Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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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 Assessing changes in spatial and temporal patterns of cropping sequences in northeast Montana(Montana State University - Bozeman, College of Agriculture, 2014) Long, John Allen; Chairperson, Graduate Committee: Rick L. Lawrence; Rick L. Lawrence, Perry R. Miller, Mark C. Greenwood and Lucy A. Marshall were co-authors of the article, 'Object-oriented crop classification using multitemporal ETM+ SLC-OFF imagery and random forest' in the journal 'GIScience and remote sensing ' which is contained within this thesis.; Rick L. Lawrence, Perry R. Miller, and Lucy A. Marshall were co-authors of the article, 'Changes in field-level cropping sequences: indicators of shifting agricultural practices' in the journal 'Agriculture, ecosystems & environment' which is contained within this thesis.; Rick L. Lawrence, Perry R. Miller, Mark C. Greenwood, and Lucy A. Marshall were co-authors of the article, 'Adoption of cropping sequences in northeast Montana: a spatio-temporal analysis' submitted to the journal 'Agriculture, ecosystems & environment' which is contained within this thesis.Initiatives to mitigate the effects of climate change have focused largely on the reduction of greenhouse gas production and on carbon capture and storage technologies. Changes in agricultural management practices have shown the ability to sequester carbon by increasing soil organic carbon and include reduced tillage intensity, decreased fallow, and changing from monoculture to rotational cropping. All have become more common in portions of the Northern Great Plains; but, despite the growth of these practices, it is unknown to what extent farmers have adopted particular cropping sequences or how they have spread temporally or spatially. My purpose here was to investigate the patterns of changing agricultural practices in northeast Montana during 2001-2012 by focusing on the increased adoption of cereal-pulse sequences and the adoption of block-managed cereal-based sequences in lieu of continuous strip-cropping. A method to identify crops via geospatial data and Landsat imagery was developed, and annual crop maps were created. Crop classifications were extracted from the maps for each field to create a 12-character string for the temporal sequence of crops, and specific 2- and 3-yr sequences were identified with a string-matching algorithm. Finally, I examined the observed spatial patterns of sequence adoption to determine if observed spatial patterns were random or were they consistent with the spread and adoption due to social interaction as described in innovation diffusion theory, adoption based on environmental factors, or neither. The major findings were: (1) cereal-fallow rotations, whether managed in blocks or by strip-cropping, no longer dominate the region; (2) there has been a substantial increase in the adoption of cereal-pulse sequences; (3) producers did not adhere strongly to specific sequences; (4) using 3-yr sequences added no additional information than 2-yr sequences; (5) the adoption of these practices was not randomly located but clustered; and (6) the adoption of these practice are not well-explained by innovation diffusion theory, although social interactions might have played a role in the early stages; the patterns are more consistent with suitability of the physical environment since available water was strongly associated with whether or not a field was managed with either practice.Item Mapping and change detection of wetland and riparian ecosystems in the Gallatin Valley, Montana using landsat imagery(Montana State University - Bozeman, College of Agriculture, 2004) Baker, Corey Ryan; Chairperson, Graduate Committee: Rick Lawrence.The location and distribution of wetlands and riparian zones influences the ecological functions present on a landscape. Accurate and easily reproducible landcover maps enable monitoring of land management decisions and ultimately a greater understanding of landscape ecology. Multi-season Landsat ETM+ imagery from 2001 combined with ancillary topographic and soils data was used to map wetland and riparian systems in the Gallatin Valley of Southwest Montana. Classification Tree Analysis (CTA) and Stochastic Gradient Boosting (SGB) decision-tree based classification algorithms were used to distinguish wetlands and riparian areas from the rest of the landscape. CTA creates a single classification tree using a one-step-look-ahead procedure to reduce variance. SGB utilized classification errors to refine tree development and incorporated the results of multiple trees into a single best classification. The SGB classification (86.0% overall accuracy) was more effective than CTA (61.7% overall accuracy) at detecting a variety of wetlands and riparian zones present on this landscape. A change detection analysis was performed for the years 1988 and 2001. The change detection used Landsat-based Tasseled Cap (TC) components and change vector analysis (CVA) to identify locations of wetland/riparian gain or loss in the 13-year period. CVA of TC brightness, greenness, and wetness components reduces the compound errors of multi-date classifications by using a threshold value to separate land cover change from spectral variability between 1988 and 2001 imagery. Only the highly changed pixels were classified using 1988 Landsat imagery and ancillary data. These change pixels were then merged with the 2001 classified image to develop a wetland/riparian map for 1988. The high overall accuracy of the 1988 classification (81%) developed with this procedure showed the benefits of this technique for mapping historical landcover features. Comparison of the 1988 and 2001 classifications identified locations where wetlands/riparian areas increased, decreased, or remained stable between these years. TC based CVA had an overall change detection accuracy of 75.8% and was able to identify areas of isolated and contiguous wetland/riparian change.Item Satellite monitoring of cropland-related carbon sequestration practices in North Central Montana(Montana State University - Bozeman, College of Agriculture, 2008) Watts, Jennifer Dawn; Chairperson, Graduate Committee: Rick L. Lawrence.This study used an object-oriented approach in conjunction with the Random Forest algorithm to classify agricultural practices set forth in carbon contract agreements associated with the Chicago Climate Exchange (CCX), including tillage (till or no-till (NT)), conservation reserve (CR), and crop intensity. The object-oriented approach allowed for per-field classifications and the incorporation of contextual elements in addition to spectral features. Random Forest is an advanced classification method that avoids data over-fitting and incorporates an internal classification accuracy assessment. Landsat satellite imagery was chosen for its continuous coverage, cost effectiveness, and image accessibility. Classification (2007) results included producer's accuracies of 91% for NT and 31% for tillage when applying Random Forest to image-objects generated from a May Landsat image. Low classification accuracies likely were attributed to the misclassification of conservation-based tillage practices as NT. Crop and CR lands resulted in producer's accuracies of 100% and 90%, respectively. Crop and fallow producer's accuracies were 95% and 82% in the 2007 classification; misclassification within the fallow class was attributed to pixel-mixing problems in areas of narrow (>100 m) strip management. A between-date normalized difference vegetation index approach was successfully used to detect areas "changed" in vegetation status between the 2007 and prior image dates; classified "changed" objects were then merged with "unchanged" objects to produce final classification maps of crop versus fallow. Resulting statistics showed that 22% of lands classified as CR had occurred outside of the Conservation Reserve Program (CRP). Field survey results were applied for tillage analysis because of low image classification rates and indicated that 56% of the evaluated region was under NT in 2007, with 44% practicing some form of tillage. Crop intensity estimates indicated that only 5% was under continuous cropping. These observations show the potential for the increased NT and continuous cropping. The application of carbon sequestration estimates to the land use data predict that approximately 59,497 t C yr⁻¹ might be sequestered through the universal adoption of NT and a 1.0 rotation (continuous cropping). Financial incentives through carbon credit programs might motivate land managers to make these management changes and to maintain CR lands.