Satellite data

Method name

Remarks

WV2 (0.5 m)

[72]

Automated Spectral-Shape Procedure

Average positional offset between delineated and manually digitized streams is about 1 pixel. NDWI_ice was discovered in order to increase the contrast between ice and water. (accuracy = 92.1%).

MODIS (250 m)

[63]

ALC approach based on object-oriented classification.

ALC mainly focuses on the changing nature of lakes. Difficulty in detecting small lakes (<0.1 km²) cannot be resolved. Lakes with partial or total ice cover posed a challenge for ALC as both lake ice and glacier ice have similar reflectance.

ASTER (15 m)

[111]

Non-parametric classification, Spectral indices NDWI using green and NIR band, and square pixel metric (SqP) method

99% accuracy attained by applying NDWI, elevation and NIR/Red band ratio to separate water features from ice debris

Landsat (30 m)

RADARSAT (7 m) [112]

Supervised MXL Classification

Difficulty in mapping small ponds which can be overcome by using high resolution imagery or a high resolution aerial photograph. User’s accuracy for water class = 95%.

MODIS (250 m)

ASTER (10 m)

Landsat ETM+ (10 m) [113]

Lakes were delineated automatically using object oriented segmentation and classification methods [57] [62] [63] [110] .

Sundal [110] had difficulty in resolving ice-covered lakes. Johannson and Brown [63] reported that as many as 18% of reported SGLs are likely to be false positive. In Selmes et al. [57] any lake <0.125 km² does not feature in the dataset. It is found to report lake area most accurately.

WV2 (0.5 m)

[77]

Spectral indices using customized NDWIs having Coastal and Blue band against NIR1 and NIR2

The coastal band produced less false positive results in comparison to the blue band during the detection of lakes. The PAN-sharpening process does affect the accuracy of feature classification.

WV2 (0.5 m)

[98]

Support vector machine (SVM), Spectral angle mapper (SAM), MXL, NN classifier, Winner takes all (WTA)

The study concluded that WTA (accuracy = 97.23%) was better for mapping water and land and SVM and NN classifier for mapping snow/ice.

IKONOS (4 m)

Hyperion

[109]

For improving the accuracy of water classification, IKONOS tasseled cap transformation is applied to wetness

Small lakes were detected due to the high spatial resolution of IKONOS image. The water class was defined by an IKONOS NDVI of greater than −0.1 (accuracy = 86.70%).