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

BATHYMETRY FROM WORLDVIEW-3 SATELLITE DATA USING RADIOMETRIC BAND RATIO

The use of optical satellite sensors allows to obtain bathymetry data on large area ina short time and in a cheap way. In addition, in particular places where it is difficult to carry out the survey by classic methods, the bathymetry from satellite data can be the only mode to obtain the depth of the backdrop. So, the aim of this research paper is to analyse the potential of the eight bands and the very high resolution of the commercial satellite WorldView-3 (WV-3) in order to obtain bathymetric data. Using WV-3 satellite data and the Stumpf method, the paper intends to investigate the possibility of obtaining bathymetric data in a specific area where the water is not particularly clear. To achieve this goal, in Geographic Information System (GIS) environment, several algorithms have been developed. The comparison between the results and the reference bathymetric data shows a huge potential of the WV-3 satellite data. The area of study concerns the port area of the city of Lisbon and a part of the river Rio Tejo, in Portugal

METODE DUAL KANAL UNTUK ESTIMASI KEDALAMAN DI PERAIRAN DANGKAL MENGGUNAKAN DATA SPOT 6 STUDI KASUS : TELUK LAMPUNG (DUAL BAND METHOD FOR BATHYMETRY ESTIMATION IN SHALLOW WATERS DEPTH USING SPOT 6 DATA CASE STUDY: LAMPUNG BAY)

Depth data can be used to produce seabed profile, oceanography, biology, and sea level rise. Remote sensing technology can be used to estimate the depth of shallow marine waters characterized by the ability of light to penetrate water bodies. One image that can estimate the depth is SPOT 6 which has three visible canals and one NIR channel with 6 meter spatial resolution. This study used SPOT 6 image on March 22, 2015. The image was first being  dark pixel atmospheric corrected by making 30 polygons. The originality of this method was to build a correlation between the dark pixel value of red and green channels with the depth of the field measurement results, made on June 3 to 9, 2015. The algorithm  derived experimentally consisted of: thresholding which served to separate the land by the sea and the correlation function. The correlation function was obtained: first correlating the observation value with each band, then calculating the difference of minimum pixel darkness value and minimum for red and green channel was 0.056 and 0.0692. The model was then constructed by using the comparison proportions, so that the linear equations were obtained in two channels: Z (X1, X2) = 406.26 X1 + 327.21 X2 - 28.48. Depth estimation results were for a 5-meter scale, the most efficient estimation with the smallest error relative mean occured in shallow water depth from 20 to 25 meters, while the result of   10 meters scale from 20 to 30 meters and the estimated depth hadsimilar patterns or could be said close to reality. This method was able to detect sea depths up to 25 meters and had a small RMS error of 0.653246 meters. Thus the two-channel method coukd offer a fast, flexible, efficient, and economical solution to map topography of the ocean floor.AbstrakData kedalaman dapat digunakan untuk menghasilkan profil dasar laut, oseanografi, biologi, dan kenaikan muka air laut. Teknologi penginderaan jauh dapat digunakan untuk mengestimasi kedalaman perairan laut dangkal yang ditandai dengan kemampuan cahaya untuk menembus badan air. Salah satu citra yang mampu mengestimasi kedalaman tersebut adalah SPOT 6 yang memiliki tiga kanal visible dan satu kanal NIR dengan resolusi spasial 6 meter. Pada penelitian ini, Citra SPOT-6 yang digunakan adalah 22 Maret 2015. Citra terlebih dahulu dilakukan koreksi atmosferik dark pixel dengan membuat 30 poligon. Originalitas dari metode ini adalah membangun suatu korelasi antara nilai dark pixel kanal merah dan hijau dengan nilai kedalaman hasil pengukuran lapangan yang dilakukan pada 3 sampai dengan 9 Juni 2015. Algoritma diturunkan secara eksperimen yang terdiri dari thresholding yang berfungsi untuk memisahkan daratan dengan lautan dan fungsi korelasi. Fungsi korelasi diperoleh pertama-tama mengkorelasikan nilai pengamatan dengan masing-masing band, kemudian menghitung selisih nilai dark pixel maksimum dan minimum untuk kanal merah dan hijau yaitu 0,056 dan 0,0692. Selanjutnya, dibangun model dengan menggunakan dalil perbandingan sehingga diperoleh persamaan linier dalam dua kanal yaitu: Z(X1,X2) = 406,26 X1 + 327,21 X2 – 28,48. Hasil estimasi kedalaman, untuk skala 5 meter, estimasi yang paling efisien dengan Mean relatif error terkecil terjadi pada kedalaman perairan dangkal dari 20 sampai dengan 25 meter, sedangkan untuk skala 10 meter dari 20 sampai dengan 30 meter dan juga hasil estimasi kedalaman yang diperoleh mempunyai pola kemiripan atau dapat dikatakan mendekati kenyataan. Metode ini mampu mendeteksi kedalaman laut hingga 25 meter dan mempunyai RMS error yang kecil yaitu 0,653246 meter. Dengan demikian, metode dua kanal ini dapat menawarkan solusi cepat, fleksibel, efisien, dan ekonomis untuk memetakan topografi dasar laut

Shallow Water Depth Inversion Based on Data Mining Models

This thesis focuses on applying machine-learning algorithms on water depth inversion from remote sensing images, with a case study in Michigan lake area. The goal is to assess the use of the public available Landsat images on shallow water depth inversion. Firstly, ICESAT elevation data were used to determine the absolute water surface elevation. Airborne bathymetry Lidar data provide systematic measure of water bottom elevation. Subtracting water bottom elevation from water surface elevation will result in water depth. Water depth is associated with reflectance recorded as DN value in Landsat images. Water depth inversion was tested on ANN models, SVM models with four different kernel functions and regression tree model that exploit the correlation between water depth and image band ratios. The result showed that the RMSE (root-mean-square error) of all models are smaller than 1.5 meters and the R2 of them are greater than 0.81. The conclusion is Landsat images can be used to measure water depth in shallow area of the lakes. Potentially, water volume change of the Great Lakes can be monitored by using the procedure explored in this research

Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods. Much of the coastal shallow water zone worldwide remains unmapped using recent techniques and is, therefore, poorly understood. Optical satellite imagery is proving to be a useful tool in predicting water depth in coastal zones, particularly in conjunction with other standard datasets, though its quality and accuracy remains largely unconstrained. A common challenge in any prediction study is to choose a small but representative group of predictors, one of which can be determined as the best. In this respect, exploratory analyses are used to guide the make-up of this group, where we choose to compare a basic non-spatial model versus four spatial alternatives, each catering for a variety of spatial effects. Using one instance of RapidEye satellite imagery, we show that all four spatial models show better adjustments than the non-spatial model in the water depth predictions, with the best predictor yielding a correlation coefficient of actual versus predicted at 0.985. All five predictors also factor in the influence of bottom type in explaining water depth variation. However, the prediction ranges are too large to be used in high accuracy bathymetry products such as navigation charts; nevertheless, they are considered beneficial in a variety of other applications in sensitive disciplines such as environmental monitoring, seabed mapping, or coastal zone management

Satellite-derived bathymetry for shallow water hydrographic mapping

Satellite-Derived Bathymetry (SDB), a new method which derives bathymetric data from multi-spectral satellite imagery, has yet to be recognised as a new acquisition method for shallow water hydrographic survey mapping. Currently, SDB has received substantial attention from researchers worldwide, but most of the studies primarily focused on remote sensing environments. The questions about precision and accuracy are always the subject of interest in the surveying field but went unreported in most of the studies. Hence, this study aims to develop an improved SDB algorithm model which is capable of delivering better accuracy for shallow water hydrographic survey mapping application in a tropical environment. High resolution multi-spectral satellite imageries from the Sentinel-2A, Pleiades and WorldView-2 of Tawau Port, Sabah and Pulau Kuraman, Labuan were derived. Both places have diverse seabed topography parameters. A conceptual model of Multi-Layer Optimisation Technique (M-LOT) was developed based on Stumpf derivation model. Accuracy assessment of M-LOT was carried out against derivation models of Lyzenga and Sumpf. Two types of accuracy assessment were involved: Statistical Assessment and International Hydrographic Organization (IHO) Survey Standard evaluation. The findings showed M-LOT model managed to achieve up to 1.800m and 1.854m Standard Deviation (SD) accuracy for Tawau Port and Pulau Kuraman respectively. In addition, M-LOT has shown a better derivation compared to Stumpf’s, where a total of 13.1% more depth samples meeting the IHO minimum standard for Tawau Port. Furthermore, M-LOT has generated an extensive increment up to 46.1% depths samples meeting the IHO minimum standard for Pulau Kuraman. In conclusion, M-LOT has significantly shown improved accuracy compared to Stumpf, which can offer a solution for SDB method in shallow-water hydrographic survey mapping application

Bathymetric ability of SPOT-5 multi-spectral image in shallow coastal water

Optical Remote Sensing offers an alternative to traditional hydrographic surveys for measuring water depth, with the advantage of low cost and large area. The multi-spectral image of SPOT-5 with the high resolution provides possibility for bathymetric mapping. In order to estimate the image's potential for the retrieval of water depth, we use single-band model and dual-band model separately to inverse the water depth with worked example from Naozhou Island in Guangdong, China. The actual depth is derived from the chart to establish inversion models and to assess the accuracy of inversed water depth based on the criterions of the mean relative error and the mean square error. The result illustrates that the dual-band model is superior to the single-band model and the red-band model is superior to the green-band model. Compared with the other segments of water depth, the mean relative errors in shallow water between 0m and 5m are the biggest, which make the overall errors of all models big. However, the dual-band model is relatively better than the single-band model in shallow water, its mean relative error is about 22%, and its mean square error is about 1.87m. We have a conclusion that the multi-spectral image of SPOT-5 has a good ability to inverse water depth, and its high resolution can describe more detail topographic information under water. Bathymetry by Remote Sensing becomes an important assistant means for traditional bathymetry methods

Estimativa da profundidade de corpos de água com o uso de dados de sensoriamento remoto

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Civil, Florianópolis, 2013.Esta pesquisa usa uma imagem captada pelo sensor ETM+ (EnhancedThematicMapper Plus), a bordo do satélite Landsat7, para calcular a profundidade na Lagoa da Conceição, em Florianópolis. São aplicados três diferentes métodos que variam em função do número de bandas usadas. O primeiro é de autoria de Polcyn (1970), usa uma banda espectral da imagem e é dos primeiros e mais clássicos modelos utilizados para estimar profundidades. O segundo método foi proposto por Stumpf (2003), usa duas bandas da imagem, e segundo seus autores tem a vantagem de reduzir os erros devido à existência de vários tipos de fundo. O terceiro método é proposto por Jupp (1989) e usa 3 bandas da imagem, aproveitando a informação captada pelo sensor e que não é usada no método de Polcyn (1970) ou Stumpf (2003). No final do trabalho é feita uma comparação entre as profundidades calculadas e profundidades medidas com ecobatímetro. As exatidões conseguidas são comparadas com as exigidas pela OHI (Organização Hidrográfica Internacional) para a confecção de cartas náuticas.Abstract : This research uses an image captured by the sensor ETM + (Enhanced Thematic Mapper Plus) on board the Landsat 7 satellite in order to calculate the depth in the Conceição Florianópolis-SC's lagoon. Three different methods were applied and varied according to number of used bands. The first one is authored by Polcyn (1970), it uses an image's spectral band. This method is one of the oldest and widely used to estimate depths. The second method was proposed by Stumpf (2003), it uses two spectral bands and according to its authors have the advantage to reduce the errors generated by the existence of various types of bottom. The third method is proposed by Jupp (1989) and uses 3-band image, it taking advantage of the most useful information to calculate the depth. At the end of the work there is a comparison between the calculated and measured echo sounder depths. The accuracies achieved are compared with those required by the IHO (International Hydrographic Organization) for making nautical charts

Mapping Nearshore Bathymetry with Spaceborne Data Fusion and State Space Modeling

Despite numerous techniques for measuring and estimating water depth, bathymetry in the nearshore zone is notoriously difficult to map. Dangerous sea states, noisy environmental conditions, and expensive survey operations, particularly in remote areas, contribute to the difficulties of obtaining data along the coast. Global datasets, derived mainly from satellite altimetry methods, do exist, but they have significant limitations nearshore. Numerous high-resolution datasets, conventionally acquired with acoustic and lidar techniques, also exist, but they cover only a small percentage of the world's coasts. Spaceborne data fusion employing multispectral satellite derived bathymetry (SDB) offers the potential to significantly reduce the global lack of nearshore bathymetry, coined the "white ribbon" by the hydrographic community, referring to the alongshore data gap on many nautical charts. A broad term, multispectral SDB spans a diverse spectrum of methods that have been used extensively in specific case studies, but the application of multispectral SDB on a global or regional scale is significantly limited by the availability of in situ reference depths needed to tune derived values. Additionally, many existing approaches only use a single multispectral image, which can result in significant errors or missing data if the image contains environmental or sensor noise, such as clouds, sediment plumes, or detector-edge artifacts. This dissertation presents two spaceborne empirical multispectral SDB methods to address shortcomings of existing SDB approaches and reduce the global shortage of nearshore bathymetry – (1) active/passive spaceborne data fusion combining MABEL/ICESat-2 and multispectral data and (2) state space modeling of Sentinel-2 and Landsat 8 multispectral data to generate gap-free models of relative SDB (rSDB) with corresponding uncertainty estimates. The recently launched ICESat-2 mission offers an opportunity for a completely spaceborne active-passive data fusion approach to nearshore bathymetry by potentially providing a global source of nearshore reference depths to tune empirical multispectral SDB algorithms. The main objectives of the ICESat-2 mission are to measure ice-sheet elevations, sea-ice thickness, and global biomass, but ICESat-2’s 532-nm wavelength photon-counting Advanced Topographic Laser Altimeter System (ATLAS) was first posited, then demonstrated capable of detecting bathymetry in certain nearshore environments. Presented in two studies conducted prior to ICESat-2’s launch, the active-passive approach is demonstrated with data from MABEL, NASA’s high-altitude ATLAS simulator system. The first study assessed the ability to derive bathymetry from MABEL and then evaluated the accuracy and reliability of MABEL bathymetry using data acquired in Keweenaw Bay, Lake Superior. The study also developed and verified a baseline model to predict numbers of bottom returns as a function of water depth. The second study completed the demonstration of the spaceborne active/passive data fusion method by synergistically fusing MABEL-derived bathymetry and Landsat 8 multispectral Operational Land Imager (OLI) imagery over the entire Keweenaw Bay study site using the Stumpf band-ratio algorithm. The study also assessed the spatiotemporal viability of the data fusion approach by characterizing the variability of global coastal water clarity as interpreted from Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data. The calculated SDB agreed with a high-resolution topobathymetric lidar dataset to within an RMSE of 0.7 m, and the spatiotemporal viability analysis indicated that the spaceborne active-passive data fusion approach may be viable over many regions of the globe throughout the course of a year. State space modeling of empirical multitemporal SDB overcomes limitations of single-image SDB by leveraging the bathymetric signal in multispectral time series to create gap-free models of relative SDB (rSDB) for an arbitrary date, enabling SDB for dates with noisy or no data. State space models (SSMs) are well established in many applications but are absent in empirical SDB literature. Consisting of a state equation, which relates consecutive state vectors, and an observation equation, which relates observations to the state vector, SSMs are typically solved using Kalman filtering techniques, which provide estimates of uncertainties along with state estimates. SSMs also provide a mechanism for data fusion by allowing an observation equation for multiple observed time series. The third study demonstrates a state space approach to empirical multispectral SDB by applying local level SSMs to Landsat 8 OLI and Sentinel-2 MSI rSDB time series, both separately and fused. A representative single-sensor SSM (Landsat 8) was transformed to SDB that agreed with a high-resolution topobathymetric lidar dataset to within an RMSE of 0.29 m, which indicates the promising performance of the state space framework. Internally consistent fused-sensor SSMs verified that state space modeling also offers a data-fusion method capable of incorporating time series from a diverse suite of multispectral sensors