Mixed grasslands in south Canada serve a variety of economic, environmental and ecological purposes. Numerical modeling has become a major method used to identify potential grassland ecosystem responses to environment changes and human activities. In recent years, the focus has been on process models because of their high accuracy and ability to describe the interactions among different environmental components and the ecological processes. At present, two commonly-used process models (CENTURY and BIOME-BGC) have significantly improved our understanding of the possible consequences and responses of terrestrial ecosystems under different environmental conditions. However, problems with these models include only using site-based parameters and adopting different assumptions on interactions between plant, environmental conditions and human activities in simulating such complex phenomenon. In light of this shortfall, the overall objective of this research is to integrate remote sensing products into ecosystem process model in order to simulate productivity for the mixed grassland ecosystem in the landscape level. Data used includes 4-years of field measurements and diverse satellite data (System Pour lObservation de la Terre (SPOT) 4 and 5, Landsat TM and ETM, Advanced Very High Resolution Radiometer (AVHRR) imagery). <p>Using wavelet analyses, the study first detects that the dominant spatial scale is controlled by topography and thus determines that 20-30 m is the optimum resolution to capture the vegetation spatial variation for the study area. Second, the performance of the RDVI (Renormalized Difference Vegetation Index), ATSAVI (Adjusted Transformed Soil-Adjusted Vegetation Index), and MCARI2 (Modified Chlorophyll Absorption Ratio Index 2) are slightly better than the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating CAI (Cellulose Absorption Index) as a litter factor in ATSAVI, a new VI is developed (L-ATSAVI) and it improves LAI estimation capability by about 10%. Third, vegetation maps are derived from a SPOT 4 image based on the significant relationship between LAI and ATSAVI to aid spatial modeling. Fourth, object-oriented classifier is determined as the best approach, providing ecosystem models with an accurate land cover map. Fifth, the phenology parameters are identified for the study area using 22-year AVHRR data, providing the input variables for spatial modeling. Finally, the performance of popular ecosystem models in simulating grassland vegetation productivity is evaluated using site-based field data, AVHRR NDVI data, and climate data. A new model frame, which integrates remote sensing data with site-based BIOME-BGC model, is developed for the mixed grassland prairie. The developed remote sensing-based process model is able to simulate ecosystem processes at the landscape level and can simulate productivity distribution with 71% accuracy for 2005.

Mixed grasslands in south Canada serve a variety of economic, environmental and ecological purposes. Numerical modeling has become a major method used to identify potential grassland ecosystem responses to environment changes and human activities. In recent years, the focus has been on process models because of their high accuracy and ability to describe the interactions among different environmental components and the ecological processes. At present, two commonly-used process models (CENTURY and BIOME-BGC) have significantly improved our understanding of the possible consequences and responses of terrestrial ecosystems under different environmental conditions. However, problems with these models include only using site-based parameters and adopting different assumptions on interactions between plant, environmental conditions and human activities in simulating such complex phenomenon. In light of this shortfall, the overall objective of this research is to integrate remote sensing products into ecosystem process model in order to simulate productivity for the mixed grassland ecosystem in the landscape level. Data used includes 4-years of field measurements and diverse satellite data (System Pour lObservation de la Terre (SPOT) 4 and 5, Landsat TM and ETM, Advanced Very High Resolution Radiometer (AVHRR) imagery). <p>Using wavelet analyses, the study first detects that the dominant spatial scale is controlled by topography and thus determines that 20-30 m is the optimum resolution to capture the vegetation spatial variation for the study area. Second, the performance of the RDVI (Renormalized Difference Vegetation Index), ATSAVI (Adjusted Transformed Soil-Adjusted Vegetation Index), and MCARI2 (Modified Chlorophyll Absorption Ratio Index 2) are slightly better than the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating CAI (Cellulose Absorption Index) as a litter factor in ATSAVI, a new VI is developed (L-ATSAVI) and it improves LAI estimation capability by about 10%. Third, vegetation maps are derived from a SPOT 4 image based on the significant relationship between LAI and ATSAVI to aid spatial modeling. Fourth, object-oriented classifier is determined as the best approach, providing ecosystem models with an accurate land cover map. Fifth, the phenology parameters are identified for the study area using 22-year AVHRR data, providing the input variables for spatial modeling. Finally, the performance of popular ecosystem models in simulating grassland vegetation productivity is evaluated using site-based field data, AVHRR NDVI data, and climate data. A new model frame, which integrates remote sensing data with site-based BIOME-BGC model, is developed for the mixed grassland prairie. The developed remote sensing-based process model is able to simulate ecosystem processes at the landscape level and can simulate productivity distribution with 71% accuracy for 2005.

Variations in Leaf Area Index (LAI) can greatly alter output values and patterns of various models that deal with energy flux exchange between the land surface and the atmosphere. Customarily, such models are initiated by LAI estimated from satellite-level Vegetation Indices (VIs) including routinely produced Normalized Difference Vegetation Index (NDVI) products. However, the accuracy from LAI-VI relationships greatly varies due to many factors, including temporal and spatial variations in LAI and a selected VI. In addition, NDVI products derived from various sensors have demonstrated variations in a certain degree on describing temporal and spatial variations in LAI, especially in semi-arid areas. This thesis therefore has three objectives: 1) determine a suitable VI for quantifying LAI temporal variation; 2) improve LAI estimation by considering both temporal and spatial variations in LAI; and 3) evaluate routinely produced NDVI products on monitoring temporal and spatial variations in LAI.<p> The study site was set up in conserved semi-arid mixed grassland in St. Denis, Saskatchewan, Canada. One 600 m - long sampling transect was set up across the rolling typography, and six plots with a size of 40 × 40 m each were randomly designed and each was in a relatively homogenous area. Plant Area Index (PAI, which was validated to obtain LAI), ground hyperspectral reflectance, ground covers (grasses, forbs, standing dead, litter, and bare soil), and soil moisture data were collected over the sampling transect and plots from May through September, 2008. Satellite data used are SPOT 4/5 images and 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) 250m, 1km as well as 10-day SPOT-vegetation (SPOT-VGT) NDVI products from May to October, 2007 and 2008. The results show that NDVI is the most suitable VI for quantifying temporal variation of LAI. LAI estimation is much improved by considering both temporal and spatial variations. Based on the ground reflectance data, the r2 value is increased by 0.05, 0.31, and 0.23 and an averaged relative error is decreased by 1.57, 1.62, and 0.67 in the early, maximum, and late growing season, respectively. MODIS 250m NDVI products are the most useful datasets and MODIS 1km NDVI products are superior to SPOT-VGT 1km composites for monitoring intra-annual spatiotemporal variations in LAI. The proposed LAI estimation approach can be used in other studies to obtain more accurate LAI, and thus this research will be beneficial for grassland modeling.

Variations in Leaf Area Index (LAI) can greatly alter output values and patterns of various models that deal with energy flux exchange between the land surface and the atmosphere. Customarily, such models are initiated by LAI estimated from satellite-level Vegetation Indices (VIs) including routinely produced Normalized Difference Vegetation Index (NDVI) products. However, the accuracy from LAI-VI relationships greatly varies due to many factors, including temporal and spatial variations in LAI and a selected VI. In addition, NDVI products derived from various sensors have demonstrated variations in a certain degree on describing temporal and spatial variations in LAI, especially in semi-arid areas. This thesis therefore has three objectives: 1) determine a suitable VI for quantifying LAI temporal variation; 2) improve LAI estimation by considering both temporal and spatial variations in LAI; and 3) evaluate routinely produced NDVI products on monitoring temporal and spatial variations in LAI.<p> The study site was set up in conserved semi-arid mixed grassland in St. Denis, Saskatchewan, Canada. One 600 m - long sampling transect was set up across the rolling typography, and six plots with a size of 40 × 40 m each were randomly designed and each was in a relatively homogenous area. Plant Area Index (PAI, which was validated to obtain LAI), ground hyperspectral reflectance, ground covers (grasses, forbs, standing dead, litter, and bare soil), and soil moisture data were collected over the sampling transect and plots from May through September, 2008. Satellite data used are SPOT 4/5 images and 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) 250m, 1km as well as 10-day SPOT-vegetation (SPOT-VGT) NDVI products from May to October, 2007 and 2008. The results show that NDVI is the most suitable VI for quantifying temporal variation of LAI. LAI estimation is much improved by considering both temporal and spatial variations. Based on the ground reflectance data, the r2 value is increased by 0.05, 0.31, and 0.23 and an averaged relative error is decreased by 1.57, 1.62, and 0.67 in the early, maximum, and late growing season, respectively. MODIS 250m NDVI products are the most useful datasets and MODIS 1km NDVI products are superior to SPOT-VGT 1km composites for monitoring intra-annual spatiotemporal variations in LAI. The proposed LAI estimation approach can be used in other studies to obtain more accurate LAI, and thus this research will be beneficial for grassland modeling.