2024 / This thesis addresses research questions from two distinct yet complementary fields—conservation science and remote sensing—through a case study in the Colombian Andes. In Chapter One, we explore the impact on forest cover of the largest and longest-running public land acquisition (PLA) program in the tropics between 2000 and 2021. Using matching and Difference-in-Differences with multiple time periods we find that as of 2021 there has been a 3.5% increase in forest cover on protected parcels and that impact increases for at least 10 to 12 years post-treatment. We also find that impact varies significantly by factors like slope, accessibility, and department. In Chapter Two, we attempt to improve the forest cover data used in Chapter One by integrating synthetic aperture radar (SAR) observations from Sentinel-1 for areas where persistent cloud cover precludes the use of optical data. We find encouraging evidence to suggest that SAR data can be used with the continuous change detection and classification algorithm to detect forest change in topographically-complex regions, but conclude that accuracy improvements and widespread workflow adoption are dependent on the accessibility of high resolution digital elevation models and improved radiometric terrain correction for Google Earth Engine.

Recently, there is a tremendous amount of high resolution imagery that wasn’t available years ago, mainly because of the advancement of the technology in capturing such images. Most of the very high resolution (VHR) imagery comes in three bands only the red, green and blue (RGB), whereas, the importance of using such imagery in remote sensing studies has been only considered lately, despite that, there are no enough studies examining the usefulness of these imagery in urban applications. This research proposes a method to investigate high resolution imagery to analyse an urban area using UAV imagery for land use and land cover classification. Remote sensing imagery comes in various characteristics and format from different sources, most commonly from satellite and airborne platforms. Recently, unmanned aerial vehicles (UAVs) have become a very good potential source to collect geographic data with new unique properties, most important asset is the VHR of spatiotemporal data structure. UAV systems are as a promising technology that will advance not only remote sensing but GIScience as well. UAVs imagery has been gaining popularity in the last decade for various remote sensing and GIS applications in general, and particularly in image analysis and classification. One of the concerns of UAV imagery is finding an optimal approach to classify UAV imagery which is usually hard to define, because many variables are involved in the process such as the properties of the image source and purpose of the classification. The main objective of this research is evaluating land use / land cover (LULC) classification for urban areas, whereas the data of the study area consists of VHR imagery of RGB bands collected by a basic, off-shelf and simple UAV. LULC classification was conducted by pixel and object-based approaches, where supervised algorithms were used for both approaches to classify the image. In pixel-based image analysis, three different algorithms were used to create a final classified map, where one algorithm was used in the object-based image analysis. The study also tested the effectiveness of object-based approach instead of pixel-based in order to minimize the difficulty in classifying mixed pixels in VHR imagery, while identifying all possible classes in the scene and maintain the high accuracy. Both approaches were applied to a UAV image with three spectral bands (red, green and blue), in addition to a DEM layer that was added later to the image as ancillary data. Previous studies of comparing pixel-based and object-based classification approaches claims that object-based had produced better results of classes for VHR imagery. Meanwhile several trade-offs are being made when selecting a classification approach that varies from different perspectives and factors such as time cost, trial and error, and subjectivity.       Classification based on pixels was approached in this study through supervised learning algorithms, where the classification process included all necessary steps such as selecting representative training samples and creating a spectral signature file. The process in object-based classification included segmenting the UAV’s imagery and creating class rules by using feature extraction. In addition, the incorporation of hue, saturation and intensity (IHS) colour domain and Principle Component Analysis (PCA) layers were tested to evaluate the ability of such method to produce better results of classes for simple UAVs imagery. These UAVs are usually equipped with only RGB colour sensors, where combining more derived colour bands such as IHS has been proven useful in prior studies for object-based image analysis (OBIA) of UAV’s imagery, however, incorporating the IHS domain and PCA layers in this research did not provide much better classes. For the pixel-based classification approach, it was found that Maximum Likelihood algorithm performs better for VHR of UAV imagery than the other two algorithms, the Minimum Distance and Mahalanobis Distance. The difference in the overall accuracy for all algorithms in the pixel-based approach was obvious, where the values for Maximum Likelihood, Minimum Distance and Mahalanobis Distance were respectively as 86%, 80% and 76%. The Average Precision (AP) measure was calculated to compare between the pixel and object-based approaches, the result was higher in the object-based approach when applied for the buildings class, the AP measure for object-based classification was 0.9621 and 0.9152 for pixel-based classification. The results revealed that pixel-based classification is still effective and can be applicable for UAV imagery, however, the object-based classification that was done by the Nearest Neighbour algorithm has produced more appealing classes with higher accuracy. Also, it was concluded that OBIA has more power for extracting geographic information and easier integration within the GIS, whereas the result of this research is estimated to be applicable for classifying UAV’s imagery used for LULC applications.

Doctor of Philosophy / Department of Geography / Kevin P. Price / A major strategy in response to rapid degradation and loss of Louisiana’s coastal wetlands has been the construction of siphon diversion projects. The diversions are designed to reintroduce nutrient enriched freshwater from the Mississippi River into wetland ecosystems to combat saltwater intrusion and stimulate marsh growth. The lack of consensus regarding the effects of river diversions on nutrient enrichment of wetland ecosystems is coupled with major concerns about eutrophication. Locating, assessing, and monitoring eutrophic marsh vegetation represent major challenges to understanding the impacts of freshwater diversions. As a result, this study was undertaken to investigate the feasibility of modeling eutrophication vulnerability of a coastal Louisiana marsh receiving turbid Mississippi River water. The major objective was to integrate remotely sensed data with field measurements of vegetation biophysical characteristics and historical ecosystem survey data to delineate landscape patterns suggestive of vulnerability to eutrophication. The initial step in accomplishing this goal was to model the spatial distribution of freshwater impacts using satellite image-based turbidity frequency data associated with siphon diversion operation. Secondly, satellite and spectroradiometer band combinations and vegetation indices optimal for modeling marsh biophysical characteristics related to nutrient enrichment were identified. Finally, satellite image data were successfully integrated with measures of historical and concurrent marsh biophysical characteristics to model the spatial distribution of eutrophication vulnerability and to elucidate the impacts of freshwater diversions.

Master of Arts / Department of Geography / J. M. Shawn Hutchinson / Windbreaks are a valuable resource in conserving soils and providing crop protection in western Kansas and other Great Plains states. Currently, Kansas has neither an up-to-date inventory of windbreak locations nor an assessment of their condition. The objective of this study is to develop remote sensing and geographic information system methods that rapidly identify and assess the condition of windbreaks in Ford County, Kansas. Ford County serves as a pilot study area for method development with the intent of transferring those methods to other counties/regions in Kansas and the Great Plains. A remote sensing technique known as object-based classification was used to classify windbreaks using color aerial photography acquired through the 2008 National Agricultural Imagery Program. Object-based classification works by segmenting imagery where areas with similar spectral, shape, and textural properties are grouped into vectors (i.e., objects) that are later used as the basis for image classification. Using this technique, 355 windbreaks, totaling nearly 1,012 acres (410 hectares), were identified in Ford County. When compared to a spatial data set of confirmed windbreak locations generated via a heads-up digitizing process, the location of windbreaks identified using object-based classification results agreed approximately 81% of the time. Mean textural and spectral values were then combined and used to place identified windbreaks into three condition categories (good, fair, and poor) using a manual classification approach. Analysis showed the area of windbreaks in good condition to be 170 hectares, with the remaining 171 hectares of windbreaks falling in the fair or poor classes. Methods detailed in this study proved successful at rapidly identifying windbreak location and for providing useful condition class results for windbreak renovation and restoration planning.

Master of Science / Department of Civil Engineering / David R. Steward / The Ogallala Aquifer is a supply of water for several municipalities in western Kansas, as well as an irrigation source for local farmers. Since the 1950’s, when the aquifer started to be pumped for irrigation, the region has seen steady declines of the groundwater table. These declines have reduced stream flow in the Arkansas and Cimarrron Rivers, and caused a redistribution of riparian phreatophytes. This thesis studies this redistribution of phreatophytes, and develops statistical relationships relating a phreatophyte’s location to depth to groundwater, increase in depth to groundwater, distance from a stream or river, and hydrologic soil group. Remote sensing was used to determine tree locations on predevelopment and post-development aerial photography. These locations were mapped using ArcGIS, and ArcAEM was used to model groundwater flow in six riparian regions taking root uptake into account. It was found that once the depth to groundwater becomes greater than about 3 m, tree population will decrease as depth to water increases. Trees were located within 700 m of the river. Areas with a dense tree population (>10% tree cover) occurred where the average depth to water ranged from 0.24-1.4 m. Areas with moderate tree density (5-10% tree cover) corresponded to an average depth to water ranging from 2.1-19 m. Areas with a low tree density (<5% tree cover) corresponded to an average depth to water ranging from 11-28 m. It was found that phreatophytes have a high likelihood of growing on hydrologic soil group A and a low likelihood of growing on hydrologic soil group B. The number of trees located on hydrologic soil group D was what would be statistically expected if tree location were independent of soil type. It was also found that tree locations could be used as an indicator of good hydraulic connectivity between surface water and groundwater. This information can be used to help guide future installation of monitoring networks and expand research projects from central Kansas to western Kansas.

Digital Elevation Models (DEMs) are digital representations of surface topography or terrain. Collection of DEM data can be done directly through surveying and taking ground control point (GCP) data in the field or indirectly with remote sensing using a variety of techniques. The accuracies of DEM data can be problematic, especially in rugged terrain or when differing data acquisition techniques are combined. For the present study, ground data were taken in various protected areas in the mountainous regions of Nepal. Elevation, slope, and aspect were measured at nearly 2000 locations. These ground data were imported into a Geographic Information System (GIS) and compared to DEMs created by NASA researchers using two data sources: the Shuttle Radar Topography Mission (STRM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Slope and aspect were generated within a GIS and compared to the GCP ground reference data to evaluate the accuracy of the satellitederived DEMs, and to determine the utility of elevation and derived slope and aspect for research such as vegetation analysis and erosion management. The SRTM and ASTER DEMs each have benefits and drawbacks for various uses in environmental research, but generally the SRTM system was superior. Future research should focus on refining these methods to increase error discrimination.

Eastern Kentucky is a 35-county region that is a part of the Cumberland Plateau of the Appalachian Mountains. With mountaintop removal and associated land cover change (LCC) (primarily deforestation), it is hypothesized that there would be changes in various atmospheric boundary layer parameters and precipitation. In this research, we have conducted sensitivity experiments of atmospheric response of a significant flash flood-producing rainfall event by modifying land cover and topography. These reflect recent LCC, including mountaintop removal (MTR). We have used the Weather Research and Forecasting (WRF) model for this purpose. The study found changes in amount, location, and timing of precipitation. LCC also modified various surface fluxes, moist static energy, planetary boundary layer height, and local-scale circulation wind circulation. The key findings were the modification in fluxes and precipitation totals. With respect to sensible heat flux (H), there was an increase to bare soil (post-MTR) in comparison to pre-MTR conditions (increased elevation with no altered land cover). Allowing for growth of vegetation, the grass simulation resulted in a decrease in H. H increased when permitting the growth of forest land cover (LC) but not to the degree of bare soil. In regards to latent heat flux (LE), there was a dramatic decrease transitioning from pre-MTR to post-MTR simulations. Then with the subsequent grass and forest simulations, there was an increase in LE comparable to the pre-MTR simulation. Under pre-MTR conditions, the total precipitation was at its highest level overall. Then with the simulated loss of vegetation and elevation, there was a dramatic decrease in precipitation. With the grass LC, the precipitation increased in all areas of interest. Then forest LC was simulated allowing overall slightly higher precipitation than grass.

Approved for public release, distribution is unlimited / The Lineate Image Near Ultraviolet Spectrometer (LINUS) is a spectral imager developed to operate in the 0.3-0.4 micron spectral region. The 2-D imager operates with a scan mirror, forming image scenes over time intervals of 10-20 minutes. Sensor calibration was conducted in the laboratory, and the system response to Sulfur Dioxide (SO2) gas was determined. The absorption profile for SO2 was measured, and curves of growth were constructed as a function of gas concentration. Test measurements were performed at the Naval Postgraduate School (NPS), from the roof of Spanagel Hall. Field observations were conducted at a coal-burning factory site at Concord, CA with the purpose of quantifying the presence of SO2. The Concord field measurement showed traces of SO2, with further analysis still required. / Civilian, DSO National Laboratories, Singapore

Throughout history floods have been part of the human experience. They are recurring phenomena that form a necessary and enduring feature of all river basin and lowland coastal systems. In an average year, they benefit millions of people who depend on them. In the more developed countries, major floods can be the largest cause of economic losses from natural disasters, and are also a major cause of disaster-related deaths in the less developed countries. Flood disaster mitigation research was conducted to determine how remotely sensed data can effectively be used to produce accurate flood plain maps (FPMs), and to identify/quantify the sources of error associated with such data. Differences were analyzed between flood maps produced by an automated remote sensing analysis tailored to the available satellite remote sensing datasets (rFPM), the 100-year flooded areas "predicted" by the Flood Insurance Rate Maps, and FPMs based on DEM and hydrological data (aFPM). Landuse/landcover was also examined to determine its influence on rFPM errors. These errors were identified and the results were integrated in a GIS to minimize landuse / landcover effects. Two substantial flood events were analyzed. These events were selected because of their similar characteristics (i.e., the existence of FIRM or Q3 data; flood data which included flood peaks, rating curves, and flood profiles; and DEM and remote sensing imagery.) Automatic feature extraction was determined to be an important component for successful flood analysis. A process network, in conjunction with domain specific information, was used to map raw remotely sensed data onto a representation that is more compatible with a GIS data model. From a practical point of view, rFPM provides a way to automatically match existing data models to the type of remote sensing data available for each event under investigation. Overall, results showed how remote sensing could contribute to the complex problem of flood management by providing an efficient way to revise the National Flood Insurance Program maps.

Approved for public release; distribution in unlimited. / PC and MNF transforms are two widely used methods that are utilized for various applications such as dimensionality reduction, data compression and noise reduction. In this thesis, an in-depth study of these two methods is conducted in order to estimate their performance in hyperspectral imagery. First the PCA and MNF methods are examined for their effectiveness in image enhancement. Also, the various methods are studied to evaluate their ability to determine the intrinsic dimension of the data. Results indicate that, in most cases, the scree test gives the best measure of the number of retained components, as compared to the cumulative variance, the Kaiser, and the CSD methods. Then, the applicability of PCA and MNF for image restoration are considered using two types of noise, Gaussian and periodic. Hyperspectral images are corrupted by noise using a combination of ENVI and MATLAB software, while the performance metrics used for evaluation of the retrieval algorithms are visual interpretation, rms correlation coefficient spectral comparison, and classification. In Gaussian noise, the retrieved images using inverse transforms indicate that the basic PC and MNF transform perform comparably. In periodic noise, the MNF transform shows less sensitivity to variations in the number of lines and the gain factor. / Lieutenant, Hellenic Navy