Todo comienza con im\u00e1genes del sat\u00e9lite Landsat, con resoluci\u00f3n de 30 metros, disponibles de forma gratuita en la plataforma Google Earth Engine y con una serie temporal de 40 a\u00f1os. Se construyeron 7.680 mosaicos en todo el l\u00edmite nacional, cada uno con decenas de millones de p\u00edxeles en total. Estos p\u00edxeles son las unidades de trabajo de MapBiomas. Las im\u00e1genes pueden contener nubes, bruma y otras condiciones que pueden afectar su calidad. Para producir una imagen limpia se seleccionan los p\u00edxeles despejados de las im\u00e1genes disponibles para el per\u00edodo seleccionado. Para cada uno de estos p\u00edxeles se extraen m\u00e9tricas que explican el comportamiento del p\u00edxel en ese a\u00f1o. Esto se hace con cada una de las 7 bandas espectrales del sat\u00e9lite, as\u00ed como con las fracciones e \u00edndices espectrales calculados. Por ejemplo, para la Banda 1 se recoge la mediana de los valores de la banda en el per\u00edodo, el valor m\u00e1ximo, el valor m\u00ednimo en el a\u00f1o y la amplitud de variaci\u00f3n. Al final, cada p\u00edxel durante un a\u00f1o lleva hasta 156 capas de informaci\u00f3n.<\/p>\n\n\n\n\n\n Mosaics covering the entire country are created for each year and are set up representing the behavior of each pixel through 156 metrics or layers of information. This mosaic set is saved as a collection of data (Asset) within the Google Earth Engine platform. These mosaics will be used in two main ways. First as source parameters for the algorithm to produce classification (see next step). It is also from this mosaic that RGB composition is derived allowing visualization of the background image in the platform MapBiomas. This composition is also used for the collection of training samples and samples for assessment of accuracy by visual interpretation.<\/p>\n\n\n\n\n\n From the image mosaics, the teams of each biome and each cross-sectional theme produce a map of each land cover class (forest, agriculture, pasture, infrastructure, water, etc). To do so, MapBiomas analysts use an automatic classifier called \"random forest'' which runs on Google Earth Engine. This system is based on machine learning for each topic to be classified, the machines are \"trained\" with samples of the targets to be classified. These samples are obtained by means of reference maps generation of maps of stable classes of the previous series of MapBiomas and by direct collection by visual interpretation of the Landsat images. The classification is made for each of the years of the series and can be saved as a single map per class where each pixel has the number of layers corresponding to the number of years of the analyzed historical series.<\/p>\n\n\n\n\n\n The spatial filter aims to increase the spatial consistency of the data by eliminating isolated or border pixels. Neighborhood rules are defined that can lead to a change in pixel classification. For example, a pixel that has less than two out of the nine neighboring pixels in the same class will be reclassified to the predominant class in the neighborhood. Each panel in each year and for each class of use is subjected to spatial filtering.<\/p>\n\n\n\n\n\n Para reducir las inconsistencias temporales, especialmente los cambios imposibles o no permitidos en la cobertura y el uso (por ejemplo, Bosque natural > No bosque > Bosque natural) y corregir las fallas debido al exceso de nubes o falta de datos, se aplican reglas de filtro temporal. Cada bioma, tema o regi\u00f3n puede tener reglas de filtro temporal espec\u00edficas. En total en la Colecci\u00f3n 2.0 se aplicaron 3 reglas. El filtro temporal se aplica a cada p\u00edxel analizando todos los a\u00f1os de la Colecci\u00f3n (40 a\u00f1os).<\/p>\n\n\n\n\n\n In this step, the maps of each class are integrated into a single map, which represents the coverage and land use of each territory for each year. Prevalence rules are applied: Thus, if the same pixel is classified into two distinct class maps, it is possible to define which one belongs to the final map. The prevalence rules may vary according to the peculiarities of the biomes, themes or regions. The integration is done for each year of the series and generates an integrated map for each year usually saved as a single ASSET with the number of annual layers of the period analyzed. The integrated map goes through a further step of spatial filtering to clean the edges and loose pixels as a consequence of the integration process.<\/p>\n\n\n\n\n\n To understand changes in land cover and land use, maps are produced with class transitions between different pairs of selected years. It is thus possible to visualize the dynamism of the territory, and answer questions such as how much of the forest has turned pasture from one year to another, for example, among other changes in the landscape Transition maps are produced pixel by pixel and after finalized also pass through a spatial filter to eliminate isolated transition or border pixels. From these maps are constructed the transition matrices for each biome, region, province, district and other territorial boundaries available on the MapBiomas Peru platform.<\/p>","protected":false},"excerpt":{"rendered":" CONOZCA LOS PASOS DE LA METODOLOG\u00cdA DE COBERTURA Y USO Aqu\u00ed detallamos la metodolog\u00eda de MapBiomas paso a paso. Para cada clase y tema tratado en el mapa existen caracter\u00edsticas espec\u00edficas que se pueden consultar en detalle en el ATBD (Documento Base Te\u00f3rico de Algoritmos) y sus anexos. Descarga la metodolog\u00eda completa – ATBD Todo […]<\/p>","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":""},"acf":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false,"trp-custom-language-flag":false,"infographic":false,"team":false},"uagb_author_info":{"display_name":"Adriel Fernandes","author_link":"https:\/\/peru.mapbiomas.org\/en\/author\/adriel-fernandes\/"},"uagb_comment_info":0,"uagb_excerpt":"CONOZCA LOS PASOS DE LA METODOLOG\u00cdA DE COBERTURA Y USO Aqu\u00ed detallamos la metodolog\u00eda de MapBiomas paso a paso. Para cada clase y tema tratado en el mapa existen caracter\u00edsticas espec\u00edficas que se pueden consultar en detalle en el ATBD (Documento Base Te\u00f3rico de Algoritmos) y sus anexos. Descarga la metodolog\u00eda completa – ATBD Todo…","_links":{"self":[{"href":"https:\/\/peru.mapbiomas.org\/en\/wp-json\/wp\/v2\/pages\/117"}],"collection":[{"href":"https:\/\/peru.mapbiomas.org\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/peru.mapbiomas.org\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/peru.mapbiomas.org\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/peru.mapbiomas.org\/en\/wp-json\/wp\/v2\/comments?post=117"}],"version-history":[{"count":11,"href":"https:\/\/peru.mapbiomas.org\/en\/wp-json\/wp\/v2\/pages\/117\/revisions"}],"predecessor-version":[{"id":1633,"href":"https:\/\/peru.mapbiomas.org\/en\/wp-json\/wp\/v2\/pages\/117\/revisions\/1633"}],"wp:attachment":[{"href":"https:\/\/peru.mapbiomas.org\/en\/wp-json\/wp\/v2\/media?parent=117"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"\/](\"https:\/\/peru.mapbiomas.org\/wp-content\/uploads\/sites\/14\/2024\/02\/ATBD_Grafico_2_EN.png\")
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