Abstract
Logging trails are one of the main components of modern forestry. However, spotting the accurate locations of old logging trails through common approaches is challenging and time consuming. This study was established to develop an approach, using cutting-edge deep-learning convolutional neural networks and high-density laser scanning data, to detect logging trails in different stages of commercial thinning, in Southern Finland. We constructed a U-Net architecture, consisting of encoder and decoder paths with several convolutional layers, pooling and non-linear operations. The canopy height model (CHM), digital surface model (DSM), and digital elevation models (DEMs) were derived from the laser scanning data and were used as image datasets for training the model. The labeled dataset for the logging trails was generated from different references as well. Three forest areas were selected to test the efficiency of the algorithm that was developed for detecting logging trails. We designed 21 routes, including 390 samples of the logging trails and non-logging trails, covering all logging trails inside the stands. The results indicated that the trained U-Net using DSM (k = 0.846 and IoU = 0.867) shows superior performance over the trained model using CHM (k = 0.734 and IoU = 0.782), DEMavg (k = 0.542 and IoU = 0.667), and DEMmin (k = 0.136 and IoU = 0.155) in distinguishing logging trails from non-logging trails. Although the efficiency of the developed approach in young and mature stands that had undergone the commercial thinning is approximately perfect, it needs to be improved in old stands that have not received the second or third commercial thinning.
Highlights
Accepted: 10 January 2022In modern timber harvesting, logging trails are crucial entities for the accurate navigations of harvesters and forwarders to penetrate into forest stands for silvicultural operations [1] in the pathway of precision harvesting
We classified forest stands concerning age, height, and thinning operations into four development categories to facilitate the detection of logging trails (Figure 1): (1) young stands before the first commercial thinning, (2) young stands that had experienced the first commercial thinning, (3) mature stands before the second commercial thinning, and (4) mature stands that had undergone the second or third thinning operation
The results of the accuracy assessment of the trained U-Net using the canopy height model (CHM), digital surface model (DSM), and digital elevation models (DEMs) datasets in distinguishing logging trails from non-logging trails demonstrate the superior performance of the DSM (Table 1)
Summary
In modern timber harvesting, logging trails are crucial entities for the accurate navigations of harvesters and forwarders to penetrate into forest stands for silvicultural operations [1] in the pathway of precision harvesting. Spotting the accurate locations of old logging trails is one the major and most challenging tasks for forest owners or operators/drivers, in the stands that have not undergone commercial thinning for a long period of time. Little is known about holistic solutions for the detection of logging trails using remote-sensing data. In Finland, rotation forest management (RFM) is the most common silvicultural method. It relies on three main phases: establishment, thinning, and final felling [2]. Forest stands are thinned two to three times between the ages of 20 and 70 years [3,4]
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