Biomass burning is a major environmental problem in Amazonia. Satellite fire detections represent the primary source of information for fire alert systems, decision makers, emissions modeling groups and the scientific community in general. Those various users create a growing demand for good quality fire data of higher spatial and temporal resolution that can only be achieved via integration of multiple satellite fire detection products. The main objective of this dissertation was to develop an integrated fire product capable of improved monitoring and characterization of fire activity in Brazilian Amazonia. 

Two major active fire detection algorithms based on MODIS and GOES data were used to meet the users demand for fire information. Large differences involving the performance of the MODIS and GOES fire products required the quantification of omission and commission errors in order to allow for appropriate treatment of individual detections produced by each data set.


Relatively small omission errors due to cloud obscuration were estimated for Brazilian Amazonia. Regional climate conditions result in reduced cloud coverage in areas of high fire activity during the peak of the dry season, therefore minimizing the effects of cloud obscuration on fire detection omission errors.

Clear sky omission and commission errors were largely dependent on the vegetation and background conditions. Relatively large commission errors occurring in high percentage tree cover areas suggested that fire detection algorithms must either be regionalized or incorporate additional tests to provide more consistent fire information across a broader range of surface conditions.

Integration of MODIS and GOES fire products using a physical parameter describing fire energy (i.e., fire radiative power) was proven difficult due to limitations involving the interplay between sensor characteristics and the types of fires that occur in Amazonia. As part of this research, a new integrated product was generated based on binary fire detection information derived from MODIS and GOES data, incorporating adjustments to reduce commission and omission errors and optimizing the complementarities among individual detections. 

These findings made a significant contribution to fire monitoring science in Amazonia and could play an important role in the development of future fire detection algorithms for tropical regions.

Chapter 1: Introduction



Biomass burning is a major environmental phenomenon influencing the global climate, with important effects on the surface energy flux and atmospheric composition, and on the Earth’s radiation budget [IPCC, 1995]. Vegetation fires are a major source of greenhouse gases including CO2 and CH4, and of chemically reactive constituents including CO and NOx [Andreae and Merlet, 2001; van der Werf et al., 2004; Crutzen, 1979; Delany et al., 1985]. Bond et al. [2004] estimated that total global emission of black carbon from biomass burning is comparable to that produced from the use of fossil fuel, whereas Penner et al. [1992] showed that carbonaceous aerosols produced during biomass burning could result in comparable radiative forcing to that of anthropogenic sulfates.

Important feedbacks between forest fragmentation and the use of fire occur at the regional level, increasing the susceptibility of altered forests to larger and more destructive fires [Cochrane et al., 1999; Nepstad et al., 1999b]. Vegetation fires can impact biodiversity through large scale tree mortality, change forest composition and affect faunal populations and alter soil nutrient pools, thereby influencing secondary forest re-growth [Barlow and Peres, 2006; Barlow et al., 2003; Cochrane and

Schulze, 1999; Hughes et al., 2000; Moran et al., 2000; Peres et al., 2003].

Vegetation fires are also found to have important social implications in tropical areas, including significant economic losses as a consequence of property damage and impacts on industry, and increased health problems among local populations

[Mendonça et al., 2004; Reinhardt et al., 2001]

In the last two decades Brazilian Amazonia has been under significant pressure as a result of high annual rates of deforestation [Laurance et al., 2004; PRODES, 2008]. Large areas of exceptionally high fire activity resulted from the wide spread use of vegetation fires to convert evergreen tropical forests into pastures or croplands or to maintain previously deforested areas [Alencar et al., 1997; Nepstad et al., 1999a; Sorrensen, 2004]. Human activities associated with those land use processes are in turn largely influenced by the regional climate conditions, characterized by high rainfall rates occurring during a relatively long wet season when fire use is rarely possible. However, during the dry season months, when there is a noticeable reduction in precipitation fires are used extensively [Schroeder et al., 2005].

Vegetation fires in Brazilian Amazonia are spatially concentrated in the major areas of deforestation [Cochrane and Laurance, 2002; Alencar et al., 2004]. Those areas often coincide with the agricultural frontiers, where investments in sustainable land use practices are scarce [Morton et al., 2006; Nepstad et al., 2001; Sorrensen, 2004]. Conservation areas serve as an important mechanism to reduce fire occurrence across large areas of tropical forest in Brazilian Amazonia [Arima et al., 2007; Nepstad et al., 2006]. However, the increasing pressure from the surrounding areas and the limited control exerted by park administration and law enforcement groups can greatly reduce the efficacy of those areas to prevent fire spread [Ferreira et al.,

1999; Laurance and Williamson, 2001; Pedlowski et al., 2005].

Brazilian Amazonia covers an area of approximately 5 Million km2 characterized by a sparse road network and limited infrastructure. In situ monitoring of fire activity is very limited and usually constrained to the immediate vicinity of a few environmental law enforcement offices [Ferreira et al., 2007]. Satellite active fire detection data represent the primary source of information on fire occurrence for county, state and federal environmental agencies, Non-Governmental Organizations (NGOs), civil society and for the scientific community.

Fire detection data can be obtained from different satellite sensors covering the region [CPTEC, 2008]. However, most users only have access to limited information describing the location and timing of detections derived from individual products. Differences among fire products and the lack of information describing data quality create major difficulties for end users [Schroeder et al., 2005]. As a result, law enforcement activities and the decision making process are significantly compromised, state and federal strategic plans to assign resources to control fire activity at the county level are negatively affected, and scientific studies based on fire detection data become subject to large uncertainties.

This dissertation investigates the performance of satellite active fire detection data for Brazilian Amazonia and analyzes the potential for integration of the different products. The main objective is to improve fire monitoring in the region by properly identifying and quantifying the major sources of error affecting individual products and by optimizing the use of multiple remote sensing fire products through data integration.


Vegetation fires in Brazilian Amazonia are largely caused by humans. The strong synergy between human activities and the fuel conditions determined by regional climate regimes create unique patterns of fire use across the region. As a result, the physical characteristics describing vegetation fires become highly influenced by the different land use and land cover types that occur in Brazilian Amazonia. The main objective of this dissertation is to quantify errors and evaluate the potential for integration of WF-ABBA and MODIS Thermal Anomalies fire detection products with attention to the regional conditions that lead to different fire regimes. The complementarities among those products are explored in order to generate improved fire detection rates with higher confidence levels.

The main hypothesis developed for this research is:

By integrating data from multiple sensors we can resolve differences between active fire detection products and increase the accuracy of vegetation fire spatial and temporal distribution

Given the considerations listed above, this dissertation is focused on the effects of cloud obscuration on detection omission errors, on the commission and omission rates associated with different land cover conditions, and on the effects of sensor characteristics which can lead to variations in detection rates and fire characterization. The dissertation is composed of the following research themes:

i)    Quantify the impact of cloud coverage on fire detection and create mechanisms to adjust the detection numbers to reflect actual fire occurrence without creating spurious fires; ii) Quantify commission and omission errors of WF-ABBA and MODIS Thermal Anomalies data for fires with different vegetation-background conditions;

iii) Evaluate fire characterization as defined by Fire Radiative Power (FRP)  as a means to integrate the WF-ABBA and MODIS Thermal Anomalies products; iv)   Quantify scan angle effects on fire detection rates of MODIS;

v)         Develop a strategy to integrate fire data from different sensors to generate an improved fire data record for Brazilian Amazonia.