Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Spatial patterns in soil depth and implications for offseason nitrogen dynamics in dryland wheat systems of central Montana(Montana State University - Bozeman, College of Agriculture, 2022) Fordyce, Simon Isaac; Co-chairs, Graduate Committee: Clain Jones and Craig Carr; Pat Carr, Clain Jones, Jed Eberly, Scott Powell, Adam Sigler and Stephanie Ewing were co-authors of the article, 'Exploring relationships between soil depth and multi-temporal spectral reflectance in a semi-arid agroecosystem: effects of spatial and temporal resolution' submitted to the journal 'Remote Sensing of environment' which is contained within this thesis.; Pat Carr, Clain Jones, Jed Eberly, Rob Payn, Adam Sigler and Stephanie Ewing were co-authors of the article, 'Spatiotemporal patterns of nitrogen mineralization in a dryland wheat system' submitted to the journal 'Agriculture, ecosystems, and environment' which is contained within this thesis.Shallow soils (< 50 cm) under dryland wheat (Triticum aestivum L.) production lose large amounts of inorganic nitrogen (N) to leaching. Crops grown in shallow soils may be more responsive to N fertilizer due to lower fertilizer recovery and suppressed mineralization, raising questions as to whether standard practices of N fertilizer rate determination can increase risks of leaching and groundwater contamination in these environments. Mineralized N can be a major nutritional supplement for wheat crops in dryland agroecosystems, so accurate estimates of mineralization inputs can have important economic and environmental implications. To assess the potential for suppressed N mineralization in shallow soils, we used spectral reflectance from up to three sensors (unmanned aerial vehicle, National Agricultural Imagery Program, and Sentinel 2) to spatially characterize soil depth on three fields in Central Montana (Chapter 2) and compared surface (0-20 cm) carbon and N cycling indices across soil depth classes (Chapter 3). Carbon dynamics were stable across depth classes while N mineralization was lower in the shallow class. Results confirm multispectral imagery as a valuable tool for non-destructively characterizing fine-scale spatial patterns in soil depth and corroborate previous findings of lower N mineralization in shallow soil environments. Given the potential for heightened fertilizer responsiveness due to lower mineralization in these environments, decision support systems for site-specific fertility management (e.g., variable rate fertilizer application) should assess the environmental consequences of leaching alongside the economic benefits of applied fertilizer rates which maximize responses of yield, quality and same-year net revenue.Item Remote sensing for wetland restoration analysis: Napa-Sonoma Marsh as case study(Montana State University - Bozeman, College of Agriculture, 2019) Bryne, Charles; Chairperson, Graduate Committee: Scott PowellHuman-caused ecosystem change and habitat loss is a major worldwide concern. Wetland loss has been remarkable worldwide and in the US. In the San Francisco Bay system, the largest estuary on the eastern rim of the Pacific Ocean and a biodiversity hotspot, more than 90 percent of the wetlands have been lost to urban development, salt production and agriculture, a loss that primarily occurred in the century following 1850. Restoration is our primary mechanism for confronting this challenge. While wetland restoration design has advanced dramatically since the early designs of the 1980s, restoration analysis and evaluation remain challenges that until now have wholly or primarily required on-site sampling. This is a major challenge for larger restoration projects, such as the Napa- Sonoma Salt Marsh restoration in California. Previous studies have indicated that the Normalized Difference Vegetation Index (NDVI) has been used in some restoration analyses with apparent success, but data is limited. To better understand its potential, this study examines issues in restoration analysis in the context of wetland restorations. By comparing pre- and post-restoration remote sensing data, I found that two sites in the Napa-Sonoma Marsh restoration demonstrated mixed NDVI results and that changes depended on subarea and whether median or maximum NDVI was analyzed. The mixed results are explained by several factors: the inherent limitations of NDVI; the large restoration size; the fact that wetlands, less vegetated, present special challenges for analysis; and the fact that it is early in the post-restoration period. The case study supports the use of remote sensing and GIS for restoration analysis and evaluation, but also emphasizes their current limitations. Many of these limitations, which hinge on the complexity of the potential data involved, are likely to be addressed in the next generation as the relevant technology continues to develop.Item Spatiotemporal mapping of mountain pine beetle infestation severity and probability of new infestation in the central U.S. Rocky Mountains(Montana State University - Bozeman, College of Agriculture, 2018) Bode, Emma Taylor; Chairperson, Graduate Committee: Rick L. LawrenceSynchronous, widespread, and severe mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks impacted forests of western North America at unprecedented levels in recent decades. Severe MPB epidemics can degrade ecosystem services and socio-economic assets. Mapping outbreak progression informs mitigation efforts and enables analysis of MPB attack processes on a landscape scale. Existing time-series methods for mapping disturbance focus on extent rather than severity. Infestation severity, expressed as within-pixel mortality percentage, is more robust for answering a variety of ecological questions. Our objectives were to: (1) map infestation severity from 2005-2015 using a time-series regression; and (2) analyze MPB attack processes by modeling new infestation probability using spatial and environmental variables in the central U.S. Rocky Mountains. We used spectral data from all available Landsat images, topographic data, and data from U.S. Forest Service aerial detection survey (ADS) polygons to model infestation severity. We collected reference data by interpreting National Agricultural Imagery Program images. We then employed logistic regression model-based recursive partitioning (MOB) to determine: (a) to what degree nearby infestation severity increased probability of new infestation; (b) the degree of variation in probability across space and time with respect to other spatial and environmental risk factors; and (c) the extent to which these effects were directional relative to prevailing winds. Validation of our infestation severity model against a randomly selected subset of the data resulted in no statistical difference between predicted and observed severity. Our raster maps allowed us to identify lower severity infestation not recorded by the ADS. The final MOB model obtained 72.1% accuracy in predicting new infestation. Nearby infestation severity strongly influenced the probability of new infestations. This effect varied with elevation, aspect, temperature, phase of the outbreak, and spatial location. Variation in probability of infestation was highest when surrounding infestation severity was low. Use of wind-informed directional effects did not improve the model. This analysis establishes the efficacy of mapping an infestation severity time series and demonstrates that severity maps facilitate novel analyses of MPB attack processes. The processes developed here can support management decisions with timely maps of MPB infestation severity and probability of new infestation.Item Effect of spectral band selection and bandwidth on weed detection in agricultural fields using hyperspectral remote sensing(Montana State University - Bozeman, College of Agriculture, 2017) Tittle, Samuel Bryant; Chairperson, Graduate Committee: Rick L. LawrencePresence of weeds in agricultural fields affects farmers' economic returns by increasing herbicide input. Application of herbicides traditionally consists of uniform application across fields, even though weed locations can be spatially variable within a field. The concept of spot spraying seeks to reduce farmers' costs and chemical inputs to the environment by only applying herbicides to infested areas. Current spot spraying technology relies on broad spectral bands with limited ability to differentiate weed species from crops. Hyperspectral remote sensing (many narrow, contiguous spectral bands) has been shown in previous research to successfully distinguish weeds from other vegetation. Hyperspectral sensor technology, however, might not currently be practical for on-tractor applications. The research objectives were to determine (1) the utility of using a limited number of narrow spectral bands as compared to a full set of hyperspectral bands and (2) the relative accuracy of narrow spectral bands compared to wider spectral bands. Answers to these objectives have the potential for improving on-tractor weed detection sensors. Reference data was provided by field observations of 224 weed infested and 304 uninfested locations within two winter wheat fields in Gallatin County, Montana, USA. Airborne hyperspectral data collected concurrently with the reference data provided 6-nm spectral bands that were used in varying combinations and artificially widened to address the research objectives. Band selection was compared using Euclidean, divergence, transformed divergence, and Jefferies-Matusita signature separability measures. Certain three and four narrow band combinations produced accuracies with no statistical difference from the full set of hyperspectral bands (based on kappa statistic analysis, alpha = 0.05). Bands that were artificially widened to 96 nm also showed no statistically significant difference from the use of 6-nm bands for both all bands and select band combinations. Results indicate the potential for bands that can differentiate weed species from crops and that the narrowest spectral bands available might not be necessary for accurate classification. Further research is needed to determine the robustness of this analysis, including whether a single set of spectral bands can be used effectively across multiple crop/weed systems, or whether band selection is site or system specific.Item Scaling and uncertainty in landsat remote sensing of biophysical attributes(Montana State University - Bozeman, College of Agriculture, 2015) Johnson, Aiden Vincent; Chairperson, Graduate Committee: Scott Powell; Paul Stoy, Nathaniel Brunsell, Stephanie Ewing, Scott Powell and Mark Greenwood were co-authors of the article, 'Change detection in the discontinuous permafrost zone using landsat: have surface features prone to pronounced methane efflux increased in spatial extent?' submitted to the journal 'Remote sensing' which is contained within this thesis.; Paul Stoy , Lucy Marshall and Joel McCorkel were co-authors of the article, 'Random uncertainty in land surface temperature calculated using landsat TM, ETM+, and TIRS' submitted to the journal 'Ecological applications' which is contained within this thesis.Monitoring environmental change is of high importance in our time of global change. Remote sensing technology provides the tools to view the ecological dynamics at a landscape scale and review the change through time with time series data availability. Creating congruence between data scales and functional scales is a long standing challenge for Earth system scientists. In this research we evaluate methods for change detection and scaling data in a discontinuous permafrost zone of central Alaska and is characterized by pronounced permafrost thaw and methane release over decadal to century timescales. The primary goal is to evaluate the applicability of Landsat satellite remote sensing for detecting bog thermal expansion over time. We implement a Random Forests classification scheme in order to separate the landscape into its various land features and bog types, many features in this landscape are developed through an aged-stage transition of thermal expansion. The results of this classification were dominated by hydrologic features, with a 0.05 increase in mean albedo, providing essentially no change in both mean Normalized Difference Vegetation Index (NDVI) and mean Brightness Temperature (BT). In addition, we attempt to capture the scales of variation within the landscape using multi-resolution methods. The scale of variance as illustrated by a wavelet analysis for NDVI show the greatest amount of variance around 4.5 km to 5 km. Brightness Temperature had three peaks of high variance between 0.06 km - 1 km including maximum variance at about 0.5 km and a pair of peaks between 3 km and 4 km. An important component of any data analysis is quantifying the uncertainty. Uncertainty quantification in remote sensing data analysis is often over looked. In a second analysis we attempt to quantify the primary sources of uncertainty in Landsat remote sensing data via simulation methods. Specifically, we evaluate the level of uncertainty contributed to the data by applying a typical atmospheric correction through Monte Carlo simulation approach to estimate the total variance within several Landsat scenes. We find the contribution of uncertainty due to the MODTRAN conversion to be between 7-27% differing by total scene variance per image.Item Wheat yield estimates using multi-temporal AVHRR-NDVI satellite imagery(Montana State University - Bozeman, College of Agriculture, 1999) Henry, Mari PatriciaItem Accuracy of saline seep mapping from color infrared aerial photographs(Montana State University - Bozeman, College of Agriculture, 1990) Beyrau, John ArthurItem Detecting and mapping leafy spurge (Euphorbia esula) and spotted knapweed (Centaurea maculosa) in rangeland ecosystems using airborne digital imagery(Montana State University - Bozeman, College of Agriculture, 2002) Driscoll, Shana GraceItem Near real-time satellite and ground based radiometric estimation of vegetation biomass, and nitrogen content in Montana rangelands(Montana State University - Bozeman, College of Agriculture, 1998) Thoma, David P.Item Characterizing rangeland using multispectral remotely sensed data and multi-scale ecological units(Montana State University - Bozeman, College of Agriculture, 2003) Maynard, Catherine Cae Lee; Chairperson, Graduate Committee: Rick Lawrence.