<p> The objective of this research was to investigate the role of land cover changes through time in influencing spatial variability of the surface urban heat island of the metropolitan area of Reno-Sparks, Nevada. Free and widely available thermal data from Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) sensor was gathered for a period between 2004 and 2017 and processed to at-satellite surface temperature. Using parcel data and the National Land Cover Database, the time series of Landsat data was sampled for areas which had undergone development during that time. This sample was cross-validated with ten iterations of equal sample size, with a mean correlation coefficient of 0.623 (standard deviation of 0.008) versus the model&rsquo;s value of 0.624. A set of generalized linear models was conducted on this sample to determine expected temperature change with land cover class. It was found that recently developed regions within Reno-Sparks are 0.6 &deg;C warmer on average than the undeveloped desert grasses and sage. When wetlands/irrigated greenery were converted to impervious surfaces, it resulted in a positive surface temperature change of over 2 &deg;C. Once developed, no significant difference was found in the surface temperature trends. This research, using remote sensing technologies, has shown that the Reno-Sparks surface urban heat island has undergone local, but measurable growth in the last fourteen years.</p><p>

Early detection of disease and insect infestation within crops and precise application of pesticides can help reduce potential production losses, reduce environmental risk, and reduce the cost of farming. The goal of this study was the advanced detection of early blight (Alternaria solani) in potato (Solanum tuberosum) plants using hyperspectral remote sensing data captured with a handheld spectroradiometer. Hyperspectral reflectance spectra were captured 10 times over five weeks from plants grown to the vegetative and tuber bulking growth stages. The spectra were analyzed using principal component analysis (PCA), spectral change (ratio) analysis, partial least squares (PLS), cluster analysis, and vegetative indices. PCA successfully distinguished more heavily diseased plants from healthy and minimally diseased plants using two principal components. Spectral change (ratio) analysis provided wavelengths (490-510, 640, 665-670, 690, 740-750, and 935 nm) most sensitive to early blight infection followed by ANOVA results indicating a highly significant difference (p < 0.0001) between disease rating group means. In the majority of the experiments, comparisons of diseased plants with healthy plants using Fisher’s LSD revealed more heavily diseased plants were significantly different from healthy plants. PLS analysis demonstrated the feasibility of detecting early blight infected plants, finding four optimal factors for raw spectra with the predictor variation explained ranging from 93.4% to 94.6% and the response variation explained ranging from 42.7% to 64.7%. Cluster analysis successfully distinguished healthy plants from all diseased plants except for the most mildly diseased plants, showing clustering analysis was an effective method for detection of early blight. Analysis of the reflectance spectra using the simple ratio (SR) and the normalized difference vegetative index (NDVI) was effective at differentiating all diseased plants from healthy plants, except for the most mildly diseased plants. Of the analysis methods attempted, cluster analysis and vegetative indices were the most promising. The results show the potential of hyperspectral remote sensing for the detection of early blight in potato plants.

The U.S. Army manages thousands of sites that cover millions of acres of land for various military training purposes and activities and often faces a great challenge on how to optimize the use of resources. A typical example is that the training activities often lead to off-road vehicle trails and paths and how to use the trails and paths in terms of minimizing maintenance cost becomes a problem. Being able to accurately extract and map the trails and paths is critical in advancing the U.S. Army's sustainability practices. The primary objective of this study is to develop a method geared specifically toward the military's needs of identifying and updating the off-road vehicle trails and paths for both environmental and economic purposes. The approach was developed using a well-known template matching program, called Feature Analyst, to analyze and extract the relevant trails and paths from Fort Riley's designated training areas. A 0.5 meter resolution false color infrared orthophoto with various spectral transformations/enhancements were used to extract the trails and paths. The optimal feature parameters for the highest accuracy of detecting the trails and paths were also investigated. A modified Heidke skill score was used for accuracy assessment of the outputs in comparison to the observed. The results showed the method was very promising, compared to traditional visual interpretation and hand digitizing. Moreover, suggested methods for extracting the trails and paths using remotely sensed images, including image spatial and spectral resolution, image transformations and enhancements, and kernel size, was obtained. In addition, the complexity of the trails and paths and the discussion on how to improve their extraction in the future were given.

Made available in DSpace on 2014-06-11T19:32:18Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-09-11Bitstream added on 2014-06-13T20:03:39Z : No. of bitstreams: 1 guimaraes_scp_dr_rcla.pdf: 6937762 bytes, checksum: 2fe16d1f38180fe9889aa65112bb66b8 (MD5) / A presente pesquisa objetivou elaboração de uma proposta de ordenamento da ocupação territorial da Sub-bacia Hidrográfica do Baixo Rio Candeias, localizada no Estado de Rondônia, utilizando ferramentas de Sensoriamento Remoto e Sistemas de Informações Geográficas na avaliação do processo de mudanças de uso da terra para subsidiar o planejamento de bacias hidrográficas. Nesta pesquisa, utilizou-se imagens de satélite digitais e analógicas e Sistema Processamento de Informações Georreferenciadas – SPRING, disponibilizados pelo Instituto Nacional de pesquisas Espaciais – INPE, no qual foram armazenadas, processadas e analisadas todas as informações inerentes a pesquisa. Inicialmente foi realizado um Diagnóstico Zero da sub-bacia, que serviu de base de dados para estabelecer e identificar as deficiências técnicas que necessitam ser complementadas em função das necessidades das comunidades abrangidas. Através da análise da rede de drenagem foi possível analisar a morfoestrutura e morfotectonica da área, identificando as falhas e fraturas, bem como, anomalias do tipo alto/baixo estrutural. Foi realizada uma caracterização das unidades fisiográficas, definidas a partir da interpretação das imagens orbitais, com identificação das formas, reconhecimento e deduções dos fenômenos na elaboração da paisagem atual e subatual. A estas informações foram agregadas, informações de pedologia de fundamental importância para entender a dinâmica e evolução da paisagem e consequentemente, na elaboração do mapa de subzonas. Os limites das Subzonas coincidiram com os limites das unidades de solos incrementadas a unidades geológicas, e como resultado definiu-se dezenove subzonas, que agruparam todas as informações (morfoestrutura e morfotectonica, fisiografia, solos, vegetação e litologia) produzidas e pesquisadas... / This research objective was to prepare a proposal of suitable land uses for the Lower Candeias River Watershed, geographically located within the State of Rondônia, Brazil, using Remote Sensing and Geographic Information Systems approaches to assess land use and land cover change processos and to provide information to support preparation of a sustainable watershed occupation plan. Satellite imagery and a Geographic Information System (SPRING) developed by the National Space Research Institute (INPE) were used to store, process, and analyze digital datasets. Initially, a “Zero Diagnostic” of the Lower Candeias River Watershed was prepared. This diagnostic was used as supporting information to identify technical weakness in the methodological approaches, which required complementary efforts given the local community and environmental characteristics. In addition, based on the river network analysis, it was possible to define the morphostructure and morphotectonic of the study area, which made possible to identify geologic faults and fractures, and low/high structural anomalies. Physiographic units were identified by analyzing satellite imagery, which included form identification, recognition and deduction of the phenomenon that were shaping current and previous landscape. The critical pedologic information were aggregated to support analysis of the dynamic and evolution of the landscape and, subsequently, to support preparation of the subzoning map of the Lower Candeias River Watershed. The subzones limits overlapped the soil unit limits and, by merging them with the geologic units, it resulted in 19 new subzones. Therefore, these new 19 subzones incorporated all information (morfoestrutura and morfotectonica, fisiografia, ground, vegetation and litologia) derived from this dissertation research. Therefore, the land use map... (Complete abstract click electronic access below)

We present results from 30 nights of observations of the intermediate-age Solar-metallicity open cluster NGC 7789 with the WFC camera on the INT telescope in La Palma. From ~900 epochs, we obtained lightcurves and Sloan r' - i' colours for ~33000 stars, with ~2400 stars with better than 1% precision. We find 24 transit candidates, 14 of which we can assign a period. We rule out the transiting planet model for 21 of these candidates using various robust arguments. For 2 candidates we are unable to decide on their nature, although it seems most likely that they are eclipsing binaries as well. We have one candidate exhibiting a single eclipse for which we derive a radius of 1.81+/0.09- Three candidates remain that require follow-up observations in order to determine their nature. Monte Carlo simulations reveal that we expected to detect ~2 transiting 3d to 5d hot Jupiter planets from all the stars in our sample if 1% of stars host such a companion and that a typical hot Jupiter radius is similar to that of HD 209458b. Our failure to find good transiting hot Jupiter candidates allows us to place an upper limit on the 3d to 5d hot Jupiter fraction of 2.6% for all stars at the 1% significance level, and similar useful limits on the hot Jupiter fraction of the different star types in our sample.

Doctor of Philosophy / Department of Agronomy / V. P. Vara Prasad / Crop assessment and monitoring is important to crop management both at crop production level and research plot level, such as high-throughput phenotyping in breeding programs. Optical sensors based agricultural applications have been around for decades and have soared over the past ten years because of the potential of some new technologies to be low-cost, accessible, and high resolution for crop remote sensing which can help to improve crop management to maintain producers’ income and diminish environmental degradation. The overall objective of this study was to develop methods and compare the different optical sensors in crop assessment and monitoring at different scales and perspectives. At crop production level, we reviewed the current status of different optical sensors used in precision crop production including satellite-based, manned aerial vehicle (MAV)-based, unmanned aircraft system (UAS)-based, and vehicle-based active or passive optical sensors. These types of sensors were compared thoroughly on their specification, data collection efficiency, data availability, applications and limitation, economics, and adoption. At research plot level, four winter wheat experiments were conducted to compare three optical sensors (a Canon T4i® modified color infrared (CIR) camera, a MicaSense RedEdge® multispectral imager and a Holland Scientific® RapidScan CS-45® hand-held active optical sensor (AOS)) based high-throughput phenotyping for in-season biomass estimation, canopy estimation, and grain yield prediction in winter wheat across eleven Feekes stages from 3 through 11.3. The results showed that the vegetation indices (VIs) derived from the Canon T4i CIR camera and the RedEdge multispectral camera were highly correlated and can equally estimate winter wheat in-season biomass between Feekes 3 and 11.1 with the optimum point at booting stage and can predict grain yield as early as Feekes 7. Compared to passive sensors, the RapidScan AOS was less powerful and less temporally stable for biomass estimation and yield prediction. Precise canopy height maps were generated from a CMOS sensor camera and a multispectral imager although the accuracy could still be improved. Besides, an image processing workflow and a radiometric calibration method were developed for UAS based imagery data as bi-products in this project. At temporal dimension, a wheat phenology model based on weather data and field contextual information was developed to predict the starting date of three key growth stages (Feekes 4, 7, and 9), which are critical for N management. The model could be applied to new data within the state of Kansas to optimize the date for optical sensor (such as UAS) data collection and save random or unnecessary field trips. Sensor data collected at these stages could then be plugged into pre-built biomass estimation models (mentioned in the last paragraph) to estimate the productivity variability within 20% relative error.

abstract: Much of Mars' surface is mantled by bright dust, which masks the spectral features used to interpret the mineralogy of the underlying bedrock. Despite the wealth of near-infrared (NIR) and thermal infrared data returned from orbiting spacecraft in recent decades, the detailed bedrock composition of approximately half of the martian surface remains relatively unknown due to dust cover. To address this issue, and to help gain a better understanding of the bedrock mineralogy in dusty regions, data from the Thermal Emission Spectrometer (TES) Dust Cover Index (DCI) and Mars Reconnaissance Orbiter (MRO) Mars Color Imager (MARCI) were used to identify 63 small localized areas within the classical bright dusty regions of Arabia Terra, Elysium Planitia, and Tharsis as potential "windows" through the dust; that is, areas where the dust cover is thin enough to permit infrared remote sensing of the underlying bedrock. The bedrock mineralogy of each candidate "window" was inferred using processed spectra from the Mars Express (MEx) Observatoire pour la Mineralogie, l'Eau, les Glaces et l'Activité (OMEGA) NIR spectrometer and, where possible, TES. 12 areas of interest returned spectra that are consistent with mineral species expected to be present at the regional scale, such as high- and low-calcium pyroxene, olivine, and iron-bearing glass. Distribution maps were created using previously defined index parameters for each species present within an area. High-quality TES spectra, if present within an area of interest, were deconvolved to estimate modal mineralogy and support NIR results. OMEGA data from Arabia Terra and Elysium Planitia are largely similar and indicate the presence of high-calcium pyroxene with significant contributions of glass and olivine, while TES data suggest an intermediate between the established southern highlands and Syrtis Major compositions. Limited data from Tharsis indicate low-calcium pyroxene mixed with lesser amounts of high-calcium pyroxene and perhaps glass. TES data from southern Tharsis correlate well with the previously inferred compositions of the Aonium and Mare Sirenum highlands immediately to the south. / Dissertation/Thesis / Masters Thesis Geological Sciences 2014

<p> If given the correct remotely sensed information, machine learning can accurately describe soil moisture conditions in a heterogeneous region at the large scale based on soil moisture readings at the small scale through rule transference across scale. This paper reviews an approach to increase soil moisture resolution over a sample region over Australia using the Soil Moisture Active Passive (SMAP) sensor and Landsat 8 only and a validation experiment using Sentinal-2 and the Advanced Microwave Scanning Radiometer (AMSR-E) over Nevada. This approach uses an inductive localized approach, replacing the need to obtain a deterministic model in favor of a learning model. This model is adaptable to heterogeneous conditions within a single scene unlike traditional polynomial fitting models and has fixed variables unlike most leaning models. For the purposes of this analysis, the SMAP 36 km soil moisture product is considered fully valid and accurate. Landsat bands coinciding in collection date with a SMAP capture are down sampled to match the resolution of the SMAP product. A series of indices describing the Soil-Vegetation-Atmosphere Triangle (SVAT) relationship are then produced, including two novel variables, using the down sampled Landsat bands. These indices are then related to the local coincident SMAP values to identify a series of rules or trees to identify the local rules defining the relationship between soil moisture and the indices. The defined rules are then applied to the Landsat image in the native Landsat resolution to determine local soil moisture. Ground truth comparison is done via a series of grids using point soil moisture samples and air-borne L-band Multibeam Radiometer (PLMR) observations done under the SMAPEx-5 campaign (Panciera 2013). This paper uses a random forest due to its highly accurate learning against local ground truth data yet easily understandable rules. The predictive power of the inferred learning soil moisture algorithm did well with a mean absolute error of 0.054 over an airborne L-band retrieved surface over the same region. The validation experiment also demonstrated a strong linkage to the soil moisture, but the algorithm suffered from a lack of training data over such a small site. However, soil moisture estimation still exhibited a mean average error (MAE) of 0.028, compared to a 0.129 MAE of a deterministic model built upon the Air Force Weather Model.</p><p>

<p> Huanglongbing (HLB) is an incurable bacterial disease that kills citrus trees and threatens to decimate California's $2.2 billion citrus industry. A solution for limiting the spread of HLB is to rapidly detect infected trees with a chemical sensor equipped Unmanned Aerial System (UAS), which lands within tree proximity and deploys an extendable boom for air-sample collection. The Agricultural UAS project is a multidisciplinary engineering effort to conduct chemical sample collection and analysis in remote locations, to study a tilt-frame UAS concept performance, and to test a novel Propeller Thrust Governing System (PTGS). Simulated flight metrics show that a tilt-frame UAS concept significantly increases endurance, range, cruising performance, and service envelope over a conventional multi-rotor UAS design. A UAS prototype has been built by integrating the following subsystems: tilt-frame aircraft design, PTGS, and an attitude control system. The PTGS is a novel subsystem designed for regulating thrust of a constant velocity, non-variable pitch propeller through the use of actuated aerodynamic surfaces (flaps) for vehicle attitude control. Experiments conducted on a custom-built force measuring platform show that a standard/inverted flap combination produces a high force-to-flap deflection angle ratio, preserves a linear response, and minimizes coupling between downwards/sideways forces. An attitude controller was designed using a cascade PID scheme with a Mahony filter for rapid attitude estimation. By modeling system dynamics and using airfoil theory, predicted dynamic response and simulated flight metrics are generated and then experimentally validated with a functional prototype vehicle. Collected flight data deviates from predicted performance by less than 5%.</p><p>

No description available.