Using Deep Learning to Detect the Need for Forest Thinning: Application to the Lungau Region, Austria

Abstract

Timely information about the need to thin forests is vital in forest management to maintain a healthy forest while maximizing income. Currently, very-high-spatial-resolution remote sensing data can provide crucial assistance to experts when evaluating the maturity of thinnings. Nevertheless, this task is still predominantly carried out in the field and demands extensive resources. This paper presents a deep convolutional neural network (DCNN) to detect the necessity and urgency of carrying out thinnings using only remote sensing data. The approach uses very-high-spatial-resolution RGB and near-infrared orthophotos; a canopy height model (CHM); a digital terrain model (DTM); the slope; and reference data, which, in this case, originate from spruce-dominated forests in the Austrian Alps. After tuning, the model achieves an F1 score of 82.23% on our test data, which indicates that the model is usable in a practical setting. We conclude that DCNNs are capable of detecting the need to carry out thinnings in forests. In contrast, attempts to assess the urgency of the need for thinnings with DCNNs proved to be unsuccessful. However, additional data, such as age or yield class, have the potential to improve the results. Our investigation into the influence of each individual input feature shows that orthophotos appear to contain the most relevant information for detecting the need for thinning. Moreover, we observe a gain in performance when adding the CHM and slope, whereas adding the DTM harms the model’s performance.