Prediction of Land Surface Temperature under Cloudy Conditions using Microwave Remote Sensing and ANN

Land surface temperature (LST) is an important variable in climate, hydrologic, ecological, biophysical and biochemical studies (Mildrexler et al., 2011). The most effective way to obtain LST measurements is through satellites. Presently, LST from moderate resolution imaging spectroradiometer (MODIS) sensor is applied in various fields due to its high spatial and temporal availability over the globe, but quite difficult to provide observations in cloudy conditions. This study evolves of prediction of LST under clear and cloudy conditions using microwave vegetation indices (MVIs), elevation, latitude, longitude and Julian day as inputs employing an artificial neural network (ANN) model. MVIs can be obtained even under cloudy condition, since microwave radiation has an ability to penetrate through clouds. In this study LST and MVIs data of the year 2010 for the Cauvery basin on a daily basis were obtained from MODIS and advanced microwave scanning radiometer (AMSR-E) sensors of aqua satellite respectively. Separate ANN models were trained and tested for the grid cells for which both LST and MVI were available. The performance of the models was evaluated based on standard evaluation measures. The best performing model was used to predict LST where MVIs were available. Results revealed that predictions of LST using ANN are in good agreement with the observed values. The ANN approach presented in this study promises to be useful for predicting LST using satellite observations even in cloudy conditions. (C) 2015 The Authors. Published by Elsevier B.V.

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Evaluating approaches and technologies for monitoring organic contaminants in the aquatic environment, Ann Arbor, MI, July 21-23, 2006: workshop proceedings

The Alliance for Coastal Technologies (ACT) convened a workshop on Evaluating Approaches and Technologies for Monitoring Organic Contaminants in the Aquatic Environment in Ann Arbor, MI on July 21-23, 2006. The primary objectives of this workshop were to: 1) identify the priority management information needs relative to organic contaminant loading; 2) explore the most appropriate approaches to estimating mass loading; and 3) evaluate the current status of the sensor technology. To meet these objectives, a mixture of leading research scientists, resource managers, and industry representatives were brought together for a focused two-day workshop. The workshop featured four plenary talks followed by breakout sessions in which arranged groups of participants where charged to respond to a series of focused discussion questions. At present, there are major concerns about the inadequacies in approaches and technologies for quantifying mass emissions and detection of organic contaminants for protecting municipal water supplies and receiving waters. Managers use estimates of land-based contaminant loadings to rivers, lakes, and oceans to assess relative risk among various contaminant sources, determine compliance with regulatory standards, and define progress in source reduction. However, accurately quantifying contaminant loading remains a major challenge. Loading occurs over a range of hydrologic conditions, requiring measurement technologies that can accommodate a broad range of ambient conditions. In addition, in situ chemical sensors that provide a means for acquiring continuous concentration measurements are still under development, particularly for organic contaminants that typically occur at low concentrations. Better approaches and strategies for estimating contaminant loading, including evaluations of both sampling design and sensor technologies, need to be identified. The following general recommendations were made in an effort to advance future organic contaminant monitoring: 1. Improve the understanding of material balance in aquatic systems and the relationship between potential surrogate measures (e.g., DOC, chlorophyll, particle size distribution) and target constituents. 2. Develop continuous real-time sensors to be used by managers as screening measures and triggers for more intensive monitoring. 3. Pursue surrogate measures and indicators of organic pollutant contamination, such as CDOM, turbidity, or non-equilibrium partitioning. 4. Develop continuous field-deployable sensors for PCBs, PAHs, pyrethroids, and emerging contaminants of concern and develop strategies that couple sampling approaches with tools that incorporate sensor synergy (i.e., measure appropriate surrogates along with the dissolved organics to allow full mass emission estimation).[PDF contains 20 pages]

Application of Drifting Buoy Technologies for Coastal Watershed and Ecosystem, Ann Arbor, Michigan, June 5-7,2005: workshop proceedings

The Alliance for Coastal Technologies (ACT) Partner University of Michigan convened a workshop on the Applications of Drifting Buoy Technologies for Coastal Watershed and Ecosystem Modeling in Ann Arbor, Michigan on June 5 to 7,2005. The objectives of the workshop were to: (1) educate potential users (managers and scientists) about the current capabilities and uses of drifting buoy technologies; (2) provide an opportunity for users (managers and scientists) to experience first hand the deployment and retrieval of various drifting buoys, as well as experience the capabilities of the buoys' technologies; (3) engage manufacturers with scientists and managers in discussions on drifting buoys' capabilities and their requirements to promote further applications of these systems; (4) promote a dialogue about realistic advantages and limitations of current drifting buoy technologies; and (5) develop a set of key recommendations for advancing both the capabilities and uses of drifting buoy technologies for coastal watershed and ecosystem modeling. To achieve these goals, representatives from research, academia, industry, and resource management were invited to participate in this workshop. Attendees obtained "hands on" experience as they participated in the deployment and retrieval of various drifting buoy systems on Big Portage Lake, a 644 acre lake northwest of Ann Arbor. Working groups then convened for discussions on current commercial usages and environmental monitoring approaches including; user requirements for drifting buoys, current status of drifting buoy systems and enabling technologies, and the challenges and strategies for bringing new drifting buoys "on-line". The following general recommendations were made to: 1). organize a testing program of drifting buoys for marketing their capabilities to resource managers and users. 2). develop a fact sheet to highlight the utility of drifting buoys. 3). facilitate technology transfer for advancements in drifter buoys that may be occurring through military funding and development in order to enhance their technical capability for environmental applications. (pdf contains 18 pages)

A novel FOCAL technique based on BP-ANN

In this paper, the feed-forward back-propagation artificial neural network (BP-ANN) algorithm is introduced in the traditional Focus Calibration using Alignment procedure (FOCAL) technique, and a novel FOCAL technique based on BP-ANN is proposed. The effects of the parameters, such as the number of neurons on the hidden-layer and the number of training epochs, on the measurement accuracy are analyzed in detail. It is proved that the novel FOCAL technique based on BP-ANN is more reliable and it is a better choice for measurement of the image quality parameters. (c) 2005 Elsevier GmbH. All rights reserved.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

Memoir of Mrs. Ann H. Judson, wife of the Rev. Adoniraum Judson, missionary to Burmah, including a history of the American Baptist Mission in the Burman Empire.

http://www.archive.org/details/memoirofmrsannhj00judsuoft