Ian Olthof

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.

Biomass measurements and relationships with Landsat-7/ETM+ and JERS-1/SAR data over Canada's western sub-arctic and low arctic

Information on biomass distribution is needed to estimate GHG emissions and removals from land use changes in Canadas north for UNFCCC reporting. This paper reports aboveground biomass measurements along the Dempster Highway transect in 2004, and around Yellowknife and the Lupin Gold Mine in 2005. The measured aboveground biomass ranges are 10–100 t ha−1 for woodlands, 1–100 t ha−1 for shrub sites, and 0.5–10 t ha−1 for grass/herbs sites. The root mean squared error (RMSE) of measurements is 21%, and the median absolute percentage error (MedAPE) is 14%. The combination of JERS backscatter and Landsat TM4/TM5 gives the best biomass equation for the Dempster Highway transect, with r 2 = 0.72 when using a one‐step approach (i.e. using all points) and 0.78 when using a two‐step approach (i.e. stratifying data into three classes: grass, shrub, and woodlands). The two‐step approach reduces the MedAPE from 53% to 33%. The validation against Yellowknife & Lupin data indicates that the equations have good transferability. The improvement of two‐step approach over the one‐step approach, however, is not significant for the validation dataset, suggesting that the one‐step approach is as good as the two‐step approach when applied over areas outside where the equations are developed. The relationships and error analysis of this study, as well as the final estimate of GHG emission/removal over Canadas north have been incorporated into Canadas 2006 UNFCCC report.

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 habitat condition changes during winter and pre-calving migration for Bathurst Caribou in northern Canada

Many factors influence the abundance of migratory tundra caribou (Rangifer tarandus). To understand their interactions with caribou abundance, we need to quantify these factors. In this study, we documented the changes in habitat conditions during winter and pre-calving migration for the Bathurst Caribou herd, using remote sensing data and ground measurements. We found there was a significant decrease in forest area which has abundant lichen, the main caribou winter diet, during recent decades due to increase in burned area, which in turn was positively correlated with summer temperature. For winter forage accessibility, we examined the annual maximum snow depth and mean ice content in snow (ICIS). There was a significant increase in ICIS during 1963–2006, but no trend in the maximum snow depth. During the pre-calving migration, the percent snow cover showed large inter-annual variations but no significant trend.

Evaluating and reducing errors in seasonal profiles of AVHRR vegetation indices over a Canadian northern national park using a cloudiness index

High-temporal coarse resolution remote-sensing data have been widely used for monitoring plant phenology and productivity. Residual errors in pre-processed composite data from these sensors can still be substantial due to cloud contamination and aerosol variations, especially over high cloud-cover areas such as the Arctic. Commonly used smoothing and filtering methods try to reform the often heavily distorted seasonal profiles of vegetation indices one way or another, instead of explicitly dealing with the errors that cause the distortion. As the distortion varies from year to year for a pixel or from pixel to pixel, so does the performance of various smoothing and filtering methods. Consequently, change detection results are likely method dependent. In this study, we investigate alternative methods in order to eliminate bias caused by cloud contamination and reduce random errors due to aerosol variations in the 10 day Advanced Very High Resolution Radiometer (AVHRR) composite data, so that accurate seasonal profiles of vegetation indices can be constructed without the need to apply a smoothing and filtering method. The best alternative method corrects cloud contaminations by spatially pairing averages of simple ratio over cloud-contaminated and clear-sky pixels in a class (SPAC). The SPAC method eliminates bias caused by cloud contamination and reduces the relative random errors to <14% near the start/end of a growing season, and to <8% during the middle growing season for the six treeless wetland and tundra classes in Wapusk National Park. In comparison, with the method whereby all pixels in a class (average all pixels in the class (AAC)) are averaged in a period, the bias could be up to 40% if all the pixels in the composite period are heavily cloud contaminated.

Image-based predictive ecosystem mapping in Canadian arctic parks

Abstract Ecological monitoring of Arctic national parks is challenging owing to their size and remote locations. Baseline ecosystem maps are a basic requirement for monitoring and are often derived from classification of remote sensing data. In many cases, however, the vegetation communities of interest overlap spectrally and cannot be separated using imagery alone. One solution is to use ancillary spatial data that are able to predict the distribution of Arctic ecosystems, which are often structured along environmental gradients. This paper presents a new image-based predictive ecosystem mapping (I-PEM) method that integrates remote sensing-based vegetation mapping with predictive terrain attributes from a digital elevation model. The approach is unique in its use of a conventional, air photo-based ecosystem map to train a decision tree classifier for mapping over a larger area of satellite coverage. I-PEM is demonstrated using SPOT HRVIR imagery over Ivvavik National Park in Yukon and Torngat Mountains National Park in Newfoundland. Results indicate that a 28-class ecosystem map derived from air-photo interpretation can be reproduced using the method with 85% or greater accuracy.

Using Satellite Remote Sensing to Assess and Monitor Ecosystem Integrity and Climate Change in Canadas National Parks

Natural Resources Canada, Parks Canada Agency and the University of Ottawa are developing standardized approaches for monitoring landscape change within and surrounding Canadas 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 Canadas National Parks and their greater ecosystems.

Climate Sensitivity of High Arctic Permafrost Terrain Demonstrated by Widespread Ice-Wedge Thermokarst on Banks Island

Ice-wedge networks underlie polygonal terrain and comprise the most widespread form of massive ground ice in continuous permafrost. Here, we show that climate-driven thaw of hilltop ice-wedge networks is rapidly transforming uplands across Banks Island in the Canadian Arctic Archipelago. Change detection using high-resolution WorldView images and historical air photos, coupled with 32-year Landsat reflectance trends, indicate broad-scale increases in ponding from ice-wedge thaw on hilltops, which has significantly affected at least 1500 km2 of Banks Island and over 3.5% of the total upland area. Trajectories of change associated with this upland ice-wedge thermokarst include increased micro-relief, development of high-centred polygons, and, in areas of poor drainage, ponding and potential initiation of thaw lakes. Millennia of cooling climate have favoured ice-wedge growth, and an absence of ecosystem disturbance combined with surface denudation by solifluction has produced high Arctic uplands and slopes underlain by ice-wedge networks truncated at the permafrost table. The thin veneer of thermally-conductive mineral soils strongly links Arctic upland active-layer responses to summer warming. For these reasons, widespread and intense ice-wedge thermokarst on Arctic hilltops and slopes contrast more muted responses to warming reported in low and subarctic environments. Increasing field evidence of thermokarst highlights the inherent climate sensitivity of the Arctic permafrost terrain and the need for integrated approaches to monitor change and investigate the cascade of environmental consequences.

Estimating Land Use Change over Canadas Arctic/Sub-Arctic Region

Estimates of land use change (LUC) and resulting greenhouse gas (GHG) fluxes in Canadas North are needed for reporting to the United Nations Framework Convention on Climate Change. This paper presents a remote sensing based approach and results for estimating non-forest LUC over Canadas Arctic/sub-Arctic zone. Spatial datasets portraying the occurrence of cultural features were used to identify high probability LUC areas within a 359 million ha northern study region. These targeted areas were analyzed using multi-temporal Landsat satellite imagery from circa 1985, 1990, and 2000. A Land Use Change Mapping System for Canadas North (LUCMAP-N) was developed by combining several image normalization and change detection techniques recently devised at Natural Resources Canadas Earth Sciences Sector (NRCan-ESS). It is estimated that the average rate of LUC during 1985-2000 over Canadas Arctic/sub-Arctic region was 666 ha/yr.