Permafrost is vulnerable to climate changes and the associated landscape changes that are enhanced by amplification processes and feedbacks unique to the Arctic. Permafrost degradation leads to important changes in terrestrial and aquatic ecosystems, and determining regions that are sensitive to permafrost degradation therefore represents an urgent issue. The Tombstone Territorial Park (TTP) and its surroundings (Ogilvie Mountains, central Yukon) represent one of those sensitive permafrost environment that should be monitored. The central Yukon is an enigmatic permafrost environment that reflects both Pleistocene and Holocene permafrost and climate conditions. The area is particularly intriguing because of the extensive presence of permafrost landforms that are more typical of areas much further north, especially ice-wedge (IW) polygons. It also represents a major transportation corridor linking multiple northern communities (Dempster Highway). Despite access along the road, and signs of permafrost degradation, there have been a limited number of studies addressing permafrost and ground ice conditions throughout the landscape. Consequently, this PhD thesis aims to characterize IW polygons, define the type and magnitude of landscape changes, and model permafrost distribution, conditions and sensitivity to climate changes in the study area. Characterization of IW polygons reveals that they occupy 2.6% of the TTP and preferentially develop in woody sedge peat, glaciofluvial and alluvial deposits along the lower reaches of the Blackstone and East Blackstone rivers on hillslopes of ≤1°. Vegetation type, surface wetness, and polygon spatial pattern are influenced by the development stage of ice-wedge polygons, while the size and angles of polygons seem independent of the development stage. A Landsat-based landscape change analysis of the TTP and surrounding region covering the 1986-2021 period shows that statistically significant spectral changes occurred in 24% of the study area, and most of these changes are associated with vegetation succession and hydrological processes (i.e., erosion and deposition). Other landscape changes included wildfires, slumps, changes to riverbanks and lake shores, earlier melting of icings in the summer, degradation on the peripheries of some ice wedge polygonal terrain, and potential insect damage to forests. The analysis reveals that the extent and magnitude of landscape changes in the study area are influenced by the geomorphic setting, ecological succession and glacial history of the region. Modeling current and future permafrost conditions in the study area using the Northern Ecosystem Soil Temperature (NEST) model indicate that permafrost has persisted through the 20th century and beginning of the 21st century and is currently present throughout the area. Modeled mean permafrost depth (113.8 ± 49.6 m), active layer thickness (2.45 ± 7 m), surface, near-surface, and deep ground temperatures (−1 ± 1.2°C, −1.6 ± 1.2°C, −2.4 ± 1.3°C, respectively) are in the range of other local and regional measurements. Predicted ALTs and permafrost depth show areas of permafrost loss by 2100 (22% of study area under RCP4.5, and 29% under RCP8.5). Permafrost degradation in the study area could greatly impact slope stability and conditions of aquatic systems, and shrubification could contribute to increased degradation.

Ordinary and weighted least squares multiple linear regression techniques were used to derive 720 models predicting Katrina-induced storm damage in cubic foot volume (outside bark) and green weight tons (outside bark). The large number of models was dictated by the use of three damage classes, three product types, and four forest type model strata. These 36 models were then fit and reported across 10 variable sets and variable set combinations for volume and ton units. Along with large model counts, potential independent variables were created using power transforms and interactions. The basis of these variables was field measured plot data, satellite (Landsat TM and ETM+) imagery, and NOAA HWIND wind data variable types. As part of the modeling process, lone variable types as well as two-type and three-type combinations were examined. By deriving models with these varying inputs, model utility is flexible as all independent variable data are not needed in future applications. The large number of potential variables led to the use of forward, sequential, and exhaustive independent variable selection techniques. After variable selection, weighted least squares techniques were often employed using weights of one over the square root of the pre-storm volume or weight of interest. This was generally successful in improving residual variance homogeneity. Finished model fits, as represented by coefficient of determination (R2), surpassed 0.5 in numerous models with values over 0.6 noted in a few cases. Given these models, an analyst is provided with a toolset to aid in risk assessment and disaster recovery should Katrina-like weather events reoccur.

The Mississippi Institute for Forest Inventory (MIFI) is the only cost-effective large-scale forest inventory system in the United States with sufficient precision for producing reliable volume/weight/biomass estimates for small working circle areas (procurement areas). When forest industry is recruited to Mississippi, proposed working circles may overlap existing boundaries of bordering states leaving a gap of inventory information, and a remote sensing-based system for augmenting missing ground inventory data is desirable. The feasibility of obtaining acceptable cubic foot volume estimates from a Landsat-derived volume estimation model (Wilkinson 2011) was assessed by: 1) an initial study to temporally validate Landsat-derived cubic foot volume outside bark to a pulpwood top estimates in comparison with MIFI ground truth inventory plot estimates at two separate time periods, and 2) re-developing a regression model based on remotely sensed imagery in combination with available MIFI plot data. Initial results failed to confirm the relationships shown in past research between radiance values and volume estimation. The complete lack of influence of radiance values in the model led to a re-assessment of volume estimation schemes. Data outlier trimming manipulation was discovered to lead to false relationships with radiance values reported in past research. Two revised volume estimation models using age, average stand height, and trees per-acre and age and height alone as independent variables were found sufficient to explain variation of volume across the image. These results were used to develop a procedure for other remote sensing technologies that could produce data with sufficient precision for volume estimation where inventory data are sparse or non-existent.

The main goal of this research is to investigate vulnerability of levees to future slump slides. In the first part, polarimetric synthetic aperture radar (PolSAR) imagery is used as input in an automated classification system for characterizing areas on the levee having anomalies. In addition, a set of in-situ soil data is collected to provide detailed soil properties over the study area. In-situ soil properties of different classes characterized by the classifier are analyzed to determine how similarities between different areas. The second part, a database including of 34 slump slides that occurred in the lower Mississippi River levee system over a period of two years is used. The impacts of rainfall as well as several spatial geometrical and geomorphological variables (including channel width, river sinuosity index, riverbank erosion, channel shape condition and distance to river) are analyzed and tested for significance and used for developing a logistic regression model.

Cotton (Gossypium hirsutum L.) and corn (Zea mays L.) spectral reflectance holds promise for deriving variable rate N (VRN) treatments calibrated with red-edge inflection (REI) type vegetation indices (VIs). The objectives of this study were to define the relationships between two commercially available sensors and the suitable VIs used to predict N status. Field trials were conducted during the 2012-2013 growing seasons using fixed and variable N rates in cotton ranging from 33.6-134.4 kg N ha-1 and fixed N rates in corn ranging from 0.0 to 268.8 kg N ha-1. Leaf N concentration, SPAD chlorophyll and crop yield were analyzed for their relation to fertilizer N treatment. Sensor effects were significant and red-edge VIs most strongly correlated to N status. A theoretical ENDVI index was derived from the research dataset as an improvement and alternative to the Guyot’s Red Edge Inflection and Simplified Canopy Chlorophyll Content Index (SI).

The discharge of sediment-laden rivers into the Mississippi Sound increases the turbidity of coastal waters. The concentration of suspended particulates is an important parameter in the analysis of coastal water quality factors. The spatiotemporal resolution associated with satellite sensors makes remote sensing an ideal tool to monitor suspended particulate concentrations. Accordingly, the presented research evaluated the validity of published algorithms that relate remote sensing reflectance (Rrs) with suspended particulate matter for the Mississippi Sound. Additionally, regression analysis was used to correlate in situ SPM concentrations with coincident observations of visible and near-infrared band reflectance collected by the MODIS Aqua sensor in order to develop a predictive model for SPM. The most robust algorithm yielded an RMSE of 15.53% (n = 86) in the determination of SPM concentrations. The application of this algorithm allows for the rapid assessment of water quality issues related to elevated SPM concentrations in the Mississippi Sound.

Glaciers are the essential source of fresh water not only to human sustenance, but it is also vital for all lifeforms on earth. Glaciers are also key components in understanding rapid changes in climate. This makes understanding of glacier mass, extent, and overall state essential. In this dissertation, the objective was to analyze the state of snow and ice masses in the mid (California) and low latitude (Chile/Argentina) western American regions using geospatial technology. This study also analyzed the effects of anomalies in snow mass on the regional agricultural practices in California’s Central Valley. In the Southern Andes, the digital elevation models from Shuttle Radar Topographic Mission (SRTM) (the year 2000) were compared with the elevation footprints from the Geoscience Laser Altimeter System (GLAS) campaign for the years 2004 through 2008. Generally, in all sub-regions, the elevation values were lower than the elevation for the year 2000, which demarcates continuous recession of ice mass in the Andean region. Also, this study quantified snow cover extent and mass balance variation in the Sierra Nevada and Mt. Shasta regions in California. To unearth anomalies in snow mass, study used digital elevation models generated from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) between the year 2000 and 2015. A remarkable reduction in snow cover extent of about 80% was observed in the studied watersheds of California. Lastly, the impacts of snow mass anomalies on the total water storage (TWS) and agriculture land cover in the California’s Central Valley were quantified and geo-visualized. The study noticed the change in the land cover area of about 20% (6993 sq.km) due to the alteration of Agriculture land to impervious land covers. Most of the change in the agriculture land cover of about 4402 sq.km occurred in the San Joaquin and Tulare Basins of southern Central Valley region. This dissertation concludes that the increased temperature in the Andes and California has adversely impacted Cryosphere components in the region in the past decade. Besides, it provides valuable insights into the changing state of cryosphere components and highlights impacts of anomalies in TWS on a billion-dollar agricultural industry.

Single-rate blanket application of fertilizer nitrogen (N) can result in an over or under application. Variable rate (VR) applications tailored to actual crop demands can improve N use efficiency in spatially variable soils. The objective of this study was to compare and improve relationships derived between corn canopy reflectance and corn N status acquired with two sensing platforms. Four fertilizer N rates were used, from deficient to excess, to create varying corn N status. Sensing and biophysical sampling was taken throughout the season for analysis and comparison to calculated vegetation indices (VIs). Results show strong relationships between VIs and corn N concentration and the strongest found using combined indices that incorporate the red-edge wavelength (720 nm). Relationships strengthened at latter growth stages. Results and models from this study demonstrate the utility of using remote sensing technologies to more accurately predict corn N status for eventual use in VR prescription development.

The Mississippi and Alabama barrier islands are sensitive landforms that are affected by hurricanes, longshore currents, and available sediment, yet these effects are difficult to quantify with traditional ground-based surveying. In this study, Landsat satellite imagery was used to evaluate changes in barrier island area and centroid position from 1990 and 2005. When hurricanes are infrequent (1999?2003), barrier islands generally increased in total area and showed only moderate repositioning of their centroid locations. However, when hurricanes were frequent (1994?1999 and 2004?2005), barrier islands showed substantial decreases in area and dramatic repositioning of their island centroid locations. This was especially true following Hurricane Katrina (2005). From 1990 to 2005, the general movement of barrier islands was westerly and most islands experienced an overall reduction in area (-18%). The results of this research are similar to findings reported in the literature and illustrate the suitability of using Landsat imagery to study geomorphic changes.

No description available.