A Cloud-based Coastal Earth Observation Framework for Regional Seagrass Environment Mapping Across The Eastern African Coastlines

Seagrasses play an important role in global coastal seascape configuration and extensive blue carbon sequestration through their connectivity with other seascape habitats. Unfortunately, their population keeps on dwindling down due to climate change and unsustainable human activities. Furthermore, the lack of seagrass distribution data and adequate level of protection hampers efforts to conserve these key ecosystems. Our study focuses on mapping seagrass distribution, bathymetry, and water quality along the East Africa coastline using Sentinel-2 satellite images on the Google Earth Engine cloud platform. We perform the seagrass mapping between the depth of 0-15 m in the country scales of Kenya, Tanzania, Mozambique, and Madagascar with a combination of large-scale in-situ and human-annotated data. The presented framework consists of big satellite data analysis, turbid zone masking, machine learning classification, and satellite-derived bathymetry (SDB) estimation. The overall accuracy of the seagrass mapping ranges between 73-89%. The SDB explains the variation in more than 60% of the validation data and features an error of less than 10% of the full mapped depth range. Our country-scale seagrass, bathymetry, and water quality inventories can support integrated science and management efforts pertaining to seascape connectivity, blue carbon spatial variability, resource conservation, and drivers of change in these optically complex natural architectures

A MACHINE LEARNING APPROACH TO MULTISPECTRAL SATELLITE DERIVED BATHYMETRY

Abstract. Bathymetry in coastal environment plays a key role in understanding erosion dynamics and evolution along coasts. In the presented investigation depth along the shore-line was estimated using different multispectral satellite data. Training and validation data derived from a traditional bathymetric survey developed along transects in Cesenatico; measured data were collected with a single-beam sonar returning centimetric precision. To limit spatial auto-correlation training and validation dataset were built choosing alternatively one transect as training and another as validation. Each set was composed by a total of ~6000 points. To estimate water depth two methods were tested, Support Vector Machine (SVM) and Random Forest (RF). The RF method provided the higher accuracy with a root mean square error value of 0.228 m and mean absolute error of 0.158 m, against values of 0.409 and 0.226 respectively for SVM. Results show that application of machine learning methods to predict depth near shore can provide interesting results that can have practical applications

GIS Untuk Integrasi Interpretasi Substrat Dasar Perairan menggunakan Penggolahan Citra ALOS-AVNIR dan Side Scan Sonar

Interpretasi substrat dasar perairan merupakan salah satu kajian penting dalam ilmu kelautan, sebagai kajian utama maupun kajian pendamping untuk kajian fenomena kelautan. Substrat dasar perairan penting untuk diketahui karena sebarannya yang sangat dinamis, di kedalaman perairan yang sama bisa saja terdapat materi substrat yang berbeda dan materi substrat yang sama bisa terdapat di rentang kedalaman yang berbeda. Interpretasi memanfaatkan citra Side Scan Sonar  dan citra satelit ALOS AVNIR-2untuk sebagian perairan Selat Sunda. Metode yang digunakan adalah pengolahan citra (image processing) identifikasi substrat perairan untuk citra Side Scan Sonar dengan Sonarwiz dan penerapan algoritma Lyzenga dan transformasi NDVI (Normalize Different Vegetation Index) untuk data citra satelit. Transformasi NDVI meningkatkan akurasi pemetaan substrat pada citra ALOS AVNIR-2. Hasil pengolahan kedua citra dioverlay menggunakan GIS untuk menampilkan visualisasi sebaran substrat perairan.Kata Kunci :ALOS-AVNIR, Side Scan Sonar, GIS, Lyzeng

ANALYSIS OF CLASSIFICATION METHODS FOR MAPPING SHALLOW WATER HABITATS USING SPOT-7 SATELLITE IMAGERY IN NUSA LEMBONGAN ISLAND, BALI

Shallow water habitat maps are crucial for the sustainable management purposes of marine resources. The use of a better digital classification method can provide shallow water habitat maps with the best accuracy rate that is able to indicate actual conditions. Experts use the object-based classification method as an alternative to the pixel-based method. However, the pixel-based classification method continues to be relied upon by experts in obtaining benthic habitat conditions in shallow water. This study aims to analyze the classification results and examine the accuracy rate of shallow-water habitats distribution using SPOT-7 satellite imagery in Nusa Lembongan Island, Bali. Water column correction by Lyzenga 2006 was opted, while object-based and pixel-based classification was used in this study. The benthic habitat classification scheme uses four classes: substrate, seagrass, macroalgae, and coral. The results show different accuracy is obtained between pixel-based classification with maximum likelihood models and object-based classification with decision tree models. Mapping benthic habitats in Nusa Lembongan, Bali, with object-based classification and decision tree models, has higher accuracy than the other with 68%

DETERMINATION OF THE BEST METHODOLOGY FOR BATHYMETRY MAPPING USING SPOT 6 IMAGERY: A STUDY OF 12 EMPIRICAL ALGORITHMS

For the past four decades, many researchers have published a novel empirical methodology for bathymetry extraction using remote sensing data. However, a comparative analysis of each method has not yet been done. Which is important to determine the best method that gives a good accuracy prediction. This study focuses on empirical bathymetry extraction methodology for multispectral data with three visible band, specifically SPOT 6 Image. Twelve algorithms have been chosen intentionally, namely, 1) Ratio transform (RT); 2) Multiple linear regression (MLR); 3) Multiple nonlinear regression (RF); 4) Second-order polynomial of ratio transform (SPR); 5) Principle component (PC); 6) Multiple linear regression using relaxing uniformity assumption on water and atmosphere (KNW); 7) Semiparametric regression using depth-independent variables (SMP); 8) Semiparametric regression using spatial coordinates (STR); 9) Semiparametric regression using depth-independent variables and spatial coordinates (TNP), 10) bagging fitting ensemble (BAG); 11) least squares boosting fitting ensemble (LSB); and 12) support vector regression (SVR). This study assesses the performance of 12 empirical models for bathymetry calculations in two different areas: Gili Mantra Islands, West Nusa Tenggara and Menjangan Island, Bali. The estimated depth from each method was compared with echosounder data; RF, STR, and TNP results demonstrate higher accuracy ranges from 0.02 to 0.63 m more than other nine methods. The TNP algorithm, producing the most accurate results (Gili Mantra Island RMSE = 1.01 m and R2=0.82, Menjangan Island RMSE = 1.09 m and R2=0.45), proved to be the preferred algorithm for bathymetry mapping

ANALYSIS OF THE PENETRATION CAPABILITY OF VISIBLE SPECTRUM WITH AN ATTENUATION COEFFICIENT THROUGH THE APPARENT OPTICAL PROPERTIES APPROACH IN THE DETERMINATION OF A BATHYMETRY ANALYTICAL MODEL

The attenuation coefficient (Kd) can be extracted by an apparent optical properties(AOP) approach to determine marine shallow-water habitat bathymetry based on an analytical method. Such a method was employed in the Red Sea by Benny and Dawson in 1983 using Landsat MSS imagery. Therefore, we applied the Benny and Dawson algorithm to extract bathymetry in shallow marine waters off Karimunjawa Island, Jepara, Central Java, Indonesia. We used the SPOT 6 satellite, which has four multispectral bands with a spatial resolution of 6 meters. The results show that three bands of SPOT 6 data (the blue, green, and red bands) can produce bathymetric information up to 30.29, 24.63 and 18.58 meters depth respectively. The determinations of the attenuation coefficients of the three bands are 0.08069, 0.09330, and 0.39641. The overall accuracy of absolute bathymetry of the blue, green, and red bands is 61.12%, 65.73%, and 26.25% respectively, and the kappa coefficients are 0.45, 0.52, and 0.13

Sunglint correction in airborne hyperspectral images over inland waters

This study assessed sunglint effects in airborne high spatial and high spectral resolution images acquired by the SpecTIR sensor under different view-illumination geometries over the Brazilian Ibitinga reservoir (Case II waters). These effects were corrected using the Goodman et al. (2008) and the Kutser et al. (2009) methods, and a variant that used the continuum removal technique to calculate the oxygen absorption band depth. The performance of each method to removing sunglint effects was evaluated by a quantitative analysis of pre- and post-sunglint correction reflectance values (residual reflectance images). Furthermore, the analysis was supported by inspection of the reflectance differences along transects placed over homogeneous masses of waters or over specific portions of the scenes affected and non-affected by sunglint. Results showed that the algorithm of Goodman et al. (2008) produced better results than the other two methods, as it approached to zero the amplitude of the reflectance values between homogenous water masses free and contaminated by sunglint. The Kutser et al. (2009) method had also good performance, except for the most contaminated sunglint portions of the scenes. When the continuum removal technique was incorporated to the Kutser et al. (2009) method, results varied with the scene and were more sensitive to atmospheric correction artifacts and instrumental signal-to-noise ratio