PRODES

Overview

PRODES (Projeto de Monitoramento da Floresta Amazônica Brasileira - Project for Monitoring the Brazilian Amazon Forest) is Brazil’s primary satellite-based deforestation monitoring system operated by INPE (National Institute for Space Research).

This dataset provides:

Amazon deforestation monitoring: Official deforestation data using satellite imagery
Multiple landscape classes: Deforestation, residual deforestation, native vegetation, hydrography, non-forest areas, clouds
Raster data: High-resolution spatial data covering entire Legal Amazon
Municipality aggregates: Affected areas per municipality for non-spatial analysis
Cumulative and incremental data: Total deforestation since 1988 and year-by-year changes
Long time series: Available from 2007 onwards (cumulative) and incrementally by year
Official baseline: Used by Brazilian government for forest policy and enforcement

PRODES is the authoritative source for Amazon deforestation statistics, used for environmental monitoring, policy evaluation, and international reporting on Brazilian forest protection.

Data Source and Methodology

PRODES monitoring: - Uses satellite imagery from multiple sources (Landsat, CBERS, others) - Automated and manual analysis to detect clear-cut deforestation - Annual assessment of forest loss - Recently (2020+) raster data published through TerraBrasilis - Data available as both raster files and aggregated by municipality

For more information, visit INPE PRODES Project and TerraBrasilis.

Available Datasets

1. deforestation

Clear-cut deforestation areas (complete forest loss).

Coverage: Entire Legal Amazon region
Time period:
- 2007: Cumulative deforestation from 1988 to 2007
- 2008-2023: Annual incremental deforestation (year-specific)
Data types:
- Raw: Raster/grid data (SpatRaster format)
- Treated: Municipality-level area aggregates
Variables: Deforested area (hectares) by municipality
Use cases:
- Track annual deforestation trends
- Identify deforestation hotspots
- Cumulative forest loss assessment
- Municipality-level deforestation analysis

2. residual_deforestation

Deforestation that was not captured in previous surveys (detected through improved methodology).

Coverage: Legal Amazon
Time period: 2010-2023
Data types: Raster or municipality aggregates
Variables: Additional detected deforestation (hectares)
Use cases:
- More comprehensive deforestation assessment
- Correct for historical detection gaps
- Full accounting of forest loss

3. native_vegetation

Remaining native forest and natural vegetation areas.

Coverage: All vegetation in Legal Amazon
Time period: 2023 (snapshot of current state)
Data types: Raster only
Variables: Forest and vegetation extent
Use cases:
- Assess remaining forest cover
- Identify conservation priority areas
- Vegetation extent mapping

4. hydrography

Water bodies and hydrographic features.

Coverage: All water features in Amazon
Time period: 2023
Data types: Raster only
Variables: Water body extent
Use cases:
- Environmental impact assessment
- Hydrology and water resource mapping

5. non_forest

Non-forest areas including savanna, grasslands, and other vegetation types.

Coverage: Legal Amazon region
Time period: 2023
Data types: Raster only
Variables: Non-forest vegetation extent
Use cases:
- Distinguish non-forest from forest areas
- Natural vegetation assessment
- Land-use classification

6. clouds

Cloud cover in satellite imagery (data quality indicator).

Coverage: Legal Amazon
Time period: 2023
Data types: Raster only
Variables: Cloud-covered areas where analysis is uncertain
Use cases:
- Assess data quality and coverage
- Identify areas needing re-analysis
- Quantify monitoring gaps

Important Data Characteristics

Raw vs. Treated Data

Raw data (raw_data = TRUE): Returns SpatRaster objects (raster grids) from TerraBrasilis
Treated data (raw_data = FALSE): Aggregated to municipality level showing total affected area
Raster resolution: Typically ~30-meter pixels
Large size: Raster data is high-resolution and may be large

Cumulative vs. Incremental Data

Year	Type	Definition
2007	Cumulative	All deforestation from 1988-2007
2008-2023	Incremental	Deforestation detected in that specific year

When analyzing time trends, be aware that 2007 includes 19 years of accumulated loss.

Data Organization

Raster format: High-resolution grid with classified pixels (deforestation, forest, etc.)
Municipality level: Treated data aggregates raster to municipality boundaries
Non-spatial: Municipality aggregates don’t retain spatial location (summarized only)

Function Parameters

1. dataset

Selects which landscape classification to download.

dataset = "deforestation"             # Clear-cut deforestation (main product)
dataset = "residual_deforestation"    # Previously undetected deforestation
dataset = "native_vegetation"         # Remaining forests and vegetation (2023 only)
dataset = "hydrography"               # Water bodies (2023 only)
dataset = "non_forest"                # Savanna and other non-forest (2023 only)
dataset = "clouds"                    # Cloud cover (2023 only)

2. raw_data

Controls data format: raster or municipality aggregates.

TRUE: Returns raw raster data (SpatRaster objects)
FALSE: Returns municipality-level aggregated area data

raw_data = FALSE  # logical

Important: Raster data is large; ensure sufficient storage and memory.

3. time_period

Specifies which year(s) to download.

Available by dataset:

Dataset	Available Years
`deforestation`	2007 (cumulative), 2008-2023 (incremental)
`residual_deforestation`	2010-2023
`native_vegetation`	2023 only
`hydrography`	2023 only
`non_forest`	2023 only
`clouds`	2023 only

time_period = 2020              # single year
time_period = c(2015, 2020)     # multiple years
time_period = 2015:2020         # range of years (for deforestation only)

4. language

Output language.

"pt": Portuguese
"eng": English

language = "eng"  # character string

Examples

Example 1: Deforestation data for a single year

# download treated deforestation data for 2023
deforestation <- load_prodes(
  dataset = "deforestation",
  raw_data = FALSE,
  time_period = 2023,
  language = "eng"
)

Example 2: Deforestation data over multiple years

# download treated deforestation data for 2008 to 2023
deforestation_series <- load_prodes(
  dataset = "deforestation",
  raw_data = FALSE,
  time_period = 2008:2023,
  language = "eng"
)

Example 3: Cumulative forest loss since 1988

# download 2007 (cumulative 1988-2007) plus all years up to 2023
all_deforestation <- load_prodes(
  dataset = "deforestation",
  raw_data = FALSE,
  time_period = c(2007, 2008:2023),
  language = "eng"
)

Example 4: Residual deforestation

# download treated residual deforestation data for 2020
residual <- load_prodes(
  dataset = "residual_deforestation",
  raw_data = FALSE,
  time_period = 2020,
  language = "eng"
)

Data Notes

Raw Raster Data Characteristics

Format: SpatRaster (from terra package)
Resolution: ~30 meter pixels
Coverage: Complete Legal Amazon region
Size: Very large files; may require significant storage/memory
Classification: Pixel values indicate land cover class (deforestation, forest, etc.)
CRS: UTM zones or standard projection; verify after loading

Municipality Aggregates

Simplified for analysis: Loses spatial detail but much smaller files
Aggregation: Total deforested area per municipality per year
Ideal for: Time series, trend analysis, comparative studies

Data Quality and Accuracy

Detection accuracy: ~95% for clear-cut deforestation
Minimum mapping unit: Small forest clearances may not be detected
Cloud contamination: Clouds may prevent detection in some areas (see clouds dataset)
Seasonal effects: Deforestation easier to detect in dry season

Limitations

Clear-cut only: Degradation and partial logging not captured (see DEGRAD for that)
Legal Amazon only: Data limited to Legal Amazon definition; doesn’t cover all forest regions
Raster data very large: May require specialized tools and significant computing resources
Historical changes: INPE occasionally revises historical estimates as methodology improves
Recent data provisional: 2023 data may be subject to revision as final processing completes