In recent years, the use of digital technologies in vegetation surveys of forests, grasslands, and other areas has attracted attention. Traditionally, researchers and technicians conducted on-site visual observations, recorded species names and counts on paper forms, or took photographs for later analysis. However, these methods have many issues, including human subjectivity, gaps in the data, and heavy workloads. At the forefront of solutions is the 3D point cloud scanning technology using laser measurement and photogrammetric analysis. By representing terrain and trees as innumerable points (point cloud data), it has become possible to visualize forest biomass and community structure in three dimensions—things that were previously invisible. This article organizes the objectives and challenges of vegetation surveys, explains the principles of point cloud scanning (LiDAR, SfM, smartphone measurements) and their applications to vegetation, compares the characteristics of ground-based, drone-based, and handheld systems, and discusses the possibilities of biomass estimation and time-series monitoring using 3D data. It also introduces the importance of high-precision positioning information essential to point cloud data and the practical improvements brought by the latest smartphone GNSS solution LRTK, considering the new era approaching vegetation surveys.

Objectives of Vegetation Surveys and Challenges of Conventional Methods

A vegetation survey is the investigation of the species, distribution, population counts, tree height, and canopy cover (the percentage of area covered) of plants growing in forests, grasslands, and similar environments. Its purposes range widely: grasping forest resources, assessing ecosystem health, formulating environmental conservation plans, and even conducting environmental assessments accompanying renewable energy development. For example, local government environmental officers regularly monitor vegetation changes to conserve biodiversity, and in forestry, measurements of forest growth and biomass (the total mass of living organisms) are used for sustainable resource management.

However, conventional vegetation survey methods have several challenges. A typical approach sets survey plots and counts plant species and numbers by visual inspection, recording them in paper field notebooks. For trees, calipers or tapes were used to measure diameter at breast height, and heights were estimated visually or with hypsometers. When surveying large forests, only some standard plots are sampled to estimate the whole, leading to inevitable survey omissions and estimation errors. Survey results are often influenced by the experience of the personnel, so observer variability and identification errors are unavoidable. For example, species identification and delineation of community boundaries require expertise and can result in differing evaluations among technicians. Furthermore, information recorded on paper or in photographs is difficult to analyze digitally later, making data sharing and accumulation cumbersome. Above all, it is difficult to directly observe the three-dimensional structure of a forest or its total biomass on site; accurate numbers historically required felling some trees and measuring their weight or volume, creating a dilemma. In this way, human-centered conventional methods had limits for comprehensive understanding of vast areas and long-term quantitative comparisons.

What Is Point Cloud Scanning: 3D Measurement by LiDAR and SfM

To solve these issues, measurement technologies collectively called 3D point cloud scanning have emerged. Point cloud data represent objects in real space as a set of many points, each containing X, Y, Z coordinates (and sometimes color or return intensity). Because the shape of the object is recorded as a density of points, complex forest structures can be modeled faithfully in 3D. Point clouds consist of tens of thousands to hundreds of millions of points; the higher the density and accuracy, the more the data can reproduce the fine details of trees and terrain at a photograph-like level. In other words, instead of flat paper vegetation maps, you can record the entire forest as a digital three-dimensional map.

Representative methods to acquire point cloud data include laser measurement (LiDAR) and photogrammetry (SfM: Structure from Motion). LiDAR irradiates the target with infrared laser light and measures distance by detecting the reflected light. The time it takes for the light to return is used to calculate the distance to the object, which is converted to spatial positions to acquire many points. By scanning the laser rapidly 360°, it is possible to collect wide-area three-dimensional point clouds in a short time. LiDAR offers high ranging accuracy, and ground-based types can even achieve millimeter-level precision (mm (in)). Also, if the wavelength is suitable, the laser can penetrate gaps in foliage to reach the ground, enabling capture of subsurface terrain and understory vegetation to some extent—another major strength.

On the other hand, SfM photogrammetry reconstructs 3D models from numerous images taken by cameras. By photographing the target area from various angles with high overlap using drones, DSLRs, or smartphones, and analyzing common points between images on a computer, point cloud data are generated. The appeal is its ease: you can start with just a camera and software, without dedicated laser equipment, making it accessible to small and medium-sized operators. With appropriate shooting conditions, very dense and precise point clouds can be obtained, and because each point can carry color information (RGB) derived from the photos, intuitive visualizations are possible (like a 3D version of aerial photographs). In practice, high-density point clouds obtained by drone photogrammetry are increasingly used in national land management for as-built management.

However, there are caveats with photogrammetry. Parts not captured by the camera cannot be reconstructed, so in forests where the canopy hides the ground or the undersides of branches, parts of the point cloud will be missing. If you want to measure the ground in densely forested areas, combining with laser scanning is desirable. Also, producing high-accuracy models often requires placing ground control points (airborne targets) beforehand, and the shooting preparation and processing time for large numbers of photos can be a drawback. Even so, as a non-destructive remote sensing technology that can measure wide areas at once, laser measurement and photogrammetry are tools that greatly advance conventional vegetation surveys.

Vegetation Information Captured by Point Cloud Data: Visualizing Biomass, Tree Height, and Community Structure

From 3D data obtained by point cloud scanning, various vegetation attributes that formerly relied on estimation can now be directly measured and visualized. For example, forest tree height can be accurately determined across an area by reading the ground surface and crown top heights from the point cloud. Traditionally, heights were measured on sample trees or estimated from fisheye photos to infer canopy height, but point clouds allow height distributions for all trees in the area. Canopy width can also be measured by analyzing airborne point clouds to determine each tree’s crown diameter and crown area—useful for assessing stand density and solar radiation simulations. With sufficiently high-density point clouds, trunks’ thickness and shapes can be captured to some degree, and research is progressing on estimating diameter at breast height and identifying species from such data.

Among the important indicators is biomass. Because point cloud data are like a complete 3D copy of the forest, you can perform sectional analyses without felling trees. For example, by estimating the volume of trunks, branches, and foliage for each tree and accounting for wood density and moisture content, it is possible to calculate the upper-layer biomass while the trees remain standing. Historically, accurate forest biomass measurement required felling trees and measuring dry weight, but point cloud measurement allows non-destructive estimation of standing biomass. This is a groundbreaking advance for evaluating forest carbon stocks and quantifying how much CO2 sequestration capacity is lost through thinning or harvesting.

Point cloud data also excel at visualizing community structure. By vertically slicing acquired point clouds, you can freely create cross-sections of a forest. This makes it easy to see the vertical distribution from the forest floor to the canopy (development of understory vegetation, stratification of shrub and tree layers, etc.). For instance, even in dense forests, a point cloud cross-section can intuitively show whether the understory is lush or whether no sunlight reaches the ground and the surface is bare. Comparing point clouds captured at different times can also capture time-series changes in vegetation. Repeated scans at fixed points annually can detect 3D differences such as how many meters trees grew over several years, newly recruited trees, or reductions due to logging or windthrow. In a sense, you create a digital twin of the forest and visualize differences from the past to scientifically track forest dynamics.

Analyzing the detailed data obtained by point cloud scanning evolves vegetation surveys from mere visual records to quantitative management. Because the forest’s current status can be represented numerically, setting conservation targets and verifying the effects of operations can be conducted based on objective data. For example, if the goal is to increase this protected area’s forest biomass by 5% over 10 years, progress can be checked against baseline data calculated from point clouds. Moreover, digitized 3D information is easy to share among stakeholders and can be overlaid on GIS maps together with other environmental information (terrain, soil, animal distributions) for comprehensive analysis. This is a change that could rightly be called the DX (digital transformation) of vegetation surveys.

Ground-Based vs Drone-Based vs Handheld: Comparison of Point Cloud Measurement Methods

Point cloud scanning encompasses various implementation methods. Here we compare the representative ground-based, drone-based, and handheld approaches and consider how they are used in vegetation surveys.

• Ground-based measurement (TLS, etc.): This method uses tripod-mounted laser scanners (terrestrial LiDAR) from the ground. From each setup point, high-density 360° point clouds are obtained, allowing extremely high-precision measurement of details such as tree height, trunk diameter, and branching. Because understory vegetation and fine terrain features obscured from the air can be recorded, it is suitable where precise data are required. However, the scannable area at one time is limited to the instrument’s line of sight, and parts shaded by obstacles cannot be acquired. Therefore, to record an entire forest without omissions, it is necessary to scan from multiple locations and perform point cloud merging (registration) during post-processing. The equipment is large, expensive, and requires specialized skills to operate, so covering a wide area requires time and cost. It is suitable for small survey plots and detailed research measurements but is not ideal for rapidly grasping entire forests of several hundred hectares.

• Drone-mounted measurement (UAV LiDAR/photogrammetry): This method uses small unmanned aerial vehicles (drones) to acquire point clouds from above. With UAV LiDAR carrying a laser scanner, three-dimensional surveying from the air can cover steep, inaccessible mountain forests and vast woodlands in a short time. Because drones fly, they can efficiently cover wide areas without walking the ground—this is their greatest advantage. With lasers, data of the ground surface can be obtained through gaps between trees, so even dense forests’ ground morphology and fallen-log distributions can be captured to some extent. However, there are constraints unique to aerial measurement. The cost of drone equipment and LiDAR sensors tends to be high, and flight permission applications and operator skills are required under aviation laws. Also, due to aircraft vibration and attitude changes during flight, point cloud accuracy and resolution tend to be somewhat lower than ground-fixed TLS. For applications requiring millimeter precision, achievement with drone-only measurement may be difficult, so the practical approach is often “use drones for wide areas and ground-based systems for precision.” Even with LiDAR, dense forests may prevent lasers from reaching the ground, creating data gaps. In that sense, drone measurement is useful for overall assessment and expanding survey range, but ground-based supplementation is sometimes necessary for detailed studies. Nonetheless, for dangerous or inaccessible locations and rapid post-disaster forest damage assessment, the speed and area coverage of drones are becoming indispensable.

• Handheld measurement (smartphones, SLAM, etc.): Recently notable are handheld devices and smartphones for point cloud measurement. Some mobile mapping systems (backpack-type LiDAR with SLAM) can scan while walking, continuously capturing 360° point clouds; using these, one can obtain continuous surrounding point clouds simply by walking through a forest. These systems remain expensive but are more mobile than ground-fixed scanners and can acquire data that fill gaps hard to capture by drones. A more accessible approach is point cloud scanning with smartphones. Since around 2020, some smartphones and tablets have begun to include small LiDAR sensors, allowing casual devices to record simplified surrounding point clouds using dedicated apps. The biggest advantages of smartphone measurement are ease and overwhelming mobility. With a pocket-sized device, you can go into the field and start a scan with one button, enabling one person to measure quickly without heavy equipment or special qualifications. After acquisition, cloud services can be used for instant sharing, enabling the 3D data scanned in the field to be shared with relevant departments the same day for action planning. However, current smartphone LiDAR has an effective range of only several meters to around a dozen meters (several ft to around a dozen ft), so the area covered at once is limited. To survey wide forestlands, you must walk and scan in segments and later merge the partial point clouds. The density and accuracy of smartphone point clouds do not match professional TLS or high-performance lasers. Therefore, they are insufficient for large-scale forest surveys covering hundreds of meters square or applications requiring millimeter precision, but conversely, for uses requiring “accuracy of several cm over ranges of several tens of meters,” they have in recent years approached a practically sufficient level. Low-cost, lightweight handheld and smartphone devices have the potential to permeate field forest censuses.

Point Cloud Data and High-Precision Positioning: Importance of GNSS Positioning

When applying 3D point cloud scanning to vegetation surveys, one must not forget the importance of positioning accuracy. No matter how detailed the point cloud data, it is a major limitation in practice if it is unclear where on a map it corresponds. For example, to compare a forest point cloud acquired in one year with one re-scanned five years later and discuss changes, both must align precisely in the same coordinate system to derive correct differences. Conventionally, terrestrial laser scanning required placing several survey control points (targets) in the field to provide geodetic coordinates to the point cloud. Similarly, photogrammetry requires ground control points or high-accuracy GPS information to align the entire model to geographic coordinates. In other words, to avoid leaving point cloud data as mere “one-off 3D models” and to integrate them with other geographic information in GIS or perform time-series comparisons, obtaining position information with centimeter-level accuracy is key.

In forests and mountainous areas, GPS positioning errors of several meters or more were common. Trees reflect and attenuate signals, disrupting satellite signals and making accurate positions difficult with handheld GPS units. However, in recent years, GNSS (Global Navigation Satellite System) high-precision positioning technologies have advanced dramatically. In particular, the RTK method (Real-Time Kinematic) can achieve position determination with errors of a few centimeters through relative positioning with a reference station. Using high-performance GNSS receivers for surveying, it has become possible to achieve centimeter-order accuracy even within forests with appropriate measures. For example, drone-mounted LiDAR records the aircraft’s trajectory with RTK-capable receivers so that the acquired point cloud receives latitude, longitude, and elevation directly. Likewise, combining RTK-GNSS with mobile SLAM scanners can correct the scanning trajectory and yield point clouds with little drift.

Now, making high-precision positioning dramatically easier is the advent of smartphone RTK. Solutions like LRTK (short for smartphone + RTK) mount a small high-precision GNSS receiver to a smartphone and use real-time correction information to improve smartphone GPS—previously off by meters—to centimeter-level accuracy instantly. If scan functions using lasers or cameras are linked in the dedicated app, point clouds obtained with a smartphone will have centimeter-precision position coordinates attached from the start. In other words, the smartphone transforms into a high-precision surveying instrument, eliminating the need for extensive post hoc reference-point adjustments. In Japan, services that augment satellite positioning (such as CLAS signals provided by the Quasi-Zenith Satellite System “Michibiki”) are supported, allowing centimeter-class positioning to continue using only satellite information even in remote mountain areas without mobile coverage. Using such technologies, surveyors can walk through a forest with a smartphone in hand, record point cloud data for trees one after another, and map each tree’s exact position and height on the spot. Without carrying heavy equipment into the mountains, digital modeling of forests with light gear and in short time becomes feasible.

Thus, the fusion of high-precision GNSS and point cloud scanning greatly enhances the practicality of vegetation surveys. Forest surveys that once required expert teams and heavy equipment can now be substituted by smartphones and small receivers, with results shared in the cloud the same day. Smartphone-based positioning technologies like LRTK simplify the entire workflow from data acquisition to analysis and are likely to broaden the pool of people capable of forest measurement and environmental monitoring. These efforts combining point cloud scanning and GNSS are driving vegetation surveys to become faster, more precise, and smarter. A new era in which biomass is visualized through 3D data is clearly arriving. To protect and utilize the forests and green spaces we live with, it is expected that next-generation vegetation surveys that wisely incorporate digital technologies will continue to expand.