Remote sensing of environment and disaster laboratory

Institute of Industrial Science, the University of Tokyo, JapanSeagrass mapping on an intertidal flat at different tide levels
using UAV and Sentinel-2Xuan Truong Trinh*, Wataru Takeuchi
Institute of Industrial Science, the University of Tokyo, Japan

Abstract: Seagrass beds have great potential to bury carbon, therefore it is necessary to monitor seagrass beds and estimate their carbon burial capacity. However, there are still many
knowledge gaps in seagrass carbon stock assessment in intertidal flats of tropical coastal zones, due to complex tidal dynamics interfering with sunlight. Previously, there was a limitation ofavailable imagery that has both sufficient spatial and temporal resolution, which limits our understanding of how the seagrass beds change under different tidal stages. Recently, the launch ofSentinel-2 satellites greatly enhanced the number of available images for analysis. By integrating Sentinel-2 images with UAV images, this paper aims to reveal how the seagrass spectral
response changes at different tide levels. To establish the ground truth with a high level of detail about seagrass species and density, a UAV was employed to capture images of the intertidal flatat low tide. With UAV-derived seagrass map as the basis, changes to its spectral responses at different tide levels was examined with time series analysis of Sentinel-2 images. It was found that
Sentinel-2 images captured at low tide have high agreement with UAV base map, while images at high tide are influenced by a constantly changing water column. The patterns in changes toseagrass beds reflectance at different tidal stages may enable further understanding of seagrass photosynthesis and carbon sequestration rate on intertidal flats.

2 Methodology

1 Introduction
Challenge:
Seagrass carbon stock in intertidal flats is uncertain due tochanging tides
Objective:
Reveal how seagrass spectral response changes at different
tide levels by integrating UAV images and Sentinel-2
imagesSignificance:
Fig. 1 Change in tidal stages from 11:00-18:00 on 2020-02-05
Enable estimation of seagrass photosynthetic activities overat Bai Bon, Phu Quoc islands, Kien Giangtime

3 Results

3.2 Sentinel-2 images at different tide levels• 107 Sentinel-2 cloud-free images in dry season
between 2018-11-01 and 2020-12-31• Dry months (November – April) were considered
to capture seagrass at maximum growth• Tide levels at image acquisition time (10:20 amlocal time) seem to have an annual cycle, high in
December, low in March
Fig. 4 Variation of tide levels obtain from TPXO global
tide model

2020-01-01
High tide (0.179m)
Overall accuracy: 79.5%

Fig. 5 True color composite and classified images from Sentinel-2 satellites• Seagrass beds clearly distinguishable from sand at low tide, and medium tide, but lessdistinct at high tide.
Dense seagrass

Fig. 3 True color composite and classified orthomosaic of the UAV
images captured at low tide on 2020-02-05, 15:00.

2020-02-05
Medium tide (-0.015m)
Overall accuracy: 94.4%

Sparse seagrass• 435 images were captured from DJI
Phantom 4• Altitude: 150m
• Spatial resolution: 4 cm
• Area covered: 66.5 ha• Tide level: -0.3m• Seagrass beds clearly visible, to eachleaf and canopy of Enhalus acoiroides

2020-03-31
Low tide (-0.255m)
Overall accuracy: 90.9%Sand

Close-up of UAV image showing eachseagrass blade is resolved.

Classified images

True color composite

3.1 UAV image acquisition classification

Fig. 2 Overall flowchart of UAV and Sentinel-2 images analysis.

Fig. 6 Reflectance of the blue, green, red, near infrared of Dense seagrass, Sparse seagrass, and sand at different tide levels• High reflectance seems to correlate with lower tide, especially in sand.

4 Discussion and future work
• The 4 cm spatial resolution of UAV enables further analysis, such as measuring canopy
density, percent cover, or species composition.
• Sentinel-2 is highly compatible with UAV images in distribution mapping of seagrass.
This allows for integrating Sentinel-2 and UAV images to extrapolate assessment thatrequires UAV’s high spatial resolution, such as percentage cover, species mapping.
• The apparent correlation between reflectance and tides may shed light to further analysis of
the water column quality, such as turbidity, suspended sediments. This may in turn reveal
further information about the bathymetry of shallow intertidal flats and the properties ofphotosynthetic activities in seagrass beds.

Future works:• Further analysis of UAV images• Examine water column effects on reflectance• Estimate bathymetry from the variation in tide level• Investigate on tidal effects on seagrass photosynthetic activities

Contact: Bw-601, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL:http://wtlab.iis.u-tokyo.ac.jp/E-mail: xuantruongtrinh93@g.ecc.u-tokyo.ac.jp)