**4. Conclusions**

94 Remote Sensing – Applications

Figure 9 shows the result of the linear regression analysis between vegetation and fire for each vegetation type. Each line in this figure with a specific color shows the degree of fit between the points distributed for both variables by type of vegetation. The results indicate that there is a gradient of fit between fire and vegetation, here named as fire gradient, which ranges from high to low coefficient of correlation (R2) following the sequence: deciduous

trees-E3 (high R2), shrubs-E2, herbaceous-E1, and evergreen trees-E4 (low R2).

E1: herbaceous; E2: shrubs; E3: deciduous trees; and E4: evergreen trees.

vegetation types.

Fig. 9. Regression of fire (independent variable) and NDVI (dependent) for each vegetation type analyzed. NDVI values range from 0 to 255 (x-axis). Fire is the daily mean value of the

The fire gradient identified above indicates that there is direct relationship between NDVI of the main vegetation types (herbaceous, shrubs and deciduous trees), which make up the Cerrado vegetation, and fire, indicating the role of fire in the maintenance of these

Fire occurs with greater intensity at the end of dry season. First of all, fire consumes part of the burk and organic matter of the plant, after the first rains, in the beginning of the rainy

At the opposite end of the fire gradient, where the evergreen trees-E4 are positioned, the fire occurs in lower proportion in these trees, however, unlike what happens with other types of vegetation, the effect of fire is pernicious, it can damage or even eliminate some species in

The multiple regression analysis indicates that there is a direct relationship between precipitation and fire, and vegetation index (NDVI) in the four vegetation types of the

density of hotspot within a 10km radius for a 16-days composite period. N = 69.

season, these partially burned plant sprouts new shoots with greater vigor.

this vegetation type according to the intensity level.

The response of vegetation NDVI is more related to the variation of fire than to variations in precipitation in Cerrado region. Vegetation NDVI responds to variation of precipitation with a time lag ranging from 16 to 48 days, while vegetation NDVI responds to variation of fire with a time lag ranging from 0 to 48 days.

The relationship between vegetation types, derived from NDVI, and precipitation, derived from TRMM, shows a gradient of positive correlations in vegetation types with low vegetation cover, herbaceous (r= 0.60) and shrubs (r= 0.51), to very little or none with high vegetation cover, deciduous trees (r= 0.31) and evergreen trees (r= 0.09). On the other hand, the relationship between vegetation and fire hotspot shows a gradient of negative correlation, which is stronger in herbaceous (r= 0.72), shrubs (r= 0.74) and deciduous trees (r= -0.73) than in evergreen trees (r= -0.52).

Our analyses show that vegetation cover increases are related to increases in precipitation and decreased in density of fire hotspots. We also found high density of fire hotspot in the dry season in deciduous trees, shrubs and herbaceous which suggesting the high removal of CO2 (greenhouse gas) of the land cover to the atmosphere somehow influencing the dynamic equilibrium of this (atmosphere) in the region of the Brazilian tropical savanna.

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**4** 

*Brazil* 

**Land Cover Change Detection in** 

**Southern Brazil Through Orbital** 

**Imagery Classification Methods** 

José Maria Filippini Alba, Victor Faria Schroder

*Brazilian Agricultural Reasearch Corporation (Embrapa)* 

Remote sensing has been considered a promising technology as support for agriculture since its beginnings, due to its contribution for a climatic perspective or for understanding of processes related to land. However, significant applications occurred only in the late twentieth century, as result of the creation of best orbital systems, with higher spatial resolution, more bands and stereoscopic capture. Several orbital platforms, as AQUA/TERRA, Quickbird and

Engineering innovations, new sensors and methods of digital image processing must be performed simultaneously so that the advances in remote sensing will be achieved. Anyway, the incorporation of orbital images on geographic information systems (GIS) and their post-processing appear as significant application since a daily life perspective, specially when classification methods are involved, because of their relation to land use,

This chapter considers classification methods applied on orbital imagery in Southern Brazil, in the coastal plain of Rio Grande do Sul state (Fig. 1), where a sequence of lagoons and lakes of different sizes occurs in the context of subtropical to temperate climate with cold

All the exposed data are related to research projects of the Embrapa Temperate Climate Research Center, Pelotas, Rio Grande do Sul state, one of the 45 research centers of the Brazilian Agricultural Research Corporation (Embrapa) spread on the national territory.

Classification methods were created in the statistical context, when a collection of objects or samples could be characterized and separated in different classes (Davis, 1986). The method

winters and hot summers, being organized according to the following four sections:

 Evaluation of rice planting area in the vicinity of Caiuba lagoon (1981 – 2009). Analysis of land cover evolution in the municipality of Montenegro (1993 – 2008).

Ikonos are examples in that sense (Moreira, 2005; Embrapa, 2009).

**1. Introduction** 

land cover and easy interpretation.

About classification methods.

Comparison and evaluation of errors.

**2. About classification methods** 

and Mauro Ricardo R. Nóbrega

Vila, D.A.; de Goncalves, L.G.G.; Toll, D.L. & Rozante, J.R. (2009). Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimates over Continental South America. *J. Hydrometeor*. Vol.10, 533–543.
