Detection of fallen logs from high-resolution UAV images

Abstract

Background: High-resolution images from unmanned aerial vehicles (UAVs) can be used to describe the state of forests at regular time periods in a cost-effective manner. The purpose of this study was to assess the performance of a line template matching algorithm, the Hough transformation, for detecting fallen logs from UAV-based high-resolution RGB images. The suggested methodology does not aim to replace any known aerial method for log detection, rather it is more oriented to the detection of fallen logs in open forest stands with a high percentage of log visibility and straightness.
 Methods: This study describes a line template matching algorithm that can be used for the detection of fallen logs in an automated process. The detection technique was based on object-based image analysis, using both pixel-based and shape descriptors. To determine the actual number of fallen logs, and to compare with the ones predicted by the algorithm, manual visual assessment was used based on six high-resolution orthorectified images. To evaluate if a line matched, we used a voting scheme. The total number of detected fallen logs compared with the actual number of fallen logs based on several accuracy metrics. To evaluate predictive models we tested the cross-validation mean error. Finally, to test how close our results were to chance, we used the Cohen`s Kappa coefficient.
 Results: The detection algorithm found 136 linear objects, of which 92 of them were detected as fallen logs. From the 92 detected fallen logs, 86 were correctly predicted by the algorithm and 24 were falsely detected as fallen logs. The calculated amount of observed agreement was equal to 0.78, whereas the expected agreement by chance was 0.61. Finally, the kappa statistic was 0.44.
 Conclusions: Our methodology had high reliability for detecting fallen logs based on total user‘s accuracy (94.9%), whereas a Kappa of 0.44 indicated there was good agreement between the observed and predicted values. Also, the cross-validation analysis denoted the efficiency of the proposed method with an average error of 16%.

Mapping Coarse Woody Debris with Random Forest Classification of Centimetric Aerial Imagery

“…Other studies mapping logs on aerial images were able to detect logs with comparable accuracy, such as Baumann et al [9] with about 68% and 95%, Duan et al [16] with 75.7% and 92.5%, Rüetschi et al [11] with 88% and 85%, and Panagiotidis et al [17] with 84.6% and 94.9% completeness and correctness, respectively. However, the accuracies in the present study reflect more challenging conditions involving CWD in diverse settings-natural and disturbed forest-at varying stages of decomposition and over a large application area.…”

Remotely piloted aircraft systems and forests: a global state of the art and future challenges

“…The overall accuracy increased steadily with the increase in the stump diameter. In the same vein as Puliti et al (2018), Panagiotidis et al (2019) assessed the detection of fallen logs from RPAS images of very high resolution. Their importance occurs mainly in areas that are difficult to access and for a quick assessment of damages such as those caused by wind.…”

Archaeological documentation of wood caribou fences using unmanned aerial vehicle and very high-resolution satellite imagery in the Mackenzie Mountains, Northwest Territories

“…Challenges in CWD field sampling techniques (Ståhl et al 2001;Woldendorp et al 2004), as well as the relevance of dead wood in a host of forest ecosystem functions (Woodall et al 2019), have sparked an interest in applying remotely sensed data for this purpose. For example, CWD objects have recently been detected with encouraging results using LiDAR point clouds (Polewski et al 2015;Joyce et al 2019), aerial photography by means of Unmanned Aerial Vehicles (UAV) (Inoue et al 2014;Duan et al 2017;Panagiotidis et al 2019), or using LiDAR combined with multispectral aerial photography (Richardon and Moskal 2016;Stereńczak et al 2017;Lopes Queiroz et al 2019). Alternatively, models can be established leveraging spectral and structural relationships between plot-level observations of CWD volume and LiDAR or winter Landsat scenes (Sumnall et al 2016;Wolter et al 2017).…”

“…Despite the use of VHR imagery and high-density point clouds, studies have reported challenges in the detection of smaller CWD objects due to high densities of small overlapping or parallel objects, occlusions, and shadowing, with reported plot-level CWD object detection rates of 23%-25% (Inoue et al 2014; Joyce et al 2019) and rates of small logs (e.g., <5 m, 20 cm DBH) as low as 5% (Inoue et al 2014; Panagiotidis et al 2019). Subarctic montane woodlands are considerably less productive than southern boreal or temperate forests whereby limited size of CWD pieces can be expected.…”