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Monitoring biological heterogeneity in a northern mixed prairie using hierarchical remote sensing methods



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Heterogeneity, the degree of dissimilarity, is one of the most important and widely applicable concepts in ecology. It is highly related to ecosystem conditions and features wildlife habitat. Grasslands have been described as inherently heterogeneous because their composition and productivity are highly variable across multiple scales. Therefore, biological heterogeneity can be an indicator of ecosystem health. The mixed prairie in Canada, characterized by its semiarid environment, sparse canopy, and plant litter, offers a challenging region for environmental research using remote sensing techniques. This thesis dwells with the plant canopy heterogeneity of the mixed prairie ecosystem in the Grasslands National Park (GNP) and surrounding pastures by combining field biological parameters (e.g., grass cover, leaf area index, and biomass), field collected hyperspectral data, and hierarchical resolution satellite imagery. The thesis scrutinized four aspects of heterogeneity study: the importance of scale in grassland research, relationships between biological parameters and remotely collected data, methodology of measuring biological heterogeneity, and the influence of climatic variation on grasslands biological heterogeneity. First, the importance of scale is examined by applying the semivariogram analysis on field collected hyperspectral and biophysical data. Results indicate that 15 - 20 m should be the appropriate resolution when variations of biological parameters and canopy reflectance are sampled. Therefore, it is reasonable to use RADARSAT-1, Landsat TM, and SPOT images, whose resolutions are around 20 m, to assess the variation of biological heterogeneity. Second, the efficiency of vegetation indices derived from SPOT 4 and Landsat 5 TM images in monitoring the northern mixed prairie health was examined using Pearson’s correlation and stepwise regression analyses. Results show that the spectral curve of the grass canopy is similar to that of the bare soil with lower reflectance at each band. Therefore, vegetation indices are not necessarily better than reflectance at green and red wavelength regions in extracting biological information. Two new indices, combining reflectance from red and mid infrared wavelength regions, are proposed to measure biological parameters in the northern mixed prairie. Third, texture analysis was applied to quantify the biological variation in the grasslands. The textural parameters of RADARSAT imagery correlated highly with standard deviation of the field collected canopy parameters. Therefore, textural parameters can be applied to study the variations within the mixed prairie. Finally, the impacts of climatic variation on grassland heterogeneity at a long time scale were evaluated using Advanced Very High Resolution Radiometer (AVHRR) , Normalized Difference Vegetation Index (NDVI), Maximum Value Composite (MVC), and SPOT Vegetation NDVI MVC imagery from 1993 to 2004. A drought index based on precipitation data was used to represent soil moisture for the study area. It was found that changes of temperature and precipitation explain about 50% of the variation in AVHRR NDVI (i.e., temporal heterogeneity) of the northern mixed prairie. Trend line analysis indicates that the removal of grazing cattle carry multiple influences such as decreasing NDVI in some parts of the upland and valley grassland and increasing NDVI in the valley grassland. Results from this thesis are relevant for park management by adjusting grassland management strategies and monitoring the changes in community sizes. The other output of the thesis is furthering the remote sensing investigation of the mixed prairie based on information of the most appropriate resolution imagery.



Northern mixed prairie, Remote sensing, Heterogeneity



Doctor of Philosophy (Ph.D.)






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