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LC-35 Abstract

Analysis of Long-term fire Dynamics and Impacts in the Amazon Using Integrated Multi-source Fire Observations

Ivan Andras Csiszar — NOAA/NESDIS Center for Satellite Applications and Research (US-PI)
Karla Maria Longo — Istituto Nacional de Pesquisas Espaciais (SA-PI)

Vegetation fire is a major process contributing to land use and land cover change globally and in particular in the Amazon region. Space-borne sensors provide valuable information on active fire detection; however, their application for quantitative studies of fire activity is still limited due to variations within and among existing systems. As a result, there is no standardized long term active fire data record available for the scientific community. Important results from the LBA-Eco Phases I and II provided improved understanding of vegetation fires as observed from satellite sensors and this knowledge can now be used to reassess conventional methods in order to create more robust fire data sets for the region and to more precisely quantify fire implications in terms of land use management and carbon emissions. The proposed research will be based on the creation of a standardized fire data record for the Amazon as derived from multiple satellite sensors and on the application of the resulting data set for the quantification of fire impacts in the region. In the first stage of this project GOES East data will be reprocessed using an updated active fire detection and characterization algorithm (version 6.0 of the WF_ABBA) that generates reduced detection errors, along with the production of an improved cloud product that will be used in combination with the fire data to help characterize fire regimes in the region. This will extend the GOES time series from the actual 2000-present (version 6.0) to 1995-present with which improved trend analysis can be achieved. Access to the extended GOES fire data records will be made open to the scientific community (LBA and others) via a data bank architecture that will be accessible online. Parallel to the processing with the WF_ABBA system, the algorithm developed at INPE for real-time detection will also be run, and the relative merits of the two processing approaches will be evaluated. In the second stage of the project an extensive data inter-comparison analysis is proposed to study the relationship between different satellite fire products. This component of the project will include the adaptation of the MODIS active fire validation methodology developed within the LBA-ECO project LC-23 'Quantifying the Accuracy of MODIS Fire Products and Establishing Their Relationship with Land Cover Dynamics', based on coincident ASTER imagery. We will develop a more generic multi-platform technique that will allow the evaluation of any fire product from moderate resolution sensors (primarily GOES in this study) based on the availability of near-coincident high resolution imagery; and the comparison of thus validated fire products with those from sensors where coincident high resolution imagery is not available (i.e. AVHRR). The linkages between moderate and coarse resolution products will be established using data fusion methods towards the creation of a unified fire diurnal cycle map that can be used to model time dependent variables (e.g.: emissions). Fire area and temperature and fire radiative power, important parameters that are related to biomass burning efficiency and emission values, will be implemented using the suite of data sets selected here. The third stage of the project will address the impacts caused by vegetation fires in the Amazon region. The extended and improved fire data products will be used to produce enhanced fire spatial and temporal distribution maps which, in combination with other parameters (e.g.: deforestation data) will be used to explain land management practices and their consequences to the environment. Emission modeling studies will also be addressed at this final phase of the proposal by feeding models with the results produced in the previous sections. Fire data from the improved time series will be fed into the emission model at CPTEC/INPE and an improved 10-year time series of biomass burning emission will be generated.

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