LBA-ECO LC-23: ASTER and MODIS Fire Detection Algorithm Comparison

LBA-ECO LC-23 contains data from a 2005 study comparing two operational fire detection algorithms applied to MODIS-Terra satellite data against higher-resolution ASTER imagery. The dataset includes the ASCII files used for logistic regression and error matrices in the evaluation. It was produced by NASA and Brazil's National Institute for Space Research (INPE).

Use Cases

• Evaluating the performance of NASA's MODIS fire detection algorithm against a higher-resolution ASTER reference.
• Comparing the Brazil INPE fire detection algorithm's characteristics with NASA's operational product.
• Conducting logistic regression analysis on fire detection confidence using the provided ASCII data.
• Building error matrices to quantify commission and omission errors in satellite fire mapping.

Strengths

• Directly supports a peer-reviewed scientific publication (Morisette et al., 2005).
• Compares two operational, publicly available fire detection products from major institutions (NASA and INPE).
• Uses high spatial resolution ASTER data as a validation benchmark.

Limitations

• Critical metadata is missing or conflicting: row counts, column names, and exact data size are unknown.
• There is a significant conflict in the 'last updated' date (2004 vs. 2026) across platforms, casting doubt on provenance tracking.
• The data's temporal coverage is limited to the years 2003-2004.

Provenance

Source

National Aeronautics and Space Administration (NASA) and Brazil's National Institute for Space Research (INPE)

Collection Method

Satellite data from MODIS-Terra and ASTER sensors, processed through two distinct fire detection algorithms.

Time Range

2003-2004

Freshness

2026-04-10 01:45:25.374374 (conflicts with 2004 date from another source)

Geography

Brazil (inferred from LBA-ECO project focus and INPE's role)