Application of Aerial Photographs and Coastal Field Data to Understand Sea Turtle Landing and Spawning Behavior at Kili-Kili Beach, Indonesia

Abstract

We investigated sea turtle landing and spawning behavior along 1.4 km of Kili-Kili Beach in East Java, Indonesia, by combining aerial photographs and field survey data. In the study, we surveyed marks of sea turtles landing and spawning on the beach and utilized aerial photographs, beach profile survey records, grain size measurements of the beach material, and tide records to understand the behavior of the turtles. Firstly, aerial photographs are processed into ortho-mosaics, and beach surfaces are classified into land cover categories. Then, we calculate the number of spawning and non-spawning instances for each category, visualizing landing positions to identify local concentrations. Spawning distances from the waterline are estimated, and beach stability is evaluated by analyzing the temporal elevation change through standard deviation. Our findings reveal preferred spawning locations on bare sand surfaces, around 8 to 45 m from the waterline, with beach elevations ranging from 1 to 5 m. The standard deviations of beach elevation were between 0.0 and 0.7 m, with a mean slope of 0.07. This information is important for effectively conserving sandy beaches that serve as spawning sites for sea turtles.

1. Introduction

Sandy beaches, which serve as spawning and hatching grounds, are crucial for the survival of sea turtles, a group of species listed as vulnerable on the International Union for Conservation of Nature (IUCN) Red List [1]. Sea turtles do not land at all beaches for spawning; it is essential to study and conserve those that function as viable spawning sites.

We believe that remote sensing techniques can contribute to these studies as researchers have increasingly employed unmanned aerial vehicles (UAVs) or drones to study sea turtles, utilizing various sensors and setups for diverse objectives. For instance, researchers have combined UAVs with boat surveys to assess the reproductive status of adult green sea turtles (Chelonia mydas) during breeding seasons [2]; used a UAV-mounted thermal infrared sensor to monitor nesting activity [3]; and captured 4K videos at 60 fps using a UAV and integrated this footage with multi-sensor bio-loggers on 22 turtles to calculate visibility rates from aerial surveys, aiming to estimate fine-scale sea turtle density in foraging areas [4].

In recent studies, [5,6] introduced an approach to examining sea turtles’ behavior on the Enshu Coast, Japan, by analyzing recorded sea turtles’ landing positions using a handheld GNSS, frequently measured beach profiles, and satellite imagery. The frequently measured beach profiles were used to observe beach morphology, while satellite imagery was utilized to assess beach surface cover, leading to significant findings. However, a limitation existed in the temporal coverage of the satellite imagery, as the beach surface cover was rarely observed as frequently as the beach profile measurements.

In the context of sea turtle landing beach research, obtaining a time series description of beach surface conditions during the landing season is essential. Having more data allows us to understand changes in coastal characteristics better. However, using satellite imagery often poses challenges. Fixed satellite orbits limit how frequently they can revisit the same area, resulting in infrequent updates, particularly in dynamic environments. In lowland tropical regions, high cloud cover often disrupts optical satellite imagery and compromises signal quality and spectral analysis [7,8,9]. To overcome these challenges, we turned to aerial photographs and terrestrial measurements of beach characteristics to examine sea turtles’ landing and spawning behavior.

We conducted research at Taman Kili-Kili Beach (Kili-Kili Beach) in the Trenggalek Regency, East Java Province, Indonesia, a beach regularly visited by olive ridley (Lepidochelys olivacea) sea turtles for their spawning [10]. Therefore, in this article, the term ‘sea turtle’ specifically refers to this species. This research has two primary objectives. First, we aim to determine whether the aerial photography configuration employed is effective for acquiring beach information necessary for studying sea turtles. Additionally, by examining these landing patterns based on the available coastal data, we hope to deepen our understanding of sea turtle spawning preferences across the beach. This information will also be instrumental in supporting conservation efforts for sea turtles.

2. Materials and Methods

2.1. Study Area

Kili-Kili Beach is a sea turtle landing beach managed by Wonocoyo Village “POKMASWAS”, a local community-based conservation group [10]. This sandy beach is approximately 1.4 km along Panggul Bay, facing the Indian Ocean in the south (Figure 1). The sea turtles begin to land in March and continue through September. However, they may occasionally halt in August. Our aerial survey and terrestrial measurements were conducted from April to September to cover the event. Kili-Kili Beach has two seasons (rainy and dry). The rainy season lasts from September to April, while the dry season lasts from May to August [11]. Sometimes, the beginning and end of the rainy season change slightly.

2.2. Aerial Photographs

Six aerial surveys were conducted monthly throughout the research period. The survey used a DJI Phantom 4 Pro v2 (non-RTK), and details of the aerial survey are in

Table 1. Details of aerial surveys.

Aerial Survey	Date	Flight Altitude (m)
1	08-04-2023	50
2	20-05-2023	50
3	17-06-2023	50
4	02-07-2023	50
5	19-08-2023	50
6	29-09-2023	50

and Figure 2.

In the aerial survey, UAV flights were set to systematically cover the Kili-Kili Beach area, divided into seven smaller sections or clusters, as illustrated in Figure 1. This setup is classified as UAV cluster tasks or UAV cluster mission planning strategy [12]. It can be referred to as the sampling-based method of cell decomposition which involves partitioning the drone’s free space into simple areas referred to as cells, enabling the easy generation of a path between two configurations in different cells [13]. We decided to apply this setup to save the battery during the mission. The UAV flew at an altitude of approximately 50 m above the surface to ensure adequate spatial resolution for this study. The flight started at cluster seven and continued to cluster one, taking off between 7 AM and 8 AM upon the aerial survey team’s arrival at the site. Concurrently, a terrestrial survey team conducted beach profiling measurements, with six predefined measurement base points. The elevation condition of the beach was used to define these locations.

Additionally, we performed differential GPS measurements at six base points for beach profiling, which were utilized as ground control points (GCPs) to geo-reference the aerial photographs (x, y coordinates). The aerial photographs were processed using the commercial SfM (Structure from Motion) software Pix4Dmapper™ version 4.84 with an educational license to produce ortho-mosaics, and ArcGIS Desktop™ 10.8.2 was used for map layouts afterward.

We use the term ‘ortho-mosaic’ to refer to a representation of an area created by stitching together multiple aerial photographs. This term is synonymous with orthophoto. An orthophoto is a photograph that has been rectified to correct for geometric distortions, allowing for accurate measurements without distortion [14,15]. This process produced six ortho-mosaics, denoted by the term ‘aerial photographs’, followed by the date. For example, Aerial Photograph 29 September 2023 indicates the ortho-mosaic resulting from the aerial survey on 29 September 2023, and it represents the sixth ortho-mosaic in total. Furthermore, ensuring consistent alignment among these ortho-mosaics is essential. To evaluate this, we compared the positions of similar objects across all ortho-mosaics. The sixth ortho-mosaic is used as the reference for this comparison, allowing us to calculate the root mean square error (RMSE) and assess the magnitude of positional error.

$$RMSE=\sqrt{{\displaystyle \frac{1}{n}}{\sum }_{i=1}^n\left[\right({E_{x,i})}^2+\left({E_{y,i})}^2\right]}$$

In the RMSE equation, E_x,i represents the error in the x direction (easting or longitude), E_y,i indicates the error in the y direction (northing or latitude), and n denotes the number of observed positions [16], in this case, the position of observed objects across the ortho-mosaics. If any of the ortho-mosaics exhibited an RMSE significantly larger than expected, we re-geo-referenced them using the sixth mosaic as the reference.

2.3. Sea Turtle Landing Records

Sea turtle landing records were collected from March 2023 to August 2023 using a handheld Garmin GPSMAP 78s by local conservation officers, who served as the surveyors, totaling sixty-three records. For these data, the surveyors patrolled the beach every morning as early as possible (typically around sunrise) to check for signs of turtle activity before they were washed away by the waves. When they found such signs, they assessed whether spawning had occurred through a manual inspection. The surveyors noted the location of spawning or non-spawning, the date, time, and any relevant details for documentation.

2.4. Beach Profile

The beach profile (cross-section) was obtained monthly on the same date as the aerial survey by an auto-level Topcon ATB4A at six positions along the coastal area. The measurement of base point positions and elevations was corrected using differential GPS. This process relied on a tidal station geodesic control point located in Pacitan, where tide measurements were taken as a reference. By aligning the beach profile data with tide data in this manner, the two datasets were synchronized. For the plot of beach profile data, we employed MATLAB™ R2024a. An example of beach profile data is shown in Figure 2.

2.5. Tide Data

Tide data are from the nearest Indonesian Geospatial Agency (BIG) station to the research area. The station’s name is Pacitan, located approximately 39 km west of Kili-Kili Beach. To support the research aims, the tide data are hourly and span from March 2023 to September 2023. The tidal range was from −1.755 m to +1.035 m above the Mean Sea Level (MSL) and categorized as meso-tidal [17]. In the analysis, the study focuses on nighttime maximum tide levels to determine the instantaneous shoreline position during sea turtle landing events that occur at night.

We do not have many tidal stations near Kili-Kili Beach, and the Pacitan Tidal Station is the closest and most accessible. The choice of Pacitan Tidal Station data is also linked to the measurement of base point positions and elevations, which were corrected using differential GPS. This process relied on a geodetic control point at the Pacitan Tidal Station, with tide measurements as the reference. Additionally, the selection of Pacitan Tidal Station allowed both survey teams to easily conduct measurements simultaneously at Kili-Kili Beach and Pacitan.

2.6. Land Cover

The land cover of the research area was determined through visual interpretation of monthly aerial photographs. We adopted the categories established by [6] and classified the land cover into four types: beach vegetation, infrastructure, bare sand, and creeks and waterways. Beach vegetation was further divided into three subtypes based on density: high-density beach vegetation (BV-H), moderate-density beach vegetation (BV-M), and low-density beach vegetation (BV-L). Infrastructure includes roads and beach protection structures (Inf), while bare sand is denoted as BS, and creeks and waterways are classified as RnW. This categorization is simple and fits with the expected detail.

2.7. Grain Size

Grain size data were obtained monthly on the same date as the aerial survey and beach profile measurement. The sand samples were obtained from 6 locations of beach profile measurements and analyzed in the laboratory. We did not measure the eastern area of cross-section 6, as it is inundated by river water during the rainy season, and sea turtles do not land there. The grain size sampling locations are marked in Figure 3.

3. Results

3.1. Overall Aerial Photographs

We successfully conducted six aerial surveys in the research area. Each aerial survey’s photographs captured with a UAV were processed using six ground control points (GCPs) with Pix4Dmapper to create an ortho-mosaic.

Table 2. The number of aerial photographs and ortho-mosaic names.

Aerial Survey	Date	Number of Aerial Photographs	Ortho-Mosaic Name
1	08-04-2023	1998	Aerial Photograph 08-04-2023
2	20-05-2023	3238	Aerial Photograph 20-05-2023
3	17-06-2023	3302	Aerial Photograph 17-06-2023
4	02-07-2023	3281	Aerial Photograph 02-07-2023
5	19-08-2023	3280	Aerial Photograph 19-08-2023
6	29-09-2023	3280	Aerial Photograph 29-09-2023

presents the number of aerial photographs from each survey and their ortho-mosaic results from the Pix4Dmapper process. Once processing was complete, we analyzed the positions of similar objects across all ortho-mosaics to verify their alignment, as seen in Figure 3, which was essential for extracting beach surface information (land cover). The sixth aerial photograph 29 September 2023 served as our reference, allowing us to calculate the root mean square error (RMSE). We aimed for an RMSE below 2. If the RMSE exceeded 2, we re-performed geo-referencing using the sixth ortho-mosaic as the baseline. The results of these processes are in

Table 3. Root mean square error (RMSE) for each ortho-mosaic after re-geo-referencing.

Number	Target	Reference	RMSE
1	Aerial Photograph 08-04-2023	Aerial Photograph 29-09-2023	1.28
2	Aerial Photograph 20-05-2023	Aerial Photograph 29-09-2023	1.93
3	Aerial Photograph 17-06-2023	Aerial Photograph 29-09-2023	1.61
4	Aerial Photograph 02-07-2023	Aerial Photograph 29-09-2023	1.21
5	Aerial Photograph 19-08-2023	Aerial Photograph 29-09-2023	1.43

, and their appearance is in Figure 4.

Figure 4 presents a series of aerial ortho-mosaic photographs of Kili-Kili Beach taken from April to September 2023. Starting from the top left, the images dated 08-04-2023 (Figure 4A), 20-05-2023, 17-06 2023, 02-07-2023, 19-08-2023, and 29-09-2023 were all captured in the morning. Figure 4A shows a gap between water and sand due to a UAV technical issue that occurred during the morning flight and continued in the afternoon. Figure 4D highlights a sandy area with a distinct tone, taken shortly after light rain, where boundaries between dry and wet sand are hard to identify, obscuring traces of the highest tide. Overall, visual differences in the photos stem from the mosaicking process and the varying number of captures per flight.

3.2. Overall Sea Turtle Landings

Figure 5 presents the sea turtle landing records from March to August 2023. The small panel provides examples of detailed date information indicating the specific landing days. Meanwhile, Figure 6 shows the monthly variation in sea turtles landing. The number of landings fluctuates monthly in the research area. The landing started in late March 2023, and the landings mostly occurred between April and June 2023. In the 2023 season, a total of 63 landings were recorded in the research area.

Figure 7 shows the longshore distribution of sea turtle landings in 2023 from west to east. Each bar represents the number of landings within about 55 m longshore. From the longshore distribution, sea turtles prefer the beach area between BP1 and BP4, followed by the area between BP5 and BP6. The occurrence of landings decreases farther east due to beach cover and profile.

3.3. Landing Preference Based on Beach Cover Category

We limit the delineation of coastal land cover or beach cover from aerial photographs to the sea pandanus (Pandanus tectorius), which grows in high density along Kili-Kili Beach from west to east, as shown in Figure 8. The area behind the sea pandanus was excluded from the analysis because it transitions into the coastal inland area, where coconut trees (Cocos nucifera) grow alongside other species such as Casuarina sp. (she-oaks). This vegetation can be recognized in aerial photographs by its unique leaf shape and treetop structure.

Figure 9 shows the beach land cover type at Kili-Kili Beach, specifically around BP1, in different months overlayed by the spawning location of the sea turtles when they existed there. This figure is an example of typical land cover at Kili-Kili Beach. Beach land cover is categorized as follows: high-density beach vegetation (BV-H), moderate-density beach vegetation (BV-M), low-density beach vegetation (BV-L), and bare sand (BS).

Sea turtles’ preferences are related to beach land cover. In the 2023 season, 63 landings were recorded, with 79.4% of the spawning occurring on areas with bare sand (BS), 7.9% with low-density beach vegetation (BV-L), and 4.8% with moderate-density beach vegetation (BW-M). Meanwhile, the non-spawning rate is 4.8% on bare sand (BS), 1.6% on low-density beach vegetation (BV-L), and 1.6% on moderate-density beach vegetation (BW-M).

3.4. Sea Turtle Spawning and Non-Spawning Distance from the Instantaneous Shoreline

Following the approach of [6], the distance (d) from the instantaneous shoreline to both sea turtle spawning and non-spawning was measured using beach profile (cross-section) data collected from field measurements and tide level records (Figure 10). Sea turtles usually come ashore at night, though the exact timing can be unpredictable. We assumed they arrived when the tide was at its highest during nighttime hours. The position of the instantaneous shoreline was determined based on the tide records, specifically using the waterline that was closest in date to the beach profile data.

Figure 11 shows the distribution of sea turtles’ positions (spawning and non-spawning) in season 2023 at Kili-Kili Beach. We estimated the following principle in Figure 10 using the six cross-sections’ data. Due to this limitation, the number of landings analyzed in the area are as follows: cross-section 1 (n = 3), cross-section 2 (n = 5), cross-section 3 (n = 6), cross-section 4 (n = 4), cross-section 5 (n = 3), and cross-section 6 (n = 2). In total, n = 23 from 63 sea turtle landings during the season, and the distribution is shown in Figure 12’s bottom panel.

Figure 12 shows the distribution of distance (d) from the instantaneous shoreline categorized by spawning and non-spawning. The maximum distance of spawning from the instantaneous shoreline is approximately 45 m, while the minimum distance is approximately 8 m. There are two non-spawning events noticeable in the figure, located approximately 15 m and 30 m from the instantaneous shoreline.

3.5. Grain Size Distribution

Surface sand samples were collected from six locations along the beach profile/cross-section measurements, which were a few meters from the measurement base point (BP), as illustrated in Figure 11. Cross-section 1 had the highest mean grain size with 0.65 mm, followed by cross-section 5 with 0.62 mm and cross-section 2 with 0.60 mm. Meanwhile, cross-section 4 had 0.59 mm, cross-section 6 had a mean grain size of 0.57 mm, and cross-section 3 had a mean grain size of 0.56 mm, the lowest among them. Overall, Kili-Kili Beach’s grain size is approximately 0.64 mm; according to grain size classification, these are categorized as coarse sand [18].

3.6. Spawning and Non-Spawning Positions and Variability of the Beach Conditions

We checked the variability of spawning and non-spawning positions around the six beach profiles, detailed in the sub-panels of Figure 11, with limits extending fifty meters east and west of each cross-section. The number of sea turtle landings analyzed at cross-sections 1 through 6 for the 2023 season reduced to three, five, six, four, three, and two, respectively. Additionally, we assessed spawning and non-spawning positions relative to beach elevation and standard deviation near the cross-sections, depicted in Figure 13.

The main panel of Figure 13 displays an overlay of beach profiles along with their mean and standard deviation, highlighting spawning and non-spawning locations. Closer to the shore, the number of missing data points increases, coinciding with a decrease in standard deviation at these positions. Additionally, the main panel of Figure 13 also illustrates the relationship between spawning and non-spawning locations concerning beach stability (expressed by standard deviation) and elevation.

Figure 13 Panel (A) shows cross-section 1, where turtles prefer beach elevations of 2–4 m, with one non-spawning location at 3.1 m. The beach surface is more stable between 15 and 40 m from the measurement point, indicated by a standard deviation of 0.1–0.4 m. The statistical analysis for the elevation between the spawning and non-spawning obtained a p-value of 0.98. Panel (B) presents cross-section 2, located east of cross-section 1, where all sea turtles spawned at elevations of 2–4 m, within 3–22 m from the measurement point, with a standard deviation of 0.2–0.7 m.

Panels (C) and (D) represent cross-sections 3 and 4, where all sea turtles spawned. In Panel (C), turtles are located 0.2–19 m from the measurement point, with elevations of 3–5 m and a standard deviation of 0–0.4 m. In Panel (D), the distance ranges from 0.5 to 20 m, with elevations of 1.6–5.2 m and a standard deviation of 0.01–0.5 m. In these two cross-sections, the p-value is 1. Panel (E) (cross-section 5) shows three landings: two spawning and two non-spawning. The spawning occurs approximately 9 m from the measurement point at an elevation of 3.7 m (SD = 0.16 m), while the non-spawning is about 10 m away at 3.2 m (SD = 0.17 m). The statistical analysis for the elevation between the spawning and non-spawning obtained a p-value of 0.05.

Panel (F) (cross-section 6) features two spawning occurrences, located at 16 m and 29 m from the measurement point, with elevations of 2 m and −0.1 m, and standard deviations of 0.4 and 0.5 m, respectively. In these cross-sections, the p-value is one because all events were classified as spawning.

Furthermore, as shown in Figure 13, the surface slope varies across each cross-section, reflecting differences in their respective conditions. When examining the mean line, we observe that the slope differs across the cross-sections. Specifically, for cross-sections 1, 2, 4, and 6, the slope is approximately 0.11. In contrast, cross-section 3 has a slope of 0.06, and cross-section 5 shows a slope of 0.08. It is also worth noting that cross-sections 1, 2, and 4 are not the same length as cross-sections 3, 5, and 6, which may influence the slope calculations. Additionally, based on data from Figure 13, we observed that the mean slope for spawning is 0.07, while for non-spawning areas it is only 0.01. This depiction suggests that sea turtles prefer specific beach characteristics when selecting spawning sites.

Figure 14 illustrates the relationship between spawning and non-spawning locations concerning beach stability and elevation, indicating that turtles have preferences for egg-laying sites, but the differences are not highly contrasted. Panel (A) shows the distribution of spawning occurrence across different elevation ranges: three occurrences at 0–2 m, nine at 2–4 m, and eight above 4 m. In contrast, non-spawning events were only observed in the 2–4 m elevation range, with just two occurrences. The statistical analysis for elevation revealed a p-value of 0.88. For beach stability, as indicated by the standard deviation ranges, the distribution of spawning occurrences is as follows: five at 0.0–0.2 m, five at 0.2–0.4 m, eight at 0.4–0.6 m, and three above 0.6 m. In comparison, non-spawning occurrences were much fewer: one at 0.0–0.2 m, one at 0.2–0.4 m, and none at 0.4–0.6 m or above 0.6 m. Lastly, the statistical analysis for the standard deviation between the two groups obtained a p-value of 0.50.

4. Discussion

4.1. Overall Sea Turtle Landing Behavior

This study used a series of aerial and terrestrial surveys to gain insights into sea turtle landing behavior. Our analysis of time series aerial photographs and coastal data revealed that sea turtles prefer to select the area west of Kili-Kili Conservation Station for their spawning, on sparsely vegetated or unvegetated berm areas (Figure 7). Kili-Kili Beach is located on the mainland tropical shores of southern East Java, with sea pandanus (Pandanus tectorius) forming a boundary for sea turtles to access the upper beach area or the backshore, as described in Figure 8 and Figure 9. Consistent with reports [19,20], olive ridleys favor spawning on similar beach characteristics.

The measurement from six positions indicates that grain size conditions are dynamic, with no extreme changes observed during the sea turtle landing season. In contrast to Kili-Kili Beach, olive ridley sea turtles in Rushikulya, India, prefer medium sand conditions [21]. Additionally, fine sand (0.1–0.25 mm) is reported as preferable for olive ridleys in Bali [22], while medium sand (0.25–0.5 mm) is noted for Aceh Jaya District, Indonesia [23]. The variation in average grain size across these locations suggests that grain size does not play a decisive role in the landing behavior of sea turtles (spawning and non-spawning).

In Figure 13, sea turtles tend to select specific elevations for spawning to avoid submersion by seawater, while their distance from the shoreline varies with beach conditions. Olive ridleys typically crawl between 10 and 20 m [24], while the study by [25] found a preference for distances of 20 to 60 m. Our evaluation of beach stability, using the standard deviation of temporal elevation changes, indicated that preferred areas experienced slight change throughout the landing season, as supported by Figure 14. Overall, the use of aerial photographs and coastal data effectively analyzed sea turtle landing behavior.

4.2. Spatial and Temporal Resolution of Aerial Photographs for Sea Turtle Study

We set the flight altitude for each aerial survey mission at 50 m. Afterward, using the default settings in Pix4Dmapper for ortho-mosaic processing, we achieved an average Ground Sampling Distance (GSD) of approximately 2.44 cm. GSD is a key factor that constrains the accuracy of photogrammetry and the ability to detect organisms [26]. Since we did not detect the sea turtles, our ortho-mosaics were sufficient for observing the beach surface, as illustrated in Figure 15.

In Figure 15, we overlaid sea turtle landing positions for that month to analyze the spatial and temporal resolution of the aerial photographs. Then, we enlarged a section of Figure 15A to provide a detailed view of the beach surface from 8 April 2023, shown in Figure 15B, where three sea turtle landing positions are visible, indicating a healthy beach. In Figure 15C, taken on 20 May 2023, only one landing is visible, along with signs of erosion and the high tide mark. The sea turtle spawning incident on 28 May 2023 is highlighted in Figure 15E. We hypothesize that turtles crawled through the area marked by the red arrow, which appears less steep than its surroundings. Although the exact timing of the erosion is unclear, this aerial view suggests that the turtles felt secure from seawater submersion despite the erosion.

Figure 15D, the ortho-mosaic from 17 June 2023, shows turtle landings below the eroded area, indicating that turtles did not spawn beyond this section, unlike on May 28. Enlarging the May 28 area in Figure 15F reveals a less steep slope, but no turtles reached it. We believe that the area in front of the erosion felt safe, which the turtles sensed. The month-long intervals between the images in Figure 15B–D offer insights into the potential of UAVs in detecting beach surface changes for beach and coastline surveys in sea turtle research, highlighted by [27,28].

Frequent collection of aerial photographs, beach profiles, tidal data, grain size data, and observations has effectively aided in monitoring sea turtle behavior at Kili-Kili Beach. However, given the flight settings, the optical sensors employed, and technical limitations experienced in the field (e.g., signal issues, no RTK), we were unable to generate a time series digital surface model (DSM) that consistently and accurately reflects the conditions. We will improve the flight setting and solve the technical limitations in the future.

For future studies, we recommend adopting weekly beach profile measurements. However, detailed time series analysis of grain size and weekly aerial surveys may not be feasible due to cost and practicality. UAV flights can be conducted as needed to enhance regular monitoring, particularly during incidents of erosion or landscape changes. While recent methods like LiDAR and aerial photogrammetry as shown by [29,30,31,32,33] provide broader, detailed beach topographical measurements, they are expensive and complex, making them less practical for frequent monitoring by small communities and volunteers at Kili-Kili Beach.

5. Conclusions

We studied sea turtle behavior at different periods by utilizing a time series of aerial photographs, sea turtle landing records, beach profile survey records, grain size measurements of the beach material, and tide records. The analysis helped us understand the behavior of the turtles on the beach. Additionally, beach stability was also evaluated by analyzing the temporal elevation change through standard deviation. Our findings indicate that sea turtles prefer spawning locations on bare sand, situated 8 to 45 m from the waterline, with beach elevations ranging from 1 to 5 m. The standard deviations of beach elevation varied from 0.0 to 0.7 m, with a mean slope of 0.07. This information is important for effectively conserving sandy beaches that serve as spawning sites for sea turtles.