Anton A. Sokolov

Parameterization of light absorption by components of seawater in optically complex coastal waters of the Crimea Peninsula (Black Sea)

The absorption of sunlight by oceanic constituents significantly contributes to the spectral distribution of the water-leaving radiance. Here it is shown that current parameterizations of absorption coefficients do not apply to the optically complex waters of the Crimea Peninsula. Based on in situ measurements, parameterizations of phytoplankton, nonalgal, and total particulate absorption coefficients are proposed. Their performance is evaluated using a log-log regression combined with a low-pass filter and the nonlinear least-square method. Statistical significance of the estimated parameters is verified using the bootstrap method. The parameterizations are relevant for chlorophyll a concentrations ranging from 0.45 up to 2 mg/m(3).

Parameterization of volume scattering function of coastal waters based on the statistical approach.

A parameterization of the volume scattering function (VSF) specific to coastal waters is proposed. We have found that the standard VSF parameterizations proposed by Fournier-Forand and Petzold do not fit our measurements obtained with a high angular resolution VSF-meter for water samples taken in the Black Sea coastal zone. We propose modeling VSF as a linear function of scattering, backscattering and particulate absorption. The statistical techniques employed allow us to retrieve the variability of VSF and to demonstrate the significance of the estimates obtained. The results of independent validation and the comparison with other commonly used parameterizations are provided.

Retrieval of forest stand attributes using optical airborne remote sensing data

Optical remote sensing data processing is proposed for the airborne images of high spectral and spatial resolution. Optimization techniques are undertaken to gain information about spatial distribution of the pixels on the hyperspectral images and the texture of the forest stands of different species and ages together with reducing redundancy of the spectral channels used. The category of neighborhood of pixels for particular forest classes and the step up method of selecting optimal spectral channels are employed in the relevant processing procedures. We present examples of pattern recognition for the forests as a result of separating pixels, which characterize the sunlit tops, shaded space and intermediate cases of the Sun illumination conditions on the hyperspectral images.

Improved technique for retrieval of forest parameters from hyperspectral remote sensing data.

This paper describes an approach of machine-learning pattern recognition procedures for the land surface objects using their spectral and textural features on remotely sensed hyperspectral images together with the biological parameters retrieval for the recognized classes of forests. Modified Bayesian classifier is used to improve the related procedures in spatial and spectral domains. Direct and inverse problems of atmospheric optics are solved based on modeling results of the projective cover and density of the forest canopy for the selected classes of forests of different species and ages. Applying the proposed techniques to process images of high spectral and spatial resolution, we have detected object classes including forests within their contours on a particular image and can retrieve the phytomass amount of leaves/needles as well as the relevant total biomass amount for the forest canopy.

Evaluation of weather research and forecasting model parameterizations under sea-breeze conditions in a North Sea coastal environment

Three atmospheric boundary layer (ABL) schemes and two land surface models that are used in the Weather Research and Forecasting (WRF) model, version 3.4.1, were evaluated with numerical simulations by using data from the north coast of France (Dunkerque). The ABL schemes YSU (Yonsei University), ACM2 (Asymmetric Convective Model version 2), and MYJ (Mellor–Yamada–Janjic) were combined with two land surface models, Noah and RUC (Rapid Update Cycle), in order to determine the performances under sea-breeze conditions. Particular attention is given in the determination of the thermal internal boundary layer (TIBL), which is very important in air pollution scenarios. The other physics parameterizations used in the model were consistent for all simulations. The predictions of the sea-breeze dynamics output from the WRF model were compared with observations taken from sonic detection and ranging, light detection and ranging systems and a meteorological surface station to verify that the model had reasonable accuracy in predicting the behavior of local circulations. The temporal comparisons of the vertical and horizontal wind speeds and wind directions predicted by the WRF model showed that all runs detected the passage of the sea-breeze front. However, except for the combination of MYJ and Noah, all runs had a time delay compared with the frontal passage measured by the instruments. The proposed study shows that the synoptic wind attenuated the intensity and penetration of the sea breeze. This provided changes in the vertical mixing in a short period of time and on soil temperature that could not be detected by the WRF model simulations with the computational grid used. Additionally, among the tested schemes, the combination of the localclosure MYJ scheme with the land surface Noah scheme was able to produce the most accurate ABL height compared with observations, and it was also able to capture the TIBL.

Regional monitoring of forest vegetation using airborne hyperspectral remote sensing data

Some results are given of the airborne applications to recognize forest classes of different species and ages for a test area based on the imaging spectrometer produced in Russia. Optimization techniques are outlined to select the most informative spectral bands for the particular subject area of the forest applications using the improved Bayesian classifier in the pattern recognition supervising procedures. A successive addition method is used in this optimization with the calculation of the probability error of the statistical pattern recognition while collecting the spectral ensembles for the known classes of forest vegetation for different species and ages. The subsequent step up method consists in fixing the level of the probability error that is not improved by adding the channels in the related computational procedures. The best distinguishable classes are recognized at the first stage of these procedures. The analytical technique called “cross-validation” is used for this purpose. The second stage is realized as a stable feature selection method based on the standard stepwise optimization approach, holdout cross-validation and resampling.

Simulation of Local Atmospheric Dynamics in the Coastal Region of Dunkerque

The structure of the lower troposphere has been studied during the sea-breeze and post sea-breeze events in an industrialized coastal area of the North Sea. Atmospheric dynamics and dispersion of pollutants in the lower troposphere have been analyzed by the experimental results of the 3D nonhydrostatic Meso-NH model in Dunkerque area (51°N, 2.20°E), in the north of France. The simulations were verified and extended by data of the measurement campaign. Ground-based remote sensing systems (lidar and sodar), surface meteorology and air quality network stations data have been employed. We illustrate the different pollution scenarios and breeze structure by the analysis of Lagrangian tracers and back trajectories.

Inventário de Emissões com Alta Resolução para a Região da Grande Vitória Utilizando o Sistema de Modelagem Integrada WRF-SMOKE-CMAQ

Atmospheric pollution from anthropogenic activities has been bothering the population of Great Vitoria Region (GVR), Espírito Santo, Brazil. Some people are particularly vulnerable to poor air quality: those affected by health conditions such as asthma, allergies, chemical sensitivity, heart disease, stroke and diabetes, as well as pregnant women, children and people of advanced age. Atmospheric pollutants complex interactions can be understood by using chemical transportation models, which require emissions inventories to provide spatial and temporal allocation of emissions. The emissions inventory of GVR on 2010 has been provided by the State Institute of Environment (IEMA). On this study, the regional sources inventory has been adapted to Sparse Matrix Operator Kernel Emissions (SMOKE) in order to be used on Air Quality Models (AQM) such as Community Multi-scale Air Quality (CMAQ) modeling system and other photoRevista Brasileira de Meteorologia, v. 33, n. 3, 521-536, 2018 rbmet.org.br DOI: http://dx.doi.org/10.1590/0102-7786333011

Recognition of Forest Species and Ages Using Algorithms Based on Error-Correcting Output Codes

The basic model of the recognition of forest inventory characteristics using spectral features is represented in the framework of the problem of hyperspectral airborne imagery processing. The algorithm of multiclass supervised classification based on the error-correcting output codes underlies this model. The support vector machine method is used as the necessary binary classifier. The method of the construction of training set by using mixed forest plots is represented. Results of the retrieval of species and age composition of forest stands from hyperspectral images are represented for the selected test area. The estimate of accuracy of the retrieval of the mixed forest composition is comparable with the accuracy of ground-based forest inventory data.

Application of airborne hyperspectral remote sensing for the retrieval of forest inventory parameters

Collecting and updating forest inventory data play an important part in the forest management. The data can be obtained directly by using exact enough but low efficient ground based methods as well as from the remote sensing measurements. We present applications of airborne hyperspectral remote sensing for the retrieval of such important inventory parameters as the forest species and age composition. The hyperspectral images of the test region were obtained from the airplane equipped by the produced in Russia light-weight airborne video-spectrometer of visible and near infrared spectral range and high resolution photo-camera on the same gyro-stabilized platform. The quality of the thematic processing depends on many factors such as the atmospheric conditions, characteristics of measuring instruments, corrections and preprocessing methods, etc. An important role plays the construction of the classifier together with methods of the reduction of the feature space. The performance of different spectral classification methods is analyzed for the problem of hyperspectral remote sensing of soil and vegetation. For the reduction of the feature space we used the earlier proposed stable feature selection method. The results of the classification of hyperspectral airborne images by using the Multiclass Support Vector Machine method with Gaussian kernel and the parametric Bayesian classifier based on the Gaussian mixture model and their comparative analysis are demonstrated.