# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## 1 Introduction  
Forest ecosystems are essential providers of services like food, water, timber, and the regulation of climate, floods, water quality, as well as biodiversity and recreation [1]. Sustainable adaptation and management of forests has become vital under current conditions of human development and climate change. Remote sensing technologies have been widely used in forest inventory and monitoring to support forest management [2]. Among various remote sensing technologies, laser scanning, or light detection and ranging (lidar), has attracted particular interest due to its unique advantage, i.e., the ability to penetrate through the foliage and capture both tree structures and the ground [3]. Lidar has been integrated into various platforms to study the forest at different scales, including space-borne satellites (e.g., Global Ecosystem Dynamics Investigation) [4], airborne systems (e.g., helicopter or plane) [5], unmanned aerial vehicles (UAVs) [6], ground mobile platforms (e.g., vehicle, backpack, handheld) [7], and ground stationary tripods [8]. Each of those lidar systems can produce point clouds with different characteristics in terms of coverage, point density, field of view, and accuracy, making them best suited for different purposes. Airborne lidar or airborne laser scanning (ALS) systems normally consist of three main components: A global navigation satellite system (GNSS) receiver for absolute positioning, an inertial measurement unit for both positioning and orientation, and a laser scanner that measures the ground by distanceand angle in the form of 3D points [9; 10]. They are usually mounted on airplanes for a large coverage while maintaining a good (cm) level of accuracy. The point density is dependent upon a few factors, e.g., scanner measurement rate and scanning mechanism, flight height and speed, swath width, and strip overlaps, hence it may vary from less than 1 point per m\({}^{2}\) to more than 50 points per m\({}^{2}\). But in general, the maximum point density is getting higher with the development of airborne laser scanners. Early studies have mostly focused on the characteristics at stand-level, such as canopy cover and height, from airborne lidar data, due to limited point density [11; 12; 13]. Now the point density is high enough to capture a sufficient number of points on each individual tree, so that individual tree detection or delineation (ITD), including tree location, size, shape and number, has drawn considerable attention [5; 14; 15; 16]. Vertical distribution, above ground biomass and other secondary properties, can be derived from those accurate delineation parameters. Therefore, ALS has been increasingly used for precise forest mapping and monitoring at landscape or regional scale [10]. Although ITD from airborne lidar is an important research topic for forest studies, it still remains as a challenge due to the complexity and heterogeneity of the forest structure and its composition. The main difficulty of ITD is tree segmentation, a step to segment the overall points into clusters that represent individual trees. There are two main strategies for tree segmentation: Raster-based and point-based [17; 18]. Earlier methods mostly adopted the first strategy, converting the 3D point clouds into canopy height models (CHMs), a raster image, then detecting tree tops using 2D image processing techniques such as local maxima, region growing and watershed [5]. The second strategy segments the trees based directly on 3D points [14; 19]. Examples include rule-based distance and height thresholding [16; 20; 21], voxel-based [22], graph-based [23], and kernel-based [24] methods. Some tried to combine both strategies to separately detect tree tops and trunks, then segment in the voxel space [25]. Segmentation methods based directly on 3D point clouds are proven to outperform those based on 2D raster conversions, such as CHM, especially for multi-layered forests [14; 19].