Spatiotemporal Analysis of Sea Surface Temperature in Bolinao

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1. SPATIOTEMPORAL ANALYSIS OF SEA SURFACE TEMPERATURE AND SEAGRASS COVER IN SANTIAGO ISLAND, BOLINAO, PANGASINAN FROM 2015-2019 USING REMOTE SENSING TECHNIQUES ENRM 240 – Aquatic Ecosystems 2nd Semester, A.Y. 2019-2020 John Andrew B. Cruz MENRM - CRM

2. OUTLINE Introduction Background of the Study Significance of the Study Objectives Review of Related Literature Methodology Study Site General Workflow References

3. INTRODUCTION

4. BACKGROUND OF THE STUDY Seagrasses Aquatic flowering plants that are usually found in shallow brackish and marine waters and can thrive in both tropical and temperate environments ( Kuo and McComb , 1989) Requires sufficient amount of light, oxygen, inorganic carbon, and nutrients, suitable water temperature, and substrate ( Greve and Binzer , 2004) More productive in tropical countries compared to temperate regions since warmer coastal waters improve the flowering and growth rate of these plants Ecosystem Services Serves as nursery and feeding areas for fishes and other aquatic organisms Prevents coastal erosion Major organic carbon sink Maintains water quality Improves fish stocks Source: publicnewsservice.org

5. BACKGROUND OF THE STUDY Status of Seagrasses In the Philippines, 18 out of 69 known seagrass species have been identified from 529 sites within its archipelagic waters (Fortes, 2012) Seagrass coverage in Bolinao , Pangasinan has declined from 30.78 sq. km. in 1993 to 14.48 sq. km. in 2013-2014 (Blanco et al., 2014) Globally, it is estimated that 29% of the seagrass extent has disappeared since 1879 ( Waycott et al., 2009) Threats to Seagrasses Coastal pollution Dredging and reclamation Overfishing and destructive fishing Coastal developments Aquaculture Eutrophication Natural disturbances Sea level rise Sea temperature rise Thermal stress due to sea temperature rise affects the spatial distribution and productivity of seagrasses, and could lead to mortality

6. BACKGROUND OF THE STUDY Remote Sensing Science and art of acquiring information about the Earth’s surface and atmosphere, without being physically in contact with the area of interest (Jensen, 2006) Utilizes sensors onboard various platforms such as satellites, aircrafts, drones, and ships in order to extract meaningful data from a large area at a given time Cost-effective method in monitoring areas frequently for spatiotemporal studies Source: gisgeography.com

7. BACKGROUND OF THE STUDY Remote Sensing of Seagrasses Landsat images are widely used in historical mapping of seagrasses because its datasets are available online from 1982 to present and it has a moderate spatial resolution (30 meters) Blanco et al. (2014) utilized historical Landsat images and to detect seagrass beds in Pangasinan and concluded that that it can be used in rapid seagrass mapping in order to examine the spatial and temporal variations in seagrass cover Source: Blanco et al. (2014)

8. BACKGROUND OF THE STUDY Remote Sensing of Sea Surface Temperature (SST) SST is the temperature measured near the water surface, ranging from 1 millimeter to 20 meters, depending on employed measurement method Satellite-derived SST values may represent the temperatures down to depths of a few meters ( Katsaros , 2003) Temporal variations in satellite-derived SST closely resemble the temperature changes recorded by in-situ data loggers along the coastal sites ( Phinn et al., 2017) Source: ncdc.noaa.gov

9. BACKGROUND OF THE STUDY Seagrass Cover and Sea Surface Temperature There are significant differences in maximum SST among different coastal habitat types, and the highest water temperatures were observed in the mudbank and shallow areas (Carlson et al., 2017) Extreme SST has contributed to seagrass mortality in the study site, and the period of elevated water temperatures was found to be longer in 2015 than in 2014 by 54 days (Carlson et al., 2017) Source: Carlson et al. (2017)

10. SIGNIFICANCE OF THE STUDY Large-scale monitoring of thermal stress in seagrass beds should be conducted in order to identify the areas that are heavily affected by sea temperature rise Utilization of satellite images, coupled with field data, for mapping the extent and distribution of seagrass beds and its sea surface temperature, is a cost-effective way of monitoring large seagrass areas compared to traditional methods Spatiotemporal seagrass extent and sea surface temperature maps that will be generated by the study can be used by coastal managers in designing more effective coastal management plans to protect and conserve seagrass beds.

11. OBJECTIVES OF THE STUDY The main objective of the study is to determine whether annual sea surface temperature variations has a significant effect to the extent and distribution of seagrass meadows in Santiago Island, Bolinao , Pangasinan. Specifically, it aims to:   Generate temporal sea surface temperature and seagrass cover maps from 2015 to 2019 using remotely sensed datasets Analyze the spatial variability of seagrasses in relation to sea surface temperature Compare the trend of average sea surface temperature and total seagrass area in Santiago Island from 2015-2019

12. METHODOLOGY

13. STUDY SITE Santiago Island Located northeast of Bolinao , Pangasinan Surrounded by a reef flat composed of limestone and carbonate sediments Home of the Giant Clam Ocean Nursery and various Marine Protected Areas (MPAs) Contains one of the most extensive seagrass beds in the Philippines Around 22, 500 ha and 8 species of seagrasses in Bolinao , Pangasinan

14. GENERAL WORKFLOW

15. DATA ACQUISITION – SATELLITE DATA EARTHEXPLORER CLIMATE ENGINE For downloading Landsat multispectral satellite images For downloading Landsat-derived sea surface temperature layers

16. DATA ACQUISITION – SATELLITE DATA May 8, 2015 April 24, 2016 May 13, 2017 April 14, 2018 April 1, 2019 LANDSAT 8 OLI MULTISPECTRAL IMAGES OF SANTIAGO ISLAND LANDSAT-DERIVED SURFACE TEMPERATURE OF SANTIAGO ISLAND

17. DATA ACQUISITION – FIELD DATA Geotagging of seagrass areas by SeaRS team (Mr. Cruz is holding the GNSS Rover equipment) Seagrass sampling points were obtained by IAMBlueCECAM Program – Project SeaRS , funded and monitored by DOST-PCIEERD and implemented by UP TCAGP Sampling points (i.e. seagrass, sand, corals, algae points) were recorded during four fieldwork activities from February to October 2018 using Garmin Handheld GPS SP80 GNSS Receiver A request letter was sent to Dr. Ariel C. Blanco (Program Leader) to allow Mr. Cruz to use the field data for the academic requirement

18. DATA PROCESSING 1. RADIOMETRIC CALIBRATION Conversion of pixel values from digital numbers to top-of-atmosphere (TOA) reflectance To reduce errors due to the sensor 2. ATMOSPHERIC CORRECTION Conversion of pixel values from TOA reflectance to bottom-of-atmosphere (BOA) reflectance To reduce atmospheric effects 3. IMAGE MASKING Masking out of land and deep water to exclude in image classification 4. WATER COLUMN CORRECTION Calculation of depth invariant index To reduce effects of varying water depths ENVI 5 Image processing and classification software

19. DATA PROCESSING 5. SELECTION OF TRAINING AND VALIDATION POINTS 6. IMAGE CLASSIFICATION Maximum Likelihood Classifier (MLC) Classifies each unknown pixel based on the probability that it belongs to a specific class 7. ACCURACY ASSESSMENT ENVI 5 Image processing and classification software 70% - training points 30% - validation points Overall Accuracy of at least 80% = accurate classification 8. SST CLIPPING Clipping of SST layers using the general seagrass extent shapefile

20. DATA ANALYSIS SPATIAL ANALYSIS Comparison of average SST at different portions of seagrass beds and other benthic features To determine whether there is a connection between seagrass distribution and spatial SST variations TEMPORAL ANALYSIS Comparison of trend of average SST and total seagrass area from 2015-2019 To determine whether there is a connection between total seagrass area and temporal SST variations

21. REFERENCES Blanco, A.C., Tamondong , A., Tagle , E., Fortes, M., Nadaoka , K. (2014). Change in Seagrass Fractional Cover in Bolinao and Anda, Philippines Derived from Landsat Images. Asian Conference on Remote Sensing , Nay Pyi Taw, Myanmar. Carlson, D. F., Yarbro , L. A., Scolaro , S., Poniatowski , M., Mcgee-Absten , V., & Carlson, P. R. (2018). Sea surface temperatures and seagrass mortality in Florida Bay: Spatial and temporal patterns discerned from MODIS and AVHRR data.  Remote Sensing of Environment ,  208 , 171–188. doi : 10.1016/j.rse.2018.02.014 Fortes, M. (2012). Historical review of seagrass research in the Philippines.  Coastal Marine Science ,  35 (1), 178–181. Greve T, Binzer T. (2004). Which factors regulate seagrass growth and distribution? In: Borum J, Duarte CM, Krause-Jensen D, Greve TM, editors. European seagrasses: an introduction to monitoring and management. The Monitoring and Management of European Beds Project (pp. 19-23). Jensen, J. R. (2014).  Remote sensing of the environment: an earth resource perspective . Nodia , India: Pearson India Education Services. Katsaros , K. B. (2003). Satellite Versus In Situ Measurements at the Air-Sea Interface.  Handbook of Weather, Climate, and Water , 885–893. doi : 10.1002/0471721603.ch47 Kuo , J. and McComb , A.J. (1989) Seagrass taxonomy, structure and development. In: Larkum , A.W.D., McComb , A.J. and Shephard, S.A., (eds.) Biology of seagrasses: a treatise on the biology of seagrasses with special reference to the Australian region (pp. 6-73). Elsevier Science Pub., Amsterdam, The Netherlands. Phinn , S. R., Kovacs, E. M., Roelfsema , C. M., Canto, R. F., Collier, C. J., & Mckenzie , L. J. (2017). Assessing the potential for satellite image monitoring of seagrass thermal dynamics: for inter- and shallow sub-tidal seagrasses in the inshore Great Barrier Reef World Heritage Area, Australia.  International Journal of Digital Earth ,  11 (8), 803–824. doi : 10.1080/17538947.2017.1359343 Sea Surface Temperature. (n.d.). Retrieved from http://www.oceanhealthindex.org/methodology/components/sea-surface-temperature Waycott , M., Duarte, C. M., Carruthers, T. J. B., Orth, R. J., Dennison, W. C., Olyarnik , S., … Williams, S. L. (2009). Accelerating loss of seagrasses across the globe threatens coastal ecosystems.  Proceedings of the National Academy of Sciences ,  106 (30), 12377–12381. doi : 10.1073/pnas.0905620106

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