In this glossary are the terms used in the description of the methodology and products of MapBiomas Amazon.



Accuracy (Precision Analysis)

Quantitative analysis of the accuracy of the mapping. Indicates the assignment error and the error area.


Set of rules and established procedures to solve a task.

Empirical decision tree

It is a classification method based on the construction of hierarchical decision rules, based on which each pixel of an image is classified. In empirical decision trees, the format and parameters of the tree are defined by the analysts, as well as the parameterization of each decision node (rule).


Collection of georeferenced maps, images or data available for processing and analysis in Google Earth Engine.

ATBD (Algorithm Theoretical Basis Document)

Document with methodological description and the algorithms used.


It refers to each layer of information in an Asset - could be maps or images.

Spectral band

Wavelength interval in the electromagnetic spectrum. The Landsat satellite has 7 spectral bands, within the visible ranges, near infrared, medium infrared and thermal infrared of the electromagnetic spectrum.

Letter or letter to the millionth

Map division of the territory in letters of 1 ° of latitude by 1.5 ° of length. This division is used to organize the processing work of MapBiomas Amazon maps. Each letter covers an area of ​​approximately 18.7 thousand square kilometers and about 20 million pixels.


It refers to the image generated by the sensor of a satellite. The approximate size of each scene is 170 km from north to south by 183 km from east to west.


Distribution of pixels in classes of a certain biome or subject.



The generic name for an automated classification method (example of a classifier is the random forest).

Code Editor

Google Earth Engine programming tool with graphical interface for visualizing the results.


Each version of the annual historical series of maps and data of land cover and use of the MapBiomas Amazon project. Collections may vary in the covered period, methodology and legend.


Mobile application developed by MapBiomas Amazon for the collection of reference data in the field.

Cloud computing

Data processing performed in a distributed way in processors available on the Internet. MapBiomas Amazon uses cloud computing through Google Earth Engine and Google Cloud Computing.

Spatial consistency

The distribution of the pixels of a certain class in an area must be consistent with the characteristics of the landscape of the place. For example, in a forest area, on a hillside, several pixels classified as water appear, indicating a spatial inconsistency.

Temporal consistency


The classification history of a pixel in time is consistent with possible or probable transitions of coverage or land use. For example, a pixel that is classified as a forest for 20 years, but in a year in the middle of the series, appears as non-forest. It is probably a classification error.


Platform for visual presentation of information and consolidated data to facilitate the tracking of information.

Feature Space

Set of spectral information used in the classification of the random forest, such as the bands, indexes and metrics used.

Spatial filter

Post-classification analysis to correct spatial consistency errors in a class.

Temporal filter

Post-classification analysis to correct errors of temporal consistency between classes and years.

Fusion Table

Data table that connects with Google tools. Very used to parameterize variables and processing rules (rules for the application of the transition filter).

Google Cloud Storage

Google tool to store large amount of information in the cloud.

Google Earth Engine

It is a geospatial analysis platform based on the cloud that allows users to visualize and analyze satellite images of the Earth's surface. All image processing and map production of MapBiomas Amazon are done on this platform.

Landsat Image

Satellite image generated by the satellites of the Landsat project of the United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA).

Spectral Index

An index resulting from mathematical operations between numerical pixel values ​​of the spectral bands of an image. For example, the Normalized Difference Vegetation Index (NDVI) is calculated as: (NIR - R) / (NIR + R) - where the NIR is the near infrared band and R is the Red band.


Routine of superposition of the classifications of the biomes and themes, generating integrated maps. The different MapBiomas Amazon classes are made separately and then integrated using prevalence rules in case one pixel receives more than one classification.

Integration Map

Final map that consolidates the maps of the biomes and themes.

Transition Map

Map that presents the main transitions of coverage and land use. It is produced from a comparison between a couple of maps (for example, 2000 and 2016). In these maps, each pixel can be classified without change or change. For each change, you receive a code that represents the class for the year T1 and the class for the year T2.

Image Mosaic

Set of good quality Landsat pixels (little cloud interference, for example) selected in a given period. MapBiomas Amazon mosaics are constructed by individually analyzing each pixel of the Landsat images available for the analysis period. In the mosaic, it seeks to represent in the best possible way the area of ​​analysis for a specific period.

Precision samples

Points collected in the images by year and classified by the interpreter with a class of coverage or use of the land.

Training samples

Points or polygons used to train the classifier.


The smallest unit in a digital image. A satellite image is composed of a matrix of pixels, each pixel with a digital value. The pixel in MapBiomas Amazon corresponds to the pixel of the Landsat images with 30 m average resolution. The area of ​​the pixel suffers variations according to latitude when farther from the equator the area tends to be smaller.


Automated routines to improve the consistency of the maps made after the classification and integration of the maps. The temporal and spatial filters are examples of post-classification.

Random Forest

Supervised classification method that is based on decision trees.


Digital image, composed of a matrix of values ​​(pixel).

Spatial resolution

Describe the level of detail of an image. Landsat images have an average spatial resolution of 30m.


Set of instructions written in programming language for a function to be executed.

Satellite Sensor

Instrument of a satellite for the remote detection of electromagnetic energy. A satellite can have multiple sensors for capturing different spectral ranges.


Digital format of spatial data file represented in vector format.

Cross-cutting themes

Classes of land-cover or land-use that occur in the different biomes and that are mapped by an independent methodology. The cross-cutting themes of MapBiomas Amazon include glaciers, water bodies, mangroves, urban infrastructure, flooded forests, pasture, and mining.


Point collection platform used for training the classifiers or precision analysis.


Web Platform developed by MapBiomas Amazon for the parametrization and classification of land use and cover maps. The platform serves as an interface between analysts' work and processing in Google Earth Engine.