Individual tree detection and characterization using 3D remote sensing

Here, we will cover individual tree detection and characterization using 3D remote sensing. Simply, it means that point clouds are collected over a forested area using airborne laser scanning (ALS) or created using photogrammetric image interpretation and further used to detect individual trees using different algorithms. After the tree detection, the attributes of interest are predicted for each tree. We try to consistently use the terms “individual tree detection and characterization” or “individual tree detection” and “individual tree characterization” separately referring to different methodological steps. In the scientific remote sensing literature, terms individual tree detection (ITD), individual tree crown approach (ITC) and single tree inventory (STI) are also often used and most often they refer to the same thing. We’ll start by discussing why we need information from single trees. Then we go through the methodological steps that are used in individual tree detection and characterization: 1) remote sensing and field data collection, 2) data types and processing, 3) tree detection algorithms, and 4) methods for prediction of tree attributes. The current methodological state-of-the-art in individual tree detection and characterization is described before we’ll finally present some example applications in biodiversity monitoring, urban forestry and wood procurement planning. Why do we need information from single trees? When looking at this kind of forested landscape (Figure 1), we easily start to think that do we really need single tree information over vast areas? Is information from single trees really needed for science, forest management planning, carbon assessments and so on? Would it be enough to have forest inventory attribute rasters or information only for forest stands? Forests in Finland are covering only 0.5% of all the forests in the world and already here in Finland, it would be challenging to handle information from each tree at the national scale. This is a non-peer reviewed preprint submitted to EarthArXiv Figure 1. ​Single forest landscape can already include millions of trees. Information from single trees is definitely required for many scientific purposes. Information on single trees is required to understand the phenomena and processes that are happening in our forests (Vastaranta et al., 2020). There is no phenomenon that is happening only at the scale of a stand or raster cell. Forest growth is a sum of tree growth processes, and forest dynamics is all about interactions between single trees. Similarly, forest attributes are composed of individual tree attributes. For example, growing stock volume is the sum of the stem volumes of single trees. In forest management and wood procurement planning, single tree information is not necessarily required, but it could be highly beneficial, because we are often interested in single tree attributes or from attributes that are directly linked to single tree attributes, such stem number, basal area, mean volume, diameter and height (Figure 2). This is a non-peer reviewed preprint submitted to EarthArXiv Figure 2​. Single tree information could be beneficial for forest management and wood procurement planning. We should move from stand and raster level information (left) towards detailed characterization of single trees (See Figure 6). CVille Kankare Private forest owners and the forest industry may also have different kinds of information needs. If we think about economic aspects, they are affected by tree quality, amount of timber assortments, forest growth and scheduling of the operations. Decision making regarding all of these could benefit from tree-level information. Even now, forest management planning calculations are computed using single tree information, but calculations are using simulated not truly measured trees. Single tree information could be beneficial for continuous cover forestry as it requires information from tree size distributions. Tree map could be obtained from single tree inventories and it could provide many additional benefits for forest management and wood procurement planning, such as improved harvesting planning and growth update as spatial distribution of the trees would be known. In addition, some things are problematic for the area-based approach (ABA) that is currently used for providing rasterand stand-level forest information (Holopainen et al., 2014; White et al., 2013). For example, accuracy of the species-specific forest inventory attributes is rather limited and that leads to inaccurate predictions for tree size distributions (Siipilehto et al., 2016). Currently there aren’t any theories or research ideas presented that could provide major improvements to ABA. Although, it should be noted that individual tree detection-based forest inventories have problems as well. We’ll cover those problems later when describing the state-of-the-art. There are also trees that need to be monitored, but are not growing in a forest environment. For example, individual tree detection and characterization methods are needed for providing information for urban forestry and management of street trees in cities (Figure 3). This is a non-peer reviewed preprint submitted to EarthArXiv Figure 3. ​City of Helsinki is updating its tree register using remote sensing-based individual tree detection and characterization techniques. © Topi Tanhuanpää To summarize, natural processes and phenomena are not happening at the raster cell level. To understand these processes, such as forest growth, single tree information is needed or at least it would be highly beneficial to use it. ​For sure, there are applications or information needs that require information from trees and then there are applications where more generalized information (e.g. rasteror stand-level forest attributes) is suitable. Thus, it is important to make informed decisions when selecting the most suitable forest mapping techniques or data sources for some specific use-case. ​If single tree information is not used, we are simplifying many things. However, it should be noted that sometimes it makes sense to simplify things. If we think about science, carbon assessments, biodiversity, habitat assessments and forestry, most probably all of these would benefit from single tree information, but certainly, it is not absolutely necessary. According to our current understanding, if we want to push things forward, then we are pushing things towards more comprehensive use of single tree information in forest sciences and forestry. Single tree inventory techniques Data acquisition and processing According to literature review by Zhen et al. (2016) active remote sensing, mainly ALS data, has been the most used remote sensing data set for single tree detection (Figure 4). However, nowadays use of photogrammetric point clouds have rapidly increased and these percentages might have changed a bit. White et al., (2013) provides a good review of the utility of the image-based photogrammetric point clouds as well as comparisons to ALS. This is a non-peer reviewed preprint submitted to EarthArXiv Figure 4. ​ Summary of remotely sensed data used in individual tree detection (Zhen et al., 2016). Here, we are not going into details of measurement principles of ALS. If you are not familiar with those, please see Hyyppä et al. (2018) or Holopainen et al. (2013, in Finnish). As the most of the tree detection methods are based on point clouds, it is important to be aware of the differences between ALS and image-based point clouds. There are also radargrammetric and interferometric point clouds, but point density in those isn’t usually suitable (dense enough) for single tree detection (Magnard et al., 2016; Vastaranta et al. 2018). It has been stated that individual tree detection would need at least 2 pulses per m​ (Kaartinen et al., 2012). This was an issue 10-years ago, but nowadays, most of the new ALS datasets are denser than that. Next countrywide ALS coverage from Finland will be more than 5 pulses per m​. Photogrammetric point clouds can be created using satellite imagery (Vastaranta et al., 2018), aerial imagery (Vastaranta et al., 2013), drone-collected imagery ​(Saarinen et al., 2018) or images collected under the canopy (Liang et al., 2014). The main difference between ALS and photogrammetric point clouds is that laser pulses can penetrate through the canopy, but the photogrammetric point clouds mainly describe the outer canopy envelope (See Figure 5). Theoretically most of the methods can describe the outer canopy surface with similar detail. If your tree detection method is only using information about the outer canopy surface, both kinds of point clouds should work. Then differences in the laser pulse density or imaging geometry are the most important things that affect tree detection. It should be noted that photogrammetric point clouds are typically much denser than ALS point clouds, but the additional point density have not led to better tree detection accuracies (​ Hirschmugl et al., 2007). In addition to remote sensing data, some amount of field-measured trees are usually needed for single tree characterization depending what kind of attributes you would like to predict for each tree. We will cover this topic a bit later, but the general rule is that you should measure all the attributes in the field that you would like to predict later. This is a non-peer reviewed preprint submitted to EarthArXiv Figure 5. Laser pulses can penetrate the canopy, but the photogrammetric point clouds mainly describe the outer canopy envelope. Small canopy gaps are also better characterized in ALS-based CHM than by photogrammetry-based CHM (DSI). The most common data types that are used for single tree detection and characterization are point cloud, raster and voxel. A point cloud is a set of data points in space. There can be multiple attributes for each point in 3D-space including return inten