**Seasonal Variability of Vegetation and Its Relationship to Rainfall and Fire in the Brazilian Tropical Savanna**

Jorge Alberto Bustamante, Regina Alvalá and Celso von Randow *Brazilian National Institute for Space Researches Brazil* 

#### **1. Introduction**

76 Remote Sensing – Applications

Zhang, M. H., Qin, Z. H., Liu, X., & Ustin, S. L. (2003). Detection of stress in tomatoes

Zhang, J. C., Huang, W. J., Li, J. Y., Yang, G. J., Luo, J. H., Gu, X. H., & Wang, J. H. (2011).

yellow rust in winter wheat. *Precision Agriculture,* 12, 716-731.

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induced by late blight disease in California, USA, using hyperspectral remote sensing. *International Journal of Applied Earth observation and Geo information*, 4, 295–

Development, evaluation and application of a spectral knowledge base to detect

The Brazilian savanna, named locally Cerrado, is the second largest Brazilian biome, covering approximately two million km2, especially in the Central Highlands (Ratter *et al*., 1997). This biome is composed predominantly of tropical savanna vegetation and is considered as one of the world's biodiversity hotspots, a priority area for biodiversity conservation in the world (Myers et al., 2000). The Cerrado region is considered the last agricultural frontier in the world (Borlaug, 2002), which has been converted in the last 50 years especially for agriculture and pasture purposes, where natural and mainly anthropogenic annual burning is a common practice. Currently, around 50% of natural vegetation in the Cerrado region has been converted to pastures and crops (PROBIO-MMA, 2007).This conversion has impacted the biological diversity, the hydrological cycle, the energy balance, the climate and the carbon dynamics at local and regional scales due to habitat fragmentation, invasive alien species, soil erosion, pollution of aquifers, degradation of ecosystems and changes in fire regimes (Klink & Machado, 2005; Aquino & Miranda, 2008). The knowledge of spatial distribution, temporal dynamics and biophysical characteristics of the vegetation types, are important elements to improve the understanding of what is the interaction like between vegetation, precipitation and fire.

The objective of this study is to determine the relationship of environmental variables, such as precipitation and fire, with spatial and temporal distribution patterns of main vegetation type of the Brazilian tropical savanna. Thus, we seek to answer the question: how environmental variables, like rain and fire, influence the main vegetation types, like herbaceous, shrubs, deciduous trees and evergreen trees, in the Cerrado biome taking in account the seasonal patterns of the variables involved?

In this study, the potential of multi-temporal satellite data, like TRMM data for precipitation, MODIS vegetation indices products for land cover mapping, and others sensors like GOES and MODIS for fire detection is explored by the use of remote sensing and geographic information systems (GIS) techniques.

#### **1.1 Seasonality of Cerrado vegetation**

Phenological parameters of vegetation, such as start and end of the growing season, are strongly influenced by atmospheric conditions (like precipitation, temperature and humidity)

Seasonal Variability of Vegetation and Its

**2. Methodology 2.1 Study area** 

**2.2 Methodology** 

and vegetation (NDVI).

considered ecotones of the two extremes.

Relationship to Rainfall and Fire in the Brazilian Tropical Savanna 79

patterns of fire occurrences, frequency, size, severity, and sometimes vegetation and fire effects as well. For example, savanna fires are often of low intensity and high frequency (often annual), while forest fires are often of low frequency (once every few centuries) and very high intensity (Bowman & Murphy, 2010). Most of the wildland fires occur by the combination of edaphic, climatic and human activities (Roy, 2004). Natural fires are generally started by lightning, with a very small percentage started by spontaneous combustion of dry fuel such as sawdust and leaves. This kind of fire is insignificants in comparison to number of fires started by humans (Roy, 2004). Most tropical fires are set intentionally by humans (Bartlett 1955, 1957, 1961) and are related to several main causative agents (Goldammer, 1988): deforestation activities (conversion of natural vegetation to other land uses, e.g. agricultural lands pastures, exploitation of other natural resources); traditional, but expanding slash-and-burn agriculture; grazing land management (fires set by graziers, mainly in savannas and open forests with distinct grass strata); use of non-wood forest products (use of fire to facilitate harvest or improve yield of plants, fruits, and other forest products, predominantly in deciduous and semi-deciduous forests); wildland/residential interface fires (fires from settlements, e.g. from cooking, torches, camp fires etc.); other traditional fire uses (in the wake of religious, ethnic and folk traditions; tribal warfare) and socio-economic and political conflicts over questions of land property and land use rights.

Satellite-borne sensors can detect fires in the visible, thermal and mid-infrared bands. These sensors have been used most extensively for detecting and monitoring fire activity from landscape to global scales (Justice et al., 2003; Diaz-Delgado et al., 2004; Allan et al., 2003; Brandis & Jacobson, 2003; Miller et al. 2003; Rollins et al., 2004; Bowman et al., 2003). Justice et al. (2003) analyzed global remote sensing data and showed that occurrence of landscape fire is not random across the world, which is strongly influenced by climatic variables, like

The study area represents almost all (more than 90%) of the Brazilian savanna (Cerrado) biome, excluding only the southern region, which is characterized by few small isolated patches of savannas with intense anthropic activities like agriculture and ranching. The Cerrado vegetation exhibits a wide range of physiognomies. Following the "forest-ecotonegrassland" concept (Coutinho, 1978), the Cerrado ranges from *campo limpo*, a grassland, to *cerradão*, a tall woodland. The intermediate physiognomies (*campo sujo* - a shrub savanna, *campo Cerrado* - a savanna woodland, and *Cerrado sensu stricto* - a woodland) are

The soil surface dries out during the dry season, leading the herbaceous and sub shrub plants suffering water stress. Thus, leaves dry out and die, while the underground plant structures are kept alive. The presence of dead leaves by water stress and also by frost greatly increases

The methodology involves the use of two spatial approaches, regional and local, to analyze the spatio-temporal relationships between environmental variables (precipitation and fire)

the litterfall and, consequently, the risk of fire (Nimer, 1977; Coutinho, 2000).

moisture deficit, wind speed, relative humidity and air temperature.

at different time scales (intrannual, inter-annual, interdecadal, and so on). Atmospheric conditions at intrannual scale influence the main phenological events that the plant experiences during the annual cycle of growth (Reed et al. 1994). At greater time scales, climate influence on the spatial and temporal distribution of vegetation (Schwartz, 1994). On the other hand, the vegetation influence atmosphere while maintaining or modifying the flows of matter and energy, albedo, roughness, CO2, which in turn affect the regional and/or global climate.

Savanna ecosystems that cover approximately 20% of the global land surface have mechanisms that control the flow of matter and energy in tropical savannas. These ecosystems are not well understood, which has hindered the inclusion of this biome in studies of regional and global modeling (Law et al., 2006).

#### **1.2 Climate and precipitation regime**

Climate patterns from intra-seasonal to decadal and century scales directly influence the timing, magnitude (productivity), and spatial patterns of vegetation growth cycles, or phenology (Reed et al., 1994; Schwartz, 1994).

The Savanna biome has a wet/dry climate. Its Köppen climate group is **Aw**. The *A* stands for a tropical climate, and the *w* for a dry season in the winter and the rainy season in the summer. During the dry season of a savanna, most of the plants shrivel up and die. Some rivers and streams dry up (Parker, 2000; Ritter, 2006). In the wet season all of the plants are lush and the rivers flow freely. The temperature of the savanna climate ranges from 20° to 30° C. In the winter, it is usually about 20° to 25° C. In summer the temperature ranges from 25° to 30° C. The savanna temperature does not change a lot, although when it does, it is very gradual and not drastic.

Because of its latitudinal position, the Brazilian savanna region is characterized by the transition between the warm climates of low latitudes and mesothermal climates of middle latitudes (Nimer, 1989). This region is considered almost homogeneous on the length and location of the dry and rainy periods (Rao & Hada, 1990). However, Castro et al. (1994) show that this region has a certain degree of heterogeneity due to the variation of length in the dry and rainy periods. This heterogeneity is determined by the interaction of atmospheric circulation systems in the lower and upper troposphere over the region. Some of these systems are: The South Atlantic anticyclone also known as South Atlantic Convergence Zone (SACZ), Polar anticyclone and Chaco low. SACZ is one of the main phenomena that determine the rainfall across the region (Satyamurty et al., 1998). In general, rainfall in the region ranges from 1000 to 1500 mm.

The climate of the Cerrado is tropical warm and semi-humid, with just two seasons, a dry one from May to September and a rainy one from October to April. Monthly rainfall in dry season (that include fall and winter) reduces considerably, reaching zero, resulting in a dry period that varies from three to five months duration (Coutinho, 2000). The rainy season (spring and summer) sometimes has short dry periods named locally "*veranicos*". The mean annual temperatures vary between 22 and 27°C and the mean annual precipitations between 600 and 2.200 mm.

#### **1.3 Fire regime and detection**

Fire is one of the most important drivers that influence vegetation function and structure. Fire incidence, in a given area or ecosystem, is part of a fire regime which has specific patterns of fire occurrences, frequency, size, severity, and sometimes vegetation and fire effects as well. For example, savanna fires are often of low intensity and high frequency (often annual), while forest fires are often of low frequency (once every few centuries) and very high intensity (Bowman & Murphy, 2010). Most of the wildland fires occur by the combination of edaphic, climatic and human activities (Roy, 2004). Natural fires are generally started by lightning, with a very small percentage started by spontaneous combustion of dry fuel such as sawdust and leaves. This kind of fire is insignificants in comparison to number of fires started by humans (Roy, 2004). Most tropical fires are set intentionally by humans (Bartlett 1955, 1957, 1961) and are related to several main causative agents (Goldammer, 1988): deforestation activities (conversion of natural vegetation to other land uses, e.g. agricultural lands pastures, exploitation of other natural resources); traditional, but expanding slash-and-burn agriculture; grazing land management (fires set by graziers, mainly in savannas and open forests with distinct grass strata); use of non-wood forest products (use of fire to facilitate harvest or improve yield of plants, fruits, and other forest products, predominantly in deciduous and semi-deciduous forests); wildland/residential interface fires (fires from settlements, e.g. from cooking, torches, camp fires etc.); other traditional fire uses (in the wake of religious, ethnic and folk traditions; tribal warfare) and socio-economic and political conflicts over questions of land property and land use rights.

Satellite-borne sensors can detect fires in the visible, thermal and mid-infrared bands. These sensors have been used most extensively for detecting and monitoring fire activity from landscape to global scales (Justice et al., 2003; Diaz-Delgado et al., 2004; Allan et al., 2003; Brandis & Jacobson, 2003; Miller et al. 2003; Rollins et al., 2004; Bowman et al., 2003). Justice et al. (2003) analyzed global remote sensing data and showed that occurrence of landscape fire is not random across the world, which is strongly influenced by climatic variables, like moisture deficit, wind speed, relative humidity and air temperature.
