Lidar has the ability to supplant or compliment many current measurement technologies in ocean optics. Lidar measures Inherent Optical Properties over long distances without impacting the orientation and assemblages of particles it measures, unlike many systems today which require pumps and flow cells. As an active sensing technology, it has the benefit of being independent of time of day and weather. Techniques to interpret oceanographic lidar lags behind atmospheric lidar inversion techniques to measure optical properties due to the complexity and variability of the ocean. Unlike in the atmosphere, two unknowns in the lidar equation backscattering at 180o (𝛽𝜋) and attenuation (c) do not necessarily covary. A lidar system developed at the Harbor Branch Oceanographic Institute is used as a test bed to validate a Monte-Carlo model to investigate the inversion of optical properties from lidar signals. Controlled tank experiments and field measurements are used to generate lidar waveforms and provide optical situations to model. The Metron EODES backscatter model is used to model waveforms. A chlorophyll based forward optical model provides a set of 1500 unique optical situations which are modeled to test inversion techniques and lidar geometries. Due to issues with the lidar system and model the goal of validating the model as well as a more mature inversion experiment were not completed. However, the results are valuable to show the complexity and promise of lidar systems. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

archives@tulane.edu / 1 / Sabrina Martinez

Accurate and replicable measurements of changes to land cover from drought conditions are essential for monitoring ecosystem disturbances. Techniques designed to measure land cover changes have been developed using data from remote sensing but with variable success. In my three study areas of southeastern parts of the American State of Texas, the change vector analysis (CVA) technique was tested on remote sensing data captured by the Landsat TM sensor taken in the years 2009, 2010, and 2011. This study monitors land use/land cover (LULC) changes due to the extreme Texas drought of 2011; the worst single year drought ever recorded in the state. The Landsat data are converted to vegetation indices; the normalized difference vegetation index (NDVI), bare soil index (BI), normalized difference moisture index (NDMI), as well as Tasseled Cap Transformations (TCT) brightness, greenness and wetness. CVA was used to determine the intensity of change (magnitude) and the type of changes that occurred (direction) between the multi-temporal data. This represents a new and improved method for calculating the direction component. Additionally, the relationship between NDVI and NDMI and between TCT variables and their application in CVA are further explored. The results show that land cover changes occurred due to an increase in precipitation in 2010 as well as considerable decrease of precipitation in 2011 resulting in the devastating drought. Validation procedures show that the CVA method was effective in capturing both magnitude of change and type of change that occurred. The remote sensing approach to monitoring drought-induced land cover changes is systematic, replicable and globally available at any time. Such a reliable methodology is essential for measuring ecosystem threats and human population vulnerability. / A Dissertation submitted to the Department of Geography in partial fulfillment of the Doctor of Philosophy. / Spring Semester 2017. / March 31, 2017. / Change vector analysis, Drought, Landsat, Remote sensing / Includes bibliographical references. / Victor Mesev, Professor Directing Dissertation; Xiuwen Liu, University Representative; Stephanie Pau, Committee Member; Xiaojun Yang, Committee Member.

Remote sensing technologies, such as satellite imagery, have proven to be a powerful tool for land cover classification when combined with machine learning algorithms. Depending on which type of sensor is used for the imagery, different properties of land cover classes may be distinguished. Because of this, a data set containing a combination of data from different sensors could potentially further improve the classification accuracy. To determine if adding data from the radar sensor on the satellite constellation Sentinel-1 to data from the multispectral optical sensor on the satellite constellation Sentinel-2 could improve the accuracy of land cover classification, a tool for combining data from both satellites was developed. The classification accuracy using the combined data was then compared to using non-combined Sentinel-2 data with a neural network and a random forest classifier. We found that the random forest classifier produced a higher accuracy than the neural network for both the combined data and non-combined data. The combined data increased the accuracy further compared to the non-combined data. However, the increase produced by the combined data was small and most likely not worth the extra computational power required to implement Sentinel-1 data to Sentinel-2 data.

To determine whether spaceborne C-band SAR data could be used alone, or in conjunction with optical data, for accurately mapping mangrove forests of the Mexican Pacific, four scenes of dual-polarized ENVISAT ASAR data, at two incidence angles, were collected for the Teacapán-Agua Brava-Las Haciendas estuarine-mangrove complex. Several combinations of these ASAR data were classified to determine the most optimal arrangement for mangrove mapping. In addition, corresponding Landsat TM data were classified using the same training sites. The overall accuracy in mapping these mangroves did improve when more than one polarization mode was employed. In general, the higher incidence angle data (∼41° vs ∼23°) provided better results. In all circumstances, the optical data alone provided higher classification accuracies. When contained as one mangrove class, the highest overall accuracy achieved using the ASAR data was 54% as compared to 76% for the optical data. When considering four separate mangrove classes, representing the four conditions typical of this system (dead, poor condition, healthy, tall healthy), overall accuracies dropped to 45% and 63%, respectively. With the limited penetration of C-band into canopies, it was difficult to separate healthy and tall healthy mangrove from palm and other terrestrial forests using the ASAR data. In addition to confusion amongst the four mangrove classes, the dead mangrove stands created considerable misclassification as they were readily misidentified with water and saltpan areas in the optical data and with agricultural lands in the ASAR data procedure. Given the advantage of ASAR for identifying dead stands from open water and saltpan, these data were then used in conjunction with the optical data to reduce the misclassification of these areas.

Given the scale and rate of mangrove loss globally, it is increasingly important to map and monitor mangrove forest health in a timely fashion. This study aims to identify the conditions of mangroves in a coastal lagoon south of the city of Mazatlán, Mexico, using proximal hyperspectral remote sensing techniques. The dominant mangrove species in this area includes the red (Rhizophora mangle), the black (Avicennia germinans) and the white (Laguncularia racemosa) mangrove. Moreover, large patches of poor condition black and red mangrove and healthy dwarf black mangrove are commonly found. Mangrove leaves were collected from this forest representing all of the aforementioned species and conditions. The leaves were then transported to a laboratory for spectral measurements using an ASD FieldSpec® 3 JR spectroradiometer (Analytical Spectral Devices, Inc., USA). R2 plot, principal components analysis and stepwise discriminant analyses were then used to select wavebands deemed most appropriate for further mangrove classification. Specifically, the wavebands at 520, 560, 650, 710, 760, 2100 and 2230 nm were selected, which correspond to chlorophyll absorption, red edge, starch, cellulose, nitrogen and protein regions of the spectrum. The classification and validation indicate that these wavebands are capable of identifying mangrove species and mangrove conditions common to this degraded forest with an overall accuracy and Khat coefficient higher than 90% and 0.9, respectively. Although lower in accuracy, the classifications of the stressed (poor condition and dwarf) mangroves were found to be satisfactory with accuracies higher than 80%. The results of this study indicate that it could be possible to apply laboratory hyperspectral data for classifying mangroves, not only at the species level, but also according to their health conditions.

Human sustainability is one of the most pressing issues of the 21st century. Coupled Human and Natural Systems (CHANS) offers a useful framework to focus on understanding the complex process and pattern that characterizes the dynamical interactions between human and natural systems. This dissertation research integrates the geospatial analysis into the CHANS framework from three perspectives: temporal, spatial, and organizational coupling. Using the temporal coupling aspect, we monitor the risk of deforestation and biodiversity threats from energy investments in Southeast Asia. We assess the energy investment evaluate changes to forest morphology and the risk to biodiversity. In terms of land cover change, we find that hydroelectric power plants tend to have more extensive biodiversity impacts than coal-fired plants, which are usually built within proximity to major population centers. Next, we explore spatial coupling by examining the spatial heterogeneity and homogeneity in home prices across Massachusetts, using Geographically Weighted Regression models with natural and socio-demographic variables. We discovered models that utilized spatial heterogeneity perform better. However, statistical tests of significance are required to determine the model specification to avoid over-fitting. In the fourth chapter, we examined a critical refugium for endangered fish species in East Africa by mapping the organizational dynamics of aquatic vegetation on Lake Kyoga, Uganda. A CHANS organizational coupling involving the natural infrastructure of aquatic vegetation and fishes can adversely impact endangered species and the surrounding human communities. Floating aquatic vegetation can protect the native fishes from predation by Nile Perch by creating hypoxic barriers between water bodies. We developed an algorithm to locate and identify various types of aquatic vegetation. Profiles of lakes are created to examine the spatiotemporal dynamics of refugia. The results are valuable in shaping strategies to conserve both fish species and human livelihoods. The fifth chapter explores emerging technologies, Virtual Reality, in communicating the complex CHANS coupling of green (trees) and gray infrastructure (gas pipelines). This chapter demonstrates the building of 3D urban landscapes from remote sensing data and the emerging use of VR to communicate, educate and empower the stakeholders on sustainability issues related to aging natural gas infrastructure and resulting methane emissions. This dissertation research aims to build a set of methodologies based on extensive geospatial data, spatially explicit models, and tools essential for operationalizing and monitoring CHANS in studies ranging from local to regional scales. Each application builds or revises a new model or algorithm to address a real-world CHANS problem.

This paper examines the issues that arise in the use of visual interpretation of Landsat data during the analysis, classification and mapping of the natural vegetation of the semi-arid Northern Cape. Initial research involved the classifying and mapping of the vegetation using conventional methods. A vegetation map, accompanying legend and descriptive key were produced. The problems encountered during this process, and the constraints of manpower, time and funds, stimulated the investigation of Landsat imagery as a means of improving the speed and accuracy of vegetation classification and mapping. A study area comprising one Landsat scene and which met certain requirements was selected: a) The area had already been surveyed and mapped at a scale of 1:250 000. b) As many vegetation units as possible were included. c) There was maximum diversity, complexity and variability in terms of soil, geology and terrain morphology. Initially a suitable mapping scale was selected, viz. 1:250 000, as it met the requirements of nature conservation authorities and agricultural planners. The scales of survey and remote sensing were based on this. The basic unit of survey was the 1:50 000 topographical map and satellite imagery at a scale of 1:250 000 was found to meet the requirements of reconnaissance level mapping. The usefulness of Landsat imagery was markedly affected by the quality of image production and enhancement. Optimum image production was vitally important and to this end, interaction between the user and the operations engineer at the Satellite Applications Centre, Hartebeeshoek was essential. All images used, were edge-enhanced and systematically corrected. While these procedures were costly, they proved to be fundamental to the success of the investigation. Precision geometric correction was not required for reconnaissance level investigation. The manual superimposition of the UTM grid, using ground control points from 1:250 000 topographical maps, proved to be accurate and convenient. Pattern recognition on single-band, panchromatic imagery was difficult. The scene lacked crispness and contrast, and it was evident that black and white imagery did not satisfy the objectives of the study. Three-band false colour composite imagery was superior to single-band imagery in terms of clarity and number of cover classes. The addition of colour undoubtedly facilitated visual interpretation. False colour composite imagery was investigated further to establish which year, season and possibly time of season would best suit the objectives of the investigation. It was found that the environmental parameters affecting reflectance are relatively stable over time and it was not necessary to acquire imagery of the same year as field surveys. However, the year of imagery should be chosen so that similar climatic conditions prevail. While, in certain instances, imagery captured during winter had advantages in separating complex mosaics, summer imagery was superior in most respects. Furthermore, given "normal" climatic conditions, the ideal period during which there was maximum contrast between and within ground classes, and thus spectral classes, was narrowed to mid-January to mid-April. Units which were acceptably heterogeneous (relatively homogeneous) in terms of reflectance levels were delineated manually on the image. This delineation was done at three levels of complexity and the units were compared with the vegetation map. A series of field trips aided the interpretation of the images, especially where discrepancies occurred between the map and the image. In general, there was a close degree of correspondence between the prepared vegetation map and the delineated image. Field investigation revealed the image units to be more accurate than those on the vegetation map, and the image served to highlight the inadequacies inherent in classifying and mapping vegetation of extensive areas with limited resources.

Throughout the Intermountain West there has been a substantial reduction in both the quantity and quality of sagebrush ecosystems. To ass ist current range management objectives, numerous efforts have been made to classify and map sagebrush communities using remotely sensed data. However, the amount of deta il provided by these broad-scale mapping projects is often limited. This research evaluated the ability of a suite of airborne and satellite imagery to detect sagebrush community structural attributes, specifically percent canopy cover, live cover, density, size-vigor, and spatial arrangement of shrubs. Field data was collected at Camp Williams National Guard Training Facility near Bluffdale, Utah, within a Wyoming big sagebrush community. High-resolution color infrared (CIR) aerial photography, panchromatic, and multi-spectral satellite imagery, including data from Orb image, IKO OS, and Landsat ETM+, were used. Comparisons were made based on the inherent spatial and spectral properties of each image. In addition to the traditional pixel-based method for classifying imagery, a relatively new object-oriented approach to measure sagebrush cover was also explored. Results indicate that the quantification of sagebrush cover can be done fairly accurately in mid-level canopy cover areas regardless of the imagery used. Confidence in the cover estimates did diminish slightly in areas where sagebrush cover was relatively sparse or extremely dense. Not all structural variables were quantifiable using the coarser imagery, due to constraints of spat ial resolution. In these instances the 0.3-meter CIR imagery was exemplified. The object-oriented approach enabled an automatic delineation of the range of variability within sagebrush stands and provided an interesting alternative to measuring sagebrush community structural attributes when compared with the more traditional pixel-based approach. This research was intended to provide a resource for anyone working wi thin sagebrush ecosystems, including rangeland managers, wildlife biologists, or other remote sensors, specifically when decisions related to the appropriate selection of remotely sensed data for some intended management application is necessary. The evaluation of wildlife habitat for sagebrush-obligate species, the direction of fire management strategies and restoration efforts, and the ident ification of appropriate grazing areas are only a few of the potential applications of this work

京都大学 / 0048 / 新制・課程博士 / 博士(地球環境学) / 甲第19878号 / 地環博第152号 / 新制||地環||30(附属図書館) / 32914 / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)教授 藤井 滋穂, 教授 高岡 昌輝, 准教授 田中 周平 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM