WATER SURFACE MAPPING: METHOD SUMMARY
Here we present a summary of the MapBiomas Water Method. Access the ATBD (Algorithm Theoretical Basis Document ) in this LINK for more methodological details.
Presentation
El objetivo principal de MapBiomas Agua es mapear la dinámica del agua superficial en todo el territorio de los países amazónicos (Panamazonía), de forma mensual y anual desde 1985 al 2024. El conjunto de datos está disponible públicamente en una plataforma web para mejorar la gestión y el uso de los recursos hídricos en toda la Panamazonía.
El mapeo de superficie de agua en los países amazónicos usó todas las escenas del satélite Landsat con una cobertura de nubes menor o igual al 70% y una resolución espacial de 30 metros. El mapeo fue conducido a una escala de sub-píxel (SWSC), con Análisis de Mixtura Espectral (SMA – por sus siglas en inglés) y reglas de clasificación empíricas basadas en una lógica fuzzy. El mapeo comprendió 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.
Organization and database
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: Fundación Amigos de la Naturaleza -FAN- (Bolivia), Fundación Gaia Amazonas -FGA- (Colombia), EcoCiencia (Ecuador), Instituto del Bien Común -IBC- (Perú), 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.
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.
Three types of products were produced by MapBiomas Water:
- Monthly and annual surface water maps;
- Surface water transition maps between “Water” and “Non-water” classes. This product was processed using the annual surface water database;
- Trend maps (increase and decrease) in surface water. This product was calculated from monthly surface water data in 5 km x 5 km grids.
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.
Method
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.
Figura 1 – Etapas de clasificación de la superficie de agua superficial.
Description of classification steps:
- Pre-processing:
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.
- Classification of Water Surface:
El algoritmo clasificador de sub-pixel de agua superficial (SWSC) original utiliza tres reglas jerárquicas de decisión binaria (ej. verdadero, falso). Debido a que el agua absorbe gran parte de la radiación electromagnética se utiliza una imagen con fracción de Shade, la combinación de GV y Soil y Cloud para clasificar los píxeles como agua superficial. Adicionalmente, se aplica una clasificación basada en lógica difusa (reglas fuzzy) independientes, en las que se determina el grado de verdad/certeza (memberships) de que un píxel Landsat es clasificado como agua. Luego se calculó 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 píxeles para producir capas de agua superficiales mensuales.
Calculando la mediana de los memberships de los píxeles entre las escenas Landsat disponibles para cada mes, se clasificaron los píxeles como agua con base a umbrales definidos. Luego se aplicaron procedimientos para restaurar falsos negativos y remover falsos positivos, basados en métricas temporales. Seguido, se aplicó un relleno de vacíos para reclasificar como agua aquellos píxeles que eventualmente fueron cubiertos por nubes o dentro de áreas donde no existían escenas Landsat durante un mes determinado, usando una combinación de dos reglas: probabilidad mediana dentro del año y la mediana decenal del mes correspondiente. Por último, la presencia de sombras de nubes u otros objetos oscuros en la escena Landsat también puede producir falsos positivos en la clasificación de agua, por lo que se aplicó un filtro de remoción para reclasificar como no agua aquellos píxeles.
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.
Figura 2 – Proceso de clasificación mensual.
- Clasificación de cuerpos hídricos
Figura 3 – Proceso de clasificación de cuerpos hídricos.
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.
Subsequently, attributes were extracted from auxiliary maps of hydroelectric plants and mining from the following entities:
Mapa de cobertura y uso de la tierra 2022 – MAATE (2022) (Ecuador)
Cartografía básica 1:50.000 y 1:100.000 – IGM (2020) (Ecuador)
MapBiomas Amazonía Colección 6
MapBiomas Ecuador Colección 3
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.
The aforementioned classes are described as follows:
Natural: Natural-origin surface water extension, including rivers, lakes, wetlands, and other water bodies.
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.
Hydroelectric: Artificial or semi-natural water bodies designed for water collection to generate hydroelectricity. This includes other types of multipurpose water big reservoirs.
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.
Aquaculture: Artificially created surface water bodies dedicated to productive activities, such as saltwater ponds for shrimp farming or freshwater ponds for fish farming.