The Design and Practice of an Experimental Teaching Case for UAV-Based Field-Data Acquisition in Outdoor Ecological Education

Abstract

Outdoor ecological practice is essential for cultivating ecological literacy; however, there is currently a relative lack of comprehensive outdoor practical teaching case designs for class-based teaching. This study describes the design of an experimental teaching case for ecological education involving UAV-based field data collection. For the scheme, we selected the Xinhui Tangerine Peel Germplasm Resources Conservation Center in Jiangmen City, Guangdong Province as the study area, utilizing the DJI Phantom 4 RTK drone, which serves as the equipment for experimental teaching. The experiment is structured into three phases: indoor preparation, field execution, and data processing. Students from four groups collaboratively conducted aerial surveys across 24 partitioned plots, with flight altitudes stratified between groups to ensure safety and data integrity. (1) In the indoor preparation phase, appropriate single-flight operational units were defined. QGIS software (version 3.26.2) was employed for zonal mission planning, and suitable flight altitudes were estimated using contour data. (2) Field experiment phase. This involved conducting a comprehensive survey of the on-site environment, selecting suitable takeoff and landing points, dividing students into teams to carry out UAV-image-acquisition tasks, and assigning different altitudes for flight routes among the teams. (3) After the fieldwork, students processed imagery using Agisoft Metashape (version 2.0.1) to generate orthomosaics and digital surface models, and engaged in ecological interpretation of the results. The experimental design ensured orderly execution, complete data coverage, and active student participation. The results indicate the approach effectively enhanced students’ UAV operational skills, outdoor problem-solving abilities, and teamwork capabilities, while deepening their ecological understanding through real-world inquiry. This case provides a replicable model for integrating UAV technology into ecological education, contributing to the transformation of ecological awareness into actionable practice.

1. Introduction

Ecological education is fundamental for addressing the global ecological crisis. Strengthening citizens’ ecological education is an important pathway to meeting environmental challenges and implementing sustainable development strategies [1]. Many countries around the world have actively responded to and practiced the concept of sustainable development by integrating ecological education into their national education systems [2,3] and offering courses related to “environmental education” and “ecological education” at different educational stages [4,5,6,7]. These ecological education practices have significantly changed people’s ecological perceptions, reshaped their ecological behaviors [8,9], enhanced citizens’ comprehensive ecological literacy, and laid a solid foundation for achieving sustainable development goals.

Ecological education is a long-term, systematic project, and ecological practice is a key component of ecological education [10,11]. Currently, outdoor ecological education and practice still face the challenge of insufficient development. Educational experiences in outdoor environments are crucial for cultivating environmental sensitivity and knowledge [12,13]. However, within the ecological education system, outdoor practical activities themed around ecological education are relatively scarce [14], resulting in the comprehensive effectiveness of ecological education being less prominent. This is manifested in the following aspects: (1) A lack of innovation in terms of the formats for ecological education. Constrained by temporal and spatial conditions, most ecological education is still delivered through indirect experiences such as classroom lectures, graphic presentations, and video viewing. There is limited use of new technologies and methods to design and develop practical project-based outdoor ecological educational activities, making it difficult for students to truly enter nature, perceive ecology, and enhance their level of ecological understanding. Research by Korcz et al. [15] shows that increasing contact with nature can raise awareness of the complexity of environmental issues, and strengthen outdoor ecological and environmental educational practices that are centered on field activities to help students form profound ecological cognition and shape long-term ecological behaviors [11]. (2) The content design of ecological education is relatively superficial and lacks systematic structuring, with insufficient emphasis on skill cultivation. Currently, ecological education often positions students as “recipients of education”, emphasizing knowledge transmission while neglecting action participation. Although ecological education institutions actively organize environmental protection activities, most of these activities remain at the superficial level of picking up litter, planting trees, or watering plants, and are lacking in terms of exploration of the deep mechanisms of ecological problems and participation in solution development. (3) Low participation and engagement among youth groups. The core of ecological education is to establish a connection between humans and nature, and enhancing citizen participation is one of its important goals [1]. The application of new technologies can help improve the technical appeal, interest, and exploratory nature of ecological education practice projects. Through new technology, young students and volunteers can identify problems, collect data, process and analyze data, propose solutions for practical projects of interest, and accumulate and enhance the practical skills needed to solve ecological problems, thereby effectively improving the outcomes of ecological education [6].

Numerous scientific training projects have fully demonstrated the many advantages of UAV technology in addressing ecological and environmental problems. (1) Convenience and Accessibility. UAVs can rapidly complete field survey tasks in complex terrains such as deserts, mountains, and rivers, providing high-precision data for various applications including forest resource surveys, mineral resource development, and territorial spatial planning [16,17,18,19]. (2) Diversity of Observation Methods. By carrying various specialized sensors (e.g., multispectral sensors and LiDAR), UAV platforms can collect multi-source data such as the spectral and echo characteristics of typical ground objects [20,21,22,23]. These data can serve as input parameters for fine-scale geographic, ecological, and environmental models, and provide data support for accuracy validation [24,25,26,27]. (3) High Resolution. UAV remote sensing effectively compensates for the limitations of satellite imagery, such as slow update cycles and relatively low spatial and spectral resolution [28]. It is gradually becoming an important means for revealing ecological processes and solving ecological and environmental problems. Leveraging the advantages of UAVs can provide significant technical support for sustainable development [29,30].

In the field of ecological restoration, UAVs can control forest fires by carrying fire-extinguishing materials [31]; equipped with multispectral sensors, they can monitor forest vegetation health; and fitted with thermal imaging sensors, they can be used for wildlife migration detection and population surveys. In forest resource management, UAVs can detect forest pests and diseases, rapidly complete large-scale forestry surveys, and contribute to sustainable forest ecosystem management [32]. In areas such as soil and water conservation, and disaster prevention and mitigation, UAVs also have significant application value. However, the application of UAVs in the field of ecological education is relatively limited. Utilizing UAV technology in ecological education can help advance the following goals: (1) Using UAVs in ecological education helps students expand their perceptual dimensions, shifting their observation perspective from the ground to the air, and shifting their perception domain from partial to holistic, and from static to dynamic. (2) UAVs help lower the participation threshold, enabling students with basic training to master ecological survey tasks that previously required specialized professional training. (3) Connecting knowledge and action, the data collected by UAVs can be transformed into actionable evidence for environmental monitoring and scientific research, achieving a closed loop of “learning for application”.

In ecological education practice, leveraging the technological advantages of UAVs, exploring their application scenarios in ecological education, optimizing the design of outdoor ecological education practice projects, and enhancing skill cultivation within ecological education can mobilize student enthusiasm, further increase citizen participation, and contribute to the sustainability of ecological education. Currently, there remains a relative scarcity of case studies on UAV-based outdoor practical teaching [33,34], highlighting an urgent need to develop UAV outdoor practical teaching solutions that integrate with the thematic focus of ecological education and offer broad reference value [35,36]. Therefore, it is of great significance to use UAVs as experimental tools to design a comprehensive outdoor practical solution suitable for experimental activities that take a class as the instructional unit [37,38].

This paper takes a citrus plantation as the outdoor practice scenario and designs an experimental teaching case suitable for class-based instruction. The case adopts an organizational model involving experimental area zoning, student grouping, and vertical stratification (by altitude) of UAV aerial survey tasks, and establishes a three-stage closed-loop project-based learning process consisting of indoor preparation, field execution, and data processing. This teaching case will provide a valuable reference for ecological education practice and help transform students’ ecological awareness into ecological action.

2. Task Demand Analysis for the Outdoor Practice Project

2.1. UAV Application Scenario—Citrus Plantations

Xinhui Tangerine Peel is a distinctive agricultural product of Jiangmen City, Guangdong Province, China. It has been recognized as a National Geographical Indication Agricultural Product and is hailed as the foremost of the “Three Treasures of Guangdong” and one of the “Top Ten Traditional Chinese Medicines of Guangdong” [39]. The Tangerine Peel industry is a pivotal pillar of Xinhui’s economy, with an estimated value reaching 890 million CNY [40]. The unique local climate, topography, and soil conditions in Xinhui shape the distinctive quality of the Citrus reticulata “Chachi” (tea-stem mandarin), forming a geographical indication agricultural industry centered on Xinhui Tangerine Peel.

This experiment selected the Xinhui Tangerine Peel Germplasm Resources Protection Center and its surrounding area as the study site (Figure 1), covering approximately 2.4 km × 1.6 km. The area contains Chachi citrus orchards, reservoirs, seedling greenhouses, related research facilities, and soil conservation structures. Land cover types include water bodies, impervious surfaces, woodland, and cultivated land. The topography features mountains, slopes, and plains. The area has relatively rich feature types, with elevations ranging from −9 m to 175 m and modest terrain relief, making it an ideal site for UAV practice. Acquiring high-precision data for this area can provide essential foundational data for planning research facilities and citrus cultivation.

2.2. Using UAVs to Conduct Citrus Distribution Surveys

The Tangerine Peel (dried tangerine peel) is a traditional Chinese medicinal material processed from citrus peels. Most citrus plantations are located in hilly areas. Due to terrain fluctuations, traditional manual ground surveys struggle to comprehensively map the spatial distribution of citrus plantations. The use of UAVs can efficiently complete the general survey of citrus plantation distribution.

In the design of this outdoor UAV-data-acquisition experiment, we propose the following questions (

Table 1. Design Approach for the Practice Plan.

Problem Description	Problem Decomposition	Corresponding Solution
Problem 1: How to quickly obtain the spatial distribution of citrus plantation?	1.1 When the target area is too large for a single UAV to complete the aerial survey task, multiple teams need to collaborate. How should the survey be planned in terms of zoning and group coordination? 1.2 How can flight safety be ensured for UAVs operated by different teams?	See Section 3.1 for solutions
Problem 2: How to design a multiple UAVs collaborative data-acquisition experiment?	2.1 During the indoor preparation phase, what auxiliary data (e.g., DEM, contour lines) are needed for aerial survey planning? What is the purpose of these auxiliary data? (e.g., assisting in setting flight path altitudes) 2.2 What are the main steps for planning an aerial survey mission using QGIS software in the indoor phase? 2.3 How can it be ensured that UAV images meet application requirements? (Flight path parameter settings)	See Section 3.2 for solutions
Problem 3: Is it necessary to assess the field environment in outdoor settings to ensure UAV flight safety?	3.1 What aspects should be included in the safety assessment?	See Section 3.3 for solutions
Problem 4: What is the UAV-image-processing workflow?	4.1 What software can be used for image stitching processing during indoor data processing? 4.2 What are the steps for stitching data using Agisoft Metashape, and what are the parameters for each step?	See Section 3.4 for solutions

). Through these questions, students are gradually guided to propose corresponding solutions.

2.3. Preparation of Experimental Conditions

(1)

UAV Equipment. The DJI Phantom 4 RTK UAV is used as the mission equipment for this practical activity. Produced by DJI, headquartered in Shenzhen, China, this product is positioned as an entry-level industry drone, with relatively simple operation, making it easier for students to quickly master UAV operation skills.

(2)

Software. QGIS software is used to delineate the aerial survey mission area and complete task zoning and group assignments.

3. Conception and Design of the Practical Plan

Following the task design approach shown in Figure 2, students participating in the experiment are guided to discuss in groups and complete the design of the following practical plan.

3.1. Experimental Organization and Design

The experiment was organized using a framework of student grouping, task zoning, and operational layering (by altitude). Grouping personnel facilitates collaborative work, ensuring each group member has hands-on UAV operation opportunities. Zoning tasks enables large-area data acquisition; typically, for the DJI Phantom 4 RTK on flat, open terrain, a single operation is limited to about 1 km². Spatially dispersing operators also enhances safety for both personnel and equipment. Layering operations by altitude helps avoid flight path overlap and prevents collisions between UAVs from different groups.

The specific design is as follows:

(1)

Students Grouping. The experiment was guided by 4 instructors. A total of 28 students were divided into 4 groups (A, B, C, and D) with 7 students each, each supervised by one instructor (

Table 2. Experimental equipment and personnel grouping.

Group	Students Number	Experimental Equipment (UAV)	Task IDs
A	7	1× DJI Phantom 4 RTK, 6 batteries	A1/A2/A3/A4/A5/A6
B	7	1× DJI Phantom 4 RTK, 6 batteries	B1/B2/B3/B4/B5/B6
C	7	1× DJI Phantom 4 RTK, 6 batteries	C1/C2/C3/C4/C5/C6
D	7	1× DJI Phantom 4 RTK, 6 batteries	D1/D2/D3/D4/D5/D6

). Each group was responsible for 6 designated plots. Task assignments and plot numbering are shown in Table 1 and Figure 2.

(2)

Tasks Zoning. The experimental area is divided into zones. The planned experimental range was approximately 2.4 km × 1.6 km (total area 3.84 km²). Based on the empirical safe flight operation range per single mission for the DJI Phantom 4 RTK (considering battery life, signal stability, and safe return-to-home requirements), and considering the complex terrain of the experimental area requiring a reduction in the operational scope per single flight, the suitable operational unit per flight was set to 400 m × 400 m (0.16 km²). Therefore, the entire experimental area was divided into a grid of 6 rows by 4 columns, resulting in 24 plots, each corresponding to one aerial survey mission per flight.

(3)

Operational Layering. Based on the Digital Elevation Model (DEM) data for the experimental area, the highest surface elevation (175 m) was extracted (Figure 3). Considering the height of obstacles such as trees and power lines, a safety margin was added, and the planned baseline flight altitude was set at 200 m (approximately 25 m above the highest point). To prevent collisions during simultaneous drone operations by different groups, the flight altitudes for the four groups were stratified with 5 m intervals: Group A at 185 m, Group B at 190 m, Group C at 200 m, and Group D at 195 m (Figure 4). The lowest altitude (185 m) remains above all obstacles, ensuring flight safety.

3.2. Indoor Preparatory Work

In the indoor preparation phase, QGIS software is used for aerial survey area planning. QGIS is an open-source geographic information system that allows for the convenient loading of online maps, providing high-resolution base maps for aerial survey mission planning. Using Digital Elevation Model (DEM) data in QGIS, two main tasks are initially completed: aerial-survey-area planning and UAV-flight-path-parameter presets. The specific steps are as follows:

3.2.1. Planning Survey Areas

(1)

Determining Single-Flight Operational Range

Battery management is paramount for safe flight. Planning the operational range must reserve sufficient battery power for a safe return. Typically, a single battery allows for stable flight for about 30 min. Therefore, the UAV’s single-flight operational range must be determined prudently based on battery capacity. Uncontrollable factors mainly arise from two aspects: first, the task area is often some distance from the takeoff/landing point, requiring the UAV to spend additional time flying to reach it; second, obstacles like mountains or trees can obstruct UAV communication. Sustained signal loss triggers automatic return function, which consumes additional battery power. Consequently, a relatively conservative flight strategy was adopted. Based on multiple pre-flight tests [41,42], the task scope per single flight was set to 400 m × 400 m. Data acquisition within this range takes approximately 14 min, leaving sufficient battery power for return and unexpected situations.

(2)

Group-based Zonal Task Planning

Load Google online maps in QGIS software to observe the imagery of the experimental area. Based on high-precision online maps, create a new Shapefile and plan the aerial survey area by drawing and editing rectangular geometries. The specific steps are as follows:

• Load the online map. In the HCMGIS plugin, select Basemaps, then add Google Imagery.
• Zoom in to the target area in Google Imagery, create a new Shapefile layer. In the parameter settings, select “Polygon” as the geometry type, and choose the projected coordinate system WGS 1984 UTM Zone 49N.
• Draw a rectangle measuring 400 m × 400 m. (1) Click the edit button to activate the Shapefile layer for editing. (2) Left click the “Add Polygon Feature” button, and select “Digitize with Line Segments” as the digitization tool. (3) Then, click to activate the advanced digitization tool. When the cursor changes to a crosshair, create the initial point. After clicking the first point, set the advanced digitization tool to snap to common angles (90°, 180°, 270°, and 360°) and fix the distance to 400 m. Sequentially create the other three corner points to form a 400 m × 400 m rectangle. This geometry represents a single-flight mission area. Copy and paste multiple rectangle geometries, drag them with the mouse to tile them across the experimental area, arranging them according to the grid layout described in Section 3.1, ensuring each rectangle corresponds to one mission plot, totaling 24 plots.
• Task numbering. Open the attribute table, add a field, set the field type to text, and number each geometry one by one. The numbers correspond to the task plots of each group, with A, B, C, and D representing group numbers and 1~6 representing task numbers. For example, A1 and B3 indicate the first flight task of Group 1 and the third flight task of Group 2, respectively.
• Estimating appropriate flight altitude. In QGIS, the DEM data is converted into contour lines, which are used to preliminarily determine the suitable flight altitude for each mission plot.
• Format conversion. DJI drones support mission modes that generate flight routes based on kmz/kml files. Therefore, the geometries in the Shapefile need to be converted one by one into kmz/kml files.
• Import data and check flight routes. Create a new folder named “DJI” in the root directory of the UAV remote controller’s memory card, and then create a folder named “kml” within the “DJI” folder. Copy the kml format aerial survey operation scope files to the “kml” folder. Start the UAV aircraft and remote controller respectively. On the remote controller, select “Import kmz task” and check if the drone flight route is generated correctly. Verify that the route area for each plot matches expectations and that there are no no-fly zone conflicts, ensuring that kml files can be directly called upon during fieldwork to generate the mission route.

3.2.2. Setting Flight Path Parameters

(1)

Setting Flight Altitude

• In QGIS, contour lines were generated from DEM data to examine ground elevations within task areas. The relationship between the ground sampling distance (GSD) and flight altitude for the visible light sensor on the DJI Phantom 4 RTK is as follows:

  $$GSD=H/36.5$$

  (1)

  where GSD (ground sampling distance) is in cm, and H (flight altitude) is in m. At 200 m altitude, the ground resolution is approximately 5.5 cm. At a flight altitude of 200 m, the ground resolution is approximately 5.5 cm. Combined with the analysis in Section 3.1, the designed flight altitudes for Groups A, B, C, and D are 185 m, 190 m, 200 m, and 195 m, respectively. All flight altitudes are above the highest point, and the 5 m interval between groups ensures vertical safety distance.

(2)

Setting Overlap Rates. To ensure image stitching quality, the forward overlap rate was set to 80%, and the side overlap rate was set to 70%. This parameter is based on the default recommended values for the DJI Phantom 4 RTK drone, adjusted slightly considering the terrain undulation to ensure sufficient matching features even in complex terrain.

(3)

Camera Parameters. Set camera parameters according to weather conditions. Select Sunny mode for sunny operations and Cloudy mode for overcast operations. White balance is set to Auto, ISO is set to 100 to reduce noise, and shutter speed is prioritized to avoid overexposure or motion blur.

3.2.3. Selecting Suitable Takeoff and Landing Points

Areas with relatively flat and open terrain, free from obstructions such as buildings and trees, relatively close to transportation lines, and without dense crowds are considered ideal takeoff and landing points for UAV field experiments. Pre-select suitable takeoff and landing points by observing Google online maps to ensure that communication between the remote controller and the aircraft remains unaffected. For each group’s six plots, it was assessed in advance whether a change of takeoff/landing point was necessary. If a ridge or dense forest obstructed the view between the plot and the takeoff/landing point, a backup takeoff/landing point was planned.

3.3. UAV-Operation-Skills Training

Section 3.1 and Section 3.2 above outline the overall design and implementation steps of the outdoor practice. To ensure students can safely use UAVs in field environments, practical UAV-operation training is provided to the students participating in the experiment. The training is conducted based on the following three aspects:

(1)

Online Simulation. The Phoenix RC simulator is used as flight simulation software. Through this software, students master basic UAV operations such as takeoff, landing, and rotation.

(2)

Understanding UAV Equipment. Students are guided to learn the basics of operating the DJI Phantom 4 UAV, including equipment assembly and flight path parameter settings.

(3)

Guided Practice. Students practice UAV operations in open areas (e.g., playgrounds, etc.).

After completing the above three stages and ensuring students have mastered UAV-operation skills, they are guided to conduct outdoor UAV-data-collection practices in citrus plantation areas (Section 3.4).

3.4. On-Site Condition Survey

Before conducting UAV-aerial-survey missions, the experimental site environment must be surveyed to determine whether it meets the conditions for safe flight.

• Meteorological conditions: wind force must be below level 5, with no heavy fog, rainfall, or snowfall.
• Environmental survey of the experimental site: observe the height of ground obstacles within the flight range, such as high-voltage lines, signal towers, and chimneys, and verify whether the preset flight route altitude is appropriate. Assess the suitability of the takeoff and landing points, ensuring they are located in relatively open areas and free from obstructions.
• Inspection of experimental equipment: before commencing UAV-flight operations, check that the UAV aircraft and remote controller are in good condition, verify that the battery level is sufficient, and confirm that the memory card is properly installed, among other checks.

Once the above conditions are confirmed to be in order, instruct students to carry out aerial survey missions in designated zones, with each group setting a different flight route altitude to conduct UAV-flight operations.

3.5. Indoor Data Processing

Perform data mosaic processing using Agisoft Metashape software. Under the “Workflow” menu, use the batch processing tool to automatically mosaic the data. In the three steps of “Align Photos,” “Build Dense Cloud,” and “Build DEM,” set the accuracy parameters to “Medium” to reduce processing time. The main steps are as follows:

(1)

Align Photos.

(2)

Build Dense Cloud.

(3)

Build DEM.

(4)

Build Orthomosaic, with the surface parameter set to “Mesh”.

4. Experimental Summary

4.1. Improvement of UAV Outdoor Practical Skills

During the phase of acquiring experimental area data using UAVs, each group was responsible for image-acquisition tasks on six experimental plots. When students carried out flight missions on the first and second plots, errors were likely to occur due to incomplete familiarity with the UAV-operation procedures, resulting in slightly longer task durations—approximately 40 min per plot. After practicing on two tasks, the time required for subsequent tasks was relatively shorter: about 20 min per task. The image-acquisition time per UAV sortie within the aerial survey range was approximately 14 min. It took about 3 h for all four internship groups to complete the aerial survey tasks, with an average time of around 30 min per task. The overall experiment proceeded smoothly, with no incidents of UAV crashes. It should be noted that, due to the relatively high altitude of plots C1 and C4 in Group C’s task area and the takeoff/landing point being located on flat ground at the slope base, communication between the aircraft and the remote controller was obstructed, leading to image gaps in some flight routes (Figure 5).

After aggregating the UAV images from each group and performing mosaic processing using Agisoft Metashape software, the image resolution after mosaic processing was approximately 5.3 cm, which is suitable for 1:1000 small-scale refined mapping. The image range fully covered the entire planned operation area. Variations in image tone were observed, primarily due to weather changes on the day of the experiment: thin clouds were present at 10 a.m., while conditions became clear around noon, resulting in uneven tones and differences in brightness and darkness across the images.

4.2. Development of Problem-Solving Skills

Selecting appropriate takeoff and landing points is a crucial prerequisite for ensuring the smooth progress of the experiment. During the phase of using UAVs to acquire images of the experimental area, the technical problems encountered by each group primarily centered on drone return incidents caused by signal loss. In terms of this experiment’s design, a single fixed takeoff and landing location may not be sufficient to support flight missions across six task plots, necessitating the relocation and changing of drone takeoff and landing points to maintain smooth signal communication between the aircraft and the remote controller. Specifically, the terrain of Group A’s task area was relatively flat, and one takeoff and landing point was sufficient to complete all tasks. Groups B and D changed their takeoff and landing point once when performing drone flight missions over different plots. Group C’s task areas (C1 and C4) were at a relatively high altitude; they carried out all six operational tasks based on one takeoff and landing point without changing it, which led to communication interruption between the drone’s remote controller and the aircraft, resulting in data loss.

4.3. Development of Team Collaboration Skills

The drone-image-acquisition experiment is an internship project focused on skill development. This experiment organized multiple experimental groups to collaboratively conduct large-scale drone-image-acquisition experiments, aiming to guide students in using drones to obtain surface spatial information data, providing effective tools for understanding and solving practical geographic problems, enhancing their ability to address real-world issues, fostering their capacity to overcome difficulties in the field, and emphasizing the cultivation of teamwork skills [43]. Overall, in this internship case, the team members cooperated in an orderly manner, and each group was able to complete the experimental tasks effectively and properly handle unexpected situations. The students who participated in the experiment have initially acquired the ability to independently design and implement drone-data-collection experiments in the field.

4.4. Enhancement of Ecological Awareness

After completing the UAV-image processing, guide students to observe the images (Figure 6). Combine the UAV orthophotos with the DSM (Digital Surface Model) images. Guide students to consider the following questions:

(1)

Identify the spatial distribution of citrus trees based on the UAV images and summarize their spatial distribution characteristics.

(2)

Analyze the reasons for the contour-based distribution of citrus forests. What is the relationship between this distribution pattern and land-use methods?

(3)

What is the connection between constructing terraces in hilly areas and the regional environmental characteristics? What are the benefits of doing so?

By reflecting on these questions, further guide students from an ecological perspective to strengthen their ecological understanding of topics such as the ecological economy, intensive land use, and soil erosion prevention. For example:

Guide students to analyze from an industrial perspective: from citrus plantations to the Chenpi industry, a geographically indicative product with regional characteristics has been formed, effectively increasing the added value of the product and benefiting economic development. From the perspective of the ecological economy, this represents a model of ecologically sustainable development.

Analyze from the perspective of land-resource management and land-use methods: in hilly areas where land resources are scarce, cultivating citrus is a typical case of intensive land-resource utilization.

The land-use method of constructing terraces follows the principle of contour farming and represents a scientific cultivation approach. The advantages are that (1) constructing terraces helps intercept runoff, conserve water and soil, and retain water for drought resistance. Guangdong has abundant rainfall. In hilly areas, establishing orchards through terracing increases vegetation coverage. During the rainy season, the root systems of citrus trees consolidate the soil, and the canopy reduces the kinetic energy of raindrops, weakening runoff erosion and helping to prevent soil erosion.

5. Discussion

5.1. Potential Application Scenarios of the Teaching Case

This paper focuses on the design of practical cases for outdoor ecological education, constructing an experimental organization framework of “zoning, grouping, and vertical stratification”. The core idea of this framework is to decompose large areas into standardized task units, divide student groups into collaborative teams, and ensure the safety of drone flights through vertical stratification of flight paths. This case can be transferred to experimental teaching scenarios requiring multi-person collaboration. The potential audience and application scenarios mainly include three aspects. (1) In the field of higher education, this case can be directly used for undergraduate experimental teaching in majors such as ecology, environment, and geography. It is suitable for class teaching and helps enhance students’ skills in ecological environment investigation [44]. In interdisciplinary education, this case can serve as a carrier for STEM education implementation [45,46]. By applying this case, students can use geographic knowledge for drone survey planning, understand the physical principles of drone flight, and analyze the scientific connotations of ecological data. (2) In the field of basic education, this case can be appropriately simplified and integrated into middle school research and practice activities for secondary school students. Currently, drone science popularization activities for teenagers are gradually emerging [47], but some activity designs remain at the level of flight performances and simple operations. This case provides a complete process for data collection and processing, addressing the shortcomings of current activity designs, namely the lack of data support and the lack of application scenarios. For example, in research design, campuses or nearby ecological parks can be selected as experimental areas, allowing students to complete simplified drone survey tasks in groups and generate orthophoto maps. On this basis, inquiry-based learning activities such as vegetation identification and land-use classification can be carried out, helping to enhance the educational value of technology research activities. (3) In the fields of community education and public science popularization, drone technology is gradually moving from professional institutions to the public sphere [48]. Applying drone technology in ecological environment protection practices will become an effective way to improve public ecological literacy.

5.2. Organizational Model of the Teaching Case

To achieve large-scale instruction in practical drone skills, this paper constructs an outdoor teaching organization model suitable for class teaching, integrating real-world scenarios and ecological education concepts. This study proposes a three-dimensional design involving the zoning of experimental areas, grouping of students, and vertical stratification of drone aerial survey tasks to address a key challenge in outdoor field experiment teaching: the contradiction between a large number of students and the high safety requirements of drone flights. Typically, when conducting drone experiments with a class as the teaching unit (e.g., a class of 20–30 students), limited experimental space can lead to some students becoming “bystanders,” while simultaneous operation by multiple individuals increases safety risks [49]. By dividing the experimental area into 24 standard plots of 400 m × 400 m, dividing the students into 4 groups, assigning 6 plot tasks to each group, and setting up four groups of flight paths at 5-m altitude intervals, this case achieves an organizational model characterized by “spatial dispersion and altitude stratification” that facilitates the safety of experiments [50,51]. The group collaboration model helps ensure that each group of students has sufficient operational opportunities, enhancing learning outcomes and skill acquisition [52,53] while avoiding the risk of drone collisions between groups [54].

5.3. Teaching Procedure Design and the Integration of Ecological Education Concepts

In terms of teaching procedure design, we have constructed a three-phase closed-loop project-based learning process encompassing indoor preparation, outdoor execution, and data processing [55]. Integrating a typical ecological landscape category—a citrus plantation—the case design emphasizes the effective connection of each procedure, extending the experimental design from the operational level of “flying the drone and capturing images” to the application level of “processing data and interpreting ecology” [56]. During the indoor preparation phase, students use QGIS software for flight planning, estimate appropriate flight altitudes based on DEM data, and apply abstract geographic information technologies to concrete tasks. In the field execution phase, students need to survey the on-site environment, assess safety risks, and carry out flight missions, testing and adjusting their indoor plans in practice. During the data processing phase, students use Agisoft Metashape to generate orthophotos and DSMs (Digital Surface Models), ultimately returning to the interpretation of ecological questions. This complete process embodies the educational philosophy of “learning by doing” [57], allowing students to experience the entire journey from project design to output generation, which helps them understand the value of technological tools in solving real-world problems [45,56]. The experiment is centered around the practical need of “how to quickly obtain the spatial distribution of a citrus forest,” guiding students to break down the problem into sub-tasks such as mission planning, safety design, data acquisition, and image processing. Ultimately, they observe the citrus distribution in the generated images, analyze contour farming, and contemplate its ecological implications. This “problem-driven” experimental design integrates skill learning with ecological cognition [47,58], avoiding the monotony of mere technical training.

From the perspective of integrating ecological education concepts, this case transforms abstract ecological ideas into actionable and observable teaching content. After image processing, the case design includes a series of guiding questions: what are the spatial distribution characteristics of the citrus forest? What is the reason for its distribution along the contour lines? What is the connection between terrace construction and the regional environment? These questions help guide students from “observing the phenomenon” to “understanding the mechanism”. The distribution of citrus trees along contour lines is not only a direct representation of land-use patterns but also a concrete manifestation of soil and water conservation measures. Terrace construction represents both an adaptation to the terrain and adherence to ecological principles. This elevation from technical operation to ecological understanding is also the core goal being pursued by ecological education.

5.4. Promotion Value

The selection of the Xinhui Chenpi (dried tangerine peel) planting area as the site for this case study holds a certain representativeness. Focusing on citrus plantations, the core of its design lies in embedding the experimental protocol within a real-world application scenario: in complex terrain conditions where ground investigation is difficult to carry out, drones can be used to rapidly acquire spatial distribution information of typical cash crops. Such application scenarios are widespread. The technical approach used in this case, which involves estimating flight altitudes based on DEM data and using QGIS for zone planning, has broad applicability [59].

Regarding the promotion and application of the case, it possesses the potential for modular decomposition. The complete “three-phase” process is suitable for university-level specialized courses, while its sub-modules can be independently applied to educational scenarios at different levels. For secondary school students, the “field execution” module can be simplified and retained, allowing students to experience drone operation and image acquisition within pre-set flight tasks designed by the teacher. For vocational colleges, the skills training module can be strengthened, with elements such as flight parameter settings and on-site safety assessment treated as independent practical projects. For public science education, the ecological interpretation module can be highlighted to enhance the public’s understanding of ecological protection. This potential for modular decomposition enables this case to serve different educational groups.

In terms of equipment requirements, this case uses the DJI Phantom 4 RTK as the experimental device. This is a relatively common industry entry-level drone [60], known for its relatively simple operation and relatively high cost-effectiveness, thus providing a good foundation for promotion. On the software side, QGIS, as open-source geographic information system software [61], requires no commercial software license, lowering the application threshold and facilitating the “low-cost replication” of the experimental case.

5.5. Limitations and Challenges

This case is oriented towards cultivating students’ experimental operation skills, and aims to provide a portable and applicable experimental teaching case from the perspective of experimental organization feasibility. In terms of content, it concludes with data processing to generate orthophotos and DSM data without deeply exploring the application scenarios of the experimental data. To some extent, the depth of the experimental content still requires further development and exploration. Further delving into the application outlets of experimental data, focusing on issues like regional development and ecological environment, might be one of the future directions for UAV practical teaching in geography majors.

Several potential challenges need attention during the promotion and replication of this experimental case. First, safety assurance is always the primary consideration. Different institutions have varying technical support capabilities and site environments, necessitating localized safety assessments based on the experience derived from this case. Second, regarding the flexibility of experiment scheduling, completing all three phases requires sufficient teaching time. How to organically integrate this process into the existing curriculum system requires curriculum design tailored to local conditions. In summary, the core objective of this case is to provide a validated, adjustable, and theme-specific experimental teaching example. It offers a reference for the use of drones in practical teaching in majors such as ecology, environmental science, and geography, while also providing a set of organizational frameworks for reference in the practice of outdoor ecological education.

6. Conclusions

This study describes the design of a UAV-based field-data-acquisition case for outdoor ecological education, using an organizational model involving area zoning, student grouping, and vertical stratification of flight altitudes, along with a three-phase closed-loop process of indoor preparation, field execution, and data processing. The design ensures operational safety and hands-on participation alongside class-based instruction.

By situating the experiment in a citrus plantation, the case integrates skill development with ecological understanding, guiding students to observe the spatial distribution patterns of citrus plantations and reflect on sustainability issues such as intensive land use and soil conservation. With entry-level UAV equipment and open-source software, the case offers a replicable model applicable to university courses, secondary education, and public science outreach, contributing to the transformation of ecological literacy into actionable practice.