environmental changes and behavior. Cryospheric lakes are classified into 5 types as depicted on Table 2. Inclement weather in the polar regions (Arctic and Antarctic), few numbers of fine-weather days in summer, and high logistic cost restricts research trips to polar Regions. Therefore, satellite RS data and aerial photography are important sources of information for monitoring the short-term and long-term changes that occur at a specific location in cryospheric regions over time. Although 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. Hence, 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.

Table 1. Types of lakes (Source: [12] ).

Table 2. Types of cryospheric lakes (Source [13] ).

2. Brief Review on RS Methods Used for Lake Feature Extraction

A brief review of the most commonly used methods employed in mapping of water feature from urban and cryospheric regions is depicted in Table 3 and Table 4. Optical satellite systems have most frequently been applied to lake or water body extraction research. The parts of the electromagnetic (EM) spectrum covered by these sensors include the visible and near-infrared (NIR) ranging from 0.4 to 1.3 μm, the short wave infrared (SWIR) between 1.3 and 3.0 μm, the thermal infrared (TIR) from 3.0 to 15.0 μm and the long wavelength infrared (LWIR) from (7 - 14 μm). The decision tree and programming method are used for extracting water body information from the flood affected region [14] . The semi-automated change detection approach is used for extracting water feature form satellite image [15] . An automatic extraction method is used for extracting water body from IKONOS and other high resolution satellite image [16] . Thresholding and multivariate regression method [17] , a conceptual clustering technique and dynamic thresholding [18] , an original entropy-based me-

Table 3. Methods used for extraction of urban lakes.

Table 4. Methods used for extraction of cryospheric lakes.

thod [19] , are also used for extraction of water bodies. The water body can be extracted by classification; unsupervised classification [20] ; the support vector machine (SVM) with one-against-one (1A1) and one-against-all (1AA) techniques are used for land cover mapping [21] . A supervised classification algorithm [22] of RS satellite image that uses the average fuzzy intra-cluster distance within the Bayesian algorithm [23] ; sometimes combinations of supervised and unsupervised classification [24] are used for water information extraction. A new index normalized optical water index (NOWI) was proposed to accurately discriminate between land and water regions in multi-spectral satellite imagery data from DubaiSat-1 [25] .

3. Importance of Cryospheric Lakes

Lake ice cover has been established as a robust indicator of local climate variability and fluctuations. Lake ice forms an essential component of the cryosphere, especially at high mountainous latitudes where a large number of lakes exist. Long-term records of lake ice have been significantly used as a proxy indicator of winter climate conditions. Previous studies have identified lake ice as a highly sensitive cryospheric component to climatic conditions [26] . Spatio-temporal changes in lake ice cover have an imperative feedback on energy exchanges between the lake surface and the atmosphere. Persistent warmer air temperatures [27] and raised snowfall had been observed in the Arctic over the last decades [28] , and found to be associated with an amplified reduction of sea-ice concentrations, thickness and extent [29] , which had been accelerated during recent years [30] . These spatio-temporal changes in the Arctic climate system have likely had an impact on ice phenology of lakes in coastal regions adjoining to the Arctic Ocean. SGLs play an important role in establishing hydrological connections that allow lubricating seasonal meltwater to reach the base of the ice sheet [33] [34] .

Among all components of glacier system, SGLs are the most straightforward to be recognized. SGLs have already been researched on the Greenland ice sheet, Svalbard, and Himalaya. SGLs on Greenland characteristically form as a response to summer melting. SGLs typically form below 1500 m altitude, in topographic low regimes in the ablation zone and can expand to numerous kilometers in size on the surface of the Greenland ice sheet. SGLs can also form in the lower ablation area of debris-covered valley glaciers. The life span of SGLs is unpredictable and in situ monitoring is less practical. Hence, RS can be effectively used for studying SGLs and their seasonal variations to address the status of glacier or ice sheet. SGLs and firn aquifers store a substantial amount of meltwater, providing a buffer between melting and mass loss to the ocean [31] [32] .

4. RS for Extracting Cryospheric Lake Features

To our knowledge, almost all of the published works on an extraction of surface lakes in cryospheric environments have used the satellite RS data. Multispectral RS has been widely utilized in cryospheric studies and have 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. Aerial photography of ice-covered terrain began during the early 20th century by expeditions to the high altitudes and was used primarily to document the progress of the expedition. Wilkins documented ice cover in the Antarctic Peninsula during the first successful flight in Antarctica by using a handheld, folding Kodak 3A camera [45] [46] . The quality of the photographs is often exceptional, but the reason behind opting for satellite RS instead of aerial photography is the harsh environment of cryosphere where frequent monitoring is difficult by the use of aerial photography which adds up to high logistics costs. Cryospheric RS applications initiated as early as 1962 with the launch of the Argon satellite. Thereafter, in the 1970s Landsat 1, 2, and 3 Multi-Spectral Scanner (MSS) images constituted an important glaciological resource [47] . Initial successes in large-scale mapping were achieved through the use of the moderate spatial resolution (1 - 2.5 km) and wide swath (2400 km) advanced very high resolution radiometer (AVHRR) images, [48] which helped reveal details about ice stream flow in West Antarctica [49] . After the original AVHRR mosaic of Antarctica, the United State Geological Survey (USGS) made subsequent improvements to the mosaic by eliminating more clouds, separating the thermal band information to illustrate surface features more clearly, and correcting the coastline of the mosaic to include the grounded ice while excluding thin, floating fast ice [50] . Another large-scale mapping has been completed with MODIS for both the Arctic and the Antarctic [51] . Most recently, Landsat imagery of Antarctica has been compiled into a single, easily accessible map-quality data set [52] and SPOT stereo imagery have been used to derive DEM of ice sheets, ice caps, and glaciers [53] . In the snow and glaciated terrain of the Himalayas, satellite RS was established as the best tool because many of the glacial lakes are located at very high altitude, cold weather, and rugged terrain conditions, making it a tedious, hazardous and time-consuming task to monitor by conventional field methods. Satellite RS technology facilitates the study of initial and qualitative hazard assessment of glacial lakes of the Himalayas systematically with a cost-to-time benefit ratio [13] .

Cryospheric lake features have been researched primarily by means of multispectral satellite images from the ASTER [54] [55] , Landsat-7 [56] , and MODIS imagery [57] instead of aerial photography despite its very high spatial resolution. The relatively high spectral reflectance response from a water body feature in the ASTER, MODIS, and Landsat multispectral bands is the foundation for employing these sensors in water body mapping and surveying applications. An accurate manual delineation of lake extent is used [55] [58] [59] when lakes are easily identifiable on images. Applications of methods that discriminate water from surrounding ice and snow are possible on optical images using semi-automatic methods that employ different spectral bands of the satellite sensors [60] - [62] . Many research studies have surveyed methods for semi-automatic or automatic lake feature extraction using medium and coarse resolution satellite RS data (e.g., [57] [63] [64] ). Automated or semi-auto- mated methods have the advantage of rapid extraction of lakes from multi-temporal images with large areal coverage [57] [62] . Although manual delineation is highly accurate [65] , it is time-consuming and thus unsuitable for wide geographical areas. Therefore, accurate manual delineation is preferable for studies over smaller areas, but studies that encompass larger areas would benefit from automatic or semi-automatic methods [63] . The most common method for deriving surface water bodies from satellite images is the density slice method, which uses single or multiple spectral bands, and multi-spectral classification [66] [67] . Frazier and Page reviewed numerous methods employed by various authors to extract water bodies from Landsat TM and MSS image classifications [40] . Yu et al. [68] investigated and discussed a few methods for deriving water information using SPOT-4 images. The thresholding and multivariate regression method, a conceptual clustering technique, the dynamic thresholding method, and entropy-based method, have been successfully implemented in surface water extraction studies [17] [18] . The water body features can be extracted by unsupervised classification [20] , supervised classification (e.g., a Bayesian algorithm) [23] , and a combination of both supervised and unsupervised classification [24] . Waldemark et al. [69] proposed a neural network (NN) approach for extracting water bodies from satellite images. In addition to the aforementioned water classification methods, there are several other original methods, such as the Decision Tree (DT) method and the step iterative method [39] [70] . It is evident that the most common methods for extracting lakes are, single band?based threshold methods, spectral index ratio (SIR)-based multiband methods, image segmentation methods, spectral-matching methods, and supervised target detection methods.

SIRs are utilized to extract a specific target or feature, and they are computed from the difference in reflectance values of the bands used to formulate the ratio [77] . Conventionally, water and vegetation have been the primary focus of normalized difference SIRs because they are simple to classify based on the difference in reflectance values, ranging from 450 nm to 750 nm. Presently, the methods for extracting lakes are based on a spectral index or multiband techniques, which are spectrum property?based methods [78] , such as the NDVI [79] and NDWI [80] - [83] . Since a single spectral index could not demarcate lakes effectively in different environments, many improved indices have been proposed to yield better results in specific environments [84] . Ouma and Tateishi [36] proposed a novel water extraction index for shoreline delineation by combining the TCW index (TCWI) and the NDWI.

A comprehensive water body information extraction technique was proposed by Wu et al. [85] through the fusion of the spectral relationships between various bands with supervised classification methods. Rogers and Kearney [86] proposed the NDWI for medium spatial resolution and high temporal resolution with MODIS multispectral satellite images (MSI). Furthermore, Xiao et al. [87] proposed a land surface water index (LSWI), while Mo et al. [88] proposed a mixed water index (MWI) by combining the NDVI and NIR data to identify water bodies in MODIS images. Lu et al. [89] recommended an integrated water body extraction technique with HJ-1A/B satellite imagery by utilizing differences between NDVI and NDWI. These modified indices have been commonly used to map surface water bodies using Landsat and MODIS images [90] - [97] . These indices are normalized, ranging from −1 to 1, in which zero acts as a threshold to discriminate water from vegetation and land surface. However, because of the complexity of cryospheric environments, various ground targets may have the same spectrum characteristics. Therefore, only one type of spectral index method cannot extract water bodies under all environmental conditions [98] .

5. RS Methodologies for Extracting Lake Features

Methodologies of a lake or water body extraction can be summarized into three groups: feature extraction using pixel-based and object-based classification, and SIRs. Nath and Deb [66] provided a comprehensive overview of methods for water extraction from high resolution satellite images. June et al. [37] developed an automatic extraction of water bodies from a Landsat TM image using DT algorithm. The proposed algorithm was based on spectral characteristics of the water body in TM images. Wang et al. [99] developed water extraction method based on texture analysis. Luo et al. [100] developed an algorithm for water extraction using Landsat TM which combines water index computation, whole-scale segmentation, whole-scale classification and local scale segmentation and classification to achieve highly-precise water extraction result [101] . The traditional pixel based digital image classification has been and is still being used for characterization and mapping the spatial extent of forests, urban, water bodies, coastal, and wetland areas [102] . In principle, three types of classification methods exist, namely unsupervised, supervised and hybrid [103] . Unsupervised classification clusters pixels in a dataset based on statistics only, without any user-defined training classes. The most commonly used unsupervised land cover/land use (LC/LU) classifier is the Iterative Self-Organizing Data Analysis (ISODATA) classification algorithm. On the other hand, several types of statistics-based supervised classification algorithms have been developed. Examples of the more popular classifiers (in increasing complexity) are parallelepiped, minimum distance, MXL, and Mahalanobis distance [104] . Hybrid methods can combine the advantages of manual, parametric and non-parametric methods in various combinations to optimize the classification process [105] . Object- based Image Analysis (OBIA), a recent image analysis approach appears to be more popularly used for the classification of LC/LU of urban areas [106] . Object-based method considers image classification based on objects such as topologic (neighborhood, context) and geometric (form, size) information [107] . The object-oriented approach analyzes objects within images as a processing unit instead of using pixels. Geographic object-based image analysis (GEOBIA), as opposed to pixel-based image processing, is also emerging as a popular classification method [108] . Studies that monitored extreme cold areas using new satellite sensors were initiated by using medium and low spatial resolution images [109] . Sundal et al. [62] proposed an automatic method based on a set of fuzzy logic membership functions to identify and map lakes. The method used a single threshold developed by Box and Ski [60] to differentiate between meltwater and ice, exploiting the different sensitivity to water of MODIS bands 1 and 3. Certain types of lakes, such as deep lakes, were particularly hard to identify. A semi- automatic method to track lakes was developed by Selmes et al. [57] . Liang et al. [64] developed an automatic method for lake identification, mapping, and tracking. Exploiting the characteristics of the changing nature of the lakes and their surroundings, Johannson and Brown [63] developed a method known as Adaptive Lake Classification (ALC); ALC, specifically targeting the identified problem lakes [110] . Methods used for water feature extraction in an urban environment and cryospheric environment are summarized in Table 5 and Table 6.

6. Discussion

We have reviewed various methods utilized for extraction of a lake or water body features in urban and cryospheric environments. In this section, we summarize and discuss the generalized trend in methods, satellite datasets and achieved accuracies for lake feature extraction. Table 7 depicts different water feature extraction methods and accuracies expressed in kappa value. For extracting urban lakes (or other urban features), per-pixel based approaches were always the primary tool because of their low cost and easy implementation. Per-pixel based classification approach was considered to be the favorite choice of most of the researchers to extract lakes.

Unfortunately, this procedure always resulted in mixed pixel’s problem. Pixel based classification approaches on high spatial resolution satellite images (e.g. SPOT, Landsat), often results in “salt and pepper” representation, which is even increasing when considering the new generation of very high spatial resolution data (e.g. IKONOS, QuickBird, GeoEye, WV2 etc.). This problem has led many researchers to incorporate segmentation, texture, context, color, and many other parameters to glide the mixed or wrongly classified pixels into their proper classes [118] . So, to overcome the limitations of pixel-based approach, a new approach was developed known as an object-oriented approach which has gained more and more interest, especially when dealing with very high spatial resolution satellite images to capture the finer details of the urban area. In almost all the case studies, object-based classification approach resulted in improved accuracy ranging from 84% to 89% (approximately). Object-based classification can, not only use spectral information of land types, but also use images’ spatial position, shape characteristic, texture parameter and the relationship between contexts, which effectively

Table 5. Technologies used for water body extraction in cryospheric environment.

Table 6. Technologies used for water body extraction in an urban environment.

Table 7. Water feature extraction methods with kappa statistics values.

avoid the “salt and pepper phenomenon” and greatly improve the accuracy of classification [119] . After reviewing numerous methods for water feature extraction in the general environment, it is apparent that common water classification methods for optical imagery could be categorized into four basic types [120] : a) thematic classification [121] , b) linear un-mixing [122] , c) single-band thresholding [123] and d) two-band spectral WI [80] [84] [86] . A Synergetic fusion of various automatic and semi-automatic methods are also proposed to improve water information extraction accuracies. The spectral band method is easy to implement, but frequently misclassify mountain shadows, urban areas or other background noise as lakes [124] . The most notable supervised classification methods used for lake extraction are MXL, DT, artificial NN and SVM [125] , while the most common unsupervised classification methods include the K-means and ISODATA [126] [127] . These methods may achieve superior accuracy than spectral band methods under some environmental conditions; however, existing ground reference datasets are required, which restrict these methods from being applied over large study regions [40] . The WIs have been extensively used because of their comparatively high accuracy in lake detection and low-cost implementation [128] . In many WIs, the lack of stability of the threshold is still a problem [120] , making it difficult to use uniformly. The lack of a reasonably stable threshold may make the classification more time-consuming and lead to a subjective choice of threshold which may also affect accuracy [41] . The design and implementation of WIs have been persistently improving [38] . Despite the fact that a number of lake extraction methods are published in the literature, the choice of method for a specific application is constrained by accuracy issues. Water classification accuracy problems are especially pronounced in areas where the background land cover includes low albedo surfaces such as shadows from mountains, buildings, and clouds. The occurrence of shadows may cause misclassification because of the resemblance in reflectance patterns, which may hamper the accuracy of the surface water mapping and change analysis [41] [129] .

Even if the object-oriented based approach is most widely researched topic in urban applications yet it is not really popular with cryospheric regions [74] . Still cryospheric applications such as feature extraction from cryospheric regions, detailed land-cover classification of cryosphere employ pixel-based approach. These days, for obtaining detailed land-cover classification or for accurate feature extraction from cryospheric region very high resolution optical satellite imagery is being used with the resolution of the order of ~0.5 - 5 m. The popular optical satellites falling in this category are: WV2, SPOT5, IKONOS, GeoEye etc.

Most commonly used methods for lake feature extraction from cryospheric region are:

a) Spectral indices making use of two satellite imagery bands,

b) Supervised classification involving MXL, parallelepiped, NN, SAM and SVM, target, WTA approach

c) Unsupervised classification: involving ISODATA technique

d) Target detection methods which include: matched filter (MF), constrained energy minimization (CEM), adaptive coherence estimator (ACE), SAM, orthogonal sub-space projection (OSP) and target-constrained interference-minimized filter (TCIMF)

e) Single band threshold and spectral relation method

Despite so many methods being developed for lake feature extraction, none of them is known to yield highly accurate results in all environments. The methods developed so far are not generic rather they are specific to either the location or the 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 open lake, which can be corrected using topographical modeling using DEM [133] -[139] . There are various other target features which possess similar spectral characteristics which result in overestimation and thus outputs an inaccurate result. Thus, after knowing the past and present of methods being developed for lake feature extraction, it’s felt that there is a strong urge for developing new methods that would yield results with higher accuracy. Also the method should be highly versatile and robust as well as dynamic, so that it can be used for extraction of all types of lakes in all environments under all situations without any change in the design of the method.

7. Summary and Conclusion

Satellite sensors of varying spatial, temporal and spectral resolutions have been used to extract and analyze information regarding surface water. Studies using ASTER and Landsat ETM+ data have focused on smaller regions with a limited number of lakes, mainly using manual delineation of lake extent. For coverage of larger areas MODIS imagery is generally been adopted. ASTER and ETM+ images have a high spatial resolution of the order of ~10 m, conducive to accurate lake area delineation, MODIS imagery is much coarser i.e. of the order of ~250 m but the temporal resolution of MODIS is higher (at least once a day rather than biweekly). Landsat series of satellites are among the most extensively used multispectral sensors in surface water extraction studies. Medium resolution satellite data (10 - 90 m) are available for cryospheric studies since the early 1970s, with the launch of the new space-borne sensors: Landsat MSS, Landsat TM and ETM+, SPOT, Terra ASTER, IRS, and more recently the Advanced Land Observing Satellite (ALOS) launched in 2006. The other group of optical satellites is high resolution, of the order of a meter and sub-meter: WV2, IKONOS, Quickbird, and Geo Eye-1, are now being brought up in use for the extraction of lakes from the cryospheric environment [140] -[150] . Now a day’s much of the work based on cryosphere such as feature extraction and land cover classification employs high resolution imagery because of its high spatial resolution, which enables to achieve finer details of the region, which is otherwise not possible by using medium resolution imagery (e.g. Landsat TM, ETM+, SPOT, ASTER etc.). After reviewing numerous methods available for cryospheric lake feature extraction, we conclude that the most popular and effective method for extracting lake is based upon spectral indices which make use of two optical bands at a time. This method is the most researched and emerging methods because of its high-term advantages which include low implementation cost, simple in understanding, easily modifiable and effective in producing stable output. Despite its potential benefits, 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. The problem of misclassification can thus be minimized by selecting appropriate threshold which would be then able to discriminate between different objects based on their precise spectral response to a greater extent. The methods which are working well for the cryospheric environment for feature extraction or land-cover 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-based as well as object-based technology, with respect to lake feature extraction and a more accurate, versatile and robust method will be developed that would work independent of location (for both urban and cryosphere, single method would be able to extract water bodies accurately) and feature (for extraction of different types of lake a single method would be able to work). And also in coming years, super high spatial and temporal resolution optical satellites will be active which will yield even micro details of a region.

Acknowledgements

We acknowledge Dr. S. Rajan, Director, NCAOR for his encouragement and motivation of this research. We also thank Ms. Prachi Vaidya, India for her constructive comments on the draft version of the manuscript. This is NCAOR contribution No. 16/2015.

Cite this paper

Shridhar D.Jawak,KamanaKulkarni,Alvarinho J.Luis, (2015) A Review on Extraction of Lakes from Remotely Sensed Optical Satellite Data with a Special Focus on Cryospheric Lakes. Advances in Remote Sensing,04,196-213. doi: 10.4236/ars.2015.43016

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