On the Use of Airborne Imaging Spectroscopy Data for the Automatic Detection and Delineation of Surface Water Bodies

Bochow, Mathias; Heim, Birgit; Küster, Theres; Rogaß, Christian; Bartsch, Inka; Segl, Karl; Reigber, Sandra; Kaufmann, Hermann

doi:10.5772/34073

Cited by 12 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sensitivity of NIR and NDWI to shadows is a well-known problem and causes misclassifications [14,16,47,[55][56][57]. Shadows of trees and buildings caused small misclassifications that were easily filtered by only taking the longest line as the water-land border.…”

Section: Discussionmentioning

confidence: 99%

Reconstruction of Lake Level Changes of Groundwater-Fed Lakes in Northeastern Germany Using RapidEye Time Series

Heine

Stüve

Kleinschmit

et al. 2015

Water

View full text Add to dashboard Cite

Groundwater-fed lakes in northeastern Germany are characterized by significant lake level changes, but for only a few lakes are in situ water level measurements available. In this study, we test the potential of RapidEye satellite images for indirectly reconstructing lake level changes. The lake levels are derived by intersecting water-land borders with a high-resolution digital elevation model (DEM). Based on Lake Fürstenseer (LF), we define requirements and limitations of the method. Water-land borders were extracted automatically from the 37 RapidEye images available for the period between 2009 and 2014. Otsu's threshold was used for the NIR band and for the normalized difference water index (NDWI). The results were validated with in situ gauging, contour lines from the DEM, and in situ Differential Global Positioning System (DGPS) measurements of the shoreline. Using an ideal shoreline subset, the lake levels could be reconstructed with decimeter accuracy using the NIR water-land border, but the levels were systematically underestimated by 0-20 cm. The accuracy of the reconstructed lake level retrieval strongly depends on the precision of the water-land border retrieval, on the accuracy of the DEM, and on the lake level itself. A clear shift of the water-land border with increasing lake level is also essential for the unambiguous reconstruction of different levels. This shift needs to be several times larger than the pixel size. The biggest challenges OPEN ACCESSWater 2015, 7 4176 for lake level reconstruction are the presence of vegetation at the shorelines, the quality of the topographic data in the underwater area, the slope of the shoreline, and shadows in combination with low solar angles.

show abstract

Section: Discussionmentioning

confidence: 99%

Reconstruction of Lake Level Changes of Groundwater-Fed Lakes in Northeastern Germany Using RapidEye Time Series

Heine

Stüve

Kleinschmit

et al. 2015

Water

View full text Add to dashboard Cite

show abstract

“…To address this problem, modified NDWI (MNDWI) [4] was proposed by replacing the NIR band used in the NDWI with the shortwave infrared (SWIR) band to extract water surface while suppressing the noise from built-up areas. It should be noted that NDWI and MNDWI are not very appropriate for delineating water bodies from urban high-spatial resolution images since some urban structures (e.g., shadow, roads, and other dark objects) also have high response for the two indexes [5]. Feyisa et al [6] developed a new automated water extraction index (AWEI), which can provide a fairly stable threshold value.…”

Section: A Spectral Analysismentioning

confidence: 99%

Combining Pixel- and Object-Based Machine Learning for Identification of Water-Body Types From Urban High-Resolution Remote-Sensing Imagery

Huang

Xie

Fang

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

125

View full text Add to dashboard Cite

Water is one of the vital components for the ecological environment, which plays an important role in human survival and socioeconomic development. Water resources in urban areas are gradually decreasing due to the rapid urbanization, especially in developing countries. Therefore, the precise extraction and automatic identification of water bodies are of great significance and urgently required for urban planning. It should be noted that although some studies have been reported regarding the water-area extraction, to our knowledge, few papers concern the identification of urban water types (e.g., rivers, lakes, canals, and ponds). In this paper, a novel two-level machine-learning framework is proposed for identifying the water types from urban high-resolution remote-sensing images. The framework consists of two interpretation levels: 1) water bodies are extracted at the pixel level, where the water/shadow/vegetation indexes are considered and 2) water types are further identified at the object level, where a set of geometrical and textural features are used. Both levels employ machine learning for the image interpretation. The proposed framework is validated using the GeoEye-1 and WorldView-2 images, over two mega cities in China, i.e., Wuhan and Shenzhen, respectively. The experimental results show that the proposed method achieved satisfactory accuracies for both water extraction [95.4% (Shenzhen), 96.2% (Wuhan)], and water type classification [94.1% (Shenzhen), 95.9% (Wuhan)] in complex urban areas.

show abstract

“…A possible explanation could be the diffuse illumination of the small water bodies by volume scattering of the surrounding trees causing the spectral shape of the water surface spectra to look similar to vegetation spectra. A more detailed discussion of the results can be found in[10].Fig. 6.…”

mentioning

confidence: 96%

Automatic detection and delineation of surface water bodies in airborne hyperspectral data

Bochow

Heim

Küster

et al. 2012

2012 IEEE International Geoscience and Remote Sensing Symposium

Self Cite

View full text Add to dashboard Cite

In this investigation we focused on the development of a new water detection algorithm based on VIS-NIR imaging spectroscopy data. By analyzing different images containing inland and ocean water types we found the slopes of the reflectance spectrum of water at specific spectral wavelengths within the VIS-NIR spectral region to be diagnostic features for surface water identification. However, the presence of these features depends on the spectral superimposition of water constituents and bottom coverage. This aspect has been considered in the development of a knowledgebased classifier. The results (probability of detection generally lies above 90%) indicate the great potential of the developed algorithm for surface water body detection and delineation within urban, rural and coastal scenes.Index Terms-imaging spectroscopy, detection and delineation of surface water bodies, water absorption, spectral features, spectral differentiation between water bodies and shadow areas

show abstract

On the Use of Airborne Imaging Spectroscopy Data for the Automatic Detection and Delineation of Surface Water Bodies

Cited by 12 publications

References 18 publications

Reconstruction of Lake Level Changes of Groundwater-Fed Lakes in Northeastern Germany Using RapidEye Time Series

Reconstruction of Lake Level Changes of Groundwater-Fed Lakes in Northeastern Germany Using RapidEye Time Series

Combining Pixel- and Object-Based Machine Learning for Identification of Water-Body Types From Urban High-Resolution Remote-Sensing Imagery

Automatic detection and delineation of surface water bodies in airborne hyperspectral data

Contact Info

Product

Resources

About