Amazon Logging Impact Study with Landsat and Field Data

This dataset combines field measurements and Landsat ETM+ satellite imagery to quantify canopy damage and recovery from selective logging in an eastern Amazon forest. The study, conducted by NASA, covers a 450-km² area in Cauaxi, Para, Brazil, and tracks post-harvest regrowth over a 0.5 to 3.5-year period. It compares reduced-impact and conventional logging practices, using spectral mixture analysis to estimate sub-pixel cover fractions of canopy, soil, and non-photosynthetic vegetation.

Use Cases

• Comparing canopy damage between reduced-impact and conventional logging methods using field and satellite-derived gap fractions.
• Modeling regional-scale forest canopy recovery rates over a 3.5-year post-harvest timeline.
• Analyzing the spatial distribution and contribution of different damage strata (roads, skids, tree falls, log decks) to overall landscape gap dynamics.
• Calibrating and validating spectral mixture models for estimating forest canopy cover from Landsat imagery using field reflectance measurements.

Strengths

• Integrates field measurements with satellite imagery (Landsat ETM+) for validation, covering a 450-km² regional study area.
• Tracks multi-temporal sequences of post-harvest regrowth from 0.5 to 3.5 years, providing a recovery timeline.
• Employs a spectral mixture model with Monte Carlo analysis to estimate high spatial resolution (sub-pixel) cover fractions.

Limitations

• Key dataset details are unknown: row count, column names, and exact file size are not provided by any source.
• Conflicting last updated dates exist: Data.gov lists 2026-03-13, while NASA Earthdata lists 1998-12-31.

Provenance

Source

National Aeronautics and Space Administration (NASA)

Collection Method

Combined detailed field study of canopy gap fraction with spectral mixture analysis of Landsat ETM+ satellite imagery.

Time Range

1996, 1998, with field measurements spanning 0.5-3.5 years post-harvest.

Freshness

2026-03-13 01:02:28.172928

Geography

Eastern Amazon forest, Cauaxi, Para, Brazil.