This study investigated the use of optical remote sensing for estimating leaf and canopy scale light use efficiency (LUE) and carbon exchange. In addition, a new leaf level model capable of predicting dynamic changes in apparent reflectance due to chlorophyll fluorescence was developed. A leaf level study was conducted to assess the applicability of passive remote sensing as a tool to measure the reduction, and the subsequent recovery, of photosynthetic efficiency during the weeks following transplantation. Spectral data were collected on newly planted saplings for a period of 8 weeks, as well as gas exchange measurements of LUE and PAM fluorescence measurements. A set of spectral indices, including the Photochemical Reflectance Index (PRI), were calculated from the reflectance measurements. A marked depression in photosynthetic rate occurred in the weeks after outplanting followed by a gradual increase, with recovery occurring in the later stages of the experimental period. As with photosynthetic rate, there was a marked trend in PRI values over the study period but no trend was observed in chlorophyll based indices. The study demonstrated that hyperspectral remote sensing has the potential to be a useful tool in the detection and monitoring of the dynamic effects of transplant shock. Relationships between hyperspectral reflectance indices, airborne carbon exchange measurements and satellite observations of ground cover were then explored across a heterogeneous Arctic landscape. Measurements were collected during August 2008, using the University of Edinburgh’s research aircraft, from an Arctic forest tundra zone in northern Finland as part of the Arctic Biosphere Atmosphere Coupling at Multiple Scales (ABACUS) study. Surface fluxes of CO2 were calculated using the eddy covariance method from airborne data that were collected from the same platform as hyperspectral reflectance measurements. Airborne CO2 fluxes were compared to MODIS vegetation indices. In addition, LUE was estimated from airborne flux data and compared to airborne measurements of PRI. There were no significant relationships between MODIS vegetation indices and airborne flux observations. There were weak to moderate (R2 = 0.4 in both cases) correlations between PRI and LUE and between PRI and incident radiation. A new coupled physiological radiative transfer model that predicts changes in the apparent reflectance of a leaf, due to chlorophyll fluorescence, was developed. The model relates a physically observable quantity, chlorophyll fluorescence, to the sub leaf level processes that cause the emission. An understanding of the dynamics of the processes that control fluorescence emission on multiple timescales should aid in the interpretation of this complex signal. A Markov Chain Monte Carlo (MCMC) algorithm was used to optimise biochemical model parameters by fitting model simulations of transient chlorophyll fluorescence to measured reflectance spectra. The model was then validated against an independent data set. The model was developed as a precursor to a full canopy scheme. To scale to the canopy and to use the model on trans-seasonal time scales, the effects of temperature and photoinhibition on the model biochemistry needs to be taken into account, and a full canopy radiative transfer scheme, such as FluorMOD, must be developed.

GIS AND REMOTE SENSING - BASED MODELS FOR DEVELOPMENT OF AQUACULTURE AND FISHERIES IN THE COASTAL ZONE: A case study in Baía de Sepetiba, BraziL. by Philip Conrad Scott The use of Geographical Information Systems (GIS) in regional development is now becoming recognized as an important research tool in identifying potential aquaculture development and promoting better use of fishery resources on a regional basis. Modelling tools of GIS were investigated within a database specifically built for the region of Sepetiba Bay (W44°50', S23°00') Rio de Janeiro - Brazil, where, water based aquaculture development potential for two native species 0 f molluscs: P ema p ema (brown mussel) and Crassostrea rhizophorae (mangrove oyster) was identified, and additionally potential for development of land-based aquaculture of the white shrimp, Litopenaeus vannamei. Taking into consideration a mix of production functions including environmental factors such as water temperature, salinity, dissolved oxygen content, natural food availability as well as shelter from exposed conditions, several aquaculture development potential areas were found. The integration of sub-models comprised of thematic layers in the GIS including human resources available, general infrastructure present, regional markets as well as constraints to aquaculture development was developed. Multi-criteria evaluation within sub-models and between sub-models resulted in identification of several distinct potential areas for mollusc aquaculture development, indicating significant production potential and job creation. Basic field environmental data were collected in field trips in 1996, 1997 and 1998. Fresh market data were collected in 2001-2002 and were used to analyse market potentiaL. The map analyses undertaken with GIS based models support the hypothesis that promising locations for aquaculture development, their extent and potential production capacity can be predicted, making GIS use a useful tool for natural resource management and decision support.

There is currently no effective tool available to quickly and economically measure a change in landmass in the setting of biomedical professionals and environmental specialists. The purpose of this study is to structure and demonstrate a statistical change-detection method using remotely sensed data that can detect the presence of an infectious land borne disease. Data sources included the Texas Department of Health database, which provided the types of infectious land borne diseases and indicated the geographical area to study. Methods of data collection included the gathering of images produced by digital orthophoto quadrangle and aerial videography and Landsat. Also, a method was developed to identify statistically the severity of changes of the landmass over a three-year period. Data analysis included using a unique statistical detection procedure to measure the severity of change in landmass when a disease was not present and when the disease was present. The statistical detection method was applied to two different remotely sensed platform types and again to two like remotely sensed platform types. The results indicated that when the statistical change detection method was used for two different types of remote sensing mediums (i.e.-digital orthophoto quadrangle and aerial videography), the results were negative due to skewed and unreliable data. However, when two like remote sensing mediums were used (i.e.- videography to videography and Landsat to Landsat) the results were positive and the data were reliable.

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<p>Environmental sensing is experiencing tremendous development due largely to the advancement of sensor technology and wireless technology/internet that connects them and enable data exchange. Environmental monitoring sensor systems range from satellites that continuously monitor earth surface to miniature wearable devices that track local environment and people's activities. However, transforming these data into knowledge of the underlying physical and/or chemical processes remains a big challenge given the spatial, temporal scale, and heterogeneity of the relevant natural phenomena. This research focuses on the development and application of novel sensing and inference techniques in air and water environments. The overall goal is to infer the state and dynamics of some key environmental variables by building various models: either a sensor system or numerical simulations that capture the physical processes.</p><p>This dissertation is divided into five chapters. Chapter 1 introduces the background and motivation of this research. Chapter 2 focuses on the evaluation of different models (physically-based versus empirical) and remote sensing data (multispectral versus hyperspectral) for suspended sediment concentration (SSC) retrieval in shallow water environments. The study site is the Venice lagoon (Italy), where we compare the estimated SSC from various models and datasets against in situ probe measurements. The results showed that the physically-based model provides more robust estimate of SSC compared against empirical models when evaluated using the cross-validation method (leave-one-out). Despite the finer spectral resolution and the choice of optimal combinations of bands, the hyperspectral data is less reliable for SSC retrieval comparing to multispectral data due to its limited amount of historical dataset, information redundancy, and cross-band correlation.</p><p>Chapter 3 introduces a multipollutant sensor/sampler system that developed for use on mobile applications including aerostats and unmanned aerial vehicles (UAVs). The system is particularly applicable to open area sources such as forest fires, due to its light weight (3.5 kg), compact size (6.75 L), and internal power supply. The sensor system, termed “Kolibri”, consists of low-cost sensors measuring CO2 and CO, and samplers for particulate matter and volatile organic compounds (VOCs). The Kolibri is controlled by a microcontroller, which can record and transfer data in real time using a radio module. Selection of the sensors was based on laboratory testing for accuracy, response delay and recovery, cross-sensitivity, and precision. The Kolibri was compared against rack-mounted continuous emission monitors (CEMs) and another mobile sampling instrument (the ``Flyer'') that had been used in over ten open area pollutant sampling events. Our results showed that the time series of CO, CO2, and PM2.5 concentrations measured by the Kolibri agreed well with those from the CEMs and the Flyer. The VOC emission factors obtained using the Kolibri are comparable to existing literature values. The Kolibri system can be applied to various open area sampling challenging situations such as fires, lagoons, flares, and landfills.</p><p>Chapter 4 evaluates the trade-off between sensor quality and quantity for fenceline monitoring of fugitive emissions. This research is motivated by the new air quality standard that requires continuous monitoring of hazardous air pollutants (HAPs) along the fenceline of oil and gas refineries. Recently, the emergence of low-cost sensors enables the implementation of spatially-dense sensor network that can potentially compensate for the low quality of individual sensors. To quantify sensor inaccuracy and uncertainty of describing gas concentration that is governed by turbulent air flow, a Bayesian approach is applied to probabilistically infer the leak source and strength. Our results show that a dense sensor network can partly compensate for low-sensitivity or high noise of individual sensors. However, the fenceline monitoring approach fails to make an accurate leak detection when sensor/wind bias exists even with a dense sensor network.</p><p>Chapter 5 explores the feasibility of applying a mobile sensing approach to estimate fugitive methane emissions in suburban and rural environments. We first compare the mobile approach against a stationary method (OTM33A) proposed by the US EPA using a series of controlled release tests. Analysis shows that the mobile sensing approach can reduce estimation bias and uncertainty compared against the OTM33A method. Then, we apply this mobile sensing approach to quantify fugitive emissions from several ammonia fertilizer plants in rural areas. Significant methane emission was identified from one plant while the other two shows relatively low emissions. Sensitivity analysis of several model parameters shows that the error term in the Bayesian inference is vital for the determination of model uncertainty while others are less influential. Overall, this mobile sensing approach shows promising results for future applications of quantifying fugitive methane emission in suburban and rural environments.</p> / Dissertation

Approved for public release, distribution is unlimited / Terrain classification is studied here using the tool of texture analysis of high-spatial resolution panchromatic imagery. This study analyzes the impact and effectiveness of texture analysis on terrain classification within the Elkhorn Slough Estuary and surrounding farmlands within the central California coastal region. Ikonos panchromatic (1 meter) and multispectral (4 meter) imagery data are examined to determine the impact of adding texture analysis to the standard MSI classification approaches. Spectral Angle Mapper and Maximum Likelihood classifiers are used. Overall accuracy rates increased with the addition of the texture processing. The classification accuracy rate rose from 81.0% for the MSI data to 83.9% when the additional texture measures were added. Modest accuracy (55%) was obtained from texture analysis alone. The addition of textural data also enhanced the classifier's ability to discriminate between several different woodland classes contained within the image. / Lieutenant Commander, United States Navy

Master of Science / Department of Geography / Douglas Goodin / Hyperspectral data has been widely used for estimates of canopy biochemical content over the past decades. Most of these studies were conducted in forests or crops with relatively uniform canopies. Feasibility of the use of hyperspectral analysis in heterogeneous canopies with diverse plant species and canopy structures remains uncertain. Spectral data at the canopy level, with mixed background noise, canopy biochemical and biophysical properties create more problems in spectral analysis than that at the leaf level. Complications of heterogeneous canopies make biochemical retrieval through remote sensing even more difficult due to more uneven spatial distribution of biochemical constituents. The objective of my research was to map canopy nitrogen content in tallgrass prairie with mixed canopies by means of hyperspectral data from in-situ and airborne measurements. Research efforts were divided into three steps: (1) the green leaf area index (LAI) retrieval, given LAI is an important parameter in scaling nitrogen content from leaves to canopies; (2) canopy nitrogen modeling from analysis of in-situ hyperspectral data; and (3) canopy nitrogen mapping based on aerial hyperspectral imagery. Research results revealed that a fine chlorophyll absorption feature in the green-yellow region at wavelengths of 562 – 600 nm was sensitive to canopy nitrogen status. Specific spectral features from the normalized spectral data by the first derivative or continuum removal in this narrow spectral region could be selected by multivariate regression for nitrogen modeling. The optimal nitrogen models with high predictive accuracy measured as low values of root-mean-square error (RMSE) were applied to the aerial hyperspectral imagery for canopy nitrogen mapping during the growth seasons from May to September. These maps would be of great value in studies on the interactions between canopy vegetation quality and grazing patterns of large herbivores in tallgrass prairie.

Measurements of soil moisture are a crucial component for understanding the global water and carbon cycle, weather forecasting, climate models, drought prediction, and agriculture production. Active and passive microwave radar instruments are currently in use for remote sensing of soil moisture. Signals of Opportunity (SoOp) based remote sensing has recently emerged as a complementary method for soil moisture remote sensing. SoOp reuses general digital communication signals allowing the reuse of allocated wireless communication signal bands for science measurements. This thesis developed a tower based SoOp instrument implementing frequencies in the P-Band and S-Band. Two field campaigns were conducted using this new instrument during the summers of 2017 and 2018 at Purdue's Agronomy Center for Research and Education.

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the degree of Master of Science in Environmental Science / Pollution originating from mine tailings is currently one of the environmental problems South Africa has to deal with. Because of the large number of tailings impoundments and their changing status, authorities are battling to keep their records and controls up to date. This project is aimed at investigating the use of remote sensing as a way of conducting surveys of mine tailings efficiently, regularly and at a low cost. Mine tailings impoundments of the Witwatersrand in Gauteng provide an ideal study area because of the large number of tailings dams of different sizes and conditions and the availability of satellite images and aerial photographs covering the area. Tailings impoundments conditions are analysed through satellite images, airborne multi-spectral data and aerial photographs captured during the Safari 2000 dry season campaign. Remote sensing interpretation of colour composites of multi-spectral bands, Principal Components and supervised and unsupervised classifications are the methods of analysis used. The overall goal of the project has been achieved through the production of a comprehensive database of tailings impoundments and their rehabilitation status, in an accessible format, containing identity, coordinates, area, rehabilitation status and owner of each tailings impoundment, map them and end up with a comprehensive database of tailings impoundment on the Witwatersrand. / AC2017