A Review of Individual Tree Crown Detection and Delineation From Optical Remote Sensing Images: Current progress and future
Juepeng Zheng, Shuai Yuan, Weijia Li, Haohuan Fu, Le Yu, Jianxi Huang
Abstract
Powered by advances of optical remote sensing sensors, the production of very high spatial-resolution multispectral images provides great potential for achieving cost-efficient and high-accuracy forest inventory and analysis in an automated way. Lots of studies that aim at providing an inventory of the level of each individual tree have generated many methods for individual tree crown detection and delineation (ITCD). This review article covers ITCD methods for detecting and delineating individual tree crowns, and systematically reviews the past and present of ITCD-related research applied to optical remote sensing images. With the goal to provide a clear knowledge map of existing ITCD efforts, we conduct a comprehensive review of recent ITCD articles to build a metadata analysis, including the algorithm, study site, tree species, sensor type, evaluation method, and so on. We categorize the methods into three classes: 1) traditional image processing methods (such as local maximum filtering and image segmentation), 2) traditional machine learning methods [such as random forest (RF) and decision tree (DT)], and 3) deep learning-based methods. With deep learning-oriented approaches contributing to a majority of the articles, we further discuss deep learning-based methods like semantic segmentation and object detection methods. In addition, we discuss four ITCD-related issues to further comprehend the ITCD domain using optical remote sensing data, such as comparisons between multisensor-based data and optical data in the ITCD domain, comparisons among different algorithms and different ITCD tasks, and so forth. Finally, this review article proposes some ITCD-related applications and a few exciting prospects and potential hot topics in future ITCD research.