Permafrost slope disturbance such as active layer detachments and retrogressive thaw slumps are a... more Permafrost slope disturbance such as active layer detachments and retrogressive thaw slumps are a major concern for arctic communities and resource development as warming temperatures have led to increasing permafrost degradation. In order to effectively assess and mitigate permafrost disturbance risk, disturbance-prone areas can be predicted through the use of susceptibility modelling. Permafrost disturbance susceptibility modelling is a quantitative assessment of the relationship between the distribution of past permafrost disturbances and a set of influencing terrain attributes that may cause slope instability. In this study we develop a universal permafrost disturbance susceptibility model using a limited disturbance inventory to test the applicability of the model for a broader region in the Canadian High Arctic. Additionally, we use the model to explore the effect of influencing terrain parameters on disturbance occurrence. To account for a large range of landscape characteris...
Permafrost slope disturbance such as active layer detachments and retrogressive thaw slumps are a... more Permafrost slope disturbance such as active layer detachments and retrogressive thaw slumps are a major concern for arctic communities and resource development as warming temperatures have led to increasing permafrost degradation. In order to effectively assess and mitigate permafrost disturbance risk, disturbance-prone areas can be predicted through the use of susceptibility modelling. Permafrost disturbance susceptibility modelling is a quantitative assessment of the relationship between the distribution of past permafrost disturbances and a set of influencing terrain attributes that may cause slope instability. In this study we develop a universal permafrost disturbance susceptibility model using a limited disturbance inventory to test the applicability of the model for a broader region in the Canadian High Arctic. Additionally, we use the model to explore the effect of influencing terrain parameters on disturbance occurrence. To account for a large range of landscape characteris...
Detailed forest ecosystem classifications have been developed for large regions of northern Ontar... more Detailed forest ecosystem classifications have been developed for large regions of northern Ontario. These ecosystem classifications provide tools for ecosystem management that constitute part of a larger goal of integrated management of forest ecosystems for long-term sustainability. These classification systems provide detailed stand-level characterization of forest ecosystems at a local level. However, for ecological approaches to forest management to become widely accepted by forest managers, and these tools to be widely used, methods must be developed to characterize and map or model ecosystem classes at landscape scales for large regions. In this study, the site-specific Northwestern Ontario Forest Ecosystem Classification (NWO FEC) was adapted to provide a landscape-scale (1:20000) forest ecosystem classification for the Rinker Lake Study Area located in the Boreal Forest north of Thunder Bay, Ontario. Multispatial resolution remote sensing data were collected using the Compact Airborne Spectrographic Imager (CASI) and analysed using geostatistical techniques to obtain an understanding of the nature of the spatial dependence of spectral reflectance for selected forest ecosystems at high spatial resolutions. Based on these analyses it was determined that an optimal size of support for characterizing forest ecosystems (i.e., optimal spatial resolution), as estimated by the mean ranges of a series of experimental semivariograms, differed based on (i) wavelength; (ii) forest ecosystem class (and at low altitude as a function of mean maximum canopy diameter (MMCD)); and (iii) altitude of the remote sensing system. In addition, maximum semivariance as estimated from the sills of the experimental semivariograms increased with density of understory. Based on the estimates for optimal spatial resolutions for six landscape-scale forest ecosystem classes, a series of spectral-spatial features were derived from the high-altitude CASI data (4 metre spatial resolution) using spatial averaging. Linear discriminant analysis for various spectral-spatial and texture feature combinations indicated that a spatial resolution of approximately 6 m was optimal for discriminating the six-landscape scale ecosystem classes. Texture features, using second-order spatial statistics that were derived from the 4 m remote sensing data, also significantly improved discrimination of the classes over the original 4 m data. Finally, addition of terrain descriptors, particularly elevation within a local region, improved discrimination of the six landscape scale ecosystem classes. It has been demonstrated that in a low-relief boreal environment, addition of textural and geomorphometric variables to high-resolution CASI reflectance data provides improved discrimination of forest ecosystem classes. Although these improvements are statistically significant, the absolute classification accuracies are not at levels suitable for operational classification and mapping. The analysis presented here represents the initiation of a complex modelling approach that is necessary for improving forest ecosystem characterization and prediction using additional primary datasets and derived datasets that possess various levels of measurement. Not only are optimal or multispatial resolution remote sensing data required, but also appropriately scaled terrain and landscape features depicting soil texture, nutrient and moisture regimes. Incorporation of these types of terrain-specific variables with reflectance data should provide further improvement in forest ecosystem classification and modelling at landscape scales.
The Kyoto Protocol has sharpened the focus on the possible role of forests in contributing to or ... more The Kyoto Protocol has sharpened the focus on the possible role of forests in contributing to or mitigating climate change. The understanding of carbon dynamics, and the prediction of future carbon stock changes, rely on an analysis of the past forest dynamics. Historical data may often be incomplete, approximate, or strongly generalized. To circumvent this problem, we have recently developed
IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004, 2004
Abstract Interferometric synthetic aperture radar (InSAR) data from RADARSAT-1 have been examined... more Abstract Interferometric synthetic aperture radar (InSAR) data from RADARSAT-1 have been examined to assess their potential for mapping terrain and changes in snow cover characteristics, relative to the limiting effects of snow on foraging by endangered Peary caribou (Rangifer tarandus). Radar is one of the few observational tools that can provide information on the changing snow pack during the dark winter months. The goal of this research is to characterize the general ensemble of terrain characteristics that may affect ...
Thermokarst, active layer detachments (ALDs), and retrogressive thaw slumps (RTSs), representing ... more Thermokarst, active layer detachments (ALDs), and retrogressive thaw slumps (RTSs), representing three forms of permafrost degradation, constitute serious risks for infrastructure and have the potential to alter environmental and ecological conditions in Arctic regions. Environmental change and increased land development pressures require innovative cost-effective methods for assessing hazard prone areas. The overall research objective of this project is to design a landscape model to predict and characterize disturbance prone areas using key physiographic controls and geospatial modeling derived from satellite imagery to efficiently produce hazard susceptibility maps. Susceptibility maps identify disturbance prone areas, which are a fundamental component of hazard management and the basis for provision of measures aimed at reducing the risks resulting from permafrost degradation. To test the validity of this modelling approach and its applicability across the Arctic, methods are be...
This study examines the distribution of laser pulse returns obtained from coincident airborne and... more This study examines the distribution of laser pulse returns obtained from coincident airborne and terrestrial lidar surveys of a closed-canopy red pine (Pinus resinosa) plantation. The purpose of this study is to improve our understanding of laser pulse sampling within closed canopies so that estimates of forest structural variables (e.g., biomass, needle-leaf area, and base-of-live-crown) can be improved at the individual tree and stand levels using lidar. The results of this study indicate the following: (1) There is a statistically significant difference between field measurements of tree height and estimates derived from the maximum laser pulse return from airborne and terrestrial lidar. In both cases, maximum laser pulse returns underestimate tree height by 1 m, on average. (2) Both terrestrial and airborne lidar are unable to discern the base of the measured live crown. Laser pulse returns from airborne lidar are biased towards the top of the tree crown, i.e., lowest laser pul...
2006 IEEE International Symposium on Geoscience and Remote Sensing, 2006
Abstract-Variability in the gross primary product (GPP) algorithm derived from the Moderate Resol... more Abstract-Variability in the gross primary product (GPP) algorithm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) is examined and compared with the same algorithm adjusted according to vegetation height influences on the light use efficiency (LUE) term within a section of the White Gull watershed, Saskatchewan Canada. GPP comparisons are made for pixels containing vegetation heights of 0 to 1m, 1 to 3m, 8 to 12m, and 12 to 24m. The results of this study find that the LUE term has an important influence ...
Permafrost slope disturbance such as active layer detachments and retrogressive thaw slumps are a... more Permafrost slope disturbance such as active layer detachments and retrogressive thaw slumps are a major concern for arctic communities and resource development as warming temperatures have led to increasing permafrost degradation. In order to effectively assess and mitigate permafrost disturbance risk, disturbance-prone areas can be predicted through the use of susceptibility modelling. Permafrost disturbance susceptibility modelling is a quantitative assessment of the relationship between the distribution of past permafrost disturbances and a set of influencing terrain attributes that may cause slope instability. In this study we develop a universal permafrost disturbance susceptibility model using a limited disturbance inventory to test the applicability of the model for a broader region in the Canadian High Arctic. Additionally, we use the model to explore the effect of influencing terrain parameters on disturbance occurrence. To account for a large range of landscape characteris...
Permafrost slope disturbance such as active layer detachments and retrogressive thaw slumps are a... more Permafrost slope disturbance such as active layer detachments and retrogressive thaw slumps are a major concern for arctic communities and resource development as warming temperatures have led to increasing permafrost degradation. In order to effectively assess and mitigate permafrost disturbance risk, disturbance-prone areas can be predicted through the use of susceptibility modelling. Permafrost disturbance susceptibility modelling is a quantitative assessment of the relationship between the distribution of past permafrost disturbances and a set of influencing terrain attributes that may cause slope instability. In this study we develop a universal permafrost disturbance susceptibility model using a limited disturbance inventory to test the applicability of the model for a broader region in the Canadian High Arctic. Additionally, we use the model to explore the effect of influencing terrain parameters on disturbance occurrence. To account for a large range of landscape characteris...
Detailed forest ecosystem classifications have been developed for large regions of northern Ontar... more Detailed forest ecosystem classifications have been developed for large regions of northern Ontario. These ecosystem classifications provide tools for ecosystem management that constitute part of a larger goal of integrated management of forest ecosystems for long-term sustainability. These classification systems provide detailed stand-level characterization of forest ecosystems at a local level. However, for ecological approaches to forest management to become widely accepted by forest managers, and these tools to be widely used, methods must be developed to characterize and map or model ecosystem classes at landscape scales for large regions. In this study, the site-specific Northwestern Ontario Forest Ecosystem Classification (NWO FEC) was adapted to provide a landscape-scale (1:20000) forest ecosystem classification for the Rinker Lake Study Area located in the Boreal Forest north of Thunder Bay, Ontario. Multispatial resolution remote sensing data were collected using the Compact Airborne Spectrographic Imager (CASI) and analysed using geostatistical techniques to obtain an understanding of the nature of the spatial dependence of spectral reflectance for selected forest ecosystems at high spatial resolutions. Based on these analyses it was determined that an optimal size of support for characterizing forest ecosystems (i.e., optimal spatial resolution), as estimated by the mean ranges of a series of experimental semivariograms, differed based on (i) wavelength; (ii) forest ecosystem class (and at low altitude as a function of mean maximum canopy diameter (MMCD)); and (iii) altitude of the remote sensing system. In addition, maximum semivariance as estimated from the sills of the experimental semivariograms increased with density of understory. Based on the estimates for optimal spatial resolutions for six landscape-scale forest ecosystem classes, a series of spectral-spatial features were derived from the high-altitude CASI data (4 metre spatial resolution) using spatial averaging. Linear discriminant analysis for various spectral-spatial and texture feature combinations indicated that a spatial resolution of approximately 6 m was optimal for discriminating the six-landscape scale ecosystem classes. Texture features, using second-order spatial statistics that were derived from the 4 m remote sensing data, also significantly improved discrimination of the classes over the original 4 m data. Finally, addition of terrain descriptors, particularly elevation within a local region, improved discrimination of the six landscape scale ecosystem classes. It has been demonstrated that in a low-relief boreal environment, addition of textural and geomorphometric variables to high-resolution CASI reflectance data provides improved discrimination of forest ecosystem classes. Although these improvements are statistically significant, the absolute classification accuracies are not at levels suitable for operational classification and mapping. The analysis presented here represents the initiation of a complex modelling approach that is necessary for improving forest ecosystem characterization and prediction using additional primary datasets and derived datasets that possess various levels of measurement. Not only are optimal or multispatial resolution remote sensing data required, but also appropriately scaled terrain and landscape features depicting soil texture, nutrient and moisture regimes. Incorporation of these types of terrain-specific variables with reflectance data should provide further improvement in forest ecosystem classification and modelling at landscape scales.
The Kyoto Protocol has sharpened the focus on the possible role of forests in contributing to or ... more The Kyoto Protocol has sharpened the focus on the possible role of forests in contributing to or mitigating climate change. The understanding of carbon dynamics, and the prediction of future carbon stock changes, rely on an analysis of the past forest dynamics. Historical data may often be incomplete, approximate, or strongly generalized. To circumvent this problem, we have recently developed
IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004, 2004
Abstract Interferometric synthetic aperture radar (InSAR) data from RADARSAT-1 have been examined... more Abstract Interferometric synthetic aperture radar (InSAR) data from RADARSAT-1 have been examined to assess their potential for mapping terrain and changes in snow cover characteristics, relative to the limiting effects of snow on foraging by endangered Peary caribou (Rangifer tarandus). Radar is one of the few observational tools that can provide information on the changing snow pack during the dark winter months. The goal of this research is to characterize the general ensemble of terrain characteristics that may affect ...
Thermokarst, active layer detachments (ALDs), and retrogressive thaw slumps (RTSs), representing ... more Thermokarst, active layer detachments (ALDs), and retrogressive thaw slumps (RTSs), representing three forms of permafrost degradation, constitute serious risks for infrastructure and have the potential to alter environmental and ecological conditions in Arctic regions. Environmental change and increased land development pressures require innovative cost-effective methods for assessing hazard prone areas. The overall research objective of this project is to design a landscape model to predict and characterize disturbance prone areas using key physiographic controls and geospatial modeling derived from satellite imagery to efficiently produce hazard susceptibility maps. Susceptibility maps identify disturbance prone areas, which are a fundamental component of hazard management and the basis for provision of measures aimed at reducing the risks resulting from permafrost degradation. To test the validity of this modelling approach and its applicability across the Arctic, methods are be...
This study examines the distribution of laser pulse returns obtained from coincident airborne and... more This study examines the distribution of laser pulse returns obtained from coincident airborne and terrestrial lidar surveys of a closed-canopy red pine (Pinus resinosa) plantation. The purpose of this study is to improve our understanding of laser pulse sampling within closed canopies so that estimates of forest structural variables (e.g., biomass, needle-leaf area, and base-of-live-crown) can be improved at the individual tree and stand levels using lidar. The results of this study indicate the following: (1) There is a statistically significant difference between field measurements of tree height and estimates derived from the maximum laser pulse return from airborne and terrestrial lidar. In both cases, maximum laser pulse returns underestimate tree height by 1 m, on average. (2) Both terrestrial and airborne lidar are unable to discern the base of the measured live crown. Laser pulse returns from airborne lidar are biased towards the top of the tree crown, i.e., lowest laser pul...
2006 IEEE International Symposium on Geoscience and Remote Sensing, 2006
Abstract-Variability in the gross primary product (GPP) algorithm derived from the Moderate Resol... more Abstract-Variability in the gross primary product (GPP) algorithm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) is examined and compared with the same algorithm adjusted according to vegetation height influences on the light use efficiency (LUE) term within a section of the White Gull watershed, Saskatchewan Canada. GPP comparisons are made for pixels containing vegetation heights of 0 to 1m, 1 to 3m, 8 to 12m, and 12 to 24m. The results of this study find that the LUE term has an important influence ...
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