Abstract
Deforestation is one of the biggest ecological challenges faced globally nowadays. From 1993 to 2020 the world lost almost 154 Mha of forests which brought about various negative conse-quences, such as an increase in greenhouse gas emissions, disruption of water cycles, increased soil erosion, disrupted livelihoods, etc. Scientists agree that artificial forestation is the only way to solve the problem on its present scale. Classic methods of artificial forestation include man-ual and automated ones or their combination. Despite its simplicity and comparably easier im-provement, classic methods also have some disadvantages that do not allow them to solve the problem, i.e., bad scalability. Automated forestation that utilizes UAVs is a new promising ap-proach that was developed in the last decades. The current paper addresses its common imple-mentations compared to classic forestation methods from the perspective of its improvement possibility based on their synthesis. Analysis of the existing experience of UAV-based foresta-tion consists of three stages: surface scanning, data processing and planting itself. The research showed that in addition to the usage of modelling and optimization algorithms at the first two stages, analogically to classic methods higher efficiency can be achieved by planting seedlings instead of seeds. The present paper suggests performing planting from the air to the defined points based on the creation and optimization of a tree situation model. Such approach requires the usage of advanced LIDAR generated land surface modelling techniques and methods of tra-jectory calculation. The article describes a step-by-step new method of tree planting automa-tion, suggesting theoretical and practical perspectives and directions for future research.
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