# Illegal Logging Detection Based on Acoustic Surveillance of Forest
## 2 Related Work  
The detection of illegal logging in forests has attracted great interest in the research community mainly due to the substantial effect it has on the environment, the economy, and society, and therefore many studies in the literature aim at automatically detecting logging in forests. A complete presentation of methods and works on environmental sound recognition can be found in [12] with many works and studies on illegal logging in various urban and forest environments in [13]. Most systems have relied on wireless sensor networks (WSNs) and have utilized sound sensors to detect the operation of chainsaws, as well as vibration sensors to specify the exact position where logging was taking place in a forest [14,15]. In [16], [PERSON] and [PERSON] presented a methodology for recognizing tree cutting in forests utilizing acoustic properties that were based on the distance between parameters, and also utilizedGaussian mixture measure (GMM), principal component analysis (PCA), and k-means clustering. Their methodology achieved satisfactory performance, reporting an accuracy of up to 92% in dense forests and up to 76% in open forests. In [17], the authors proposed a three-tier architecture that could be used for monitoring a forest. The architecture was aimed at continuously monitoring a forest area to recognize illegal logging by using chainsaw noise identification methods on wireless sensor networks. In addition to the detection of chainsaw noises, the authors also presented methods that could localize the position of the noise of the chainsaws which were based on the time difference of arrival (TDOA) paired with multilateralism. Finally, the work utilized neural networks to efficiently identified acoustic signals of the chainsaws. In [18], the authors presented a prototype system aimed at detecting illegal logging, which was based on the utilization of both vibration and sound sensors. Sound sensors were utilized to spot chainsaws, and vibration sensors were used to specify the falling of trees in forests. The Arduino Nano framework was utilized and GSM modules provided information to the guard patrols in the forests. The study results pointed out that the value of 63.4 dB for the chainsaws, as well as the threshold of 4400 for the vibration sensors, were suitable for detecting of illegal logging. In [15], the authors designed and introduced a system that was based on wireless sensor networks and various sensors to detect and recognize illegal cutting of trees. In the nodes of the network, sound and vibration sensors were employed. The Xbee Pro S2C module was utilized as a communication medium and the Arduino Nano was used for data processing procedures. The system introduced by the authors was tested in a small forest and open area scenarios and the findings showed that the authors' work was cost-efficient and had a promising performance. In [19], the authors presented a methodology for recognizing chainsaws and for specifying their position. The authors detected sound signals of chainsaws in soil and air, as well as the time difference of the arrival of the two waves in the two mediums. The sound wave from chainsaws was detected via microphone and geophone sensors. The methodology relied on a correlation to determine the time difference and to specify the distance between the sound source to a specific sensor, and also to specify the direction mainly by preformation microphone rotations. The system that was built based on the authors' methodology was energy efficient, and the testing phase reported an accuracy of 95%. Authors, in [14], addressed logging detection and introduced a method that used vibration and sound sensors to detect illegal tree logging in mountains. In their work, they utilized simple subtraction of two data to obtain differential signal strength as a feature of the vibration. The results from this experimental study indicated that the method could distinguish between vibrations of sawing wood and vibrations of human bodies.