**3. Conclusion**

detection of salinity through its effect on the vegetation. Richardson et al. [86] specified that an inverse relationship is observed between reflectance and salinity, as salt content induces less plant cover (decreasing of density, LAI and height) and sometimes slight salt deposition on surface associated with vegetation have similar reflectance as that of normal cropped area. Salt tolerant plants are good references of salinity level on salt marshes but necessitate good calibration [30]. Contrasted associations of vegetation and bare soils can be more useful for salinity detection than individual surface types. Remotely sensed imagery cannot be used to classify and assess soil profile. Spectral characteristics of the earth surface features that are indicative of subsurface conditions can be analyzed. Satellite multi-spectral data denote changes that aid in locating mapping units; they hold great promise for soil surveys and landuse planning [87]. Some relationships have been established to relate soil properties and spectral data while most of these properties have been from the surface soil, subsurface properties that influence some surface characteristics were considered. Satellite sensors observe only the ground surface, actually both subsurface and surface soil conditions are influenced by common genetic factors [88]. Both subsurface conditions and surface conditions are plant canopy. Therefore, when satellite imagery depicts a pattern based on a different spectral response, it

is not unreasonable to attempt some inferences about subsurface soil patterns [70].

There are many procedures commonly used for the classification of remote sensing images and this depends on the radiometric information in the image bands. The traditionally used classification method is a pixel-based approach and is one of the procedures based on conventional statistical techniques and it performs well. Pixel based approach is based on conventional statistical techniques, such as parallelepiped, maximum likelihood and minimum distance procedures [57]. In pixel-based classification, two kinds of traditional classification methods-unsupervised classification and supervised classification are used. Ideally, pixels are expected to be to a degree, more or less grouped in the multispectral space in clusters corresponding to different land cover types [89]. It is a classic classification approach that classifies an image pixel by pixel and one pixel can only be classified into one class, thus produces

The basic characteristics of digital image acquired by remote sensing method are composed of pixels. According to [24], the intensity of each pixel corresponds to the mean radiance measured electrically over the ground area corresponding to each pixel. Each pixel has digital number (DN) corresponding to the average radiance measured in this pixel. This number from quantizing the original electrical signal from the sensor result into positive integer values using a process termed analogue-to-digital signal conversion [90]. The DNs comprising of a digital image are recorded over numerical ranges as 0–255, 0–511, or higher. These ranges correspond to the set of integers that were recorded using 8-, 9-, and 10-bit binary computer coding scales, respectively. In such numerical formats, the image can be analyzed with the aid of computer [91]. A digital image is a 2-dimension array of elements; the corresponding

*2.10.6. Image classification*

76 Multi-purposeful Application of Geospatial Data

is a hard classification [67].

*2.10.7. Classification of digital satellite data*

The chapter demonstrates possibility of use of high technology in particular remote sensing and GIS technology in land cover/land use and soil salinity monitoring with demonstration of advantages of use such technology in similar problem solving. Detection of soil salinity by conventional means of soil survey requires a great deal of time, but the application of geospatial analysis using remote sensing and GIS techniques minimize time consuming and offer the possibility assessment, modeling and mapping of irrigated land. The chapter also worked on general subjects with reflection of the cycle of satellite data use with a variety of application, indexes for registration and data processing stage. The fact is that the use of space technology advances in land classification are commonly used instrument for soil monitoring which is one of the suitable and flexible instrument from a wide point of view. The instrument makes it possible to perform results conveniently for users. In addition, the application of these indexes is a good indicator of soil salinity in irrigated lands, which may influence decision on reclamation of soil salinity and used as an input for agricultural land management. Irrigation managers, planners, farmers and government agencies for smart agriculture can use models and maps generated through geospatial analysis.
