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A sensor installation and evaluation programme at the Institute of Ocean Sciences, Sidney, BC, Canada, shows that the weather buoys operating round Canada's coasts can be used as important new sources of surface information for satellite sensor calibration and validation. The standard instruments on the buoys provide real-time measurements to forecasters--of wind, waves, water and air temperature and air pressure. The present programme is evaluating the addition of sensors measuring chlorophyll fluorescence, Photosynthetically Available Radiation (PAR), salinity and water-leaving radiance. Time-series of these data are also of value for a variety of fisheries, ecosystem and climate studies. Data can be viewed on the web at http://www-sci.pac.dfo-mpo.gc.ca/ecobuoys. [ABSTRACT FROM AUTHOR]

Land cover classification over large geographic areas using remotely sensed data is increasingly common as a result of the requirements of national inventory and monitoring programmes, scientific modelling and international environmental treaties. Although large-area land cover products are more prevalent, standard operational protocols for their validation do not exist. This paper provides a framework for the accuracy assessment of large-area land cover products and synthesizes some of the key decision points in the design and implementation of an accuracy assessment from the literature. The fundamental components of a validation plan are addressed and the framework is then applied to the land cover map of the forested area of Canada that is currently being produced by the Earth Observation for Sustainable Development programme. This example demonstrates the compromise between the theoretical aspects of accuracy assessment and the practical realities of implementation, over a specific jurisdiction. The framework presented in this paper provides an example for others embarking on the assessment of large-area land cover products and can serve as the foundation for planning a statistically robust validation. [ABSTRACT FROM AUTHOR]

This paper evaluates annual fire maps that were produced from NOAA-14/AVHRR imagery using an algorithm described in a companion paper (Li et al., International Journal of Remote Sensing, 21 , 3057-3069, 2000 (this issue)). Burned area masks covering the Canadian boreal forest were created by compositing the daily maps of fire hot spots over the summer and by examining Normalized Difference Vegetation Index (NDVI) changes after burning. Both masks were compared with fire polygons derived by Canadian fire agencies through aerial surveillance. It was found that the majority of fire events were captured by the satellite-based techniques, but burnt area was generally underestimated. The burn boundary formed by the fire pixels detected by satellite were in good agreement with the polygons boundaries within which, however, there were some fires missed by the satellite. The presence of clouds and low sampling frequency of satellite observation are the two major causes for the underestimation. While this problem is alleviated by taking advantage of NDVI changes, a simple combination of a hot spot technique with a NDVI method is not an ideal solution due to the introduction of new sources of uncertainty. In addition, the performance of the algorithm used in the International Geosphere-Biosphere Programme (IGBP) Data and Information System (IGBPDIS) for global fire detection was evaluated by comparing its results with ours and with the fire agency reports. It was found that the IGBP-DIS algorithm is capable of detecting the majority of fires over the boreal forest, but also includes many false fires over old burned scars created by fires taking place in previous years. A step-by-step comparison between the two algorithms revealed the causes of the problem and recommendations are made to rectify them. [ABSTRACT FROM AUTHOR]

Air temperature (Ta) is an important climatological variable for forest research and management. Due to the low density and uneven distribution of weather stations, traditional ground-based observations cannot accurately capture the spatial distribution ofTa, especially in mountainous areas with complex terrain and high local variability. In this paper, the daily maximumTain British Columbia, Canada was estimated by satellite remote sensing. Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data for the summer period (June to August) from 2003 to 2012 were collected to estimateTa. Nine environmental variables (land surface temperature (LST), normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), latitude, longitude, distance to ocean, altitude, albedo, and solar radiation) were selected as predictors. Analysis of the relationship between observedTaand spatially averaged remotely sensed LST indicated that 7 × 7 pixel size was the optimal window size for statistical models estimatingTafrom MODIS data. Two statistical methods (linear regression and random forest) were used to estimate maximumTa, and their performances were validated with station-by-station cross-validation. Results indicated that the random forest model achieved better accuracy (mean absolute error, MAE = 2.02°C,R2 = 0.74) than the linear regression model (MAE = 2.41°C,R2 = 0.64). Based on the random forest model at 7 × 7 pixel size, daily maximumTaat a resolution of 1 km in British Columbia in the summer of 2003–2012 was derived, and the spatial distribution of summerTain this area was discussed. The satisfactory results suggest that this modelling approach is appropriate for estimating air temperature in mountainous regions with complex terrain. [ABSTRACT FROM AUTHOR]

Information on biomass distribution is needed to estimate GHG emissions and removals from land use changes in Canada's 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 Canada's north have been incorporated into Canada's 2006 UNFCCC report. [ABSTRACT FROM AUTHOR]

The objective of this paper is to study, on a weekly basis, the explanatory power of one satellite-based measurement, the Normalized Difference Vegetation Index (NDVI), for wheat yield modelling in 40 census agricultural regions (CAR) in the Canadian Prairies during the whole growing season using 16 years of NOAA AVHRR satellite data (between 1987 and 2002). We also explore the relative value of NDVI compared with a land-based measurement, the Cumulative Moisture Index (CMI). By developing a series of weekly wheat yield models over the course of the growing season, we are able to determine the accuracy of different models. Our findings indicate that NDVI possesses explanatory power 4 weeks earlier in the season than CMI. [ABSTRACT FROM AUTHOR]

This paper describes single-date and multi-date land-cover classification accuracy results using segment-based, gap-filled Landsat 7 Enhanced Thematic Mapper data compared with Landsat 5 Thematic Mapper data captured one day apart. Maximum likelihood and Decision tree classification algorithms were evaluated. The same training and verification sets of ground data were used for each classification evaluation. For the comparison with the single-date classification, an average decrease of 2.8% in the classification accuracy was obtained with the use of the gap-filled Landsat data. Area estimates for the mid-summer images differed, on average, from 0.6% to 1.9% for a four-class and eight-class classification, respectively. A multi-date land-cover classification was also completed with the addition of a late spring Landsat 5 image, resulting in an average decrease in classification accuracy of 1.8%. [ABSTRACT FROM AUTHOR]

This paper presents a sampling design for monitoring spatial and temporal changes in forest health in the Upper Elbow River Basin, in Alberta, Canada. The procedure involved a combination of cumulative effects assessment and remote sensing techniques for selecting sampling sites based on physical and anthropogenic features. Normalized difference vegetation index (NDVI) was the indicator of forest health. Unique combinations of slope, aspect, soil moisture, NDVI, vegetation type, and the zone of influence of human activities were used to select pairs of sampling sites. Each pair consisted of a site within the zone of influence and one outside the zone. Spatial discrimination analysis was the method used for reclassification. The analysis suggested that 58 pairs would be appropriate for monitoring. NVDI was negatively correlated with dry soils and increased with the slope. To various extents, most of the species displayed NDVI values between 0.20 and 0.59. The density of linear disturbances (km/km2) was estimated and it showed that one of the three sub-catchments, the Bragg Creek, has levels of human disturbance above the value considered optimal for wildlife habitat suitability. [ABSTRACT FROM AUTHOR]

This paper is dedicated to the application of uncertainty analysis on the EQeau model. EQeau is a semi‐empirical model that uses satellite C‐band SAR data to estimate Snow Water Equivalent. The uncertainty issues of this model's application have not yet been addressed. The uncertainty analysis permits the evaluation of the impact of input data and model parameters on the model outputs and the assessment of the implications and limitations of the model's application. [ABSTRACT FROM AUTHOR]

The spectral reflectance of agricultural crops is affected significantly by sub-pixel scale spectral contributions of background soils and shadows as viewed by a remote sensing instrument. This has meant the potential of remote sensing imagery has not been fully realized for extracting biophysical information and assessing ecological stress using methods such as vegetation indices (VIs). In this paper, we address this problem explicitly using spectral mixture analysis (SMA) to quantify the area abundance of plants, soils and shadows at sub-pixel scales with the aim of improving extraction of plant biophysical and structural information from remote sensing data. Different measurement strategies were tested in the field for acquiring reference endmember spectra of crop vegetation, soil and shadows using a field spectroradiometer for a set of potato plots in western Canada. Endmember measurements included sunlit and shadowed spectra of in situ crop targets, optically thick stacks and data from excised leaves, as well as cultivated, rough and compacted soils. All possible combinations of crop, soil and shadow endmember spectra were analysed using SMA to derive sets of sub-pixel scale component fractions from radiometer spectra acquired from a boom truck over replicate plot samples with a sensor field of view of 1.05 m. Digital video image frames captured simultaneously with the radiometer data were used to determine ground proportions of crop, soil and shadow for independent validation of the SMA fractions. Endmember fractions derived from excised leaves, cultivated soil and shadowed vegetation spectra showed the best agreement with ground truth data, with differences of only ±3.3%. These sub-pixel scale fractions were used in regression analyses to predict leaf area index, biomass and plant width with an average r 2 value of 0.85 from SMA shadow fraction, which was a substantial improvement over the best VI results from NDVI, NGVI and SR (average r 2 = 0.53). Perspectives on SMA at different stages in the growing season and for different crop types are provided with a recommendation that further SMA research is warranted for local to regional scale agricultural crop monitoring programmes. [ABSTRACT FROM AUTHOR]

Up-to-date information on the distribution and changes associated with wetlands is essential for their effective conservation and management. Satellite remote sensing is arguably the only practical way for mapping and monitoring wetlands in a timely manner over a large area, and therefore has been chosen by the Canadian Wetland Inventory (CWI) initiative to map Canada's wetlands and by the Environment and Sustainable Development Indicators (ESDI) initiative to monitor the extent of wetlands indicators. As a contribution to these initiatives, this paper presents a rule-based wetland mapping method using Landsat-7/ETM+, two-season Radarsat-1/SAR images and DEM data. Three study sites in eastern Canada were selected to test this new method. The test results indicate that the classification accuracy of the new method ranges from 71% to 92%, for open bog, open fen, treed bog, marsh and swamp at the three sites. In comparison, two traditional methods based on Landsat-7/ETM+ alone or both Landsat-7/ETM+ and Radarsat-1/SAR have accuracies of 24-89% and 78-85% respectively. [ABSTRACT FROM AUTHOR]

To deal with the extremely high data rate and huge data volume generated onboard a hyperspectral satellite, the Canadian Space Agency (CSA) has developed two fast on-board data compression techniques for hyperspectral imagery. The CSA is planning to place a data compressor on-board a proposed Canadian hyperspectral satellite using these techniques to reduce the requirement for onboard storage and provide a better match to available downlink capacity. Since the compression techniques are lossy, it is essential to assess the usability of the compressed data and the impact on remote sensing applications. In this paper. 11 hyperspectral data users covering a wide range of application areas and a variety of hyperspectral sensors assessed the usability of the compressed data using their well understood datasets and predefined evaluation criteria. Double blind testing was adopted to eliminate bias in the evaluation. Four users had ground truth available. They qualitatively and quantitatively compared the products derived from the compressed data to the ground truth at compression ratios from 10:1 to 50:1 to examine whether the compressed data provided the same amount of information as the original for their applications. They accepted all the compressed data. The users who did not have ground truths available evaluated the compression impact by comparing the products derived from the compressed data with those derived from the original data. They accepted most of the compressed data. [ABSTRACT FROM AUTHOR]

Currently, a model (referred to as a monthly model) employing monthly temperature and precipitation data is used by the Canadian Wheat Board to estimate spring wheat yields for the Canadian Prairies. The model uses a cumulative moisture index as an explanatory variable. In this paper, the performance of the monthly model was improved by first developing a daily model by employing daily, instead of monthly, data for the 1975-1996 period and then by developing a hybrid model which incorporated into the daily model an additional variable derived from the National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR)-based composited Normalized Difference Vegetation Index (NDVI) data for the 1987-1996 period. Out of the seven variables derived, two variables - the average NDVI during the heading phenological-phase and the average NDVI during the entire growing season - were found to be the best. The start and the end of the heading phase were estimated using a biometeorological time scale model. The performance of models was tested on five crop districts (1b, 3bn, 4b, 6a, and 9a) of Saskatchewan on the basis of coefficient of determination, R[sup 2]. While using 1975-1996 data, the values for R[sup 2] were 0.43, 0.82, 0.73, 0.71 and 0.00 in the case of the daily model as opposed to 0.20, 0.71, 0.57, 0.58, and 0.00 in the case of the monthly model for districts 1b, 3bn, 4b, 6a, and 9a, respectively. While using 1987-1996 data, the values of R[sup 2] were 0.79, 0.96, 0.83, 0.95, and 0.39 in the case of the hybrid model as opposed to 0.13, 0.70, 0.75, 0.50, and 0.00 in the case of the monthly model for districts 1b, 3bn, 4b, 6a, and 9a, respectively. For district 9a, which experiences an adequate supply of soil moisture, the concept of cumulative soil moisture index was not found to hold well for yield estimation. [ABSTRACT FROM AUTHOR]