Abstract

Site preparation by mounding is a commonly used silvicultural treatment that improves tree growth conditions by mechanically creating planting microsites called mounds. Following site preparation, an important planning step is to count the number of mounds, which provides forest managers with an estimate of the number of seedlings required for a given plantation block. In the forest industry, counting the number of mounds is generally conducted through manual field surveys by forestry workers, which is costly and prone to errors, especially for large areas. To address this issue, we present a novel framework exploiting advances in Unmanned Aerial Vehicle (UAV) imaging and computer vision to estimate the number of mounds on a planting block accurately. The proposed framework comprises two main components. First, we exploit a visual recognition method based on a deep learning algorithm for multiple object detection by pixel-based segmentation. This enables a preliminary count of visible mounds and other frequently seen objects on the forest floor (e.g., trees, debris, accumulation of water) to be used to characterize the planting block. Second, since visual recognition could be limited by several perturbation factors (e.g., mound erosion, occlusion), we employ a machine learning estimation function that predicts the final number of mounds based on the local block properties extracted in the first stage. We evaluate the proposed framework on a new UAV dataset representing numerous planting blocks with varying features. The proposed method outperformed manual counting methods in terms of relative counting precision, indicating that it has the potential to be advantageous and efficient under challenging situations.