A Novel Approach for Coarse-to-Fine Windthrown Tree Extraction Based on Unmanned Aerial Vehicle Images

Abstract

Surveys of windthrown trees, resulting from hurricanes and other types of natural disasters, are an important component of agricultural insurance, forestry statistics, and ecological monitoring. Aerial images are commonly used to determine the total area or number of downed trees, but conventional methods suffer from two primary issues: misclassification of windthrown trees due to the interference from other objects or artifacts, and poor extraction resolution when trunk diameters are small. The objective of this study is to develop a coarse-to-fine extraction technique for individual windthrown trees that reduces the effects of these common flaws. The developed method was tested using UAV imagery collected over rubber plantations on Hainan Island after the Nesat typhoon in China on 19 October 2011. First, a coarse extraction of the affected area was performed by analyzing the image spectrum and textural characteristics. A thinning algorithm was then used to simplify downed trees into skeletal structures. Finally, fine extraction of individual trees was achieved using a line detection algorithm. The completeness of windthrown trees in the study area was 75.7% and the correctness was 92.5%. While similar values have been reported in other studies, they often include constraints, such as tree height. This technique is proposed to be a more feasible extraction algorithm as it is capable of achieving low commission errors across a broad range of tree heights and sizes. As such, it is a viable option for extraction of windthrown trees with a small trunk diameter.

A New Individual Tree Crown Delineation Method for High Resolution Multispectral Imagery

“…Yang et al [3] proposed a watershed segmentation method for ITC delineation from high-resolution multispectral aerial imagery, but only part of the crowns in the image was delineated in experiment, and the whole delineation accuracy was unknown. Duan et al [32] performed a coarse extraction of tree crowns with respect to image spectral and textural characteristics and achieved an accuracy of 84% on unmanned aerial vehicle images. Asner et al [33] detected dead trees in Hawaiian forests successfully using a spectral mapping index.…”

Estimating Coarse Woody Debris Volume Using Image Analysis and Multispectral LiDAR

“…Since the advent of high-speed, large-data computing, a variety of automated remote-sensing solutions have been implemented to identify and measure forest structure and components through the analysis of imagery collected with aircraft and satellites. Remote sensing of CWD has been achieved with variable degrees of success on satellite data [8,9] and high-resolution piloted and unpiloted aircraft imagery [10][11][12]. These studies commonly use pixel-based or geographical object-based image analysis (GEOBIA) solutions to process aerial images using linear models, classification trees, and machine learning to extract information regarding CWD objects.…”

“…If human interpreters could identify at most 90% of relatively large CWD during leaf-off, which represents good visibility conditions, one would expect automated detection to perform worse on more difficult conditions or smaller CWD. Some of the most successful recent studies of automated detection of CWD on forests, such as those of Duan et al [11], Stereńczak et al [14], and Lopes Queiroz et al [12], have developed sophisticated methods for the detection of visible CWD on aerial images, but have based their accuracy metrics of what is visible in aerial photos and not from field data, and therefore their efficacy based on ground truth remains unknown.…”

An Automatic Tree Skeleton Extracting Method Based on Point Cloud of Terrestrial Laser Scanner

“…In recent years, TLS has been more mature with the development of computer technology and digital image processing technology [1][2][3][4][5]. TLS could obtain the threedimensional information of the object by measuring many sample points of the objects [6][7][8].…”