In this glossary are the terms used in the description of the methodology and products of MapBiomas.
| Word | Definition |
| Algorithm | 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). |
| Asset | 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. |
| Band | It refers to each layer of information of an Asset - be it maps or images. |
| Spectral band | Interval between two wavelength values in the electromagnetic spectrum. Landsat has several spectral bands each one covering a range of the electromagnetic spectrum. |
| Chart or Millionth chart | Mapping 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 MapBiomas map processing work. Each map unit covers an area of approximately 18,700 square kilometers and about 20 million pixels.2 y cerca de 20 millones de píxeles. |
| Classification | Assignment (distribution) of the pixels of a satellite image in thematic classes of the legend. |
| Classifier | Generic name for an automated classification method (an example of a classifier is Random Forest). |
| Code Editor | Online integrated development environment (IDE) that is part of Google Earth Engine and that allows the development of Earth Engine applications through scripts and the visualization of the results through a graphical interface. |
| Collection | Version of the time series of maps and data on land cover and use of the MapBiomas Colombia project. Collections may vary in the period analyzed, methodology and legend. |
| Collect Mobile | Mobile application developed by MapBiomas for the collection of reference data in the field. |
| Cloud computing | Data processing carried out in a distributed manner on processors available on the Internet. MapBiomas uses cloud computing through Google Earth Engine and Google Cloud Computing. |
| Spatial consistency | Consistency in the spatial distribution of pixels of a class with the characteristics of the local landscape. For example, the occurrence of some glacier pixels in the middle of a forest indicates a spatial inconsistency. |
| Temporal consistency | Classification history of a pixel to a certain class over time and consistency with possible or probable land cover or land use transitions. For example, a pixel that has been classified as forest for 20 years, but appears as non-forest in one year in the middle of the series, is probably an inconsistency or classification error. |
| Dashboard (control panel) | Platform for visual presentation of the consolidated data that helps track the information. |
| Scene | 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. |
| Accuracy | Quantitative analysis of the accuracy of the mapping. Indicates the assignment error and the error area. |
| 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 are done on this platform. |
| Landsat Image | Satellite image generated by the satellites of the Landsat - Landsat project is a joint effort 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. |
| Integration | Routine of superposition of the classifications of the biomes and themes, generating integrated maps. The different MapBiomas 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 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. |
| Training Samples | Points or polygons used to train the classifier. |
| Precision Samples | Points collected in the images by year and classified by the interpreter with a class of coverage or use of the land. |
| Píxel | 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 corresponds to the pixel of the Landsat images with 30m average resolution. The area of the pixel suffers variations according to latitude when farther from the equator the area tends to be smaller. |
| Post-classification | 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. |
| Raster | 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. |
Scripts | 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. |
| Shapefile | Digital format of spatial data file represented in vector format. |
| Transversal Themes | Classes of use that occur in the different biomes and that are mapped by a single process for the whole country. The cross-sectional themes of MapBiomas include forest plantation, pasture, agriculture, non-vegetated area and water bodies. |
| WebCollect | Point collection platform used for training the classifiers or precision analysis. |
| Workspace | Web Platform developed by MapBiomas 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. |