Darren Pouliot

Trends in vegetation NDVI from 1 km AVHRR data over Canada for the period 1985-2006

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 <7%. When compared with results from other studies, similar trends were found over northern areas, while in southern regions the results were less consistent. Local assessment of potential causes of trends in each region revealed a stronger influence of climate in the north compared to the south. Southern regions with strong positive trends appeared to be most influenced by land cover change.

Detecting long-term changes to vegetation in northern Canada using the Landsat satellite image archive

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

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 (EO) data in climate studies include, among others, analyses of the earths radiation budget (Earth Radiation Budget Experiment (ERBE), Scanner for Radiation Budget (ScaRaB), and Cloud and the Earths Radiant Energy System (CERES)), cloudiness (International Satellite Cloud Climatology Project (ISCCP)), vegetation research (Global Inventory Modeling and Mapping Studies (GIMMS)), and the National Oceanic and Atmospheric Administration – National Aeronautics and Space Administration (NOAA–NASA) Pathfinder Program. Despite several attempts, the great potential of the advanced very high resolution radiometer (AVHRR) 1 km satellite data for climate research remains substantially underutilized. To address this issue, the generation of a comprehensive satellite data archive of AVHRR data and products at 1 km spatial resolution over Canada for 1981–2004 (24 years) has been initiated, and a new system for processing at level 1B has been developed. This processing system was employed to generate baseline 1 day and 10 day year-round clear-sky composites for a 5700 km × 4800 km area of North America. This region is centred over Canada but also includes the northern United States, Alaska, Greenland, and surrounding ocean regions. The baseline products include top-of-atmosphere (TOA) visible and near-infrared reflectance, TOA band 4 and band 5 brightness temperature, a cloud – clear – shadow – snow and ice mask, and viewing geometry. Details of the data processing system are presented in the paper. An evaluation of the system characteristics and comparison with previous results demonstrate important improvements in the quality and efficiency of the data processing. The system can process data in a highly automated manner, both for snow-covered and snow-free scenes, and for daytime and nighttime orbits, with high georeferencing accuracy and good radiometric consistency for all sensors from AVHRR NOAA-6 to AVHRR NOAA-17. Other processing improvements include the implementation of advanced algorithms for clear sky – cloud – shadow – snow and ice scene identification, as well as atmospheric correction and compositing. At the time of writing, the assembled dataset is the most comprehensive AVHRR archive at 1 km spatial resolution over Canada that includes all available observations from AVHRR between 1981 and 2004. The archive and the processing system are valuable assets for studying different aspects of land, oceans, and atmosphere related to climate variability and climate change.

Approaches for optimal automated individual tree crown detection in regenerating coniferous forests

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 implemented for approaches 1–3, and a watershed segmentation algorithm was applied in approach 4. Detection accuracy was evaluated using standardized methods. The highest accuracies for each dataset were obtained with approaches based on local scale representations where the regenerating structure favored such approaches. However, more consistent accuracies across all datasets were obtained with the optimum global scale approach. Post-detection merging of adjacent crown segments produced the poorest results. Error sources and the advantages and disadvantages of each approach are discussed in terms of developing more operational methods for automated tree detection in regenerating forests.

Multitemporal land cover mapping for Canada: methodology and products

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-resolution (30 m) reference data shows that land cover data produced with this new approach have an overall accuracy similar to that of other 1 km resolution land cover maps of Canada, but this product maintains high consistency between years, with a thematic resolution of 12 classes. An analysis of spatial and temporal patterns of land cover disturbances demonstrates the potential application of the multitemporal land cover time series.

Development of a circa 2000 land cover map of northern Canada at 30 m resolution from Landsat

Previous land cover maps covering northern Canada have been of insufficient spatial or thematic detail to address emerging northern issues such as wildlife habitat, land use planning, and fine-scale land cover dynamics. Mapping northern land cover requires medium-resolution (~30 m) remote sensing data to effectively characterize cover types that are spatially heterogeneous and cannot be consistently represented at coarser (250 m – 1 km) scales. In this paper, we present a land cover map of northern Canada at 30 m spatial resolution suitable for application in northern land use planning, wildlife habitat assessment, and climate change impact assessment and adaption. Orthorectified circa 2000 Landsat data were acquired from the Centre for Topographic Information, with coverage from the treeline to the northern tip of Ellesmere Island, and combined into 16 radiometrically balanced large-area mosaics. A stratified unsupervised cluster labelling approach was used for map generation. Literature on northern land cover and vegetation mapping and numerous northern vegetation surveys were examined to define a land cover legend containing 15 classes. Field data gathered during several campaigns were used in conjunction with other available medium-resolution land cover maps to develop a dataset for training and validation. Standardized accuracy assessment is limited due to the cost of field data acquisition and the small archive of reference data in northern regions. Comparison with field data used only to aid cluster labelling suggests 81.5% accuracy for 76 plots, and examination of subpixel land cover distribution within each 1 km Circumpolar Arctic Vegetation Map (CAVM) class shows good agreement. The map is publicly available through the Natural Resources Canada Geogratis portal in 1 : 250 000 scale National Topographic System (NTS) map sheets.

A method for trend-based change analysis in Arctic tundra using the 25-year Landsat archive

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 km 2 study area in northern Yukon, Canada where index and fractional changes are related to growth of vascular plants and coastal erosion.

Monitoring Cumulative Long-Term Vegetation Changes Over the Athabasca Oil Sands Region

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.

Phase‐dependent climate–predator interactions explain three decades of variation in neonatal caribou survival

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.

Land change attribution based on Landsat time series and integration of ancillary disturbance data in the Athabasca oil sands region of Canada

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-cover time series was also created to provide information on “from-to” change. A basic accuracy assessment of the land cover showed it to be of moderate accuracy, approximately 69%. Results show that fire was the major cause of change in the region. As expected, surface mine development and related activities have increased since 2000. Insect damage has become a more significant agent of change in the region. Further investigation is required to determine if insect damage is greater than past historical events and to determine if industrial development is linked to the increasing trend observed.