[A-CG41] Coastal Ecosystems 2. Coral reefs, seagrass and macroalgal beds, and mangroves

[ACG41-P05] Towards the detection of salinity stress on mangroves: Relationship between the number of yellow-color salt-excreting leaves on mangrove trees and soil salinity

*Akiko Watanabe¹, Takashi Nakamura¹, Kristian R. Monay¹, Yuta A. Takagi¹ (1.Tokyo Institute of Technology)

Keywords:Mangrove, Yellow-color salt-excreting leaves, Drone survey, Species coverage classification, Pore water salinity

Mangroves have the potential to mitigate global warming by sequestering organic carbon and to reduce coastal disasters through their dense and complex root structures. It is therefore important to understand the basics of mangrove ecology. It is well known that mangroves can inhabit in the sallow coastal zone brackish zone because of their higher salinity tolerance. One of the important mechanisms for achieving such high salinity tolerance in some mangrove species is that salt absorbed by the roots is transported to the old yellow-leaves and ejected with the leaves by detaching from the main body. Therefore, it is considered that if salt entering their body becomes high, the number of the yellow-leaves might increase. To verify this hypothesis, the relationships between porewater salinity, the number of yellow leaves, and species components were examined.
For obtaining porewater salinity in the mangrove area, porewater was corrected by Rhizon samplers at 20 stations in the Fukido River Mangrove area in Iriomote Ishigaki National Park, Okinawa, Japan. Stations were installed in a 100mx100 m area, each station 10-20m apart. Each station consists of one or two mangrove species, Rhizophora stylosa and Bruguiera gymnorhiza. To identify mangrove species and to estimate ratio of yellow-leaves to green-leaves in wide range of the mangrove area, aerial photos were taken by drone (Phantom 4 pro, DJI), and an orthomosaic map was created from the photos. To process the images (Image Analyst, 2011), 20 images were cropped with the station average plotted at the center and a size of 10 mx10 m using QGIS. Then, mangrove species in each image were visually identified according to the observed features. In the image analysis, the ratio of the specified color to the number of pixels in the image was derived. The chosen image analysis is unique in that it uses the LAB color model, which shows one dimension only in terms of brightness, rather than the RGB color model. LAB seemed better as this analysis could differentiate yellow leaves and lighted green leaves. Adding a modification to the existing MATLAB script to only detect the needed color % in the image with the factor of A and B, the color percentage of yellow leaves and all leaves on each 10mx10m image with one species was calculated.
The relative coverage of each species was found to correlate with salinity. The fraction of B. gymnorhiza was found to decrease as salinity increased according to the function f=-4.0511S+33.857 where f is the B. gymnorhiza fraction (%) and S is salinity (PSU) (R-squared = 0.4826). The mean yellow leaf fraction of R. stylosa was determined to be 1.85% (SD = 0.777%). The mean yellow leaf fraction of B. gymnorhiza was determined to be 0.910% (SD = 0.448%). These results show that R. stylosa has both higher salinity tolerance and higher yellow leaf fraction than B. gymnorhiza. The difference of more than twice the yellow leaf fraction was greater than expected, allowing for clear differentiation between the species. Furthermore, it was found that the total yellow leaf fraction could be linearly interpolated between the two values proportional to the species coverage. In conclusion, our results show that the distribution of the two mangroves is controlled by salinity, and we propose that their species distribution patterns may be determined using drone surveys by assessing the percent yellow leaf fraction according to the relationship discovered in this study.