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No description available.

At present one of the challenges of soil erosion research in South Africa is the limited information on the location of gullies. This is because traditional techniques for mapping erosion which consists of the manual digitization of gullies from air photos or satellite imagery, is limited to expert knowledge and is very time consuming and costly at a regional scale (50-10000km²). Developing a robust, reliable and accurate means of mapping gullies is a current focus for the Institute for Soil, Climate and Water Conservation (ISCW) of the Agricultural Research Council (ARC) of South Africa. The following thesis attempted to answer the question whether “medium resolution multi-spectral satellite observations, such as Landsat TM, combined with information extraction techniques, such as Vegetation Indices and multispectral classification algorithms, can provide a semi-automatic method of mapping gullies and to what level of accuracy?”. More specifically, this thesis investigated the utility of three Landsat TM-derived Vegetation Index (VI) techniques and three classification techniques based on their level of accuracy compared to traditional gully mapping methods applied to SPOT 5 panchromatic imagery at selected scales. The chosen study area was located in the province of KwaZulu-Natal (KZN) South Africa, which is considered to be the province most vulnerable to considerable levels of water erosion, mainly gully erosion. Analysis of the vegetation indices found that Normalized Difference Vegetation Index (NDVI) produced the highest accuracy for mapping gullies at the sub-catchment level while Transformed Soil Adjusted Vegetation Index (TSAVI) was successful at mapping gullies at the continuous gully level. Mapping of gullies using classification algorithms highlighted the spectral complexity of gullies and the challenges faced when trying to identify them from the surrounding areas. The Support Vector Machine (SVM) classification algorithm produced the highest accuracy for mapping gullies in all the tested scales and was the recommended approach to gully mapping using remote sensing

At present one of the challenges of soil erosion research in South Africa is the limited information on the location of gullies. This is because traditional techniques for mapping erosion which consists of the manual digitization of gullies from air photos or satellite imagery, is limited to expert knowledge and is very time consuming and costly at a regional scale (50-10000km²). Developing a robust, reliable and accurate means of mapping gullies is a current focus for the Institute for Soil, Climate and Water Conservation (ISCW) of the Agricultural Research Council (ARC) of South Africa. The following thesis attempted to answer the question whether “medium resolution multi-spectral satellite observations, such as Landsat TM, combined with information extraction techniques, such as Vegetation Indices and multispectral classification algorithms, can provide a semi-automatic method of mapping gullies and to what level of accuracy?”. More specifically, this thesis investigated the utility of three Landsat TM-derived Vegetation Index (VI) techniques and three classification techniques based on their level of accuracy compared to traditional gully mapping methods applied to SPOT 5 panchromatic imagery at selected scales. The chosen study area was located in the province of KwaZulu-Natal (KZN) South Africa, which is considered to be the province most vulnerable to considerable levels of water erosion, mainly gully erosion. Analysis of the vegetation indices found that Normalized Difference Vegetation Index (NDVI) produced the highest accuracy for mapping gullies at the sub-catchment level while Transformed Soil Adjusted Vegetation Index (TSAVI) was successful at mapping gullies at the continuous gully level. Mapping of gullies using classification algorithms highlighted the spectral complexity of gullies and the challenges faced when trying to identify them from the surrounding areas. The Support Vector Machine (SVM) classification algorithm produced the highest accuracy for mapping gullies in all the tested scales and was the recommended approach to gully mapping using remote sensing

>Magister Scientiae - MSc / Gully erosion and headcut migration has been at the crux of wetland degradation in South Africa. The resulting erosion and draining effect has seen more than 50% of wetlands in the country degraded. This study investigated the degradation of indigenous Palmiet, peat forming, wetlands through headcut erosion. This was done by exploring the relationship between headcut migration rate and morphodynamic characteristics through the use of multiple regression analysis. Wetlands investigated in this study occurred in the Kromme River catchment and Nuwejaars River catchment, in the Eastern Cape and Western Cape respectively. Morphodynamic characteristics assessed include headcut dimensions, gully characteristics, soil characteristics and drainage basin characteristics. These parameters were determined either through infield assessment, image analysis or laboratory analysis. Three headcut migration rate types were calculated through a combination of infield measurements and image analysis techniques executed within ArcGIS. These migration rate types include apex advancement (m/a), gully expansion (m2/a) and volume erosion (m3/a). Statistical analysis revealed significant relationships between morphodynamic characteristics and both volume erosion and gully expansion. Morphodynamic characteristics such as drop height, apex width, gully width, drainage rate and sand content were found to have a direct relationship with migration rates, whereas characteristics such as average drainage basin slope, clay content, silt content, SOM content and soil saturation were found to have an indirect relationship with headcut migration rates. Results provide insight into the headcut migration process, its influencing factors and the potential for headcut migration rate prediction. An evaluation of these results using WET-Health found that the wetland management tool captures wetland geomorphic controls to an accuracy of 68% and 70%. Furthermore, the influence of morphodynamic characteristics on migration rates contributes to the wetland rehabilitation process as it allows for the identification of headcut sites most susceptible to erosion. This will then allow for timely wetland rehabilitation, decreasing the rate of net wetland degradation and improving the management and efficiency of wetland restoration.

The upper Colorado River drainage system yields approximately 104,000 acre-feet of silt annually to the Colorado River. In an attempt to reduce the silt load, federal land management agencies have installed numerous land surface treatments. A study was conducted to measure the economic benefits of the land treatments near Cisco, Utah, and to compare them to the treatment costs and to develop predictive criteria for estimating the optimum intensity of treatment. The economic evaluation was done in a benefit-cost framework and the criteria for estimating optimum intensity of treatment was done in a production-function framework. The land treatments were found to be effective in retaining silt, but treatment apparently resulted in decreased livestock carrying capacity. Over-all, the land treatments were found to be uneconomical.

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Master of Science / Department of Biological & Agricultural Engineering / Stacy L. Hutchinson / Gully erosion is a serious problem on military training lands resulting in not only soil erosion and environmental degradation, but also increased soldier injuries and equipment damage. Assessment of gully erosion occurring on Fort Riley was conducted in order to evaluate different gully location methods and to develop a gully prediction model based on logistic regression. Of the 360 sites visited, fifty two gullies were identified with the majority found using LiDAR based data. Logistic regression model was developed using topographic, landuse/landcover, and soil variables. Tests for multicollinearity were used to reduce the input variables such that each model input had a unique effect on the model output. The logistic regression determined that available water content was one of the most important factors affecting the formation of gullies. Additional important factors included particle size classification, runoff class, erosion class, and drainage class. Of the 1577 watersheds evaluated for the Fort Riley area, 192 watersheds were predicted to have gullies. Model accuracy was approximately 79% with an error of omission or false positive value of 10% and an error of commission or false negative value of 11%; which is a large improvement compared to previous methods used to locate gully erosion.

Master of Science / Department of Biological and Agricultural Engineering / Stacy Hutchinson / Military maneuvers result in significant physical and environmental impacts to the landscape. These impacts generally result in a loss of vegetative cover and increased watershed runoff and rate depending on vehicle speed, turning radius, and soil moisture content. Unless adequately monitored or mitigated, this increased runoff can lead to excessive soil erosion and gully formation. Past studies have revealed that these gullies can impact water quality from excessive erosion and create concerns regarding soldier safety. In order to better understand how gullies form and evolve overtime on military installations, a study is being conducted at Fort Riley, KS. In 2010, approximately forty gullies were identified, assessed, and measured using common erosion monitoring and surveying techniques. These gully locations, and any newly formed gullies, were remeasured using these same methods in 2012 to determine the rate of growth for each site with respect to width, depth, and headcut. Of fifty-nine gullies total, twenty one were initially included in this study. Upon further analysis including the utilization of watershed characteristics and land management techniques, eleven of the 21 utilized gullies were deemed appropriate to include in predictive assessment, as these eleven systems exhibited singular headcut migration. Multiple Regression Analysis was utilized to produce predictive equations for Headcut Growth. This equation [Headcut Growth = 0.666 + 0.137(Watershed Slope) – 0.478(Training Intensity) + 0.757(log[Watershed Area]) – 0.278(Drainage Density) – 0.0138(Above Ground Biomass Change) + 0.187(Burning Frequency] resulted in a model relationship of approximately 90%, with Watershed Slope being the most significant variable when an output Headcut Growth was reached.

Master of Science / Department of Computing and Information Sciences / Daniel A. Andresen / The purpose of this project is to develop an Android application for Fort Riley soldiers so as to support the theme “War Fighters Supporting War Fighters by Caring for Military Training Lands”. There are four core features in this application: 1. Find a Gully 2. Report a Gully 3. View Current Weather 4. View Current Satellite Image Features are explained in brief below. 1. Find a gully In this feature based on the current location of soldier, application will display all gullies near that location using Google Maps API. Soldier can also view the gully details by tapping the gully icon. 2. Report a Gully In this feature, soldier can report a new gully i.e.; gully which is not already present on the map. This gully will be stored as unverified gully in the database. Once this gully is verified it will be changed to verified gully and it will be plotted on the Google map. 3. View Current Weather In this feature, soldier can view the current weather conditions of Fort Riley. 4. View Current Satellite Image In this feature, soldier can view the current satellite image of Fort Riley.