Seafloor mapping

Seafloor mapping, also called seabed imaging, is the measurement of water depth of a given body of water. Bathymetric measurements are conducted with various methods, from sonar and Lidar techniques to buoys and satellite altimetry. Various methods have advantages and disadvantages and the specific method used depends upon the scale of the area under study, financial means, desired measurement accuracy, and additional variables. Despite modern computer-based research, the ocean seabed in many locations is less measured than the topography of Mars. Seafloor mapping, also called seabed imaging, is the measurement of water depth of a given body of water. Bathymetric measurements are conducted with various methods, from sonar and Lidar techniques to buoys and satellite altimetry. Various methods have advantages and disadvantages and the specific method used depends upon the scale of the area under study, financial means, desired measurement accuracy, and additional variables. Despite modern computer-based research, the ocean seabed in many locations is less measured than the topography of Mars. At the beginning of the twentieth century mapping the seafloor was a very difficult task. The mapping of the sea floor started by using sound waves, contoured into isobaths and early bathymetric charts of shelf topography. These provided the first insight into seafloor morphology, though mistakes were made due to horizontal positional accuracy and imprecise depths. In 1957, Marie Tharp, working alongside with Bruce Charles Heezen created the first three-dimensional physiographic map of the world's ocean basins. Tharp's discovery was made at the perfect time. It was one of many discoveries that took place near the same time as the invention of the computer. Computers, with their ability to compute large quantities of data, have made research much easier, include the research of the world's oceans. There has been a boom in the underwater environmental exploration; rather than simply creating a map, scientists are attempting to visualize the entire seafloor with maximum possible detail. Computers are put to good use here with their help, researchers have managed to store and analyze large quantities of data. This led to the creation of the first digital map of the world ocean bed in 1970. Constantly developing technology allows computing to take place in the special equipment required for 'high-resolution orthoimagery'. This means researchers may no longer need to use sound frequencies to conduct marine exploration. This method was later upgraded to Airborne Laser Bathymetry (ALB). ALB provides images that are both higher quality and in color.. The improvements to these research methods and the large amount of data received, stored and computed all led to the creation of one of the first color images of the underwater environment created on a computer. Another form of mapping the seafloor is through the utilisation of satellites. The satellites are equipped with hyper-spectral and multi-spectral sensors which are used to provide constant streams of images of coastal areas providing a more feasible method of visualising the bottom of the seabed. The data-sets produced by Hyper-Spectral (HS) Sensors tend to range between 100-200 spectral bands of approximately 5 - 10 nm bandwidths. Hyper-Spectral Sensing, or imaging spectroscopy, is a combination of continuous remote imaging and spectroscopy producing a single set of data. Two examples of this kind of sensing are AVIRIS (Airborne visible/infrared imaging spectrometer) and HYPERION. More information on Hyper-Spectral Imaging can be found here (Hyperspectral imaging). The application of HS sensors in regards to the imaging of the seafloor is the detection and monitoring of chlorophyll, phytoplankton, salinity, water quality, dissolved organic materials, and suspended sediments. However this does not provide a great visual interpretation of coastal environments. The other method of satellite imaging, multi-spectral (MS) imaging, tends to divide the EM spectrum into a small number of bands, unlike its partner Hyper-Spectral Sensors which can capture a much larger number of spectral bands. More information on multi-spectral sensing can be found at Multispectral image.