Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas
2. Materials and Methods
2.1. Study Site
2.2. Satellite Data
2.3. Field Data
2.3.1. Training and Validation Data
2.3.2. Auxiliary Data
- Cloud mask: We use the QA60 bitmask band to mask opaque and cirrus clouds and scale S2 L1C TOA images by 10,000 (Figure 4a,b).
- Land mask: Although here we utilize the buffered coastline shapefile of Greek waters to mask out terrestrial Greece, we include a classification and regression tree (CART) classifier  in the GEE code that future users could employ to mask out their terrestrial part. The classifier is applied on a b3-b8-b11 composite and the user should train it with relevant pixels over land and water.
- Image composition: We apply image composition which yields a new pseudo-image composite whose pixels are the first quartile (Q1) of the median values of the cloud corrected and masked for land images of step 2. The purpose of this approach is to decrease noncorrected image artefacts by the previous steps.
- Sunglint correction: We further correct the atmospherically corrected image composites with the sunglint correction algorithm of . Following a user-defined set of pixels of variable sunglint intensity, the algorithms equals the corrected for sunglint composite to the initial first quartile composite minus the product of the regression slope of b8 against b1-b4 and the difference between b8 and its minimum value (Figure 4c).
- Depth invariant indices calculation: To compensate the influence of variable depth on seabed habitats, we derive the depth invariant indices [26,27] for each pair of bands with reasonable water penetration (b1-b2, b2-b3, b1-b3) with the statistical analysis of . Prior to the machine learning-based classification, we apply a 3 × 3 low pass filter in the depth invariant as well as the sunglint-corrected input to minimize remaining noise over the optically deep water extent which would have caused misclassified seagrass pixels otherwise (Figure 4d).
4.1. On Global Mapping and Monitoring of Seagrasses, and the Results of the Present Case Study
4.2. The Good, the Bad, and the Best Practices of the Proposed Cloud-Based Workflow
- Selection of a suitable time range; the suitability relates to possible available in situ data to run the machine learning classifiers, the atmospheric, water surface and column conditions of the study area, but also the season of maximum growth of the seagrass species of interest, especially for change detection studies. Here, we have chosen one month of Sentinel-2 imagery within the period of better water column stratification of the Greek Seas.
- Selection of suitable points that will represent land and water for land masking (if needed), polygons over deep water (for atmospheric correction), variable sunglint intensity (for sunglint correction), and sandy seabed of variable depth (for the depth invariant index calculation).
- Accurate in situ data that will cover all the existing habitats within the study extent for training of the machine learning classifications and validation with an ideally independent data set to reduce potential bias; here, we design remotely sensed, homogenous 4 × 4 (1600 m2) polygons for the training of the machine learning model and employ an independent point-based data set for the validation. We also decided to design deep-water polygons to minimize possible misclassifications with seagrasses.
- Method-wise, the herein image-based, empirical algorithms (e.g., dark pixel subtraction, sunglint correction, depth invariant indices) contain inherent assumptions in their nature and necessitate a sufficient selection of pixels to produce valuable results. Concerning the sunglint correction, specifically, an image composition spanning a large period of time can amplify the artificiality of the produced pseudo-composite, causing the sunglint correction algorithm to be unable to capture any existing interference by this phenomenon.
- Data-wise, there is a threefold problem with Sentinel-2 applications in the remote sensing of optically shallow benthos and broadly aquatic extent. First, the tile limits of Sentinel-2 data are visible due to differences in viewing angles (odd and even detectors feature a different viewing angle) that produce striping. In turn, this artifact could severely impact classification output as it alters neighboring reflectances. A first possible solution for striping could be the application of pseudo-invariant feature normalization using a tile as a reference image and all the others as the slave ones—a theoretically, computationally expensive operation within GEE. A second solution is to split the initial study area into subareas—ultimately every tile within the visible stripes—where we could select polygons and run the classifier. The second data-related issue is the coastal aerosol band 1, which is originally in 60-m resolution in comparison to the 10-m resolution of all the other visible bands. Although on-the-fly reprojected to 10-m for visual purposes and integral towards coastal habitat mapping and SDB due to its great penetration, it causes artefacts upon application of the depth invariant indices of [26,27]. Therefore, we only utilise b2-b3 index during the classification step. This could be solved through the implementation of a downscaling approach of band 1  into the existing workflow which is under exploration in terms of computation time efficiency. The third and last data-wise issue is the selection of training and validation data. We designed as homogeneous as possible polygons that represent seagrasses, sands, rocks, and deep water based on very high spatial resolution images; however, these will be as accurate as our experienced eye will dictate to us. Figure 2 shows that at all band-to-band scatterplots, the designed polygons of seagrass and non-seagrass beds are not well-differentiated and may have caused misclassifications. Generally, the collection of field data for the classification of remote sensing of aquatic habitats is expensive, time-consuming, and sparse today. More efforts should be driven towards allocating funding for accurate and high resolution in situ data and/or advocating the sharing of open datasets that would permit regional to global projects. The search for open access data on seagrass from relevant data repositories reveals a high number, however a fraction of these are potentially suitable for use in the remote sensing domain. Therefore, it is mandatory to urge a collaborative action between seagrass and remote sensing scientists, which will galvanize the development of a protocol that could be easily adapted in any seagrass bioregion for the designation of accurate and well documented with metadata, in situ data for seagrass mapping using the present workflow.
4.3. Future Endeavours
- Basin- (Mediterranean) to global-scale mapping and monitoring of seagrasses and related biophysical variables (specifically the climate change-related carbon sequestration): The expected lifespan of Sentinel-2 and its succeeding complementary twin mission (7.25 + 7.25 years) would unravel issues related to open and free, high spatial resolution data availability and allow intra-annual (seasonal) to interannual monitoring activities in the optically shallow grounds of seagrasses for 14.5 years by 2029, which marks the end of the announced UN decade of ocean science .
- Improvement of certain stages of the present workflow: (a) Incorporation of a more sophisticated atmospheric correction algorithm like Py6S , (b) Implementation of optimization approaches for simultaneous derivation of benthic reflectance and bathymetry based on the semianalytical inversion model of [40,41], (c) Inclusion of best available pixel (BAP) approach within span of well-stratified column period which use pixel-based scores, according to both atmospheric-, season- and sensor-related issues, to produce a composite with the best available pixel , (d) Incorporation of object-based segmentation and classification methods to improve classified outputs. The main drawback of the first three improvements is they would possibly lower the time efficiency of the present version of the chain due to the higher demand in computational power based on the need to implement look up tables and/or run radiative transfer codes.
- Integration of seagrasses and other coastal habitats to the analysis ready data (ARD) era: Recent advances in optical multispectral remote sensing (e.g., Sentinel-2, Landsat 8, Planet’s Doves), cloud computing and machine learning classifiers can enable multiscale, multitemporal and sensor-agnostic approaches where all the aforementioned data will be preprocessed to a high scientific standard (Cloud Optimized GeoTIFF; ), further harnessing past, present and future remotely sensed big data and facilitating the near real-time measurements of physical changes of these immensely valuable habitats for Earth.
Conflicts of Interest
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Traganos, D.; Aggarwal, B.; Poursanidis, D.; Topouzelis, K.; Chrysoulakis, N.; Reinartz, P. Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas. Remote Sens. 2018, 10, 1227. https://doi.org/10.3390/rs10081227
Traganos D, Aggarwal B, Poursanidis D, Topouzelis K, Chrysoulakis N, Reinartz P. Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas. Remote Sensing. 2018; 10(8):1227. https://doi.org/10.3390/rs10081227Chicago/Turabian Style
Traganos, Dimosthenis, Bharat Aggarwal, Dimitris Poursanidis, Konstantinos Topouzelis, Nektarios Chrysoulakis, and Peter Reinartz. 2018. "Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas" Remote Sensing 10, no. 8: 1227. https://doi.org/10.3390/rs10081227