In this paper, we describe a method for the prediction of concentration of chlorophyll-a (Chl-a) from satellite data in the coastal waters of Kaštela Bay and the Brač Channel (our case study areas) in the Republic of Croatia. Chl-a is one of the parameters that indicates water quality and that can be measured by in situ measurements or approximated as an optical parameter with remote sensing. Remote sensing products for monitoring Chl-a are mostly based on the ocean and open sea monitoring and are not accurate for coastal waters. In this paper, we propose a method for remote sensing monitoring that is locally tailored to suit the focused area. This method is based on a data set constructed by merging Sentinel 2 Level-2A satellite data with in situ Chl-a measurements. We augmented the data set horizontally by transforming the original feature set, and vertically by adding synthesized zero measurements for locations without Chl-a. By transforming features, we were able to achieve a sophisticated model that predicts Chl-a from combinations of features representing transformed bands. Multiple Linear Regression equation was derived to calculate Chl-a concentration and evaluated quantitatively and qualitatively. Quantitative evaluation resulted in $R^2$ scores 0.685 and 0.659 for train and test part of data set, respectively. A map of Chl-a of the case study area was generated with our model for the dates of the known incidents of algae blooms. The results that we obtained are discussed in this paper.

This paper presents a semantic conceptual framework and definition of environmental monitoring and surveillance and demonstrates an ontology implementation of the framework. This framework is defined in a mathematical formulation and is built upon and focused on the notation of observation systems. This formulation is utilized in the analysis of the observation system. Three taxonomies are presented, namely, the taxonomy of (1) the sampling method, (2) the value format and (3) the functionality. The definition of concepts and their relationships in the conceptual framework clarifies the task of querying for information related to the state of the environment or conditions related to specific events. This framework aims to make the observation system more queryable and therefore more interactive for users or other systems. Using the proposed semantic conceptual framework, we derive definitions of the distinguished tasks of monitoring and surveillance. Monitoring is focused on the continuous assessment of an environment state and surveillance is focused on the collection of all data relevant for specific events. The proposed mathematical formulation is implemented in the format of the computer readable ontology. The presented ontology provides a general framework for the semantic retrieval of relevant environmental information. For the implementation of the proposed framework, we present a description of the Intelligent Forest Fire Video Monitoring and Surveillance system in Croatia. We present the implementation of the tasks of monitoring and surveillance in the application domain of forest fire management.

In this paper we are investigating the possibility of usage of remote sensing satellite data, more precisely sentinel-3 OLCI and SLSTR data, for assessment of bathing water quality. In this research we used data driven approach and analysis of data in order to pinpoint aspects of remote sensing data that can be useful for bathing water quality assessment. For this purpose we collected satellite images for period from start of June till end of September of 2019 and results of in-situ measurement for the same period. Results of in-situ measurement were correlated with satellite images bands and analyzed. We propose a simple method for rapid assessment of possible deterioration of bathing water quality to be used by public health authorities for better planning of in situ measurements. Results of implementation of predictive models based on k-nearest neighbour (KNN) and decision tree (DT) are described.

Modern forest fire surveillance systems offer automatic observers as an assistance to human monitoring. Intelligent algorithms analyze video stream, trying to find early visual signs of fire, which are smoke during the day and flames during the night, with large expected detection distance. In the early stage of fire, smoke occupies a very small part of the image. Degradations like mist, dust, camera shake, pronounced sunlight effects and dirt on camera lenses lowers the quality of images. In this phase smoke is often hardly distinguishable even for a human operator responsible for confirming an alarm. All of the abovementioned make detecting early visible signs of a forest fire a complex task. Deep learning algorithms applied to emerging smoke footage typically perform poorly compared to other problems, with a high false alarm rate. In this paper, we study the possibility of using other available pieces of information that define the context and dynamic characteristics of an image. This information is merged into a multi-channel image. The information content of the resulting data set is evaluated by applying the same neural network architecture to original RGB images collected from surveillance cameras and compiled multichannel images. The obtained results encourage further research in this direction.

In this paper were analyzed the applicability of regression models to the problem of real time viewership prediction. In this paper the analysis is based on precise viewership data obtained from the national company responsible for maintaining the broadcasting infrastructure. The viewership data and the TV schedule archive data were preprocessed to create a dataset on which the analysis is performed. Analysis was performed on the correlation of the number of viewers with time series trends and TV schedule features. The results were compared with several implemented and trained models, and compared with the performance of various tested models. In this paper is described the methodology of building the models and discussed the obtained results.