) in Newfoundland, Canada. Daily survival rates were higher and rose more quickly with calf age during the population growth period compared to the decline. Population size (negatively) and calf weight (positively) affected survival during the decline but neither had a detectable influence during the growth phase. Sex, climate and plant productivity (the latter two derived from the North Atlantic Oscillation and Normalized Difference Vegetation Index, respectively) exerted minimal influence during either phase. Predation was the dominant source of mortality. The mean percentage of calves killed by predators was 30 % higher during the decline compared to the growth phase. Black bears (Ursus americanus) and lynx (Lynx canadensis) were the major predators during the population increase but this changed during the decrease to black bears and coyotes (Canis latrans). Our findings are consistent with the hypothesis that Newfoundland caribou experienced phase-dependent survival mediated proximally by predation and competition for food.

This study uses two remotely sensed vegetation indices to investigate cumulative long-term changes of undisturbed vegetation in the Athabasca Oil Sands region of Alberta, Canada, between 1984 and 2012. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Wetness Index (NDWI) were derived from both Landsat and MODIS time series, for comparative purposes and to increase confidence in detected trends. Trend analysis of undisturbed forest areas, i.e., area without abrupt changes revealed a consistent decrease in vegetation condition, quantified by an average reduction of 18.6% (SD=5.02% ) in NDVI and of 31.0% (SD=10.06%) in NDWI, over the 28-year period. The study does not conclusively associate the trends with any single stressor, but seeks to quantify the spatial and temporal distribution of cumulative effects resulting from a variety of natural and anthropogenic causes. Examination of the temporal pattern of trends showed an increase in the occurrence of decreasing trends in the last 10 years. The decreasing trends were more frequent closer to mining developments for both the Landsat and MODIS time series. Climate change was not considered a major causal factor as climate normalized trends had little effect on the results. The trend analysis undertaken can be used to enhance in situ monitoring programs for site selection of additional monitoring facilities particularly regarding potential cumulative effects, provide an indication of likely future short-term changes in the region, and to aid in the development of mitigation measures.

Detailed information on the spatial and temporal distribution of land cover is required to evaluate the effects of land cover change on environmental processes. The development of temporally consistent land cover time series (LCTS) from satellite-based earth observation has proven difficult because multi-year observations are acquired under different conditions resulting in high inter-annual reflectance variability. This leads to spurious differences in land cover when standard approaches for image classification are applied to generate multi-year land cover data. To reduce this effect, a common solution has been to first detect change and update a base map for only these change areas. As long as the change commission error is low, this approach will ensure high consistency between maps in the time series. Here we present an approach for change-based LCTS development following from previous research, but with significant advancements in change detection, training, classification, and evidence-based refinement. The method was applied to generate an annual LCTS covering Canada spanning 2000–2011 that is consistent between years and can be used to identify dominant change transitions. Assessment of the LCTS was challenging because multiple maps needed to be evaluated and can be prohibitive particularly for annual time series covering several years. Three approaches were undertaken involving visual examination, comparison with a reference sample derived from Landsat, and comparison with the MODIS Global LCTS V5.1. Visual assessment revealed high inter-map consistency and logical temporal change trajectories of land cover classes. Comparison with the reference sample showed an accuracy of 70% at the 19 class thematic resolution. Accounting for mixed pixels by considering the first or second reference land cover label as correct increased the accuracy to 80%. Comparison with the MODIS Global LCTS showed that the Canada LCTS achieved higher inter-map consistency and accuracy as expected with national relative to global land cover products.

Systematic error in long-term satellite data records resulting from inter-sensor differences or other persistent influences such as satellite orbital drift can greatly affect the use of these data to monitor land surface dynamics and trends. In this research an identification and correction procedure for systematic error is developed and used to evaluate the NOAA AVHRR long-term satellite data record produced by the Canada Center for Remote Sensing (CCRS). The record is composed of observations acquired by seven AVHRR sensors during the period 1985–2011. It includes two types of AVHRR sensor: AVHHR-2 flown onboard NOAA-9, -11, and ‐14, and AVHRR-3 onboard NOAA-16, -17, -18 and ‐19. Systematic error between sensors was identified through evaluation of synchronized nadir overpass (SNO) observations. The first order systematic error correction was derived from SNO comparison and then further optimized using a reference calibration target. Examination showed considerable difference between AVHRR-2 and AVHRR-3 measurements, which are largely attributed to differences in sensor design characteristics, uncertainty in sensor radiometric calibration, and imperfections in data processing. The results also show overall higher consistency between data from missions with AVHRR-3 than with AVHRR-2 sensors. The developed approach for correction of systematic error in time series was validated based on statistical analysis of eight independent pseudo-invariant targets not used for the initial correction development. Trends in these targets largely caused by the difference between AVHRR-2 and ‐3 sensors are shown to be removed or reduced after the correction was applied.

To extract information about the Earth's surface from Earth Observation data, a key processing step is the separation of pixels representing clear-sky observations of land or water surfaces from observations substantially influenced by clouds. This paper presents an algorithm used for this purpose specifically for data from the AATSR sensor on ENVISAT. The algorithm is based on the structure of the SPARC cloud detection scheme developed at CCRS for AVHRR data, then modified, calibrated and validated for AATSR data. It uses a series of weighted tests to calculate per-pixel cloud presence probability, and also produces an estimate of cloud top height and a cloud shadow flag. Algorithm parameters have been optimized for daytime use in Canada, and evaluation shows good performance with a mean daytime kappa coefficient of 0.76 for the ‘cloud’/‘clear’ classification when compared to independent validation data. Performance is independent of season, and is a dramatic improvement over the existing AATSR L1B cloud flag for Canada. The algorithm will be used at CCRS for processing AATSR data, and will form the basis of similar processing for data from the SLSTR sensors on Sentinel-3.

Remote sensing has provided evidence of vegetation changes in Arctic tundra that may be attributable to recent climate warming. These changes are evident from local scales as expanding shrub cover observed in aerial photos, to continental scales as greening trends based on satellite vegetation indices. One challenge in applying conventional two date, satellite change detection in tundra environments is the short growing season observation window, combined with high inter-annual variability in vegetation conditions. We present an alternative approach for investigating tundra vegetation and surface cover changes based on trend analysis of long-term (1985-present) Landsat TM/ETM+ image stacks. The Tasseled Cap brightness, greenness, and wetness indices, representing linear transformations of the optical channels, are analysed for per-pixel trends using robust linear regression. The index trends are then related to changes in fractional shrub and other vegetation covers using a regression tree classifier trained with high resolution land cover. Fractional trends can be summarised by vegetation or ecosystem type to reveal any consistent patterns. Example results are shown for a 3 000 km2 study area in northern Yukon, Canada where index and fractional changes are related to growth of vascular plants and coastal erosion.

Accurate and precise detection of phenology events is needed to assess trends in seasonal vegetation development indicative of climate or other environmental change processes. In this research, detection accuracy of start of season (SOS) phenology for deciduous forest across Eastern Canada was assessed using satellite time series and in situ PlantWatch observations. Several aspects were evaluated regarding performance of phenology information extraction: 1) effect of compositing period, 2) individual performance of the Advanced Very High Resolution Radiometer (AVHRR) and the Medium Resolution Imaging Spectrometer (MERIS) sensors, and 3) performance for these sensors combined. The AVHRR and MERIS sensors were used as they are overlapping operational missions with planned future continuity. Three approaches to utilizing the multi-sensor data were tested: 1) inter-calibrating NDVI data between sensors and using the multi-sensor data stream to detect SOS, 2) combining independently derived SOS estimates from AVHRR and MERIS based on a weighted average, and 3) combining approaches 1 and 2. Comparison with in situ observations of leaf out and first bloom showed that combining independent SOS estimates from AVHRR and MERIS was better than using the inter-calibrated multi-sensor data. Combining SOS estimates from both sensors reduced error by 1–2 days compared to the individual sensor results. Composite periods from 7 to 11 days produced the best results for leaf out with a mean absolute error (MAE) of 5 days. Results for first bloom were not as good as those for leaf out, producing a MAE of 6.5 days. For first bloom, compositing periods greater than 11 days did not increase error at the same rate as seen for leaf out. However, the larger MAE observed for first bloom may have masked this effect.

Climate change is expected to have significant impacts on northern vegetation, particularly along transition zones such as the treeline. Studies of vegetation composition and change in this ecotone have largely focussed on local analysis of individual trees using labour intensive stand reconstruction techniques, which are spatially limited and do not consider vegetation types other than trees. Remote sensing may be well suited to monitoring recent changes across the treeline because it captures integrated changes of all vegetation life forms over large spatial extents. This research examines treeline vegetation composition and change along the western subarctic treeline mapped by Timoney et al. (1992) using a 1 km resolution, 22-year AVHRR archive from 1985–2006. While most remote sensing studies on vegetation change in arctic and subarctic regions only exploit information contained in the Normalized Difference Vegetation Index (NDVI), we examine long-term reflectance trends in AVHRR bands 1 and 2 in addition to NDVI. The GeoSail canopy reflectance model is used to map treeline composition by combining information from 22-year summertime and early springtime composite images. A set of spectral change vectors are then generated from GeoSail simulations and used to classify trends in AVHRR along the treeline to estimate vegetation change. Evaluation of vegetation composition against the MODIS Vegetation Continuous Fields (VCF) product that has been recently validated along the treeline reveals good spatial correspondence. Temporal trends are shown to agree with literature on tundra–taiga vegetation dynamics in recent decades. Evidence is presented that suggests replacement of bare surfaces with herb, conifer decline along the southern treeline, increased shrubiness, and increased conifer recruitment and growth in the north.

Managing groundwater resources requires quantification of several complex atmosphere/surface properties affecting recharge, including precipitation, temperature, soils, land cover and land cover change. In this review we elaborate on some of the remote sensing techniques commonly applied for generating information about changes in land cover, over large geographical areas, which are often required for hydrogeological studies. Two case studies are presented: the Chateauguay River Basin (Quebec) and Casselman Township (Ontario). Both examples represent agriculture dominated landscapes that require a unique mapping approach to minimize confusion between agriculture and forest classes. The first example demonstrates a procedure for mapping land cover aerial extent and change using remote sensing data acquired off the peak of the growing season where agricultural fields can be most effectively discriminated from forests. In the second example three classification methods are compared for mapping specific crop ...

Research on change detected has largely focused on method development and evaluation in a temporally dependent manner where training and validation data are from the same temporal period. Monitoring over several change periods needs to account for increased variability resulting from possible combinations of atmosphere, sensor, and surface conditions. Training a change method for each monitoring period (i.e. a dynamic approach) is an option, but can be costly to develop the needed training datasets and may not be warranted if sufficient accuracy can be obtained without retraining (i.e. a static approach). In this research the potential of change detection using a static approach suitable for near-real time annual monitoring was evaluated. The research assessed the influence of feature set size, radiometric normalization, incorporation of temporal information, and change object size and sub-pixel fraction on accuracy. The static approach was based on a decision tree developed using 250 m MODIS data from 2005 to 2006 and applied annually for the period 2001–2005. Change results between years were combined and compared to reference data representing change from 2001 to 2005 to evaluate monitoring performance. Results revealed high accuracy for the decision tree change model development from 2005 to 2006 (bootstrap cross-validation KAPPA = 0.91), with lower accuracy (KAPPA = 0.80) when applied for monitoring from 2001 to 2005. Radiometric normalization increased monitoring accuracy (KAPPA = 0.86). Further improvement was achieved with the incorporation of temporal contextual tests to combine the 2001–2005 inter-annual change maps (KAPPA = 0.90), but required a time lag of 1 year. An alternative temporal test that was not restricted by the 1 year time lag produced slightly lower accuracy (KAPPA = 0.88). Evaluation of the effect of object size on detection accuracy showed that accuracy for objects less than 7 pixels was strongly related to object size, with objects less than 3 pixels having low detection rates. The effect of sub-pixel change fraction was found to be dependent on object size with larger objects reducing detection error across the range of fractions evaluated.

Canada's national parks system includes 43 terrestrial parks covering 3% (276,275 km2) of the country's landmass and representing its full range of natural regions. Considering the vast and often remote areas under protection, Parks Canada Agency envisions Earth Observation technology to be the basis for a Park Ecological Integrity Observing System (Park-EIOS), and integral component of a larger national parks ecological integrity (EI) monitoring program. Park-EIOS is planned for operational use beginning in 2008 and includes coarse filter EI indicators corresponding to landscape pattern, succession and retrogression, net primary productivity, and focal species distributions within parks and their surrounding greater park ecosystems. A primary input to produce all four indicators is a time series of land cover information derived from medium (~ 30 m) resolution, Landsat-class sensors. This paper describes a generic, end-to-end change detection framework developed for Park-EIOS, labelled Automated Multi-temporal Updating through Signature Extension (AMUSE). AMUSE involves radiometric normalization steps, production of a baseline land cover, change vector analysis to identify changed pixels, and a new constrained signature extension approach to update the land cover of changed areas. We present the method and results applied to six pilot parks using time series of Landsat TM/ETM+ imagery from 1985–2005.

Long-term changes in the Normalized Difference Vegetation Index (NDVI) have been evaluated in several studies but results have not been conclusive due to differences in data processing as well as the length and time of the analysed period. In this research a newly developed 1 km Advanced Very High Resolution Radiometer (AVHRR) satellite data record for the period 1985-2006 was used to rigorously evaluate NDVI trends over Canada. Furthermore, climate and land cover change as potential causes of observed trends were evaluated in eight sample regions. The AVHRR record was generated using improved geolocation, cloud screening, correction for sun-sensor viewing geometry, atmospheric correction, and compositing. Results from both AVHRR and Landsat revealed an increasing NDVI trend over northern regions where comparison was possible. Overall, 22% of the vegetated area in Canada was found to have a positive NDVI trend based on the Mann-Kendal test at the 95% confidence level. Of these, 40% were in northern ecozones. The mean absolute difference of NDVI measurements between AVHRR and Landsat data was

Variability and trends in lake ice dynamics (i.e. lake ice phenology) are related to climate conditions. Climate influences the timing of lake ice melt and freeze onset, ice duration, and lake thermal dynamics that feedback to the climate system initiating further change. Phenology records acquired in a consistent manner and over long time periods are required to better understand variability and change in climate conditions and how changes impact lake processes. In this study, we present a new technique for extracting lake ice phenology events from historical satellite records acquired by the series of Advanced Very High Resolution Radiometer (AVHRR) sensors. The technique was used to extend existing in-situ measurements for 36 Canadian lakes and to develop records for 6 lakes in Canada's far north. Comparison of phenology events obtained from the AVHRR record and in-situ measurements show strong agreement (20 lakes, 180 cases) suggesting, with high confidence especially in the case of break-up dates, the use of these data as a complement to ground observations. Trend analysis performed using the combined in-situ and AVHRR record ∼ 1950–2004 shows earlier break-up (average — 0.18 days/year) and later freeze-up (average 0.12 days/year) for the majority of lakes analyzed. Less confidence is given to freeze-up date results due to lower sun elevation during this period making extraction more difficult. Trends for the 20 year record in the far north showed earlier break-up (average 0.99 days/year) and later freeze-up (average 0.76 days/year). The established lake ice phenology database from the historical AVHRR image archive for the period from 1985 to 2004 will to a certain degree fill data gaps in the Canadian in-situ observation network. Furthermore, the presented extraction procedure is not sensor specific and will enable continual data update using all available satellite data provided from sensors such as NOAA/AVHRR, MetOp/AVHRR, MODIS, MERIS and SPOT/VGT.

A mapping methodology is presented for generating a land cover time series from coarse spatial resolution earth observation data. Historically, this has been a difficult task because of inconsistencies that can arise between maps due to inherent noise present in satellite observations. The new methodology reduces the inconsistency by incorporating several information sources unique to the presented approach of updating an existing land cover map backward and forward in time. It consists of change detection and a local evidence classification decision rule that incorporates the local spectral similarity for each class, local land cover proportions, and expected class changes based on the previous class and change direction. The methodology has been implemented to produce land cover maps of Canada for 1985, 1990, 1995, and 2000 from data acquired by the series of National Oceanic and Atmospheric Administration (NOAA) – advanced high-resolution radiometer (AVHRR) sensors. Accuracy assessment based on medium-...

Relative radiometric normalization has long been performed to generate consistency among individual Landsat scenes for production of composites containing multiple scenes. Normalization methods have relied on matching identical and assumed invariant features in both images of an overlapping pair, or on invariant targets that are not necessarily the same features. Problems with overlap normalization methods include sensitivity to outliers in overlap data caused by atmospheric or land cover change between scenes, which can lead to radiometric error propagation across a mosaic caused by a normalized scene becoming a reference for the subsequent scene entered into the mosaic. Solutions to such problems include interactive outlier removal to generate a normalization function using a ‘no change’ data set and methods that are robust against outliers to automatically generate normalization functions with minimal user input. This paper compares two normalization methods that use a robust regression technique called Theil-Sen with an established overlap normalization method. The first method uses Theil-Sen regression to generate a normalization function between overlap regions, while the second uses Theil-Sen to normalize to coarse-resolution composite reflectance data from the SPOT VEGETATION (VGT) sensor. The results of the normalizations were evaluated in two ways: (1) using statistics generated between overlap regions; and (2) separately using coarse-resolution data as a reference. Both overlap normalization methods performed almost identically; however, Theil-Sen was faster and easier to implement than its traditional counterpart due to its insensitivity to outliers and capability for full automation. While overlap and coarse-resolution normalizations each outperformed the other when evaluated against its calibration set, error propagation caused by outliers in overlap samples was avoided in the normalization to coarse-resolution imagery. Advantages offered by normalization to coarse-resolution data using robust regression, including full automation, make this method particularly attractive for generation of large area mosaics containing 100 Landsat scenes or more.

Automated tree detection provides a means to acquire information on tree abundance and spatial distribution, both of which are critical for evaluating the status of regenerating forests. It is also often a precursor to automated tree delineation, which typically utilizes image data surrounding a detected crown point. However, obtaining consistently accurate detection results has proven difficult owing to errors associated with image scale. In this paper, four approaches that reduce this scale dependence are evaluated, including (1) determination of optimum global image smoothing to apply predetection, (2) determination of optimum local image smoothing to apply predetection, (3) determination of the optimal local window size for use in the detection algorithm, and (4) post-detection merging of initially defined crown segments. Each approach was applied to three datasets acquired by different sensors and with different regenerating forest conditions. A common local maximum tree detection algorithm was imple...

Climate can have direct and indirect effects on population dynamics via changes in resource competition or predation risk, but this influence may be modulated by density- or phase-dependent processes. We hypothesized that for ungulates, climatic conditions close to parturition have a greater influence on the predation risk of neonates during population declines, when females are already under nutritional stress triggered by food limitation. We examined the presence of phase-dependent climate-predator (PDCP) interactions on neonatal ungulate survival by comparing spatial and temporal fluctuations in climatic conditions, cause-specific mortality and per capita resource limitation. We determined cause-specific fates of 1384 caribou (Rangifer tarandus) from 10 herds in Newfoundland, spanning more than 30 years during periods of numerical increase and decline, while exposed to predation from black bears (Ursus americanus) and coyotes (Canis latrans). We conducted Cox proportional hazards analysis for competing risks, fit as a function of weather metrics, to assess pre- and post-partum climatic influences on survival on herds in population increase and decline phases. We used cumulative incidence functions to compare temporal changes in risk from predators. Our results support our main hypothesis; when caribou populations increased, weather conditions preceding calving were the main determinants of cause-specific mortality, but when populations declined, weather conditions during calving also influenced predator-driven mortality. Cause-specific analysis showed that weather conditions can differentially affect predation risk between black bears and coyotes with specific variables increasing the risk from one species and decreasing the risk from the other. For caribou, nutritional stress appears to increase predation risk on neonates, an interaction which is exacerbated by susceptibility to climatic events. These findings support the PDCP interactions framework, where maternal body condition influences susceptibility to climate-related events and, subsequently, risk from predation.

The development of temporally consistent land cover time series from satellite-based earth observation has proven difficult due to variability in sensor observations. This leads to spurious land cover differences between maps when standard supervised classification approaches are applied. To reduce this effect, a common solution has been to first detect change and update a base map for only these change areas. Assessing the accuracy of land cover time series is challenging because multiple maps need to be assessed for both land cover classification and change detection accuracies. Regarding a change based updating approach; accuracy is close to that of the original base map for a time series where only a small percent change occurs. Over longer periods where significant change has accumulated the accuracy becomes more dependent on the change and update labeling accuracy. Thus, accuracy for a change based approach can be seen as a function of the base map, change detection, and update accuracies. A specific formulization is developed to summarize these components and applied to investigate accuracy of a 250 m resolution time series for Canada. Results show that the time series accuracy was in a large degree predetermined by the base map accuracy because there was only a small amount of change over the period and the base map and update accuracies were similar. Increasing the update accuracy by a few percent, within the precision of its estimation, would improve the accuracy of the time series evaluated as it is extend in time.

Recent northern vegetation changes caused by climate warming have been well documented, using experimental plot warming to examine vegetation-specific changes and satellite image data to examine overall trends. Previous remote sensing efforts have employed the Normalized Difference Vegetation Index (NDVI) from AVHRR, whose 1 km to 8 km pixel size is too large for examination of broad scale vegetation-specific responses because of mixing within the pixel footprint. In this paper, we reconcile differences between field- and remote sensing-based approaches by using both medium-resolution (30 m) and coarse resolution (1 km) data to study 20 years of vegetation-specific responses to northern climate warming (1986 to 2006). Trends are compared among vegetation communities from two separate Landsat classifications in Canada’s eastern and western forest-tundra transition zone, as well as a 1 km AVHRR database recently developed over Canada. A comparison of absolute trends among mapped vegetation communities revealed lichen-dominated communities consistently exhibiting lower trends than those dominated by vascular plants, with both exhibiting increasing NDVI. Our results and those obtained from experimental warming suggest that the magnitude difference in NDVI increase between lichen and vascular vegetation is related to increasing vigor and biomass of vascular vegetation, in contrast to physiological impairment of lichen due to the short-term secondary effect of temperature on moisture. In the longer term, succession from lichen to vascular is likely responsible for the small observed NDVI increase over lichen-dominated regions. The fact that both Landsat and AVHRR exhibited similar relative vegetation-specific trends in NDVI suggests that this phenomenon may be widespread in the North.

Natural Resources Canada, Parks Canada Agency and the University of Ottawa are developing standardized approaches for monitoring landscape change within and surrounding Canada's National Parks using Earth observation. This paper focuses on remote sensing methodologies developed at the CCRS for three types of ecological indicators: Landscape Pattern, Succession & Retrogression, and Net Primary Productivity (NPP), using La Mauricie National Park to demonstrate the methods and results. Landscape pattern analyses are discussed in relation to landscape metric stability, scaling, and selection. Major vegetation disturbances through time were examined using a hybrid change detection technique combining vegetation index differencing and constrained signature extension. Ecosystem productivity measures were developed using a remote sensing-based modeling approach known as EALCO (ecological assimilation of land and climate observations). It is anticipated that this pilot study will produce new automated EO processing methods that culminate in an operational remote sensing-based system for monitoring the ecological integrity of Canada's National Parks and their greater ecosystems.

Analysis of coarse resolution (~1?km) satellite imagery has provided evidence of vegetation changes in arctic regions since the mid-1980s that may be attributable to climate warming. Here we investigate finer-scale changes to northern vegetation over the same period using stacks of 30?m resolution Landsat TM and ETM + satellite images. Linear trends in the normalized difference vegetation index (NDVI) and tasseled cap indices are derived for four widely spaced national parks in northern Canada. The trends are related to predicted changes in fractional shrub and other vegetation covers using regression tree classifiers trained with plot measurements and high resolution imagery. We find a consistent pattern of greening (6.1?25.5% of areas increasing) and predicted increases in vascular vegetation in all four parks that is associated with positive temperature trends. Coarse resolution (3?km) NDVI trends were not detected in two of the parks that had less intense greening. A range of independent studies and observations corroborate many of the major changes observed.

Generating historical AVHRR 1 km baseline satellite data records over Canada suitable for climate change studies Rasim Latifovic, Alexander P. Trishchenko, Ji Chen, William B. Park, Konstantin V. Khlopenkov, Richard Fernandes, Darren Pouliot, Calin Ungureanu, Yi Luo, Shusen Wang, Andrew Davidson, and Josef Cihlar Pages 324-346 Abstract. Satellite data are an important component of the global climate observing system (GCOS). To serve the purpose of climate change monitoring, these data should satisfy certain criteria in terms of the length of observations and the continuity and consistency between different missions and instruments. Despite the great potential and obvious advantages of satellite observations, such as frequent repeat cycles and global coverage, their use in climate studies is hindered by substantial difficulties arising from large data volumes, complicated processing, and significant computer resources required for archiving and analysis. Successful examples of satellite earth observation (...