In this glossary are terms commonly used in describing Mapbiomas's methodology and products

Accuracy (Accuracy Analysis)Quantitative analysis of mapping accuracy. Indicates the allocation error and the area error.
AlgorithmSet of rules and procedures established to solve a task.
Accuracy SamplesPoints collected over the anual mosaics and visually classified by the interpreter as belonging to a specific land use land cover class.
Training SamplesPoints or polygons used to train the classifier
Empirical Decision TreeA cascade of parameters set to define the pixel classification. In the empirical decision trees the format and parameters of the tree are defined by the analysts, as well as the parameterization of each decision node.
AssetCollection of maps, images or georeferenced data available for processing and analysis in Google Earth Engine.
ATBD (Algorithm Theoretical Basis Document)Document with methodological description and the algorithms used.
BandIt refers to each layer of information of an Asset - either maps or images.
Spectral BandInterval between two wavelengths in the electromagnetic spectrum. Landsat has several spectral bands each one covering a range of the electromagnetic spectrum.
CAR Cadastro Ambiental Rural
Chart or Millionth ChartMapping division of the Brazilian territory is defined by IBGE and integrated to the International Map of the World (IMW) or Millionth Map. This division is used to organize the work of processing MapBiomas maps. Each chart covers an area of approximately 18,700 square kilometers and about 20 million pixels.
SceneRefers to the image generated by the sensor of a satellite. To cover the Brazilian territory, 380 Landsat scenes are required.
ClassificationDistribution of pixels in classes of a given biome or theme.
ClassifierGeneric name for an automated classification method (example of a classifier is Random Forest).
Code EditorGoogle Earth Engine programming tool with graphical interface for viewing the results.
CollectMobileMobile application developed by MapBiomas for the collection of reference data in the field.
ColeçãoEach version of MapBiomas annual mapping data. The collections may vary in the period, methodology and legend.
Cloud ComputingData processing performed on distributed processors available on the world wide computer network. In MapBiomas the cloud computing process runs through Google Earth Engine and Google Cloud Computing.
Spatial ConsistencyDistribution of pixels of a certain class in space must be consistent with landscape characteristics of the place. For example, in the middle of a hillside forest area several pixels appear as water indicating a spatial inconsistency.
Temporal ConsistencyClassification history of a pixel in time is consistent with possible or probable transitions of land use and land cover. For example, a pixel that is classified as forest for 20 years but in a year in the middle of the series appears as non-forest. This is likely to be a misclassification.
Dashboard (control panel)Platform for visual presentation of information and consolidated data for easy tracking of information.
Feature SpaceSet of spectral information, indices and metrics used in Random Forest classification.
Spacial FilterPost-classification analysis used to correct errors of spatial consistency in a class.
Temporal FilterPost-classification analysis to correct temporal consistency errors between classes and years.
Fusion TableTabular data that connects with Google tools. Used to parameterize variables and processing rules (rules applied during the transition filter).
Google Cloud StorageGoogle's tool for storing lots of cloud information.
Google Earth EnginePlatform for analysis and visualisation of scientific spatial data on the Earth's surface, in cloud computing. All image processing and production of MapBiomas maps is done on this platform.
Landsat ImageImage generated by a set of Landsat satellites - launched by NASA and operated by the American Geological Survey.
IntegrationOverlap routine of classes in order to generate an integrated maps. Different classes of MapBiomas are made separately and then integrated using prevalence rules
Integration MapFinal map consolidating maps of biomes and themes.
Transition MapMap showing the main transitions of land use and land cover. It is produced from a comparison of a pair of maps (eg 2000 x 2016). In these maps each pixel can be classified as change or no change. For each change, it receives a code that represents the class in t1 and the class in t2.
Image MosaicSet of Landsat pixels with good quality (little cloud interference, for example) selected in a given period. The MapBiomas mosaics are constructed by individually analyzing each pixel of the Landsat images available for the period. In the mosaic, we try to represent the analysis area for the specified period in the best possible way. In MapBiomas image mosaics generally represent the period of one year.
Spectral IndexA spectral index is the result of mathematical operations between numerical values of pixels from the spectral bands of a sensor. For example, the Normalized Difference Vegetation Index (NDVI) is calculated by: (NIR - R) / (NIR + R) - NIR being the near infrared band and R is the Red band.
PixelThe smallest unit in a digital image. A satellite image is composed of an array of pixels, each pixel with a digital value. The pixel in MapBiomas corresponds to the pixel of Landsat images with 30m resolution. The area of the pixel undergoes variations according to its latitude. Further away from the equator the distorted will be the area.
Post-ClassificationAutomated routines to improve the consistency of maps performed after classification and map integration. The temporal and spatial filters are examples of post classification.
Random ForestSupervised classification method that is based on decision trees.
RasterDigital image, composed of an array of values (pixel).
Spatial resolutionDescribes the level of detail of an image. Landsat (TM) images have an average spatial resolution of 30m.
ScriptsSet of instructions written in a programming language for a function to be executed.
Satellite SensorSatellite instrument responsible for the remote sensing of electromagnetic energy. A satellite may have multiple sensors for picking up different spectral ranges.
ShapefileFile format with a set of spatial data in vector format.
Transversal ThemeLand use land cover classes that occur transpassing the limits of different biomes. The cross-cutting themes of MapBiomas include agriculture, pasture, planted forest, urban area, mining, mangrove, apincum, aquaculture, beach and dunes.
WebCollectPlatform used to collect points for the training of the classifier or accuracy analysis.
WorkspaceWeb platform developed by MapBiomas for parameterization and classification of land use and land cover maps. The platform serves as an interface between analyst work and the cloud processing on Google Earth Engine.