Open Access

Muhammed Sinan

• The area covered with forests and trees is an important indicator of the state of the environment. There are multiple difficulties that forests face which cause them to decline. It is possible to track the physiological stress caused by biotic or abiotic stimuli in forests. At present, one of the major problems is the large number of dead trees that directly damage neighbouring trees. If a thorough overview of the affected area and the number of damaged trees can be quickly recorded and incorporated into the planning of forest management measures. Dead trees can be identified appropriately using Remote Sensing (RS) and Artificial Intelligence (AI) approaches. AI has developed a number of image segmentation algorithms that can classify dead trees from RS data, such as unmanned aerial vehicles (UAVs) images. One of the Machine Learning (ML) algorithm, Deep Learning (DL), is becoming increasingly popular due to its outstanding image segmentation performance and various image processing methods. From this perspective, this research aimsThe area covered with forests and trees is an important indicator of the state of the environment. There are multiple difficulties that forests face which cause them to decline. It is possible to track the physiological stress caused by biotic or abiotic stimuli in forests. At present, one of the major problems is the large number of dead trees that directly damage neighbouring trees. If a thorough overview of the affected area and the number of damaged trees can be quickly recorded and incorporated into the planning of forest management measures. Dead trees can be identified appropriately using Remote Sensing (RS) and Artificial Intelligence (AI) approaches. AI has developed a number of image segmentation algorithms that can classify dead trees from RS data, such as unmanned aerial vehicles (UAVs) images. One of the Machine Learning (ML) algorithm, Deep Learning (DL), is becoming increasingly popular due to its outstanding image segmentation performance and various image processing methods. From this perspective, this research aims to utilize one of the DL models, U-net, to segment the dead spruce (Picea abies) trees in the UAV orthophotos. The network was trained using several experiments as preliminary tests by altering the pixel size, loss functions, the number of parameters, etc. As an outcome, the semantic image segmentation using U-net architecture and the combination of an appropriate training strategy for dead spruce tree detection on UAV orthophotos were successful. However, the prediction model revealed a minor overfitting, which may be fixed by adding more training data sets and modifying the U-net architecture. Even though the constructed model and the available data were used to produce a very effective visual explanation of the classification.…