Water Information Extraction Research Articles

Comparative Analysis of Automatic Water Identification Method Based on Multispectral Remote Sensing

In this paper, from the water spectral reflectance characteristics, I select five multi-spectral remote sensing water information extraction methods, issue comparative analysis and applicability assessment in five typical experiment areas based on Landsat data. I have made a comprehensive comparison and algorithm applicability analysis on the five kinds of methods. The main conclusions are the following: MNDWI and The spectral relationship method between multi-band have the most widely applicability, MNDWI always use in the building shadow and bare land. The spectral relationship method between multi-band has the special effects on the mountain shadow areas. NDVI has an apparent superiority in the dense forest and grassland, the low-cost of single-band threshold make it have more advantages in the great plains areas, compromise and low noise of NDWI can be used in complicated areas.

THE APPLICATION OF CBERS-02B DATA TO THE DISASTER MONITORING OF THE “5, 12” WENCHUAN EARTHQUAKE: THE DYNAMIC MONITORING OF THE TANGJIASHAN BARRIER LAKE

In this paper,the authors used multi-temporal CCD data from the CBERS-02B satellite and adopted the human-computer interaction to extract water information from the Tangjiashan barrier lake. Statistic analysis was employed to detect the change of the lake area so as to understand its variation and trend. The result can provide some quantitative basis for the study of geological hazards situation and hazards prevention.

Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery

The normalized difference water index (NDWI) of McFeeters (1996) was modified by substitution of a middle infrared band such as Landsat TM band 5 for the near infrared band used in the NDWI. The modified NDWI (MNDWI) can enhance open water features while efficiently suppressing and even removing built‐up land noise as well as vegetation and soil noise. The enhanced water information using the NDWI is often mixed with built‐up land noise and the area of extracted water is thus overestimated. Accordingly, the MNDWI is more suitable for enhancing and extracting water information for a water region with a background dominated by built‐up land areas because of its advantage in reducing and even removing built‐up land noise over the NDWI.

The methods of extracting water information from spot image

Some techniques and methods for deriving water information from SPOT-4(XI) image were investigated and discussed in this paper. An algorithm of decision-tree (DT) classification which includes several classifiers based on the spectral responding characteristics of water bodies and other objects, was developed and put forward to delineate water bodies. Another algorithm of decision-tree classification based on both spectral characteristics and auxiliary information of DEM and slope (DTDS) was also designed for water bodies extraction. In addition, supervised classification method of maximum-likelyhood classification (MLC), and unsupervised method of interactive self-organizing dada analysis technique (ISODATA) were used to extract waterbodies for comparison purpose. An index was designed and used to assess the accuracy of different methods adopted in the research. Results have shown that water extraction accuracy was variable with respect to the various techniques applied. It was low using ISODATA, very high using DT algorithm and much higher using both DTDS and MLC.