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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]

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]

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]

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]

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]

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]

Normalized Difference Vegetation Index (NDVI) time series data are used by agricultural agencies for many essential operational crop monitoring programmes. But optical sensors often miss key growth stages due to cloud cover interference, impacting the performance of operational activities. Although the synergistic use of optical and Synthetic Aperture Radar (SAR) imagery can provide time series data, any SAR-optical integration necessitates building a relationship between these two data sources. The objective of this study was to use a semi-empirical Canopy Structure Dynamics Model (CSDM), Growing Degree Days (GDD), and SAR parameters calibrated to optical NDVI to derive daily estimates of canola crop condition over an entire growing season. RADARSAT-2 Fine Quad-pol and RapidEye images were collected over three years for a study site in western Canada. Object-based image analysis was applied to study the relationship between the optical and SAR time series data. Significant correlations were documented between a number of SAR parameters and optical NDVI, specifically a ratio of backscatter intensities (HH-HV)/(HH+HV), a ratio of volume to surface scattering extracted from the Freeman Durden decomposition, and Entropy from the Cloude-Pottier decomposition. Correlations (r-values) between these SAR parameters and optical NDVI ranged from 0.63 to 0.84 for the three years of data. Based on this analysis, a simple statistical model was used to relate SAR parameters to optical NDVI, creating a SAR-calibrated NDVI (SARcal-NDVI). A CSDM was fit to the SARcal-NDVI for each canola field, constructing a temporal vegetation index curve which captured canopy development from emergence to senescence. Coefficients of determination (R2) were 0.87 0.86 and 0.82 for entropy, the volume-surface scattering ratio, and the ratio of backscatter intensities (HH-HV)/(HH+HV), respectively, demonstrating a good model fit. The CSDM describes well the temporal evolution of SARcal-NDVI. Using the CSDM, SARcal-NDVI and GDD, the canola condition can be estimated for any given day in the growing season. In fact when the CSDM was used to estimate SARcal-NDVI for the exact days of RapidEye acquisitions, correlations with optically derived NDVI were high. The strongest correlations with RapidEye NDVI were reported for the volume-surface scattering ratio (R2 of 0.69 and RMSE of 0.15). The SARcal-NDVI estimated from the CSDM was also physically meaningful. Field-based biomass was significantly correlated (R2 of 0.79) with the SARcal-NDVI calculated using the volume-surface scattering ratio. Although further research is needed to extend this method to other crops, these results demonstrate that SAR data can be used to estimate vegetation conditions and when coupled with a CSDM, integrated into current monitoring operations based on optical NDVI. As a next step, the research team will be assessing SARcal-NDVI in a national operational programme which reports on crop yields using modelling with optical-based NDVI. [ABSTRACT FROM AUTHOR]

The objective of map regionalization is to group contiguous objects on a map into larger entities sharing similar properties or relationships, resulting in homogeneous regions that are easier to interpret. We propose a strategy to interactively incorporate human perception of homogeneous regions to improve unsupervised regionalization processes. The approach fits within the well-known segmentation/clustering framework. The method operates on a categorical map, introduces a contour detector for boundaries delineation with better resolution power than a regular grid tessellation to initiate a region growing process, and integrates the role of a human analyst for better classification of homogeneous areas through a semi-supervised clustering (SSC) method. This last step is achieved using pairwise clustering constraints on regions identified by the analyst on the monitor. The potential of the proposed strategy is illustrated with data extracted from the Earth Observation for the sustainable development of forests (EOSD) map of Canada. Comparisons with a recently introduced algorithm for map regionalization are provided for three different spatial scales at different steps of the method. [ABSTRACT FROM AUTHOR]

Past emission of metal-bearing particulate matter, sulphur dioxide (SO2), and sulphuric acid by base metal smelters in the Sudbury region led to widespread loss of vegetation, contamination of soils, and formation of black coatings on rock surfaces. These black coatings formed through the incorporation of smelter-borne particulate matter into the partly dissolved uppermost layers of siliceous minerals on exposed rock, and are characterized by high heavy-metal content. This study involved assessment of the reflectance properties of black coatings in the Sudbury region, and determination of the geographic distribution of coatings through supervised classification of reflectance data derived from a Landsat Enhanced Thematic Mapper Plus (ETM+) image. Classifications involved the use of the Spectral Angle Mapper (SAM), Maximum Likelihood, and Feedforward Backpropagation Neural Network algorithms. The reflectance spectra of black coatings in the Sudbury region are relatively flat and featureless, and are characterized by reflectance values less than ~13% across the visible, near-infrared, and short-wave infrared. Spectral properties are similar to those of magnetite, a spinel-group mineral known to be present in Sudbury coatings. The presence of carbon-rich soot particles may be an important influence on the reflectance properties of coatings. SAM classification results are characterized by the widespread mislabelling of uncoated urban and open-pit sites as mantled by black coatings, and neural network results problematically mislabel some uncoated wetland sites as coated. Results generated by the Maximum Likelihood algorithm most usefully depict the distribution of exposed black coatings in the Sudbury region. The mapping of black coatings using remote-sensing methods can provide useful information on the spatial character of environmental degradation in the vicinity of smelters, and should be helpful in the monitoring of environmental recovery where emissions have been reduced or eliminated. [ABSTRACT FROM AUTHOR]

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

In Canada, forest companies and government are faced with the important challenge of monitoring forest stand health. This task is especially difficult when the objective is to monitor the health of mature deciduous stands. Mapping methods for tree health have been proposed using multispectral and hyperspectral airborne sensors; however, acquiring airborne data over large areas remains costly. In addition, some studies have pointed out that forest dieback is characterized by multi-causality. Therefore, we propose a large-scale mapping method which includes a model to parameterize several factors influencing forest vigour. A high-spatial resolution satellite image was fused with a series of biophysical parameters using the Dempster–Shafer theory (DST). The study was performed over mature deciduous forests in the province of Québec, Canada. The fusion of a Satellite Pour l’Observation de la Terre (SPOT)-5 high resolution geometric (HRG) image with a surface deposit map and an ice storm damage-intensity map provided the best results, improving the overall accuracy by 15.1%, when compared with a K‐nearest-neighbour (KNN) algorithm using the SPOT-5 image alone. Moreover, the DST improved the accuracy of the vigour class identification, halving the standard deviation when compared with the KNN method. This study shows how the DST can be used to model the influence of biophysical parameters when combined with multispectral information to better assess the health of mature deciduous stands. [ABSTRACT FROM AUTHOR]

Ice, Cloud, and land Elevation Satellite (ICESat) / Geosciences Laser Altimeter System (GLAS) waveform data are used to estimate biomass and carbon on a 1.27 × 106 km2 study area in the Province of Quebec, Canada, below the tree line. The same input datasets and sampling design are used in conjunction with four different predictive models to estimate total aboveground dry forest biomass and forest carbon. The four models include non-stratified and stratified versions of a multiple linear model where either biomass or (biomass)0.5 serves as the dependent variable. The use of different models in Quebec introduces differences in Provincial dry biomass estimates of up to 0.35 G, with a range of 4.94 ± 0.28 Gt to 5.29 ± 0.36 Gt. The differences among model estimates are statistically non-significant, however, and the results demonstrate the degree to which carbon estimates vary strictly as a function of the model used to estimate regional biomass. Results also indicate that GLAS measurements become problematic with respect to height and biomass retrievals in the boreal forest when biomass values fall below 20 t ha-1 and when GLAS 75th percentile heights fall below 7 m. [ABSTRACT FROM AUTHOR]

The use of remote-sensing techniques in the discrimination of rock and soil classes in northern regions can support a diverse range of activities, such as environmental characterization, mineral exploration and the study of Quaternary paleoenvironments. Although images with low spectral resolution can commonly be used in the mapping of classes possessing distinct spectral properties, hyperspectral images offer greater potential for discrimination of materials characterized by more subtle reflectance properties. In an effort to better constrain the utility of broadband and hyperspectral datasets in high-latitude research, this study investigated the effectiveness of Landsat Thematic Mapper (TM) and EO-1 Hyperion data for discrimination of lithological classes at eastern Melville Island, Nunavut, Canada. TM data were classified using a standard neural-network algorithm, and both TM and Hyperion data were linearly unmixed using ground-truth spectra. TM classification results successfully discriminate between classes over much of the study area, although with incomplete separation between clastic and carbonate materials. TM unmixing results are poor, with useful class separation restricted to vegetation and red-weathered sandstone classes. Hyperion results effectively depict the fractional cover of end members, although the abundance images of several classes contain background abundance values that overestimate surface exposure in some areas. For the study area and surface classes involved, noisy hyperspectral data were found to be of greater utility than higher-fidelity broadband multispectral data in the generation of fractional abundance images for an inclusive set of surface-cover classes. [ABSTRACT FROM AUTHOR]

The goal of this study is to develop an efficient method to retrieve vegetation biophysical properties based on ground LAI measurements and satellite data, and thus avoid the labour-intensive and time-consuming process for collecting biomass and canopy height in the future. The field data was conducted in Grasslands National Park (GNP), Saskatchewan, Canada. The two vegetation indices, ATSAVI and RDVI, were derived from SPOT 4 HRV images to estimate LAI and to prepare LAI and biophysical maps for the GNP. The results demonstrated strong relationships between LAI and selected vegetation indices. However, a detailed accuracy assessment indicated that ATSAVI was likely to be better in estimating and mapping LAI than the RDVI. The accuracy of the LAI map was calculated to be 66.7%. The significant relationship between measured LAI and the biophysical data solves the difficulty for mapping biophysical information due to insufficient sampling coverage for GNP. [ABSTRACT FROM AUTHOR]

The goal is to determine the extent to which heterogeneous inland wetland vegetation communities and their dominant species, as well as adjacent upland vegetation types, can be mapped using 4-m hyperspectral Compact Airborne Spectrographic Imager (CASI) data. Two classification algorithms, the maximum-likelihood classifier (MLC) and the spectral angle mapper (SAM), are applied to CASI data acquired over an inland wetland complex located in southern Ontario, Canada. Application of the MLC algorithm to all bands of the CASI data produced better classification results than use of the SAM. Using the MLC, 10 classes were identified with an overall accuracy of 92%. This approach permitted differentiation between areas of shrub-dominated vegetation communities, floating aquatic communities, emergent aquatics and shallow open water. In the SAM classification, 11 image-derived spectral endmembers were generated. Wetland classes identified were shrub-dominated wetlands, floating aquatic vegetation communities, shallow open water and moderately turbid shallow open water. Upland vegetation types were accurately mapped with both algorithms. Reasons why the SAM did not perform as well as the MLC in this complex environment are suggested. It is concluded that high-resolution hyperspectral data can provide information needed by wetland managers about inland wetland plant communities and their dominant species. [ABSTRACT FROM AUTHOR]

The mixed prairie in Canada is characterized by its low to medium green vegetation cover, high amount of non-photosynthetic materials, and ground level biological crust. It has proven to be a challenge for the application of remotely sensed data in extracting biophysical variables for the purpose of monitoring grassland health. Therefore, this study was conducted to evaluate the efficiency of broadband-based reflectance and vegetation indices in extracting ground canopy information. The study area was Grasslands National Park (GNP) Canada and the surrounding pastures, which represent the northern mixed prairie. Fieldwork was conducted from late June to early July 2005. Biophysical variables - canopy height, cover, biomass, and species composition - were collected for 31 sites. Two satellite images, one SPOT 4 image on 22 June 2005, and one Landsat 5 TM image on 14 July 2005, were collected for the corresponding time period. Results show that the spectral curve of the grass canopy was similar to that of the bare soil with lower reflectance at each band. Consequently, commonly used vegetation indices were not necessarily better than reflectance when it comes to single wavelength regions at extracting biophysical information. Reflectance, NDVI, ATSAVI, and two new coined cover indices were good at extracting biophysical information. [ABSTRACT FROM AUTHOR]

Two promising techniques for estimating Leaf Area Index (LAI) using remote sensing are Linear Spectral Mixture Analysis (LSMA) and Modification of Spectral Vegetation Indices (MSVI). The Normalized Distance Method (ND), which uses principles employed by the LSMA and MSVI techniques, is introduced in this study. These three methods are applied to a region of montane forest in Kananaskis Country, Alberta, Canada, in order to estimate LAI. In situ measurements of LAI in 10 deciduous and 10 coniferous plots, and a SPOT-4 image taken at the height of the growing season, provided test data that produced relationships for LAI in pure stands of either coniferous or deciduous vegetation using each of the three methods. All methods exhibited varying degrees of performance and demonstrated significant dependence on vegetation type. The ND method produced relationships with coefficients of determination (R 2) of 0.86 and 0.65 for coniferous and deciduous vegetation, respectively; the MSVI method (when using the adjusted Normalized Difference Vegetation Index) produced relationships with R 2 values of 0.79 and 0.59 for coniferous and deciduous vegetation, respectively; and the LSMA technique produced relationships with R 2 values of 0.83 and 0.0 for coniferous and deciduous vegetation, respectively. [ABSTRACT FROM AUTHOR]

We have designed a system that predicts species richness in the mixed wood boreal forest of Canada. The system is based on a simple multivariate linear model that uses four landscape characteristics as independent variables: canopy species type, distance from the nearest ridgeline, time since the last fire and canopy stem density. The model is shown to provide statistically significant estimates of richness when using observed independent variables. We developed models for estimating the four landscape characteristics from geospatial data consisting of remotely sensed imagery and a digital elevation model. We ran the model at the stand scale and the pixel scale and found that stand scale predictions were be more accurate that pixel scale predictions. We produced a map of vegetation species richness for Prince Alberta National Park in central Saskatchewan Canada that is consistent with our expectations. We also estimated the uncertainty in the four landscape characteristic estimates and developed a methodology for propagating this uncertainty through the system to produce estimates of uncertainty in the pixel-based richness predictions. While the uncertainty is significant, the estimation and management of uncertainty in a mapping system of this type represents an innovation. [ABSTRACT FROM AUTHOR]

Multilayer feed-forward (MLF) neural networks were employed to estimate air temperatures in Southern Québec (Canada) using Advanced Very High Resolution Radiometer (AVHRR) images. The input variables for the networks were the five bands of the AVHRR image, surface altitude, solar zenith angle, and Julian day. The estimation was carried out using a dataset collected during the growing season from June to September 2000. Levenberg--Marquardt back-propagation (LM-BP) was used to train the networks. The early stopping method was applied to improve the LM-BP and to generalize the networks. Bands 4 and 5, which are used for retrieval of surface temperature, were the most critical components for the estimation. The contribution of Julian day to the precision of estimated air temperature was much superior to that of altitude and solar zenith angle for the dataset of inter-seasonal air temperatures. The network using all five bands, Julian day, altitude, and solar zenith angle provided the best results, with 22 nodes in the hidden layer. In the time series of estimated and station air temperatures, the difference between the temperatures was generally maintained within 2°C on various canopies, even during steep variations in August and September. [ABSTRACT FROM AUTHOR]

A practical, hydrologic model (DPHM-RS) is developed for the semi-arid climate of the Canadian Prairies that could adequately account for a river basin's terrain features by sub-dividing it to sub-basins of uneven shapes and sizes (semi-distributed) based on topographic information derived from the digital terrain elevation (DTED) data. Even though computationally modest, DPHM-RS is scientifically vigorous, can effectively assimilate remotely sensed (RS) data, and has most of its parameters determined through RS data and measurements. The hydrologic processes are estimated for each land cover and then aggregated according to percentage of each land cover present within each sub-basin. As evapotranspiration (ET) usually dominates the hydrology of the Canadian Prairies, ET from each land cover is estimated at three levels by the two-source model that separately considers evaporation from soil and plants. The soil moisture at the top active and the transmission zones are estimated by a water budget approach, while the groundwater dynamics by the topographic soil index obtained from DTED. The surface runoff from each sub-basin is routed to the channel network by a kinematic wave response function, and then routed to the basin outlet by the Muskingum-Cunge model. DPHM-RS, is applied to the Paddle River Basin (265 km 2 ) of Central Alberta divided to five sub-basins. It was calibrated with hourly hydroclimatic and RS data collected in the summer of 1996 and validated with data of 1997 and 1998. In both stages, there are good agreements between simulated runoff at the basin outlet with the observed, between simulated surface temperature and net radiation with the observed, between soil moisture and that retrieved from Radarsat-SAR data, and between simulated ET and that estimated by water balance. Encouraging results from these multi-criteria assessments demonstrate the feasibility of semi-distributed, physics-based hydrologic modelling in the dry climate of Canadian Prairies, and the usefulness of RS and DTED data in basin hydrology. [ABSTRACT FROM AUTHOR]

A complete set of Advanced Very High Resolution Radiometer (AVHRR) data (75 images) is used to retrieve aerosol optical depth (AOD) over dense vegetation and over lake water in the visible AVHRR channel. The present approach for remote sensing of aerosols from the National Oceanic and Atmospheric Administration (NOAA)-11 AVHRR sensor is based on the detection of atmospherically dominated signals over dark surface covers such as dense dark vegetation (DDV). Such targets were identified using the reflective portion of the middle-wave AVHRR channel 3 signal. When a fixed DDV surface reflectance is subtracted from the observed reflectance after correction for all other atmospheric effects, the remaining part, which is due to aerosols, is inverted to derive aerosol optical thickness using a look-up table (LUT) similar to that used in water surface inversion. The algorithm was applied to the daily afternoon NOAA-11 AVHRR (1 km×1 km) data acquired from the end of May to mid-August 1994 over the Canadian 1000 km×1000 km Boreal Ecosystem Atmosphere Study (BOREAS) domain. A validation analysis using five ground-based Sun photometers within the studied area shows the good performance of the retrieval algorithm. The approach allows detailed analysis of the AOD spatio-temporal behaviour at the regional scale useful for climate and transport model validation. [ABSTRACT FROM AUTHOR]

A large abandoned tailings deposit at a mine site near Timmins, Ontario, Canada has produced significant damage in an adjacent forest due to contamination and wind stress. Significant forest structure changes were measured between 1997 and 1999. A multivariate image-based forest structure index (FSI) was developed using canonical correlation analysis of 1997 field and airborne digital camera data. FSI included decreasing canopy closure and leaf area index, and increasing blown down and standing dead structure measures associated with image spectral, textural and radiometric fraction variables. An image model predicting FSI achieved an R 2 =0.66. The model equation was then applied to 1999 airborne imagery to predict FSI for each plot. Comparing the 1999 image predicted FSI to that calculated from field data showed that the model was strong in predicting positive or no forest structure changes, but not increased structure degradation. The latter was due to the presence of herbaceous and shrub vegetation that had developed during the two-year period in open plots near the tailings where blow down was significant. The next research phase will derive means to separate these two signals in forests of open overstory. [ABSTRACT FROM AUTHOR]

In March 1996, a multispectral aircraft survey of the coastal waters off Vancouver Island was carried out using a Compact Airborne Spectrographic Imager (CASI). This survey was combined with in situ measurements of water properties (phytoplankton composition, phytoplankton pigments, absorption spectra of phytoplankton, and concentration of dissolved organic carbon, or DOC). Comparison of the phytoplankton absorption data from this experiment with similar data from other regions shows that phytoplankton community has a significant impact on the spectral form and magnitude of absorption spectra, when normalized to unit chlorophyll-a. Concurrent measurements of in situ properties and aircraft data were obtained at eight stations. The in situ measurements of phytoplankton absorption and estimates of downwelling irradiance based on a clear-sky atmospheric-transmission model are used as inputs to a model of water-leaving irradiance. The modelled irradiances are compared with the remotely sensed values of water-leaving radiances. The observed differences between model and observation are used to evaluate the potential influence of DOC on water-leaving radiance. Practical difficulties of separating the phytoplankton signal from that of the coloured component of DOC (also known as yellow substance) are examined. Algorithms for estimation of the concentration of chlorophyll-a (the major phytoplankton pigment) can be based on their absorption or fluorescence properties. The distribution of chlorophyll-a in the study area is estimated using both these approaches, and possible causes for the observed discrepancies are discussed. [ABSTRACT FROM AUTHOR]

The Hudson Bay Lowlands of Manitoba contain a wide range of vegetation types that reflect local variations in climate, geological history, permafrost, fire, wildlife grazing and human use. This study, in Wapusk National Park and the Cape Churchill Wildlife Management Area, uses a Landsat-5 TM image mosaic to examine landscape-level vegetation classes. Field data from 600 sites were first classified into 14 vegetation classes and three unvegetated classes. Principal component analysis was used to examine the spectral properties of these classes and identify outliers. Multiple discriminant analysis was then applied to determine the statistical significance of the vegetation classes in spectral space. Finally, redundancy analysis was used to determine the amount of vegetation variance explained by the spectral reflectance data. We advocate this adaptive learning approach to vegetation mapping, by which the researcher employs an iterative strategy to carefully examine the relationship between ground and spectral data. This approach is labour intensive, but has the advantage of producing vegetation classes that are spectrally separable, decreasing the likelihood of errors in classification caused by overlap between classes. [ABSTRACT FROM AUTHOR]

Multisource data consisting of satellite imagery, topographic descriptors derived from DEMs, and GIS inventory information have been used with a detailed, field-based landcover classification scheme to support a quantitative analysis of the spatial distribution and configuration of grizzly bear (Ursus arctos horribilis) habitat within the Alberta Yellowhead Ecosystem study area. The map is needed to determine if bear movement and habitat use patterns are affected by changing landscape conditions and human activities. We compared a multisource Evidential Reasoning (ER) classification algorithm, capable of handling this large and diverse data set, to a more conventional maximum likelihood decision rule which could only use a subset of the available data. The ER classifier provided an acceptable level of accuracy (ranging to 85% over 21 habitat classes) for a level 3 product, compared to 71% using a maximum likelihood classifier. [ABSTRACT FROM AUTHOR]

In 1997, the Canada Centre for Remote Sensing acquired RADARSAT-1, SPOT and IRS-1C imagery over an agricultural site in western Canada. These data were used to address the information content of RADARSAT-1 imagery for mapping crop type and for providing information on crop condition, and to explore the implications of crop growth stage on crop monitoring with radar imagery. The use of radar for crop mapping is particularly attractive because of its all weather capability and the sensitivity of microwaves to canopy structure and moisture. Results from this study indicated that multi-date RADARSAT-1 imagery, with or without satellite optical imagery, can provide accurate information about crop types, although timing of image acquisition was important. Regression analysis established that some indicators of crop vigour - in particular leaf area index and crop height - were correlated with backscatter. The highest correlations were for wheat and potatoes. However, backscatter was insensitive to variations in corn growth and only moderately sensitive to differences in indicators of canola crop condition. Nevertheless, this study clearly demonstrates that multi-temporal RADARSAT-1 imagery can be used to provide useful crop information. [ABSTRACT FROM AUTHOR]

Inexpensive desktop scanners are frequently used for the scanning of small format aerial photographic images. However, these scanners do not always conform to the spectral and spatial quality required for subsequent image analysis. This Letter outlines simple tests that can be conducted to characterize the properties of desktop scanners and illustrates the performance of a specific instrument. Spectral and spatial artifacts introduced by this scanner are outlined. It is important for image analysts to be aware of these scanner properties, and decide in each case whether the use of these instruments is warranted for quantitative applications. [ABSTRACT FROM AUTHOR]

Underwater kelp seasonal variation is assessed through the comparative analysis of HRV and Thematic Mapper (TM) images of Baie des Chaleurs between Caps-Noirs and Pointe-Bonaventure, Quebec. The total biomass is estimated, based on the morphology of the dominant species Laminaria longicruris. Kelp-covered and kelp-free areas are differentiated from each other in water depth of 0-6 m and 0-7 m with the HRV and TM images, respectively. The median biomass estimated for the kelp-covered category of the classified image is (1500_400)g m[sup -2]. The multidate image shows a spatial variation of the kelp beds in 45% of the area. Areas where no change occurred occupy at least 70 ha, while growth and decay of kelp are observed in much smaller areas, in shallow water and at the boundary of kelp beds. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive investigation of fires across the Canadian boreal forest zone by means of satellite-based remote sensing. A firedetection algorithm was designed to monitor fires using daily Advanced Very High Resolution Radiometer (AVHRR) images. It exploits information from multichannel AVHRR measurements to determine the locations of fires on satellite pixels of about 1 km2 under clear sky or thin smoke cloud conditions. Daily fire maps were obtained showing most of the active fires across Canada (except those obscured by thick clouds). This was achieved by first compositing AVHRR scenes acquired over Canada on a given day and then applying the fire-detection algorithm. For the fire seasons of 1994-1998, about 800 NOAA/AVHRR daily mosaics were processed. The results provide valuable nation-wide information on fire activities in terms of their locations, burned area, starting and ending dates, as well as development. The total burned area as detected by satellite across Canada is estimated to be approximately 3.9, 4.9, 1.3, 0.4 and 2.4 million hectares in 1994, 1995, 1996, 1997 and 1998, respectively. The peak month of burning varies considerably from one year to another between June and August, as does the spatial distribution of fires. In general, conifer forests appear to be more vulnerable to burning and fires tend to grow larger than in deciduous forests. [ABSTRACT FROM AUTHOR]