**11. Acknowledgments**

350 Remote Sensing – Applications

In this figure, it can be seen that thanks to the water column correction, the classes are better defined, with mixing among them—caused by interference by the depth of the water column—avoided to whatever extent possible. The ISODATA algorithm more accurately selects and groups clusters, eliminating this problem. This visualization again confirms the advantage of performing water column corrections to obtain better results for the processes

The study shows that the application of new remote sensing methods is crucial to the preprocessing of images in order to identify submerged aquatic ecosystems. This is because when quantitative information is mapped or derived from satellite images of aquatic environments, the depth of the water causes spectral confusion and therefore significantly affects the measurements of submerged habitats. Water column correction minimizes this effect, which enables distinguishing the classes of benthic ecosystems present in the Chinchorro Bank and demonstrates improvement especially in zones representing more variation in depth. Thus, water column correction is an indispensible pre-processing method

The water column correction method used in this study uses the majority of the spectral information while disregarding the characteristics of the water surrounding the reef, such that the spectral values are transformed from a band pair into a depth-invariant index. This should be applied in relatively clear water (type 1 or type 2), as is the case of the Chinchorro Bank. Using this process, the attenuation effect of the water column was minimized, which is one of the primary problems with the segmentation of images of submerged ecosystems. Traditional, unsupervised classification methods, such as ISODATA, have difficulty detecting subclasses, that is, this type of classifier makes it complicated to detect pixels between very close classes with distributions that share an overlapping zone. When classifying benthonic habitats in the Chinchorro Bank, it was possible to observe that the classes with less concentration of pixels were masked by those with greater amounts. This may be because standard methods, such as ISODATA, use moving mass center techniques

to locate the classes and, thus, what are called subclasses become undetectable.

In general, the data from remote sensors are used for mapping reef habitats. Although the classification presented here was quite general—only 4 classes were determined—the results show that the Landsat 7-ETM+ images are able to identify different classes in submerged benthonic environments. Although the classification resulted in visually optimal results, the need to incorporate statistical validation of the data is important, so as to determine the accuracy of the classification performed in comparison to the reality; this was not possible for this study because an adequate database of in situ sampling was not available. Nevertheless, because of the visual comparison with classes identified by studies such as those by Aguilar-Perera & Aguilar Dávila (1993), Chávez and Hidalgo (1984) and Jordán (1979) and the consistency with the theory of the zonation of benthic bottoms based on depth, it can be concluded that the classifications obtained by ISODATA successfully determined the majority of the benthonic cases defined in this study of the Chinchorro Bank. Coral reefs are being threatened worldwide by a combination of natural and anthropogenic impacts. Although the natural impacts are intense, there are intermediate time lapses that

to classify benthic bottoms.

in the cartography of submerged aquatic ecosystems.

**10. Conclusions** 

The authors would like to thank the Mexican Navy (SEMAR), Deputy Department of Oceanography, Hydrography and Meteorology (Dirección General Adjunta de Oceanografía, Hidrografía y Meteorología) for the information provided regarding bathymetry and the field sampling of sand data. We also thank Dr. Juan Pablo Carricart Ganivet and Janneth Padilla Saldívar for the information and geographic basis from the Comprehensive Management of the Chinchorro Bank: Geographic survey and geomorphologic characterization of the reef.

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**15** 

*Iran* 

**Predictability of Water Sources** 

, Somayeh Talebi and Farshad Amiraslani

**Using Snow Maps Extracted from** 

**the Modis Imagery in Central Alborz, Iran** 

*Faculty of Geography, Department of Cartography, University of Tehran, Tehran* 

Snow reserves in mountainous basins are important and reliable water resources in Iran. Identification of their quality is necessary because of an increasing value of freshwater and utilization of water recourses. About 60 percent of surface water and 57 percent of ground water sources in Iran flows in snowy regions (Rayegani, 2005). The water produced from snowmelt process provides soil water, ground water reserves and water in lakes and rivers. Since snow cover is one of the most important sources of provided water, an accurate prediction and timing of snow runoff is necessary for the efficient management and

The science of snow hydrology, compared to other branches of hydrology science, has a relatively shorter history due to difficulties accompanied with snow measurement. The correct analysis of snow issues needs a set of observations and statistics in snow-gauging. Currently, however, there are no regular and comprehensive snow measurement procedures in most parts of Iran. Measurements are only limited to those snowy basins recharging important dams; even these measurements are carried out in scattered points

The measurement range of these stations is limited to 2000-3000 m asl heights. Thus, in mountainous Iran, current distribution of stations would not seem to be adequate. In such conditions, study of snow reserves and identification of snow melting trend in most basins would be accompanied with limitations. Consequently, measuring snow cover using ground methods will be difficult and costly. Remote sensing technology has many applications in various environmental and earth resources studies including ice and snow research. These applications have been increased recently as a result of unique technical advantages such as multi-temporal imagery acquired in various wavelengths, extent of spatial coverage, and improvement of computer hardwares for interpretation and extraction of information. Regarding snow research, remote sensing technology can provide

Seyed Kazem Alavipanah**\***

**1.1 Snow reserves and remote sensing** 

decision- making in water supply.

rather than an entire dam catchment area.

**1. Introduction** 

 \*

Corresponding Author

underwater light fields. *Applied Optics*, Vol. 32, No. 36, (December 1993) pp. 7484- 7504

