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This paper presents different approaches to map bark beetle infested forests in Croatia. Bark beetle infestation presents threat to forest ecosystems. Due to large unapproachable area, it also presents difficulties in mapping infested areas. This paper analyses available machine learning options in open-source software QGIS and SAGA GIS. All options are performed on Copernicus data, Sentinel 2 satellite imagery. Machine learning and classification options are maximum likelihood classifier, minimum distance, artificial neural network, decision tree, K Nearest Neighbor, random forest, support vector machine, spectral angle mapper and Normal Bayes. Kappa values respectively are: 0.71; 0.72; 0.81; 0.68; 0.69; 0.75; 0.26; 0.60; 0.41 which shows highest classification accuracy for artificial neural networks method and lowest for support vector machine accuracy. [ABSTRACT FROM AUTHOR]

Since the 1990s, remote sensing images have been used for land cover classification combined with Machine Learning algorithms. The satellites and airborne sensors pass over a specific point of land surface periodically, it is possible to assess the change in land cover over a long time. With the advent of Machine Learning (ML) methods, automated land cover classification has been at the center of research for the last few decades. From 2015 forward, a technical shift has been noticed with the emergence of several branches of Neural Networks (NN) and Deep Learning (DL). This paper examines current practices, problems, and trends in satellite image processing. This formal review focused on the summarization of major classification approaches from 1995. Two dominant research trends have been noticed in automated land cover classification, e.g., per pixel and subpixel analysis. Classical machine learning algorithms and deep learning methods are mainly used for per-pixel analysis, whereas fuzzy logic algorithms are used for sub-pixel analysis. The current article includes the research gap in automated land cover classification to provide comprehensive guidance for subsequent research direction. [ABSTRACT FROM AUTHOR]

Wireless spectrum is a limited resource, and the rapid increase in demand for wireless communication-based services is increasing day by day. Hence, maintaining a good quality of service, high data rate, and reliability is the need of the day. Thus, we need to apportion the available spectrum in an efficient manner. Dual-Function Radar and Communication (DFRC) is an emerging field and bears vital importance for both civil and military applications for the last few years. Since hybridization of wireless communication and radar designs provoke diverse challenges, e.g., interference mitigation, secure mobile communication, improved bit error rate (BER), and data rate enhancement without compromising the radar performance, this paper reviews the state-of-the-art developments in the spectrum shared between mobile communication and radars in terms of coexistence, collaboration, cognition, and cooperation. Compared to the existing surveys, we explore an open research issue on radar and mobile communication operating with mutual benefits based on collaboration in terms of spectrum sharing. Additionally, this paper provides important perspectives for future research of DFRC technology. [ABSTRACT FROM AUTHOR]