**8. References**

114 Remote Sensing – Applications

with values greater or equal than 50%. The rest of the classes presented low accuracy with values in the interval 0 – 25%. The correlation coefficient of the quantity of control points and the accuracy was 0.43, suggesting few dependence between both variables. Anyway, a critical case occurred with the class "Native forest" with 11 control points and only two hits. An explanation for the low accuracy of the classification process and some specific classes is the shadow derived from the steep topography, causing confusion among classes and inconsistent results. A improvement of the results is obtained when principal components are considerer before classification, with a potential increment of accuracy of 10% (Schroder

Forestry 9 5 56% Pastures/SWVC 11 6 55% Water 2 0 0% Perennial crops 7 4 57% Native forest 11 2 18% Urban/outcrops 4 2 50% Anual crops 4 1 25% **Total 48 20 42%** 

Table 7. Results of the confusion matrix for the process of classification in the municipality of Montenegro, Rio Grande do Sul state, Brazil (Schroder & Filippini-Alba, 2010b). SCC =

Two categories of change detection techniques (Lu et al., 2004) were considered in this chapter, all of them including classification methods: Post-classification comparison and

The strategy with GIS isolated the poligon corresponding to the "Potential area for agriculture", then, the interference between some pair of classes was eliminated, by instance, wetlands and rice crops. The post-classification comparison allowed a rapid approach about the region with minor accuracy (preliminary results). Definition of the method used depends on the ratio between cost and efficiency according to the designed

Errors associated to classification methods are mainly due to the spectral answer, by undefinition of classes or occurrence of pixels of transition, because the errors derived from digitalization were insignificant. Atmospheric conditions and the regional topography also

Land cover changes in a dynamic way, sometimes with significant transformation rates of one class to another, as the discussed cases confirm. Truth of field appears as an optimal method to improve results, but the cost of process, in time, financial and human resources is

**Control points SCC Accuracy** 

& Filippini-Alba, 2010b).

samples correctly classified.

influence the process of classification.

**6. Conclusion** 

strategy with GIS.

objectives.

incremented.


**5** 

*Spain* 

**Mapping Soil Salinization of** 

Jose Navarro-Pedreño and Ignacio Gómez *Department of Agrochemistry and Environment,* 

*University Miguel Hernández of Elche* 

Ignacio Melendez-Pastor, Encarni I. Hernández,

**Agricultural Coastal Areas in Southeast Spain** 

Soil salt content is a key factor that determines soil chemical quality together with soil reaction, charge properties and nutrient reserves (Lal et al., 1999). An adequate salt supply is essential for an optimum development of photosynthetic mechanism and other biochemical processes in plants (Sitte et al., 1994). Soil salt content constitutes an environmental problem when salt accumulation generates drastic changes in soil physical and chemical properties, adversely affecting soil productivity and plant growth (Richards, 1954; Qadir et al., 2000).

Salinization affects about 30% of the irrigated land of the world, decreasing this area approximately 1-2% per year due to salt-affected land surfaces (FAO, 2002). In Europe, about 1-3 million hectares of the land are affected by salinization (European Commission, 2003), and most of these areas are situated in the Mediterranean basin. In Spain, about 18% of the 3.5 million hectares of irrigated land are severely affected or at serious risk of soil salinization (European Commission, 2002). Soil salinization is a frequent problem in arid and semiarid regions like Southeast Spain (Hernández Bastida et al., 2004). In these areas, agriculture with a great water requirement combined with high water tables and an adverse climate (increased occurrence of extreme drought events) have forced irrigation with poor quality water, causing processes of soil degradation and salinization, limiting crop growth

Evaluating the spatial variability of basic soil properties in saline soils, and mapping spatial distribution patterns of these soil properties helps to make effective site-specific management decisions (Ardahanlioglu et al., 2003). Accordingly, remote sensing techniques and geographic information systems (GIS) have introduced a new era for soil resources assessment and monitoring in terms of information quality (Mermut and Eswaran, 2001). *A priori* knowledge of spectral characteristics of remotely sensed materials is fundamental to any valuable quantitative analysis (Ben-Dor et al., 1997). The variety of absorption processes occurring in the soil and their wavelength dependence allow us to derive information about the chemistry of the minerals composing it from the reflected or emitted light (Clark, 1999). Reflectance spectra of soils are attributed to numerous soil properties. There are no narrow absorption bands linked to soil salinity status, since it is

and the production capacity (Pérez-Sirvent et al., 2003; Acosta et al., 2011).

**1. Introduction** 


http://www.infoteca.cnptia.embrapa.br/handle/doc/884278

