ARナビゲーションで迷わない植生調査：広大な調査地も漏れなくカバー

Vegetation surveys are important fieldwork for recording plant species and distributions in natural environments such as forests, wetlands, urban green spaces, and post-disaster sites, and for understanding ecosystem conditions. In extensive sites, it is difficult for surveyors to accurately grasp their own positions and survey extents, and the risk of getting lost or overlooking parts of the area is ever-present. Also, if the location information of the collected data is offset, the accuracy of vegetation maps declines, making re-surveys or comparisons difficult. A promising solution to these challenges—allowing field teams to survey “without getting lost and without missing areas”—is the combination of AR navigation and high-accuracy GNSS positioning. This article explains in detail the spatial-recognition and navigation issues in vegetation surveys and how AR navigation combined with high-accuracy GNSS (such as RTK) can address them.

植生調査の基礎と広大な現場での課題

First, let’s cover the basics of vegetation surveys. In vegetation surveys, surveyors list plant species growing in the target area, classify vegetation types, record cover (approximate ground cover percentage), and evaluate the current condition and changes of the ecosystem. The work is carried out by specialists from environmental survey companies, university researchers, forestry workers, and municipal natural conservation staff, and the results are used as baseline material for forest management plans, conservation measures, and environmental assessments. In large areas such as mountain forests or wetlands, it is necessary to walk ranges from several hectares to several tens of hectares while plotting vegetation distributions on maps or setting up survey plots at regular intervals to record vegetation in detail.

However, wide-area field surveys come with difficulties in spatial awareness and navigation. Inside forests visibility is limited, making it hard to know where you are on a map. In wetlands or post-disaster sites the terrain can be complex and landmarks scarce, so it’s not easy to determine walking routes. The larger the survey area, the harder it is to mentally manage “how far we’ve surveyed” and “which areas remain un-surveyed,” leading to human errors such as survey omissions (overlooked areas) or conversely duplicate surveys (re-surveying the same location). In steep, uneven mountain forests, travel takes longer than straight-line distance would suggest, risking the inability to cover planned areas within the scheduled time. Given these challenges, the introduction of technologies that enable precise location awareness and efficient navigation has long been desirable in vegetation surveys.

目視・紙地図・従来型GPSではなぜミスが起きるのか

Traditionally, field position checks and navigation in vegetation surveys have relied on surveyors’ experience and intuition, paper topographic maps, and handheld GPS devices. However, visual observation, paper maps, and typical GPS methods cannot completely prevent survey omissions or location-offset errors. There are several reasons for this.

First, when moving across large natural areas using only a paper map and compass, there is a risk of losing your current location. In forests, trees block distant landmarks, making it difficult to estimate your position on a map. Even experienced surveyors can, within dense woodland, end up in situations such as “I thought I surveyed the east-facing slope of such-and-such stream, but actually I skipped part of the slope.” Also, the method of shading surveyed areas on a paper map with a pencil or marker makes it time-consuming to confirm exactly how far you’ve shaded in the field, and especially in team work, mismatches in recognition can lead to coverage gaps.

Next, the position information from general GPS devices or smartphones carries errors on the order of several meters (several ft). In open places this may be acceptable, but in forests with dense trees or in urban parks with tall buildings satellite signals are obstructed, and positional offsets of 5–10 m (16.4–32.8 ft) or more are not uncommon. Therefore, even if you record “a plot was placed at this point” relying on GPS, it may actually refer to a point several meters away. When attempting to revisit later, GPS coordinates alone may not allow you to return to the exact spot, requiring time-consuming searching. If the survey target is a rare plant’s natural habitat or a small-scale site, even slight positional offsets can cause misidentification of the area subject to conservation measures. In short, the accuracy of conventional GPS yields only vague “around here” location information and undermines the precision required for vegetation surveys.

Furthermore, human attention has limits. As fatigue accumulates during long outdoor work, map-reading errors and recording mistakes tend to increase. Especially in monotonous landscapes like vast wetlands or post-disaster sites, you can even lose your sense of which direction you are facing. When these factors combine, it becomes difficult to maintain survey comprehensiveness and accuracy, and the valuable results of fieldwork may contain omissions or errors.

スマホで実現するARナビゲーションとは？現場で威力を発揮する理由

A new means of solving these field problems is smartphone-based AR navigation. AR (Augmented Reality) navigation overlays digital information onto the camera view on a smartphone or tablet screen to provide intuitive guidance. In vegetation survey fields, using AR navigation allows you to display survey points and routes “superimposed on the actual scene,” making position awareness far easier compared to relying only on maps and compasses.

For example, if you register planned survey plot locations or area boundaries on a digital map in advance, simply pointing a smartphone camera on site will make markers or boundary lines appear on the screen as if “there is an invisible flag standing there.” Surveyors can then move toward those markers, so even in dense thickets the exact direction to proceed becomes immediately clear. Previously, surveyors had to stare at the arrow on a GPS receiver and imagine “how many meters left,” but with AR navigation the next target is visualized in the real scene, making it easier to perceive distance intuitively. Detour routes that skirt valleys or wetland edges can also be displayed along the actual terrain in AR, enabling smooth route selection that a paper map alone would not make obvious.

The strength of AR navigation is that information is visual and intuitive. Even inexperienced survey assistants can follow arrows or 3D markers displayed on the smartphone and reach points with precision comparable to veterans. In other words, stable survey quality that does not depend on individual intuition or skill can be achieved for anyone. If you no longer need to place map-reading experts in each site based on specific terrain conditions, personnel allocation becomes more flexible and overall team efficiency improves.

高精度GNSSとの組み合わせで向上する位置精度と再訪性

To make AR navigation a true asset, positioning accuracy is critically important. The digital guidance shown on a smartphone screen depends on the correspondence between data coordinates and real-world space. The key here is the introduction of high-accuracy GNSS (Global Navigation Satellite System) positioning.

While conventional GPS accuracy can produce the meter-level errors described above, recent high-accuracy positioning methods such as RTK (Real Time Kinematic) have made it possible to pinpoint positions down to a centimeter-level error range. RTK-GNSS achieves high accuracy by calculating the distance difference between a fixed base station and the rover (surveyor-side) in real time, canceling out satellite positioning error factors. In Japan, by using augmentation signals provided by the quasi-zenith satellite Michibiki (QZSS) such as CLAS or network-type RTK correction services, compatible smartphones or GNSS receivers can easily perform high-accuracy positioning.

Combining high-accuracy GNSS with AR navigation results in almost no discrepancy between the digital display and actual position. For example, when approaching a survey target point, the marker shown on the smartphone screen and the actual ground point will coincide within an error of several tens of centimeters (within several inches). This makes it possible to pinpoint rare plant individuals or fixed observation plots. During re-surveys you can stand virtually in the same position as the initial survey and start observations, allowing you to accurately capture temporal vegetation changes. With normal GPS, you could only specify “survey this general area” roughly and risk looking at slightly different areas on revisit, but high-accuracy GNSS allows you to reproduce the survey area spatially with precision.

Moreover, improved positioning accuracy dramatically enhances AR display stability. With meter-level errors, AR markers can wobble on the screen or appear to float off the real object, but if centimeter-level accuracy (half-inch accuracy) is assured, AR guidance stays synchronized with the real landscape and behaves like a physical signpost on site. This builds trust in the field and allows surveyors to rely on the digital guide with confidence.

スマホ＋ARナビ×高精度GNSSで誰でもできる精度の高い調査

By building a system that combines smartphones and high-accuracy GNSS devices for AR navigation, you can create an environment where anyone can conduct high-precision vegetation surveys without specialized equipment or advanced surveying skills. Previously, precise position-fixing and surveying required transits, high-performance GPS receivers, and specialized technicians. Today, palm-sized GNSS receivers connected to a smartphone enable centimeter-level positioning (half-inch accuracy), and that data can be used for real-time AR displays.

This configuration’s advantages are its portability and ease of operation. Smartphones are tools everyone is familiar with and can be operated intuitively without special training. High-accuracy GNSS receivers are compact and lightweight, and surveyors can carry them on backpacks or poles. Initial setup in the field takes only a few minutes, avoiding time-consuming equipment adjustments. In other words, you can devote more time and attention to the core of the survey—plant observation and recording.

Also, AR navigation streamlines team work. If each member imports the shared survey plan data (survey plots, route information, etc.) into their smartphones, everyone can move while viewing the same AR guidance, reducing the chance of missed or duplicate coverage. Tasks that used to rely on verbal coordination or paper maps can be semi-automatically allocated by digital maps and AR displays. This lets veterans focus on key checks while new staff reliably fill coverage areas by following AR guidance.

点群データや写真記録との連携で記録・報告も効率化

AR navigation plus high-accuracy GNSS contributes not only to on-site guidance but also to the advancement of data recording. By utilizing smartphone cameras and LiDAR sensors, you can digitally record information collected during surveys on site and link it to precise locations.

For example, a photo of vegetation status within a survey plot will have high-accuracy coordinates and orientation information tagged to it. When preparing reports later in the office, you can unambiguously identify “which point and which direction this photo was taken from,” enhancing the persuasiveness of explanatory materials. Also, by using built-in LiDAR functions (on supported models) or close-up photos, you can record the condition of rare species or ground cover as 3D point cloud data. Point cloud data can store a precise digital copy of the terrain and vegetation structure on site, making it useful for area measurements, cross-section analysis, and comparisons over time. It is revolutionary that information once recorded as sketches in field notebooks or flat photos can now be accumulated in three-dimensional, quantitative form.

Because recorded data is digital, real-time sharing and speedy reporting are also possible. After finishing surveys, you can upload data from smartphones to cloud storage and instantly share information with colleagues in the office. This eliminates the need to bring paper records back for manual input, greatly streamlining daily reports and result compilation. Visual reports showing photos and point clouds plotted on maps will be powerful tools when explaining findings to clients and stakeholders.

ARナビ＋高精度測位の活用事例と期待される効果

So, what outcomes can be expected when AR navigation and high-accuracy GNSS are introduced? Here are some illustrative cases and effects.

• Large-scale forest vegetation monitoring: An environmental survey company conducts regular monitoring in tens of hectares of mountain forest. The survey team divides areas according to AR-displayed survey zones and walks every section without omission. As a result, they covered the planned area with zero oversights and reduced work time by 20% compared to before. After the survey, each person’s movement track was recorded, making it immediately clear who surveyed which route.

• Rare plant surveys in wetlands: Municipal natural conservation staff survey habitats of rare aquatic plants across a wide wetland. AR navigation visually displays boundaries of protected areas that must not be entered while guiding them sequentially to the points to be surveyed. Because AR also indicated safe routes that avoided boggy ground, they moved efficiently. Thanks to high-accuracy GNSS, coordinate reliability was high, and recorded habitat locations were directly reflected in conservation area maps, aiding future follow-up surveys and conservation planning.

• Urban green space tree inventory: A university research team conducts a tree survey in a large city park. Using smartphones plus GNSS, they obtain accurate locations for each tree while assigning tree numbers via AR display and collecting data. The survey data was reflected in a cloud map in real time, letting researchers in distant labs monitor progress. This reduced communication loss between field and office and eliminated duplicate data整理. The generated tree inventory was shared immediately with the city and management contractors, demonstrating improved information sharing.

• Vegetation recovery monitoring in post-disaster sites: After major landslides or wildfires, teams assess ecosystem recovery. Traditional maps may be useless when terrain has dramatically changed, but AR navigation can display virtual survey grids and target points on site so surveyors can systematically traverse the area without confusion. High-accuracy GNSS allowed precise delineation of hazardous area boundaries, enabling safe yet extensive coverage. On such sites rapid data collection is required, and because AR enables intuitive overlays of old and new data, understanding vegetation change and evaluating restoration effects can be done swiftly.

As these examples show, introducing AR navigation plus high-accuracy GNSS can be expected to produce multifaceted effects such as prevention of survey omissions, reduction of labor, enhanced safety, and improved data sharing. Above all, for field staff the peace of mind of “not getting lost” and “being able to reliably reach points” leads to higher productivity and allows greater focus on the survey work itself.

おわりに：LRTKが切り拓く誰でもできる精密測量の世界

The use of AR navigation and high-accuracy GNSS in vegetation surveys is transforming fieldwork. Spatial recognition and positioning tasks that once relied on a few experienced individuals are becoming reproducible processes for anyone through digital technology, dramatically improving the quality and quantity of field data. Solutions like LRTK (an all-in-one high-accuracy positioning tool) that allow positioning, recording, and sharing with a single smartphone greatly lower the barrier to field deployment. With LRTK, simple surveying that combines centimeter-level GNSS positioning, 3D scanning, and AR guidance is possible without relying on specialized equipment. If you are considering DX (digital transformation) for vegetation surveys, try bringing such smartphone-based high-accuracy positioning tools to the field. Experience firsthand the benefits that precise navigation and positioning can bring. For more details on LRTK features and case studies, see the [LRTK official site](https://lrtk.lefixea.com/).