Estimating with precision coastal marine properties such as primary production, particulate and dissolved carbon, and red tide concentrations is a challenging but important part of marine research. It benefits not only the local communities, but also provides an important input to various global biogeochemical modeling efforts. Due to the complexity of coastal environments resulting from temporal variability of tidal and riverine influences, it is useful to develop and deploy an automated sensor network that provides real-time feedback. It can be used to validate remote sensing models to retrieve in-water constituents, and provide calibration and validation for atmospheric correction of satellite sensors. For turbid waters, satellite observations in the infrared part of the spectrum can not be used to estimate atmospheric aerosol concentration because the water is not black as is found for clearer waters. This research contribution introduces a modeling effort for a turbid coastal harbor area using a semi-analytical hyperspectral remote sensing algorithm for Case 2 waters to process data from the Autonomous Marine Optical System (AMOS). Retrieved results are then compared with field sample measurements showing satisfactory closure between measurements and theory. A time series of AMOS data over a one-month time span is examined, revealing significant variations in biological activity. A sensitivity analysis of the model is performed to expose the limitations and possible improvements to AMOS measurements in the future.

<p>Wetland monitoring and preservation efforts have the potential to be enhanced with advanced remote sensing acquisition and digital image analysis approaches. Progress in the development and utilization of Unmanned Aerial Systems (UAS) and Unmanned Aerial Vehicles (UAV) as remote sensing platforms has offered significant spatial and temporal advantages over traditional aerial and orbital remote sensing platforms. Photogrammetric approaches to generate high spatial resolution orthophotos of UAV acquired imagery along with the UAV?s low-cost and temporally flexible characteristics are explored. A comparative analysis of different spectral based land cover maps derived from imagery captured using UAV, satellite, and airplane platforms provide an assessment of the Huntington Beach Wetlands. This research presents a UAS remote sensing methodology encompassing data collection, image processing, and analysis in constructing spectral based land cover maps to augment the efforts of the Huntington Beach Wetlands Conservancy by assessing ecological and temporal changes at the Huntington Beach Wetlands.

Dramatic land-cover changes have occurred in a broad range of spatial and temporal scales over the last decades. Satellite remote sensing, which can observe the earth's surface in a consistent manner, has been playing an important role in monitoring and evaluating land-cover changes. Meanwhile, optical remote sensing, a common approach to acquiring land-cover information, is limited by weather conditions and thus is greatly constrained in areas with frequent cloud cover and rainfall. Recent advances in polarimetric SAR (PolSAR) provide a promising means to extract timely information of land-cover changes regardless of weather conditions. SAR satellite can pass through an area from different orbits, namely ascending orbit and descending orbit. The PolSAR images from the same orbit will have similar backscattering even with different incident angles. But if images are acquired from different orbits, the backscattering will vary greatly, which causes many difficulties to land cover change detection. The proposed algorithms in this study can perform land cover change detection in three situations: 1) repeat-pass images (image from the same orbit and with same incident angle, 2) images from the same orbit but with different incident angle, and 3) images from different orbits. Using images from different orbits will largely reduce the monitoring interval which is important in the surveillance of natural disasters. The present study proposes 1) a sub-pixel automatic registration technique, 2) an automatic change detection technique and 3) an iterative framework to process a time series of PolSAR images that can be applied to the PolSAR images from different orbits. Firstly, automatic registration is crucial to the change detection task because a small positional error will largely degrade the accuracy of change detection. The automatic registration technique is based on the multi-scale Harris corner detector. To improve the efficiency and robustness, the orientation angle differencing method is proposed to reject outliers. This differencing method has been proved effective even in the experiment of using PolSAR images from different orbits when less than 5% of the feature point matches are correct. Secondly, the change detection technique can automatically detect land-cover conversions and classify the newly input image. Hierarchical segmentation has been applied in the change detection which generates objects within the constraint of the previous classification map. Multivariate kernel density estimation is applied to classify newly input PolSAR image. The experiments show that the proposed change detection technique can mitigate the effect of polarimetric orientation shift when the PolSAR images are from different orbits, and it can achieve high accuracy even when complex local deformation is appeared. Lastly, the iterative framework, which integrates the automatic registration and automatic change detection techniques, is proposed to process a time series of PolSAR images. In the iterative process, no obvious decrease of classification accuracy is observed. Therefore, the proposed framework provides a potential treatment to derive land-cover dynamics from a time series of PolSAR images from different orbits. / published_or_final_version / Urban Planning and Design / Doctoral / Doctor of Philosophy

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The Okavango Delta, an internationally recognized wetland, is undergoing natural and anthropogenic change at a variety of spatio-temporal scales. The objective of this research was to utilize remotely sensed imagery to assess the spatio-temporal distribution of flooding and fire and their subsequent influences on vegetation as represented by vegetation index trajectories in the Okavango Delta. The characterization of the spatiotemporal dynamics of vegetation spectral response via a time-series of remotely sensed data not only informs ecosystem and disturbance theory but also presents new methodological applications for multi-temporal change analysis. Disentangling these components from a signal is critical for better assessing the interrelationships among climatic oscillations, disturbance regimes, and human management on ecosystem response. This research tested six hypotheses regarding flooding and fire, and found that the largest number of fires occurred either within 5 km of the border to the Wildlife Management Areas or within the active (flooded a minimum of every two years) floodplains. These hypotheses indicate that burning is highest where people have accessinto the management areas and where the natural resources are plentiful. Periodicities from vegetation signal time-series did not confirm published climate-driven periodicities of 3, 8, and 18-years but did reveal seasonal (6 month) and quasi-decadal periodicities. Vegetation trajectories were more predictable with increasing flood frequency and duration, but were less predictable with increased fire frequency. The fact that increased burning resulted in less predictable behavior indicates the potential of quantifying the anthropogenic influence on the landscape using remotely sensed imagery. Flooding and fire were not statistically correlated to the residual dynamics, refuting the conceptualization of flooding and fire as disturbance and supporting the interpretation of flooding and fire as disturbance regimes. This research thus contributes methodologically and theoretically to the ecology literature by operationalizing tests for disturbance versus disturbance regimes via spatio-temporal characterization. Further, this work extends change detection techniques typically implemented with coarser spatial resolution but more frequently acquired imagery by using harmonic regression and wavelet analysis with Landsat data. Lastly, this work provides a temporally rich assessment of recent vegetation, flooding, and fire trends for improving management efforts of the Okavango Delta.

ITC/USA 2014 Conference Proceedings / The Fiftieth Annual International Telemetering Conference and Technical Exhibition / October 20-23, 2014 / Town and Country Resort & Convention Center, San Diego, CA / The Mars Rover Design Team at Missouri University of Science and Technology developed a multifunctional rover for the Mars Society's University Rover Challenge. The main processor of the rover controls various rover subsystems based on commands received from a base station, acquires data from these subsystems, collects primary location and environmental data, and transmits information to the base station. The methodology and technical design of the processor hardware and software will be described in the overall context of the collaborative team development. The paper will also discuss the process, challenges and outcomes of working with limited resources on a student design team.

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The vegetation index products from the Moderate Resolution Imaging Spectroradiometer (MODIS) are designed to provide consistent, spatial and temporal comparisons of global vegetation conditions. The objective of this dissertation was to validate the robustness and global implementation of two MODIS VI algorithms, including the normalized difference vegetation index (NDVI) and "enhanced" vegetation index (EVI). Their performances have been evaluated in: (1) the normalization of canopy background (brightness) variations and the extraction of biophysical parameters across different canopy structures; (2) the characterization of seasonal vegetation profiles (phenological, intra-annual); and (3) spatial and temporal discrimination of vegetation differences (inter-annual). The validation was accomplished through multiple means, including canopy radiative transfer models which were utilized to extract pure vegetation spectra and "true" VI value free of background contamination for varying canopy structures and vegetation amount. The experimental field- and airborne-based radiometry and satellite imagery at multiple spatial resolutions were also coupled and scaled-up for comparison with coarse spatial resolution MODIS VI products to quantify characteristics of semiarid rangeland vegetation. The results showed that NDVI was advantageous in yielding biophysical relationships applicable across varying canopy types, but required knowledge of soils for biophysical estimations. The EVI provided biophysical relationships sensitive to canopy structure, thus requiring knowledge of canopy type for biophysical assessments. The MODIS VI products were successfully validated, radiometrically, by coupling field and the MODLAND Quick Airborne Looks (MQUALS) observations to high spatial resolution imagery (AVIRIS and ETM+), and appeared robust across the two parallel sites for depicting their ecological equivalents. MODIS multitemporal VI profiles were able to depict phenological activity, length of the growing season, peak and onset of greenness, and leaf turnover. Among the sensors tested, spatial resolution was found to be most important for discriminating the major land cover subtypes within the two parallel semiarid rangelands, and spectral resolution had major effects on capturing seasonal contrast due to atmosphere influences. The validation strategy utilized in this study to successively aggregate the integrity-inherent multiple fine spatial resolution data to the coarse MODIS pixel sizes appeared to perform well, thus showing potentials in the validation of other satellite products.

The use of active and passive remote sensing systems for relating forest spatial patterns to fire history was tested over one of the Arizona Sky Islands. Using Landsat Thematic Mapper (TM), Shuttle Imaging Radar (SIR-C), and data fusion I examined the relationship between landscape metrics and a range of fire history characteristics. Each data type (TM, SIR-C, and fused) was processed in the following manner: each band, channel, or derived feature was simplified to a thematic layer and landscape statistics were calculated for plots with known fire history. These landscape metrics were then correlated with fire history characteristics, including number of fire-free years in a given time period, mean fire-free interval, and time since fire. Results from all three case studies showed significant relationships between fire history and forest spatial patterns. Data fusion performed as well or better than Landsat TM alone, and better than SIR-C alone. These comparisons were based on number and strength of significant correlations each method achieved. The landscape metric that was most consistent and obtained the greatest number of significant correlations was Shannon's Diversity Index. Results also agreed with field-based research that has linked higher fire frequency to increased landscape diversity and patchiness. An additional finding was that the fused data seem to detect fire-related spatial patterns over a range of scales.

Optimal scale of study and scaling are fundamental to ecological research, and have been made easier with remotely sensed (RS) data. With access to RS data at multiple scales, it is important to identify how they compare and how effectively information at a specific scale will potentially transfer between scales. Therefore, my research compared the spatial, spectral, and temporal aspects of scale of RS data to study biophysical properties and spatio-temporal dynamics of the northern mixed prairie vegetation. I collected ground cover, dominant species, aboveground biomass, and leaf area index (LAI) from 41 sites and along 3 transects in the West Block of Grasslands National Park of Canada (GNPC; +49°, -107°) between June-July of 2006 and 2007. Narrowband (VIn) and broadband vegetation indices (VIb) were derived from RS data at multiple scales acquired through field spectroradiometry (1 m) and satellite imagery (10, 20, 30 m). VIs were upscaled from their native scales to coarser scales for spatial comparison, and time-series imagery at ~5-year intervals was used for temporal comparison. Results showed VIn, VIb, and LAI captured the spatial variation of plant biophysical properties along topographical gradients and their spatial scales ranged from 35-200 m. Among the scales compared, RS data at finer scales showed stronger ability than coarser scales to estimate ground vegetation. VIn were found to be better predictors than VIb in estimating LAI. Upscaling at all spatial scales showed similar weakening trends for LAI prediction using VIb, however spatial regression methods were necessary to minimize spatial effects in the RS data sets and to improve the prediction results. Multiple endmember spectral mixture analysis (MESMA) successfully captured the spatial heterogeneity of vegetation and effective modeling of sub-pixel spectral variability to produce improved vegetation maps. However, the efficiency of spectral unmixing was found to be highly dependent on the identification of optimal type and number of region-specific endmembers, and comparison of spectral unmixing on imagery at different scales showed spectral resolution to be important over spatial resolution. With the development of a comprehensive endmember library, MESMA may be used as a standard tool for identifying spatio-temporal changes in time-series imagery. Climatic variables were found to affect the success of unmixing, with lower success for years of climatic extremes. Change-detection analysis showed the success of biodiversity conservation practices of GNPC since establishment of the park and suggests that its management strategies are effective in maintaining vegetation heterogeneity in the region. Overall, my research has advanced the understanding of RS of the northern mixed prairie vegetation, especially in the context of effects of scale and scaling. From an eco-management perspective, this research has provided cost- and time-effective methods for vegetation mapping and monitoring. Data and techniques tested in this study will be even more useful with hyperspectral imagery should they become available for the northern mixed prairie.