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Given the urgent priority around protecting the forests and limiting the impacts of the climate change, the constant monitoring of forests towards the achievement of accurate and timely detection of infestations and the catastrophic action of invasive insects, pests and fungi is an important and challenging task. More precisely, new species of insects that are introduced or already existing insect species whose population multiply uncontrollably into the forest area, affect tree growth, their survival, as well as the quality of forest biomass and constitute a serious threat to the mechanisms of such forest ecosystems. Thus, new concepts are needed that will overcome difficulties faced by existing remote sensing techniques and that would allow the timely and accurate health determination process of forest regions, assisting scientists and authorities to take action in order to protect the forests. In this paper, we propose a monitoring approach, which uses high resolution RGB aerial images and combines different Region Convolution Neural Networks (R-CNNs) architectures, namely Faster R-CNN and Mask R-CNN and fuses their bounding box outcomes in order to more accurately localize candidate infected trees’ regions whilst increasing the number of the candidate trees that have been detected as infected. Subsequently, the candidate detected trees are modelled through the higher order linear dynamical systems (h-LDS) and descriptors are extracted for each candidate region. Finally, the h-LDS descriptors are classified using an SVM classifier for the estimation of the infected trees. The study area includes parts of the suburban pine forest of Thessaloniki city (Greece) named Seich Sou, which suffers the last months an infestation of high significance and intensity by a bark and wood destroying insect (Tomicus piniperda). Although this insect was recorded in the specific ecosystem many years ago, its population increased uncontrollably after the degradation of the ecosystem due to human intervention and lack of protection and management strategy. Experimental results, through their outperforming existing state-of-the-art algorithms, demonstrate high potential and perspectives of the proposed methodology of low cost and time consumed, to contribute to the sustainable management, protection and recovery of a forest ecosystem.
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