Segmentation and morphology of open water bodies from multispectral images

Lira, Jorge

doi:10.1080/01431160600702384

Cited by 83 publications

(39 citation statements)

References 16 publications

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“…Optical images have been widely proved to be an excellent tool to discriminate continental water bodies [10,11] from other targets, or to monitor inland water characteristics [12][13][14][15][16], but their use is less frequent for shallow wetlands covered by aquatic plants [17,18]. Many previous studies have used optical images to reveal and assess the damage caused by catastrophic floods as well [19,20].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Monitoring of the Flooding Regime and Hydroperiod of Doñana Marshes with Landsat Time Series (1974–2014)

et al. 2016

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This paper presents a semi-automatic procedure to discriminate seasonally flooded areas in the shallow temporary marshes of Doñana National Park (SW Spain) by using a radiommetrically normalized long time series of Landsat MSS, TM, and ETM+ images . Extensive field campaigns for ground truth data retrieval were carried out simultaneous to Landsat overpasses. Ground truth was used as training and testing areas to check the performance of the method. Simple thresholds on TM and ETM band 5 (1.55-1.75 µm) worked significantly better than other empirical modeling techniques and supervised classification methods to delineate flooded areas at Doñana marshes. A classification tree was applied to band 5 reflectance values to classify flooded versus non-flooded pixels for every scene. Inter-scene cross-validation identified the most accurate threshold on band 5 reflectance ( < 0.186) to classify flooded areas (Kappa = 0.65). A joint TM-MSS acquisition was used to find the MSS band 4 (0.8 a 1.1 µm) threshold. The TM flooded area was identical to the results from MSS 4 band threshold < 0.10 despite spectral and spatial resolution differences. Band slicing was retrospectively applied to the complete time series of MSS and TM images. About 391 flood masks were used to reconstruct historical spatial and temporal patterns of Doñana marshes flooding, including hydroperiod. Hydroperiod historical trends were used as a baseline to understand Doñana's flooding regime, test hydrodynamic models, and give an assessment of relevant management and restoration decisions. The historical trends in the hydroperiod of Doñana marshes show two opposite spatial patterns. While the north-western part of the marsh is increasing its hydroperiod, the southwestern part shows a steady decline. Anomalies in each flooding cycle allowed us to assess recent management decisions and monitor their hydrological effects.

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Section: Introductionmentioning

confidence: 99%

Long-Term Monitoring of the Flooding Regime and Hydroperiod of Doñana Marshes with Landsat Time Series (1974–2014)

et al. 2016

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“…To address the variation in the optimal thresholds and noise contamination issues, some studies have combined WIs with other methods, including color-space transformation [15], principal components analysis [16], image segmentation [16], topographic masking [17], linear discriminant analysis [18] and a combination of digital image processing and geographical information system techniques [5].…”

Section: Introductionmentioning

confidence: 99%

An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

et al. 2014

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Abstract:The water index (WI) is designed to highlight inland water bodies in remotely sensed imagery. The application of WI for water body mapping is mainly based on the thresholding method. However, there are three primary difficulties with this method: (1) inefficient identification of mixed water pixels; (2) confusion of water bodies with background noise; and (3) variation in the threshold values according to the location and time of image acquisitions. Considering that mixed water pixels usually appear in narrow rivers or shallow water at the edge of lakes or wide rivers, an automated method is proposed for extracting rivers and lakes by combining the WI with digital image processing techniques to address the above issues. The data sources are the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) images for three representative areas in China. The results were compared with those from existing thresholding methods. The robustness of the new method in combination with different WIs is also assessed. Several metrics, which include the Kappa coefficient, omission and OPEN ACCESSRemote Sens. 2014, 6 5068 commission errors, edge position accuracy and completeness, were calculated to assess the method's performance. The new method generally outperformed the thresholding methods, although the degree of improvement varied among WIs. The advantages and limitations of the proposed method are also discussed.

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“…Landsat imagery [19]. A Linear Discriminant Analysis Water Index (LDAWI) composed of the green, red, NIR and SWIR bands of SPOT-5 images to map lake surface [20] has also been used to compare in water indices.…”

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confidence: 99%

Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Húmeda Biosphere Reserve in Central Spain

et al. 2016

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Abstract:The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ satellite images for the reference period 2013-2015 were jointly used with ground-truth datasets. Several inverse modeling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2%. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the European Water Framework Directive and the Habitats Directive.

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Segmentation and morphology of open water bodies from multispectral images

Cited by 83 publications

References 16 publications

Long-Term Monitoring of the Flooding Regime and Hydroperiod of Doñana Marshes with Landsat Time Series (1974–2014)

Long-Term Monitoring of the Flooding Regime and Hydroperiod of Doñana Marshes with Landsat Time Series (1974–2014)

An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Húmeda Biosphere Reserve in Central Spain

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