Background & Summary

The European Council’s Directive 92/43/EEC (Habitats Directive)1 requires monitoring habitats and species of community interest listed in Annexes I and II based on specific protocols. Based on this Directive, an extensive system of protected areas, the Natura 2000 Network, was also established. Effective monitoring of the habitats, particularly inside this Network, is crucial for their conservation in the face of increasing human impact on natural environments2,3,4. Nevertheless, monitoring is a complex task requiring experience, knowledge and specific skills5. Nowadays, human operators are the only ones able to apply the protocols. Artificial robotics represents a new challenge for repetitive and time-consuming activities characteristic of habitat monitoring. This paper presents a new dataset related to robotic monitoring of the Mediterranean coastal dune as defined in the Habitat Directive1. According to the Annex I of the Directive, habitats are identified by a four-digit codex. The initial digit broadly signifies the type of environment, which, in this context, is the coastal sand dunes and inland dunes habitat. The subsequent digits provide more specificity, 2110 corresponds to Embryonic shifting dunes and 2120 to Shifting dunes with Ammophila arenaria (white dunes). Steep ecological gradients characterize these habitats which turn on a delicate balance between sand erosion and deposit6,7. The zonation of plant communities across the gradients depends on species tolerance and adaptation to dune morphology. Specifically, the two monitored habitats are located on the active dunes where dune-building grasses work to build and maintain the entire system using their roots as trap sand.

These habitats are facing a multitude of threats at a global, European and national level8,9,10. The main issues are land use change and landscape fragmentation, mechanical disturbances, trampling, invasive species, sea-level rise and extreme meteorological events. Effective monitoring at the local scale relies on selected species, including typical species (TS)11, which can reflect a favourable status of the structure and function of the habitat and on species having a negative role, such as alien invasive species12,13. The standard data collection is based on field sampling in plots (1 m × 1 m) along transects5,14. TS and aliens are identified inside each plot, and the cover is estimated.

The presented data were acquired using the quadrupedal robot ANYmal C15 (Fig. 1), which was equipped with cameras and sensors to collect photos and videos of the dune environment. The use of robotic technology in habitat monitoring offers several advantages, including the ability to collect data from remote and inaccessible locations, and the potential for collecting data at a higher temporal resolution.

Fig. 1
figure 1

The robot ANYmal C in habitat 2120 with heighlited the sensors used for acquiring data. These sensors are one Velodyne VLP-16 puck lite, two FLIR Blackfly BFS-GE-16S2C-BD2 wide angle cameras, and four Intel RealSense D435 RGB-D cameras.

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This data descriptor presents a new dataset related to robotic monitoring of the Mediterranean coastal dune environment of the EU habitats 2110 and 2120. The data were jointly collected in Sardinia, Italy (Fig. 2), by plant scientists and roboticists using the ANYmal C robot and are made publicly available through a repository16. Similarly to the dataset acquired in other habitats17,18,19, this data descriptor has the objective of improving current monitoring approaches by providing to researchers from different sectors the material to design, train, and test their algorithms. The combination of expertise coming from robotic engineers and plant scientists highlights the importance of multidisciplinary collaboration in habitat monitoring and conservation.

Fig. 2
figure 2

Data gathering location.

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Methods

The data used in this study were collected in Platamona, Sardinia, Italy. The study area falls within the Natura 2000 network, Special Areas of Conservation (SAC) ITB010003 - Stagno e Ginepreto di Platamona (Fig. 2). The field work was carried out over four days, from the 16th to the 19th of May 2022. Indeed this is the best period for sampling dune vegetation5.

The data were gathered using the robot ANYmal C (Fig. 1), developed by ANYbotics AG. The robot is capable of collecting habitat information using its onboard sensors while moving in an autonomous or teleoperated manner. ANYmal C is equipped with four Intel RealSense D435 RGB-D cameras (https://www.intelrealsense.com/depth-camera-d435/) that are placed one per side of the robot body. These cameras can register high-resolution images and videos at 30 fps. Two wide-angle FLIR Blackfly BFS-GE-16S2C-BD2 cameras are also present, one on the front and one on the back of the system. In addition, the robot mounts a Velodyne VLP-16 puck lite LiDAR (https://velodynelidar.com/products/puck-lite/) on the rear-top part of its body, which allows for the acquisition of a 3D map of the environment. The state of the robot during data acquisition was recorded as ROS bag files using the robot’s dedicated ROS interface, as per the official ROS documentation (http://wiki.ros.org/rosbag).

Before executing the field data collection, we identified suitable locations using a Geographical Information System (GIS) environment and a detailed habitat map. Thanks to this information and previous knowledge about the area, the botanic team selected for data acquisition two suitable sample areas within the SAC with an habitat coverage of at least 70%. This selection took into account also the accessibility for both the robot and the human team.

The dataset provided in this data descriptor consists of three sets of data: 3D mapping data, species data, and monitoring mission data. Details on the acquisition methods of these data sets are provided in the subsequent sections of this paper.

Species data

We collected data for four indicator species, including the TSs of the two habitats5, namely Calamagrostis arenaria (L.) Roth (habitat 2120) and Thinopyrum junceum (L.) Á.Löve and Achillea maritima (L.) Ehrend. & Y.P.Guo (habitat 2110) and one alien invasive. We refer to World Flora Online Portal (http://www.worldfloraonline.org) for the nomenclature, but Table 1 reports also the common name of these indicator species for the convenience of non-plant-expert readers. T. junceum sand couch-grass, and A. maritima, cottonweed, are salt-tolerant species living at the base of the dunesands. C. arenaria, European beach grass, is a perennial plant up to 120 cm tall with horizontal and vertical rhizomes. It is a keystone species crucial in dune construction and maintenance. Concerning the alien invasive species, we collected data for Carpobrotus acinaciformis (L.) L.Bolus. This species, native to South Africa, was introduced in Europe in the 1900s, becoming the most abundant invasive species in dune habitats20. The impact of this species on coastal habitats is well documented20,21. For each indicator species, at least 130 pictures and 13 videos were recorded using the RGB-D front camera. In the case of C. arenaria, 160 more images and 16 more videos were recorded using the front wide camera due to the species size. Table 1 reports the number of photos and videos provided in the present dataset.

Table 1 The three species typical of the habitats 2110 and 2120 and the alien species with the number of photos and videos for each of them.
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The process of data acquisition involved the following steps: firstly, the plant experts followed the instructions provided in22 to recognize the indicator species mentioned earlier. Following this, a robotic specialist remotely operated the robot and directed it towards the identified instance of the species. Due to this procedure, each photograph of a specific species necessarily includes at least one instance of it, but also other indicator species may be present. Figure 3 presents an illustration for each of the four species discussed previously.

Fig. 3
figure 3

The four indicator species of habitats 2110 and 2120. These pictures were taken by the on-board depth cameras of the robot. (a–c) are typical species. (d) is an alien species.

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3D Mapping data

The second section of the dataset consists of 3D spatial data obtained through a LiDAR sensor and shared in the form of point clouds. These point clouds provide information on the dimensions and shapes of the surrounding environment. The acquisition of this set of data is performed while the robot is guided by an operator. First, the robot is placed in selected study area, and GPS data, date, and weather information are annotated to enable data comparison with past and future surveys. The employed sensor was GPS Garmin Montana 680, and the measurements were taken with at least 3 m accuracy. The geographic coordinate system is EPSG 4326. Next, the mapping process begins through the ANYmal’s ROS Graphical User Interface, and ANYmal is directed by the operator to move forward, execute a 180° turn, and return to its starting position. The point cloud is stored at the end of this procedure.

Mapping data of five different areas in habitats 2110 and 2120 are presented through this data descriptor. Table 2 provides additional information on these.

Table 2 Details of the mappings: date, time, weather information, and georeferencing information (EPSG: 4326).
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Monitoring mission data

The final section of the dataset pertains to the autonomous monitoring mission carried out by the robot in Platamona in the same plots surveyed by plant scientists. GPS coordinates (EPSG 4326) were recorded to enable comparisons with past and future surveys. Weather information was also recorded. This could be employed to estimate sunlight.

For the autonomous monitoring, a 3D map of the environment obtained through the mapping process described in the Section “3D Mapping Data” was used to assist the robot in localizing itself within the environment. The robot followed a straight path going in the direction from the shoreline to the hinterland, as shown in Fig. 4. At each meter, the robot stopped, and four photos (one per each of the four RGB-D cameras) were taken, along with videos recorded by the same cameras, and ROS bags (http://wiki.ros.org/rosbag) of robot status information. A total of six autonomous plots have been performed. More information regarding them can be found in Table 3. This table specifies also the mapping associated to each plot. Finally, Table 4 lists the ROS topics used to store the robot status information. This includes the robot’s position, velocity and battery charge, and the joint positions, velocities, accelerations, torques and currents.

Fig. 4
figure 4

Example of robot motion during the autonomous monitoring mission: 6 m transect. The robot starts pointing towards the opposite direction of the sea, and it moves on a straight line (dashed blue path) till the final goal (red flag). At each waypoint (blue marks) the robot stops. For the entire mission, the RGB-D cameras record a video, the robot state is stored as ROS bag files. Shorter and longer transects can be performed changing the number of waypoints.

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Table 3 Details of the six monitoring missions.
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Table 4 List of ROS topics from which we recorded the robot status during mapping and autonomous missions.
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Data Records

The data are provided through16 at https://doi.org/10.5281/0.8314728. An example code to use the data saved in the ROS bag files is also on GitHub (https://github.com/mpollayil/Code-for-Habitat-Data-Analysis) and on23.

We provide three sets of structured data, represented in the tree diagram in Fig. 5. Each subtree includes a README.txt file that describes the corresponding data set. The set “Species Data” includes pictures of the four indicator species found in habitats 2110 and 2120. Each subfolder named after the indicator species contains a “Photos” and a “Videos” subfolder containing the relevant files. In the case of Calamagrostis arenaria, an additional directory layer is present to distinguish between data acquired with the RGB-D camera, i.e. “depth” folder, and with the wide camera, i.e., “wide” folder Table 1 displays the number of pictures for each indicator species.

Fig. 5
figure 5

Hierarchical directory structure of the dataset. The symbol [⋯] specifies that the content structure of that folder is similar to the other same-level folders.

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The set “3D Mapping Data” comprises five Mapping subfolders, each containing point clouds in the “Point clouds” folder and the stored robot status in the “Robot Status” folder.

The final set is “Monitoring Mission Data”, which includes all the information gathered during autonomous monitoring missions executed by the robot. This set is divided into six subfolders for the six plots, each one including the robot status and acquired data.

Data formats

This section provides technical details about the data file formats.

.jpg

Images acquired by the robot are saved in the jpg format, a prevalent image standard that can be accessed using widely accessible image viewers across different operating systems. Both species pictures and photos recorded during monitoring missions are in this format.

.avi

Videos registered with the robot’s on-board cameras are stored in avi format, and they can be be accessed using widely accessible video players across different operating systems.

.ply and .pb

Three-dimensional mappings are saved as point clodus and provided to users in two standards formats: ply and pb. Ply is the Polygon File Format, a common file extension for 3D models that can be accessed through common software such 3D modeling tools, e.g., MeshLab, or math processing products, e.g., MATLAB (https://www.mathworks.com/products/matlab.html). Pb is the most widely used point cloud format within ROS, particularly in ANYmal research (https://www.anymal-research.org/).

.bag

ROS employs topics (http://wiki.ros.org/rostopic) to transmit the robot information and data. Data flowing in ROS topics can be stored and shared as bag files. Table 4 summarizes the topics included in the provided bag files. More details about these topics can be found on the ANYmal Research site (https://www.anymal-research.org/). Figure 6 displays some of measured quantities during the monitoring mission Plot5. Within ROS, there exist various tools for extracting, visualizing, and analyzing the data saved in bag files. MATLAB (https://www.mathworks.com/products/matlab.html) can also be a valid tool for researchers outside of robotics to handle this file type.

Fig. 6
figure 6

Graphs of the robot status extracted from the ROS bag of the Plot5. The robot moved as shown in Fig. 4. For the sake of readibility, only the first 40 s have been plotted.

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Template script for data extraction

In Code 1, we show a template example of a MATLAB script for extracting data from ROS bags and reading the content of a specific ROS topic. The ROS toolbox and MATLAB 2022a (https://www.mathworks.com/products/matlab.html) or later versions can be used to run this example. Table 4 reports the topics that can be extracted from the bags. The ANYmal Research Website (https://www.anymal-research.org/) describes these issues in detail, as well as the specifics of the sensors mounted on the robot. To access this, however, a research collaboration with ANYbotics is required.

Code 1.

Example of MATLAB script to extract data from ROS bag files.

We must emphasize that being part of ANYmal Research is not required to access the data offered in this research. Also, script displayed in Code 1 and the code provided by this data descriptor are sufficient to extract, visualize, and analyze the supplied data.

Technical Validation

During the fieldwork, the robotics engineering team — Franco Angelini (FA), Mathew Jose Pollayil (MJP), and Manolo Garabini (MG) — and the plant science team — Simonetta Bagella (SB), Maria Carmela Caria (MCC), and Giovanni Rivieccio (GV) — both offered quality assurance. Each author kept tabs on the data gathering process and thoroughly examined the final dataset for mistakes and inconsistencies.

It is worth mentioning that that the data are presented in their raw state, without any post-processing that could jeopardize their authenticity. The next section discusses a few choices that guarantee the validity of the data acquisition.

Area selection

The sampling sites have been chosen by the team of plant scientists, who are expert on the local flora and plants. Platamona, located in the north-west of Sardinia, in the centre of the Gulf of Asinara, is a shoreline made of fine sand. It is the longest and deepest beach along the Sardinian coastline: about 15 km long and 10 to 30 m wide. The two habitats 2110-Embryonic shifting dunes and 2120-Shifting dunes with Ammophila arenaria (white dunes) occupy large surfaces in the dunal system.

Time selection

The survey time, 16th–19th of May 2022, established by the plant scientists, was the most suitable for the quick identification of the species to be monitored based on the presence of the flowers and the optimal development of vegetative apparatus.

Species data validity

TS for habitats 2110 and 2120 have been selected in accordance with EU guidelines5. Plant scientists SB, MCC and GR identified them in the fields and in laboratory using the Flora Manual22 ensuring the correct species classification.

Mapping data validity

The data that the robot saved during mapping is presented in its unprocessed raw form. Since ANYmal uses the stored map to locate itself in the environment using the Simultaneous Localization and Mapping (SLAM) algorithm, its authenticity is confirmed by the lack of any localization errors communicated by the robot during this operation.

Monitoring data validity

Scientific journals24 has been used to drive the procedures employed during monitoring missions, assuring the authenticity of the data gathered during them. To follow these rules, the robot imitates field relevé work done by plant biologists. Additionally, the robot is programmed to alert users in case of potential data acquisition errors. The data are supplied for analysis without any alterations, therefore there is no chance of post-processing-related corruption.

Database validity

After the data collection was finished, the database was created. We only included accurate, clean data. Each database entry was thoroughly reviewed by both teams to assure its accuracy. The dataset’s photos, videos, and classification in the “Species Data” section were all confirmed by the plant specialists. Similar to this, the robotic engineering team verified the accuracy of the point clouds and robot status information by reviewing them and executing test scripts.

Usage Notes

The provided data has a multidisciplinary scope and can benefit both robotic and botanical research. Data contained in the “Species Data” section can be employed by computer scientists to test or train machine learning techniques to detect and classify flora, similarly to25,26. Engineers could also employ point cloud and robot status information contained in the “3D Mapping Data” section to validate their navigation or locomotion algorithms. Finally, the section “Monitoring Mission Data” contains georeferenced information regarding plots that could be used by computer scientists to validate their plant species classification algorithms or by plant scientists to evaluate the state of habitat conservation and to compare them to other plots, such as historical or future data from the same locations.