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Underwater imaging is challenging because of the significant attenuation of light due to absorption and scattering of light in water. Using polarization properties of light is one of the options for improving image quality. We present results of imaging of a polarized target in open ocean (Curacao) and coastal (NY Bight) waters. The target in the shape of a square is divided into several smaller squares, each of which is covered with a polarizing film with different polarization orientations or transmission coefficients was placed on a mirror and imaged under water by a green-band full-Stokes polarimetric video camera at the full range of azimuth angles against the Sun. The values of the Stokes vector components from the images are compared with the modeled image of the target using radiative transfer code for the atmosphere-ocean system combined with the simple imaging model. It is shown that even in clear water the impact of the water body on the polarized underwater image is very significant and retrieval of target polarization characteristics from the image is extremely challenging.

This paper provides a synergetic approach between numerical modeling and remote sensing of bio-optical water properties. The work demonstrates that appropriate data-assimilation schemes make numerical modeling a suitable and reliable tool for filling the gaps arising due to satellite imagery unavailability and/or cloud covering. In this research we apply the Princeton Ocean Model to the Sea of Azov, assimilating bio-optical indexes ( index 34 and b bp (555)) from MODIS L2 products. These data identify the presence of suspended matter (mineral suspended matter from river discharges or resuspending as a result of a strong wind), and suspended matter of biological origin. The ad hoc assimilation/correction scheme allows for prediction (and reanalysis) of transport and diffusion of the bio-optical tracers. Results focus on the ability of the method to provide spatial maps that overcome the general issues related to Ocean Color imagery (e.g., cloud cover) and on the comparison between the assimilating and the non-assimilating runs. Methods of joined information analysis are discussed and the quality of model forecasts is estimated depending on the intervals of the satellite data assimilation. Hydrodynamic modeling of the Sea of Azov was carried out for the period of 2013–2014 applying meteorological data of the regional weather forecasting system SKIRON/Eta . The analysis of data coherence helps to detect negative changes to the sea waters, predict them and forecast typical areas and territories subject to anthropogenic impact. The successive data-assimilation algorithm is proved to improve the forecast of suspended matter transfer.

The land use in China has an important impact on environment and the Chinese economic development. The sustainable development has been recognized as the key element to keep China's development with success in the future. This paper uses ecological footprint and biological capacity as indicators to measure regional sustainable degree of land use. The ecological footprint and the biological capacity of land use in Chongqing municipality with 28 million populations have been calculated from 1999 to 2007. The ecological footprint and the biological capacity of land use in Chongqing municipality had obvious changed from 1999 to 2007. The change degree of ecological footprint affected by land use is more prominent than the change degree of biological capacity. The fossil energy land possesses the most proportion, which exceeds 58% in average. The cropland is also an important effect to ecological footprint, which achieves to 35% in proportion. The results from the research can inform the Chongqing municipality government in more detail enhances the land protection in the balance of urban development with society, ecology, environment and resource.

Complex mission specifications can be often specifiedthrough temporal logics, such as Linear Temporal Logic and itssyntactically co-safe fragment, scLTL. Finding trajectories thatsatisfy such specifications becomes hard if the robot is to fulfilthe mission in an initially unknown environment, where neitherlocations of regions or objects of interest in the environmentnor the obstacle space are known a priori. We propose an algorithmthat, while exploring the environment, learns importantsemantic dependencies in the form of a semantic abstraction,and uses it to bias the growth of an Rapidly-exploring randomgraph towards faster mission completion. Our approach leadsto finding trajectories that are much shorter than those foundby the sequential approach, which first explores and then plans.Simulations comparing our solution to the sequential approach,carried out in 100 randomized office-like environments, showmore than 50% reduction in the trajectory length. QC 20210803

Hydrometeor classification system using fuzzy logic technique based on dual-polarization radar measurements is presented. In this study, five radar measurements (horizontal reflectivity, differential reflectivity, specific differential phase, correlation coefficient, and linear depolarization ratio), and height relating to environmental melting level are used as input variables of the system. The hydrometeor classification system chooses one of nine different hydrometeor categories as output. The system presented in this paper is a further development of an existing hydrometeor classification system model developed at Colorado State University. The hydrometeor classification system is evaluated by comparison against the in situ sample data collected by instrumentation on T-28 aircraft during Severe Thunderstorm Electrification and Precipitation Study (STEPS).

Exploration of an unknown environment is a fundamental concern in mobile robotics. This paper presents an approach for cooperative multi-robot exploration, fire searching and mapping in an unknown environment. The proposed approach aims to minimize the overall exploration time, making it possible to localize fire sources in an efficient way. In order to achieve this goal, the robots should cooperate in an effective way, so they can individually and simultaneously explore different areas of the environment while they identify fire sources. The proposed approach employs a decentralized frontier based exploration method which evaluates the cost-gain ratio to navigate to target way-points. The target way-points are obtained by an A* search variant algorithm. The potential field method is used to control the robots motion while avoiding obstacles. When a robot detects a fire, it estimates the flame's position by triangulation. The communication between the robots is done in a decentralized control way where they share the necessary data to generate the map of the environment and to perform cooperative actions in a behavioral decision making way. This paper presents simulation and experimental results of the proposed exploration and fire search method and concludes with a discussion of the obtained results and future improvements. 1

Recent developments in environmental studies are related to worldwide ecological problems associated with anthropogenic impacts on the biosphere. Pollution is an undesirable product of human activity. Industrial, agricultural, forestry, and transportation all generate substances and by-products that are considered pollutants. Remote sensing technologies are an effective tool in numerous environmental investigations relevant to ecosystems preservation, biodiversity conservation and other problems of global importance. In agriculture, remote sensing is used for assessing plant growth, condition, and for identification of stress situations. This paper is devoted to the study of the impact of heavy metal contamination on species performance and the possibility to detect pollution stress from measurements of plant spectral characteristics. A main goal is to study the relationships between the stress factor and plant spectral features, and to assess the ability of various spectral indicators to detect plant heavy metal-induced stress. Multispectral measurements were performed over spring barley and pea plots subjected to Ni and Cd pollution. Significant correlations were observed between plant bioparameters and different spectral features. Meaningful statistical relationships were established between the heavy metal pollution amounts, plant bioparameters and spectral properties that allow detection and quantification of the stress factor affect on plant performance.

We address a new approach to the problem of improvement of the quality of remote sensing (RS) images obtained with several imaging systems/methods as required for end-user-oriented environmental resource management. We present the elaborated end-user-oriented software that provides the necessary tools for numerical implementation/simulation of different RS image reconstruction-fusion paradigms. In this paper, we present the computational methodology and software that performs RS image enhancement/fusion using the recently developed non parametric high-resolution techniques, in particular, regularized constrained least squares method, weighted constrained least squares , robust Bayesian minimum risk , and robust maximum entropy (ME) methods. We develop a modified ME neural network (NN)-oriented technique to perform the reconstruction-fusion tasks in a computationally efficient manner. We also present a quantitative and qualitative characterization of the performance of the developed MENN reconstruction/fusion algorithms evaluated through software simulations, along with their comparison with the previously developed regularized inverse filtering and NN-based image reconstruction techniques that do not accomplish the data/method fusion. Simulation examples are reported to illustrate the good overall performances of the end-user-oriented fussed image reconstruction achieved with the elaborated software in application to the real-world 2-dimensional RS imagery.

There is a growing demand for an automatic surveillance system for road traffic data and industrial workroom environments. These data are required for surveillance and control. The problem of diagnostic intruders in a dangerous areas, knocks generally to the illumination changes. From the beginning of this work, it was stated that, the device had to supervise a robotic environment, in real time, in order to detect the abnormal situations. This paper describes implementation of a fast algorithm of surveillance system that performs tracking of robot's manipulator arm and detection of moving objects. The aim of this work is to avoid collision between human and moving machines. This paper presents a new approach of surveillance allowing unpredictable robotics tasks and tolerant independent illumination changes. We present in our paper an original method to modelize the scene by an image spatial sampling and an algorithm to detect moving objects. The detection is based on the observation of changes between a reference and the current images.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.