Automatic detection of tree cutting in forests using acoustic properties

Deforestation is cutting trees of forests on a huge scale, often resulting in loss of habitat of millions of wild animals. About 30% of earths land is still covered with forests but due to deforestation we are losing them at the rate of about half the size of England per year. In forests, tree cutting activities are illegal but due to shortage of manpower and other resources, governments are not very successful in curbing this menace. One way to stop this is to detect the tree cutting process in an early stage so that timely measures can be taken to stop the same. The simplest method of early detection of tree cutting is to regularly monitor the forest area either manually or using some automatic techniques. As tree cutting generates lot of noise, it can be detected by regularly monitoring the acoustic signals inside the forest. An acoustic signature can provide valuable information about the activities of any intruder inside the forest. This paper proposes an algorithm for automatic detection of tree cutting in forest. The proposed algorithm is based on distance between parameters, along with K-means clustering, GMM and PCA for comparison. The efficiency of proposed algorithm is 92%. (C) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Automated summary

Deforestation is a significant issue that results in the loss of habitat for millions of wild animals. Detecting tree cutting in its early stages and taking timely measures can help prevent deforestation. This paper proposes an algorithm for automatically detecting tree cutting, with an efficiency of 92%.