{"id":455,"date":"2023-12-05T15:28:37","date_gmt":"2023-12-05T15:28:37","guid":{"rendered":"https:\/\/staging-ecuador.mapbiomas.org\/?page_id=455"},"modified":"2025-09-17T00:08:44","modified_gmt":"2025-09-17T00:08:44","slug":"metodologia-mapbiomas-agua","status":"publish","type":"page","link":"https:\/\/ecuador.mapbiomas.org\/en\/metodologia-mapbiomas-agua\/","title":{"rendered":"MapBiomas Water Method"},"content":{"rendered":"

WATER SURFACE MAPPING: METHOD SUMMARY<\/strong><\/p>\n\n\n\n

Here we present a summary of the MapBiomas Water Method. Access the ATBD (Algorithm Theoretical Basis Document ) in this LINK<\/a> for more methodological details.<\/p>\n\n\n\n

Presentation<\/strong><\/p>\n\n\n\n

El objetivo principal de MapBiomas Agua es mapear la din\u00e1mica del agua superficial en todo el territorio de los pa\u00edses amaz\u00f3nicos (Panamazon\u00eda), de forma mensual y anual desde 1985 al 2024. El conjunto de datos est\u00e1 disponible p\u00fablicamente en una plataforma web para mejorar la gesti\u00f3n y el uso de los recursos h\u00eddricos en toda la Panamazon\u00eda. <\/p>\n\n\n\n

El mapeo de superficie de agua en los pa\u00edses amaz\u00f3nicos us\u00f3 todas las escenas del sat\u00e9lite Landsat con una cobertura de nubes menor o igual al 70% y una resoluci\u00f3n espacial de 30 metros. El mapeo fue conducido a una escala de sub-p\u00edxel (SWSC), con An\u00e1lisis de Mixtura Espectral (SMA \u2013 por sus siglas en ingl\u00e9s) y reglas de clasificaci\u00f3n emp\u00edricas basadas en una l\u00f3gica fuzzy.  El mapeo comprendi\u00f3 el periodo de 1985 a 2024, en la escala mensual, con un total de 396.000 escenas Landsat procesadas y analizadas en la plataforma Google Earth Engine. <\/p>\n\n\n\n

Organization and database <\/strong><\/p>\n\n\n\n

The overall coordination of MapBiomas Water is led by Imazon and RAISG, while technical and operational coordination is directed by Geokarten. The reconstruction of the monthly historical series of surface water was carried out by specialists from all biomes of the Amazonian countries, under the leadership of the following institutions: Fundaci\u00f3n Amigos de la Naturaleza -FAN- (Bolivia), Fundaci\u00f3n Gaia Amazonas -FGA- (Colombia), EcoCiencia (Ecuador), Instituto del Bien Com\u00fan -IBC- (Per\u00fa), Provita y Wataniba (Venezuela), Alliance of Bioversity International y CIAT (Guianas y Suriname). The surface water mapping algorithm was developed by Imazon and adapted by MapBiomas Water in this initial phase of work. <\/p>\n\n\n\n

The development of the MapBiomas Water control panel (dashboard) was conducted by Geodatin and includes significant contributions from the MapBiomas Water working group and platform users in the design thinking process. <\/p>\n\n\n\n

Three types of products were produced by MapBiomas Water: <\/p>\n\n\n\n

    \n
  1. Monthly and annual surface water maps;<\/li>\n\n\n\n
  2. Surface water transition maps between \u201cWater\u201d and \u201cNon-water\u201d classes. This product was processed using the annual surface water database;<\/li>\n\n\n\n
  3. Trend maps (increase and decrease) in surface water. This product was calculated from monthly surface water data in 5 km x 5 km grids. <\/li>\n<\/ol>\n\n\n\n

    The dashboard (link) consists of maps, statistics, and visualization, analysis, and data access tools. It is possible to view the data on an annual and monthly scale, as well as obtain it in different territorial units. Finally, the dashboard also provides a link to access the MapBiomas Water data API. <\/p>\n\n\n\n

    Method<\/strong><\/p>\n\n\n\n

    The following diagram illustrates the main stages in the process of classifying surface water in the Amazonian countries, involving a surface water sub-pixel classifier (SWSC), decision tree, and post-classification procedures to generate annual and monthly surface water datasets. <\/p>\n\n\n\n

    Figura 1 \u2013 Etapas de clasificaci\u00f3n de la superficie de agua superficial.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Description of classification steps:<\/strong><\/p>\n\n\n\n

      \n
    1. Pre-processing: <\/strong><\/li>\n<\/ol>\n\n\n\n

      Consists in the selection of Landsat scenes from the sensors: Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI); applying cloud and shadow masking to each scene and excluding scenes with more than 70% cloud cover. The visible, near and mid-infrared spectral bands were selected for the application of the Mixture Spectral Model (MEM). The result of the MEM is a set of compositional bands for each pixel of the Landsat image, for the Vegetation, Non-Photosynthetically Active Vegetation (NPV), Soil, Shade, and Cloud components. Water behaves as a dark body (i.e. low reflectance) in Landsat images and therefore has a high percentage of the Shadow component in the pixel. The edges of lakes, rivers, and humid environments, such as floodplains, present a mixture of Shadow (water), Vegetation, and Soil, which allows the detection of water in environments with these types of materials. <\/p>\n\n\n\n

        \n
      1. Classification of Water Surface:<\/strong><\/li>\n<\/ol>\n\n\n\n

        El algoritmo clasificador de sub-pixel de agua superficial (SWSC) original utiliza tres reglas jer\u00e1rquicas de decisi\u00f3n binaria (ej. verdadero, falso). Debido a que el agua absorbe gran parte de la radiaci\u00f3n electromagn\u00e9tica se utiliza una imagen con fracci\u00f3n de Shade, la combinaci\u00f3n de GV y Soil y Cloud para clasificar los p\u00edxeles como agua superficial. Adicionalmente, se aplica una clasificaci\u00f3n basada en l\u00f3gica difusa (reglas fuzzy) independientes, en las que se determina el grado de verdad\/certeza (memberships) de que un p\u00edxel Landsat es clasificado como agua.  Luego se calcul\u00f3 el grado de verdad promedio para obtener un mapa continuo de memebership con valores que oscilan entre 0 y 1. En base a estos memberships se clasifican los p\u00edxeles para producir capas de agua superficiales mensuales. <\/p>\n\n\n\n

        Calculando la mediana de los memberships de los p\u00edxeles entre las escenas Landsat disponibles para cada mes, se clasificaron los p\u00edxeles como agua con base a umbrales definidos. Luego se aplicaron procedimientos para restaurar falsos negativos y remover falsos positivos, basados en m\u00e9tricas temporales. Seguido, se aplic\u00f3 un relleno de vac\u00edos para reclasificar como agua aquellos p\u00edxeles que eventualmente fueron cubiertos por nubes o dentro de \u00e1reas donde no exist\u00edan escenas Landsat durante un mes determinado, usando una combinaci\u00f3n de dos reglas: probabilidad mediana dentro del a\u00f1o y la mediana decenal del mes correspondiente. Por \u00faltimo, la presencia de sombras de nubes u otros objetos oscuros en la escena Landsat tambi\u00e9n puede producir falsos positivos en la clasificaci\u00f3n de agua, por lo que se aplic\u00f3 un filtro de remoci\u00f3n para reclasificar como no agua aquellos p\u00edxeles. <\/p>\n\n\n\n

        Annual surface water maps include an identification between permanent and seasonal water, this classification is based on thresholds corresponding to the number of months in which a pixel is classified as water. For the first case, a frequency >= 6 months is considered, and for the second, a frequency between 1 to 5 months. <\/p>\n\n\n\n

        Figura 2 \u2013 Proceso de clasificaci\u00f3n mensual.<\/p>\n\n\n\n

        \"\"<\/figure>\n\n\n\n
          \n
        1. Clasificaci\u00f3n de cuerpos h\u00eddricos<\/strong><\/li>\n<\/ol>\n\n\n\n

          Figura 3 \u2013 Proceso de clasificaci\u00f3n de cuerpos h\u00eddricos.<\/p>\n\n\n\n

          \"\"<\/figure>\n\n\n\n

          For the classification of water bodies, the following information extracted from the annual mapping of water surface (permanent) was used: i) the first and last occurrence of the water body in the year, ii) the total frequency of the water surface in the historical series, and iii) the annual frequency. This information was organized into raster data and used in an object segmentation algorithm.<\/p>\n\n\n\n

          Subsequently, attributes were extracted from auxiliary maps of hydroelectric plants and mining from the following entities:<\/p>\n\n\n\n

          Mapa de cobertura y uso de la tierra 2022 \u2013 MAATE (2022) (Ecuador)<\/p>\n\n\n\n

          Cartograf\u00eda b\u00e1sica 1:50.000 y 1:100.000 \u2013 IGM (2020) (Ecuador)<\/p>\n\n\n\n

          MapBiomas Amazon\u00eda Colecci\u00f3n 6 <\/p>\n\n\n\n

          MapBiomas Ecuador Colecci\u00f3n 3<\/p>\n\n\n\n

          Water body segments were classified using the Random Forest algorithm into five categories: natural, other artificial, hydroelectric, mining, and aquaculture. Additionally, a \"false positives\" class was included to eliminate persistent overestimations in the annual and monthly surface maps. Spatial and frequency filters were then applied; further samples were taken, and manual polygon delineations were conducted to improve the results.<\/p>\n\n\n\n

          The aforementioned classes are described as follows:<\/p>\n\n\n\n

          Natural: Natural-origin surface water extension, including rivers, lakes, wetlands, and other water bodies.<\/p>\n\n\n\n

          Other Artificial: Artificially constructed water bodies such as small reservoirs, storage tanks, canals, or ponds, intended for agricultural production, water treatment, recreation, drinking water supply, among others.<\/p>\n\n\n\n

          Hydroelectric: Artificial or semi-natural water bodies designed for water collection to generate hydroelectricity. This includes other types of multipurpose water big reservoirs.<\/p>\n\n\n\n

          Mining: Artificial water bodies associated with surface areas used for extracting rock or mineral materials. No distinction is made between industrial or artisanal, legal or illegal, metallic or non-metallic operations. Most are alluvial.<\/p>\n\n\n\n

          Aquaculture: Artificially created surface water bodies dedicated to productive activities, such as saltwater ponds for shrimp farming or freshwater ponds for fish farming.<\/p>","protected":false},"excerpt":{"rendered":"

          MAPEO DE LA SUPERFICIE DE AGUA: S\u00cdNTESIS DEL M\u00c9TODO Aqu\u00ed presentamos una s\u00edntesis del m\u00e9todo desarrollado y aplicado por MapBiomas Agua. Para mayor informaci\u00f3n de los detalles metodol\u00f3gicos, acceda al ATDB (Documento Base de la Teor\u00eda del Algoritmo) en este LINK. Presentaci\u00f3n El objetivo principal de MapBiomas Agua es mapear la din\u00e1mica del agua superficial […]<\/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:\/\/ecuador.mapbiomas.org\/en\/author\/adriel-fernandes\/"},"uagb_comment_info":0,"uagb_excerpt":"MAPEO DE LA SUPERFICIE DE AGUA: S\u00cdNTESIS DEL M\u00c9TODO Aqu\u00ed presentamos una s\u00edntesis del m\u00e9todo desarrollado y aplicado por MapBiomas Agua. Para mayor informaci\u00f3n de los detalles metodol\u00f3gicos, acceda al ATDB (Documento Base de la Teor\u00eda del Algoritmo) en este LINK. Presentaci\u00f3n El objetivo principal de MapBiomas Agua es mapear la din\u00e1mica del agua superficial…","_links":{"self":[{"href":"https:\/\/ecuador.mapbiomas.org\/en\/wp-json\/wp\/v2\/pages\/455"}],"collection":[{"href":"https:\/\/ecuador.mapbiomas.org\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/ecuador.mapbiomas.org\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/ecuador.mapbiomas.org\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/ecuador.mapbiomas.org\/en\/wp-json\/wp\/v2\/comments?post=455"}],"version-history":[{"count":10,"href":"https:\/\/ecuador.mapbiomas.org\/en\/wp-json\/wp\/v2\/pages\/455\/revisions"}],"predecessor-version":[{"id":989,"href":"https:\/\/ecuador.mapbiomas.org\/en\/wp-json\/wp\/v2\/pages\/455\/revisions\/989"}],"wp:attachment":[{"href":"https:\/\/ecuador.mapbiomas.org\/en\/wp-json\/wp\/v2\/media?parent=455"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}