<p> African savannas cover roughly half of the continent, are home to a great diversity of wildlife, and provide ecosystem services to large populations. Savannas showcase a great diversity in vegetation structure, resulting from variation in climatic, edaphic, topographic, and biological factors. Fires play a large role as savannas are the most frequently burned ecosystems on Earth. To study how savanna vegetation structure shifts with environmental factors, it is necessary to gather site data covering the full gradient of climatic and edaphic conditions. Several earlier studies have used coarse resolution satellite remote sensing data to study variation in woody cover. These woody cover estimates have limited accuracy in drylands where the woody component is relatively small, and the data cannot reveal more detailed information on the vegetation structure. We therefore know little about how other structural components, tree densities, crown sizes, and the spatial pattern of woody plants, vary across environmental gradients. </p><p> This thesis aimed to examine how woody vegetation structure and change in woody cover vary with environmental conditions. The analyses depended on access to very high spatial resolution (&lt;1 m) satellite imagery from sites spread across African savannas. The high resolution data combined with a crown delineation method enabled me to estimate variation in tree densities, mean crown size and the level of aggregation among woody plants. With overlapping older and newer imagery at most of the sites, I was also able to estimate change in woody cover over a 10-year period. I found that higher woody plant aggregation is associated with drier climates, high rainfall variability, and fine-textured soils. These same factors were also indicative of the areas where highly organized periodic vegetation patterns were found. The study also found that observed increases in woody cover across the rainfall gradient is more a result of increasing crown sizes than variation in tree density. The analysis of woody cover change found a mean increase of 0.25 % per year, indicating an ongoing trend of woody encroachment. I could not attribute this trend to any of the investigated environmental factors and it may result from higher atmospheric CO₂ concentrations, which has been proposed in other studies. The most influential predictor of woody cover change in the analysis was the difference between potential woody cover and initial woody cover, which highlights the role of competition for water and density dependent regulation when studying encroachment rates. The second most important predictor was fire frequency. </p><p> To better understand and explain the dominant ecosystem processes controlling savanna vegetation structure, I constructed a spatially explicit model that simulates the growth of herbaceous and woody vegetation in a landscape. The model reproduced several of the trends in woody vegetation structure earlier found in the remote sensing analysis. These include how tree densities and crowns sizes respond differently to increases in precipitation along the full rainfall range, and the factors controlling the spatial pattern of trees in a landscape.</p><p>

<p> This study uses a small Unmanned Aircraft System (sUAS) equipped with a multispectral sensor to assess various Vegetation Indices (VIs) for their potential to monitor iron chlorosis levels in a grain sorghum crop. Iron chlorosis is a nutritional disorder that affects numerous varieties of crops and plants that are grown on high-pH, calcareous soils and greatly affects crop yield. The objective of this project is to find the best Vegetation Index (VI) to detect and monitor iron chlorosis.</p><p> A series of flights were completed over the course of the growing season and processed using Structure-from-Motion (SfM) photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red-edge, and near-infrared wavelengths. A series of ground data collection methods were used to analyze stress and chlorophyll levels and grain yield, correlating them to sUAS-acquired four-band multispectral imagery covering the area of interest for ground control and precise crop examination.</p><p> 25 Vegetation Indices (VIs) were calculated using the collected reflectance maps and soil-removed reflectance maps (a supervised classification was used to remove soil via a binary classification). The separability for each VI was then calculated using a two-class distance measure, determining which contained the largest separation between the pixels representing iron chlorosis and healthy vegetation. The field-acquired levels of iron chlorosis were used to conclude which VIs achieved the best results for the dataset as a whole and at each level of chlorosis (low, moderate and severe). It was concluded that the MERIS Terrestrial Chlorophyll (MTCI), Normalized Difference Red Edge (NDRE), and Normalized Green (NG) indices achieved the highest amount of separation between the iron chlorotic and healthy plant populations, with the NG being the most popular for both soil-included and soil-removed VIs, with soil-removed VIs reaching higher levels of separability.</p><p>

Magister Scientiae - MSc (Biodiversity and Conservation Biology) / The Richtersveld falls within the succulent karoo and dester biomes. This studu made use of remote sensing technologies in order to investigate possible vegetation cover changes that have taken place over time, and which have manifested through a combination of threats to the region. Te aims of the study were adressed using three key questions that sought to gainan understanding of the relationship between vegetation response and moisture, in order to interpret teporal and spatial vegetyation cover changes. A spartially and temporarily representative remotely sensed dataset was used together with techniques that are repeatable and able to quantify change with a limited human bias. / South Africa

This dissertation integrates the use of both transformational and derivational analogy into a general problem solving system. As the system faces new problems, cases are retrieved, adapted and subsequently generalized in order to enhance its performance. Empirical results show that the system can successfully address problems in simple domains and scales up smoothly to create solutions to problems in more complex domains including the management and processing of remote sensing and geographic information systems data for natural resource applications. Most case-based reasoning systems rely on sophisticated indexing schemes and adaptation rules to find solutions to new problems. As a result, they expend considerable effort in retrieving and adapting cases to new problems. The approach presented in this dissertation introduces the use of generalization to case-based reasoning. Once a case has been retrieved and adapted to a new problem, the system will generalize the old case with the new case by using an algorithm similar to least general generalization. As the system gains experience, the case-base is generalized and, as is shown by the experimental results, the number of cases required to solve problems is significantly reduced. To show that the approach scales up to real world problems, the system, dubbed PALERMO (Planning and LEarning for Resource Management and Organization), has been implemented and integrated into the SEIDAM environment. SEIDAM (System of Experts for Intelligent DAta Management) is a complex system that uses several AI approaches to manage large quantities of remote sensing and geographic data. It draws on expert system technology, software agents and case-based reasoning to gather and process remote sensing and digital geographic data. One of the goals of SEIDAM is to use remote sensing data to update digital forest cover maps to assist in land use decision making.

The Earth's Ionosphere frequently disrupts Space to Earth communication such as Global Navigation Satellite Systems (GNSS) and international telecommunications critical to a modern technological world. As human society has become heavily dependent on GNSS services, timely and accurate space weather characterization and forecasts are needed. This is particularly true at mid-latitudes, such as the contiguous United States (US), where population density is greatest, hence technological interruptions most impactful. As a conducting layer, the ionosphere delays radio signals by refraction, and in some circumstances causes wave interference due to diffraction off density irregularities. Ionospheric refraction can be used to estimate the path-integrated plasma density, referred to as the Total Electron Content (TEC). Maps of TEC constructed from ground-based receiver networks provide a global and time-dependent image of ionospheric dynamics. While refraction scales with radio-frequency and dual-frequency GNSS receivers routinely compensate for this effect. Radio receivers, including GNSS monitors, are being used to monitor and quantify these effects, producing climatological maps of ionospheric irregularities. However, efforts have focused on low- and high-latitude regions as they are continuously perturbed by geophysical processes related to the orientation of the Earth’s magnetic field. The region in-between has a much more nuanced space-time connection to geomagnetic disturbances. As a consequence, no dedicated observatories are operating today at mid-latitudes. This dissertation provides a fundamental analysis of this underexplored territory in the burgeoning field of space weather. In this dissertation, we develop signal processing techniques to leverage data from geodetic GNSS receivers to study ionospheric irregularities and scintillation, and their connection to spatiotemporal variations in TEC. Newly introduced data source covers areas of Central America and the Caribbean, contiguous US, and Alaska. We applied these techniques initially to study the ionospheric effects of the 2017 solar eclipse and terrestrial weather patterns. We then focused our effort on a long term study of geomagnetic storm effects at mid-latitudes. Eight years of data have been processed in the last solar cycle (2012-2019), and nine profound space weather events were identified. The newly constructed maps were used in conjunction with TEC maps to provide a critical spatial context for understanding the origin of the irregularities. The observations revealed several types of space weather events that affected the area, including a poleward expansion of equatorial plasma bubbles near local midnight, a single plasma bubble expanding poleward while trailing the terminator, and a newly observed mid-latitude phenomena we termed mid-latitude density striations. We also discovered evidence for expansion into and coupling with processes in the near Earth magnetosphere. All events occurred during geomagnetic storms, with an average strength of Dst=-125 nT, and Kp=6+. The events were recorded at all seasons. One event showing mid-latitude density striations was analyzed in greater detail using both GNSS-derived products, and in-situ measurements of plasma parameters in the ionosphere and conjugate magnetosphere. While the large scale TEC projection closely resembles the expected characteristics of an equatorial plasma bubble, we observed that the electric field peaked at the density gradients instead of in the trough, and the density irregularities lagged the trough formation by about one hour. Morphology of TEC irregularities measured by a ground-based GNSS receiver was compared to the first GNSS scintillation observations at mid-latitudes from 2001. We found the large scale density structures, as well as the respective location of scintillation, closely resemble the mid-latitude density striations. We suggest the narrow density, and electric field perturbations were likely caused by the penetration of a substorm-induced electric field to lower latitudes. We conclude the dissertation by discussing the implication of such space weather events on modern technology.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2005. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (November 15, 2006) Vita. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 2006. / Typescript. Includes bibliographical references (leaves 127-131).