<p>Eutrophication, which is the most important degradation in water bodies, has been traced to the imposed loading of nutrients. Of interest is the fact that the process is often accompanied with undesirable effects, one of which is primarily the increased algae production at the surface and accumulation of biomass at the bottom and the secondary responses, which include a., change in species composition b. change in the biogeochemical cycle c. shift in the seasonal pattern and magnitude variability.</p><p>The biogeochemical cycle in response to hydrodynamic alterations may occur internally; however external loading often fosters the process over large spatial scales. In the quest of validating the above statement, we hypothesized that there is no difference in the mean concentration of nutrients in the Gulf of Bothnia from the overall mean concentration.</p><p>The analysis was done with a probability mapping method, in which all stations were grouped into a lattice. The cells are constructed using a grid system, i.e. x and y axis (longitude and latitude). Basically the method statistically tested for variables deviating from the over mean concentration. The variables analyzed are DIN, DSi, DIP and DIN: DSi.</p><p>Results of the analysis showed significant spatial variations in the nutrient distribution in the Gulf of Bothnia; such differences were observed in the coastal to the deep zones of the Gulf.</p>

Eutrophication, which is the most important degradation in water bodies, has been traced to the imposed loading of nutrients. Of interest is the fact that the process is often accompanied with undesirable effects, one of which is primarily the increased algae production at the surface and accumulation of biomass at the bottom and the secondary responses, which include a., change in species composition b. change in the biogeochemical cycle c. shift in the seasonal pattern and magnitude variability. The biogeochemical cycle in response to hydrodynamic alterations may occur internally; however external loading often fosters the process over large spatial scales. In the quest of validating the above statement, we hypothesized that there is no difference in the mean concentration of nutrients in the Gulf of Bothnia from the overall mean concentration. The analysis was done with a probability mapping method, in which all stations were grouped into a lattice. The cells are constructed using a grid system, i.e. x and y axis (longitude and latitude). Basically the method statistically tested for variables deviating from the over mean concentration. The variables analyzed are DIN, DSi, DIP and DIN: DSi. Results of the analysis showed significant spatial variations in the nutrient distribution in the Gulf of Bothnia; such differences were observed in the coastal to the deep zones of the Gulf.

Remotely sensed imagery is one of the most important data sources for large-scale and multi-temporal agricultural, forestry, soil, environmental, social and economic applications. In order to accurately extract useful thematic information of the earth surface from images, various techniques and methods have been developed. The methods can be divided into parametric and non-parametric based on the requirement of data distribution, or into global and local based on the characteristics of modeling global trends and local variability, or into unsupervised and supervised based on whether training data are required, and into design-based and model-based in terms of the theory based on which the estimators are developed. The methods have their own disadvantages that impede the improvement of estimation accuracy. Thus, developing novel methods and improving the existing methods are needed. This dissertation focused on the development of a feature-space indicator simulation (FSIS), the improvement of geographically weighted sigmoidal simulation (GWSS) and k-nearest neighbors (kNN), and their assessment of land use and land cover (LULC) classification and probability (fraction) mapping of percentage vegetation cover (PVC) in Duolun County, Xilingol League, Inner Mongolia, China. The FSIS employs an indicator simulation in a high-dimensional feature space and expends derivation of indicator variograms from geographic space to feature space that leads to feature space indicator variograms (FSIV), to circumvent the issues existing in traditional indicator simulation in geostatistics. The GWSS is a stochastic and probability mapping method and considers a spatially nonstationary sample data and the local variation of an interest variable. The improved kNN, called Optimal k-nearest neighbors (OkNN), searches for an optimal number of nearest neighbors at each location based on local variability, and can be used for both classification and probability mapping. Three methods were validated and compared with several widely used approaches for LULC classification and PVC mapping in the study area. The datasets used in the study included a Landsat 8 image and a total of 920 field plots. The results obtained showed that 1) Compared with maximum likelihood classification (ML), support vector machine (SVM) and random forest (RF), the proposed FSIS classifier led to statistically significantly higher classification accuracy of six LULC types (water, agricultural land, grassland, bare soil, built-up area, and forested area); 2) Compared with linear regression (LR), polynomial regression (PR), sigmoidal regression (SR), geographically weighted regression (GWR), and geographically weighted polynomial regression (GWPR), GWSS did not only resulted in more accurate estimates of PVC, but also greatly reduced the underestimations and overestimation of PVC for the small and large values respectively; 3) Most of the red and near infrared bands relevant vegetation indices had significant contributions to improving the accuracy of mapping PVC; 4) OkNN resulted in spatially variable and optimized k values and higher prediction accuracy of PVC than the global methods; and 5) The range parameter of FSIVs was the major factor that spatially affected the classification accuracy of LULC types, but the FSIVs were less sensitive to the number of training samples. Thus, the results answered all six research questions proposed.

Impacts of humans on ecosystems in western United States have necessitated ecological restoration, which includes the development of native seed that can be used for revegetation efforts. Development of such seed sources are costly and time consuming. This study describes the use of non-parametric multiplicative regression analysis (NPMR) to develop a predictive model for occurrence of sulfur-flower buckwheat (Eriogonum umbellatum Torrey) population seed collection. This perennial forb species is of interest for seed source development in the western United States. Presence and absence data for E. umbellatum was taken from the Utah Division of Wildlife Resources Big Game Range Trend project as well as herbarium specimens across Utah, U.S.A. NPMR, a statistical niche modeling system that selects the best predictor variables and develops probability of occurrence estimates multiplicatively, was used to select predictor variables from spatially explicit data made available in a Geographic Information System (GIS). Two models were created using NPMR, one with a suggested default minimum average neighborhood size and the other with a less-restricted minimum average neighborhood size. GIS maps of models were created, artificially classified into low, medium, and high probability areas, and validated in the field in Tooele County, Utah. Of 68 possible physiographic, climatic, and soil variables provided for analysis, NPMR selected 4 variables for the default minimum average neighborhood model and 10 variables for the less restricted neighborhood model. The default model had a higher descriptive statistic (log β value) and mapped a larger area than the less restrictive neighborhood model. When increased minimum neighborhood sizes were selected during the development of the probability maps, the resulting areas of probability prediction decreased. The presence rates of E. umbellatum in field-validated test sites were 7.4%, 12.0%, and 28.6% for the low, medium, and high probability sites, respectively. Although presence rates of field validated data were lower than the predicted probability ranges for those same sites, presence rates increased with increased probability ranges. Using the generated model can reduce the cost and time necessary to locate plants compared to searching for species populations using an undirected approach.