Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey

Sarp, Gulcan; Özçelik, Mehmet

doi:10.1016/j.jtusci.2016.04.005

Cited by 184 publications

(78 citation statements)

References 28 publications

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“…Other approaches rely on machine-learning algorithms to extract water bodies from optical imagery. Prevalent supervised classification algorithms that have been used include Random Forests [14,15], neural networks [16], decision trees [17], support vector machines [18,19] and the perceptron model [20]. Classification-based approaches may achieve higher accuracy than thresholding methods; however, ground truth data are required to select appropriate training samples.…”

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confidence: 99%

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

et al. 2018

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Satellite data offer the opportunity for monitoring the temporal flooding dynamics of seasonal wetlands, a parameter that is essential for the ecosystem services these areas provide. This study introduces an unsupervised approach to estimate the extent of flooded areas in a satellite image relying on the physics of light interaction with water, vegetation and their combination. The approach detects automatically thresholds on the Short-Wave Infrared (SWIR) band and on a Modified-Normalized Difference Vegetation Index (MNDVI), derived from radiometrically-corrected Sentinel-2 data. Then, it combines them in a meaningful way based on a knowledge base coming out of an iterative trial and error process. Classes of interest concern water and non-water areas. The water class is comprised of the open-water and water-vegetation subclasses. In parallel, a supervised approach is implemented mainly for performance comparison reasons. The latter approach performs a random forest classification on a set of bands and indices extracted from Sentinel-2 data. The approaches are able to discriminate the water class in different types of wetlands (marshland, rice-paddies and temporary ponds) existing in the Doñana Biosphere Reserve study area, located in southwest Spain. Both unsupervised and supervised approaches are examined against validation data derived from Landsat satellite inundation time series maps, generated by the local administration and offered as an online service since 1983. Accuracy assessment metrics show that both approaches have similarly high classification performance (e.g., the combined kappa coefficient of the unsupervised and the supervised approach is 0.8827 and 0.9477, and the combined overall accuracy is 97.71% and 98.95, respectively). The unsupervised approach can be used by non-trained personnel with a potential for transferability to sites of, at least, similar characteristics.

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confidence: 99%

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

et al. 2018

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“…In geosciences, especially in remote sensing, multispectral imagers provide spectral signatures from natural objects depending on their physical conditions, like the approved NDWI for water recognition (Feyisa et al, 2014;Gao, 1996;Li et al, 2014;Sarp and Ozcelik, 2016). Currently, it seems to be obvious that the application of mobile multispectral imaging using smartphones is not possible due to technical reasons.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Environmental Time Lapse Image Sequences for the Determination of Shore Lines Captured by Hand-Held Smartphone Cameras

Kröhnert

Meichsner

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The relevance of globally environmental issues gains importance since the last years with still rising trends. Especially disastrous floods may cause in serious damage within very short times. Although conventional gauging stations provide reliable information about prevailing water levels, they are highly cost-intensive and thus just sparsely installed. Smartphones with inbuilt cameras, powerful processing units and low-cost positioning systems seem to be very suitable wide-spread measurement devices that could be used for geo-crowdsourcing purposes. Thus, we aim for the development of a versatile mobile water level measurement system to establish a densified hydrological network of water levels with high spatial and temporal resolution. This paper addresses a key issue of the entire system: the detection of running water shore lines in smartphone images. Flowing water never appears equally in closerange images even if the extrinsics remain unchanged. Its non-rigid behavior impedes the use of good practices for image segmentation as a prerequisite for water line detection. Consequently, we use a hand-held time lapse image sequence instead of a single image that provides the time component to determine a spatio-temporal texture image. Using a region growing concept, the texture is analyzed for immutable shore and dynamic water areas. Finally, the prevalent shore line is examined by the resultant shapes. For method validation, various study areas are observed from several distances covering urban and rural flowing waters with different characteristics. Future work provides a transformation of the water line into object space by image-to-geometry intersection.

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“…resolution [3,18,34,35]. However, to best of our knowledge, many scientists who are pursuing research on lakes in regions of high interest, such as the Qinghai-Tibetan Plateau, use mainly higher spatial resolution images in different periods [3,[36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Lake Area Changes and Their Influence on Factors in Arid and Semi-Arid Regions along the Silk Road

et al. 2018

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Abstract:In the context of global warming, the changes in major lakes and their responses to the influence factors in arid and semi-arid regions along the Silk Road are especially important for the sustainable development of local water resources. In this study, the areas of 24 lakes were extracted using MODIS NDVI data, and their spatial-temporal characteristics were analyzed. In addition, the relationship between lake areas and the influence factors, including air temperature, precipitation, evapotranspiration, land use and land cover change (LULCC) and population density in the watersheds, were investigated. The results indicated that the areas of most lakes shrank, and the total area decreased by 22,189.7 km 2 from 2001 to 2016, except for those of the lakes located on the Qinghai-Tibetan Plateau. The air temperature was the most important factor for all the lakes and increased at a rate of 0.113 • C/a during the past 16 years. LULCC and the increasing population density markedly influenced the lakes located in the middle to western parts of this study area. Therefore, our results connecting lake area changes in the study region highlight the great challenge of water resources and the urgency of implementation of the green policy in the One Belt and One Road Initiative through international collaboration.

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Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey

Cited by 184 publications

References 28 publications

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

Segmentation of Environmental Time Lapse Image Sequences for the Determination of Shore Lines Captured by Hand-Held Smartphone Cameras

Lake Area Changes and Their Influence on Factors in Arid and Semi-Arid Regions along the Silk Road

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