AI-Accelerated Decision Making in Forest Management

Abstract

This paper is a continuation of the research on the application of artificial intelligence in counting trees with the use of methods for the automatic analysis of photogrammetric data in forests of the continental region. This paper is devoted to the AI application in accelerating decision making processes in forest management. It also discusses how RGB imagery from drones could replace aerial and satellite hyperspectral imagery and automatically detect unhealthy and dead trees. Experimental research was conducted to verify whether Faster R-CNN can automatically detect and classify snag and trees weakened by diseases on aerial RGB data, enabling a quick response to forest-threatening factors. The research is based on photogrammetric data taken in areas of forest districts subordinate to the Regional Directorate of State Forests in Zielona Gora. Non-metric imagery data was collected from drones and small airplanes with a photogrammetric container and postprocessed with respect to the photogrammetric constraints. The results show that in specific cases aerial and satellite hyperspectral imagery can be replaced by RGB orthomosaics in order to decrease the time needed for forestry treatments.