Abstract
Water on the Earth’s surface is an essential part of the hydrological cycle. Water resources include surface waters, groundwater, lakes, inland waters, rivers, coastal waters, and aquifers. Monitoring lake dynamics is critical to favor sustainable management of water resources on Earth. In cryosphere, lake ice cover is a robust indicator of local climate variability and change. Therefore, it is necessary to review recent methods, technologies, and satellite sensors employed for the extraction of lakes from satellite imagery. The present review focuses on the comprehensive evaluation of existing methods for extraction of lake or water body features from remotely sensed optical data. We summarize pixel-based, object-based, hybrid, spectral index based, target and spectral matching methods employed in extracting lake features in urban and cryospheric environments. To our knowledge, almost all of the published research studies on the extraction of surface lakes in cryospheric environments have essentially used satellite remote sensing data and geospatial methods. Satellite sensors of varying spatial, temporal and spectral resolutions have been used to extract and analyze the information regarding surface water. Multispectral remote sensing has been widely utilized in cryospheric studies and has employed a variety of electro-optical satellite sensor systems for characterization and extraction of various cryospheric features, such as glaciers, sea ice, lakes and rivers, the extent of snow and ice, and icebergs. It is apparent that the most common methods for extracting water bodies use single band-based threshold methods, spectral index ratio (SIR)-based multiband methods, image segmentation methods, spectral-matching methods, and target detection methods (unsupervised, supervised and hybrid). A Synergetic fusion of various remote sensing methods is also proposed to improve water information extraction accuracies. The methods developed so far are not generic rather they are specific to either the location or satellite imagery or to the type of the feature to be extracted. Lots of factors are responsible for leading to inaccurate results of lake-feature extraction in cryospheric regions, e.g. the mountain shadow which also appears as a dark pixel is often misclassified as an open lake. The methods which are working well in the cryospheric environment for feature extraction or landcover classification does not really guarantee that they will be working in the same manner for the urban environment. Thus, in coming years, it is expected that much of the work will be done on object-based approach or hybrid approach involving both pixel as well as object-based technology. A more accurate, versatile and robust method is necessary to be developed that would work independent of geographical location (for both urban and cryosphere) and type of optical sensor.
Highlights
Water on the Earth’s surface is an essential part of the hydrological cycle
The methods which are working well in the cryospheric environment for feature extraction or landcover classification does not really guarantee that they will be working in the same manner for the urban environment
This method is the most researched and emerging methods because of its high-term advantages which include low implementation cost, simple in understanding, modifiable and effective in producing stable output. It does suffer from few limitations, which include high location or feature dependency which makes it working only for a specific application, high misclassification which usually occurs because of objects possessing similar spectral characteristics and the commonly known misclassified objects are mountain shadows or hill shadows which possess dark pixel that almost appear similar to a water body and often get misclassified and results in a false positive result
Summary
Water on the Earth’s surface is an essential part of the hydrological cycle. Water resources include surface water, groundwater, inland water, rivers, lakes, transitional waters, coastal waters and aquifers [1]. We are essentially looking at the extraction of information on lakes because water resources have been degraded and exhausted in last few years. Being able to access the spatial distribution and geographical extent information on lakes in real time has great significance in limnology and for understanding interactions between regional hydrology and climate change [9]. The cryosphere refers to those parts of the Earth containing water in its frozen state: snow, glaciers, permafrost, seasonally frozen ground, lake and river ice, sea ice, ice sheets, and shelves. RS data can never replace aerial photographs, which provide images at a resolution as high as 0.2 - 0.3 m, it is suitable for lake-feature extraction in the cryospheric regions, where frequent aerial photography is difficult because of the extremely harsh environment and the high logistical costs. Development of automated or semi-automated feature extraction methods using RS data is much needed for continuous monitoring of the geographical features in a cryospheric environment
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