Airborne and Terrestrial Laser Scanning in Forests

Dear Colleagues,

Nowadays, airborne and terrestrial laser scanning are widely utilized to collect point cloud data for hectare- or larger-scale forest resource surveys, aiding in the estimation of tree and stand attributes, detection and delineation of tree crowns, analysis of forest borders, changes and its ecosystem and biodiversity, etc.

In this Special Issue, we welcome new research progress and contributions for forest-related research using airborne and terrestrial laser scanning (or combining other earth observation data). We look forward to novel datasets, new algorithm design or broader application domains in forest resource surveys.

Traditional pipelines for extracting different forest structural attributes from point cloud data include pulse-based or voxel-based gap probability methods (used for the estimation of plant or leaf area indexes of forest stands) and geometrical modeling (used for explicit reconstruction of individual tree structure). In addition, radiative transfer models (RTMs) also improve the accuracy of the retrieval of forest biophysical properties when coupled with earth observation data.

Over the recent years, artificial intelligence, especially the machine learning and deep learning model, has had a significant effect on processing point cloud data and shown great potential in forest resource surveys. Moreover, fusing other multi-modal data (such as optical remote sensing data or GIS data) with point cloud data (collected from UAV, airborne and spaceborne) has become a popular way to improve the performance in regional and large-scale forest resource surveys.

We are looking for papers that focus on forest-related surveys using point cloud data from airborne and terrestrial laser scanning (or combining other kinds of remote sensing data or GIS data), including forest parameter estimation (such as diameter at breast height, tree height, aboveground biomass, leaf area index, etc.), tree detection (such as location, delineation, counting, species classification, etc.) and large-scale forest surveys (such as forest borders, change detection, forest ecosystem, biodiversity, etc.)

Dr. Juepeng Zheng
Dr. Zhouxin Xi
Dr. Zhen Ye
Dr. Shangshu Cai
Guest Editors

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