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    Mangroves are highly productive ecosystems that provide important ecosystem services, are strategic allies in carbon capture and storage, conserve different plant and wildlife species, are producers of aquatic species such as crabs and shrimp, and local communities have developed strong economic, cultural and identity ties. Despite their great ecological, economic, and social importance, mangroves are threatened by natural and anthropogenic factors, hence the importance of their constant monitoring. Remote sensing technology has demonstrated its ability to map changes in mangroves and technological advances allow faster application of mapping methodologies, optimizing costs and time. To facilitate the sustainable management of mangroves, an open tool based on remote sensing data and machine learning was developed on the Google Earth Engine platform (MANGLEE). MANGLEE was tested in the mangroves of Guayas, Ecuador. Mangrove cover maps were obtained for the years 2018, 2020 and 2022 as well as the mangrove change maps for the two periods 2018-2020 and 2020-2022. This publication is possible by the support of the people of the United States through the United States Agency for International Development (USAID). The content of this publication is the responsibility of its authors and does not necessarily reflect the views of USAID or the Government of the United States of America.

  • Amazonia holds the largest continuous area of tropical forests with intense land use change dynamics inducing water, carbon, and energy feedbacks with regional and global impacts. Much of our knowledge of land use change in Amazonia comes from studies of the Brazilian Amazon, which accounts for two thirds of the region. Amazonia outside of Brazil has received less attention because of the difficulty of acquiring consistent data across countries. We present here an agricultural statistics database of the entire Amazonia region, with a harmonized description of crops and pastures in geospatial format, based on administrative boundary data at the municipality level. The spatial coverage includes countries within Amazonia and spans censuses and surveys from 1950 to 2012. Harmonized crop and pasture types are explored by grouping annual and perennial cropping systems, C3 and C4 photosynthetic pathways, planted and natural pastures, and main crops. Our analysis examined the spatial pattern of ratios between classes of the groups and their correlation with the agricultural extent of crops and pastures within administrative units of the Amazon, by country, and census/survey dates. Significant correlations were found between all ratios and the fraction of agricultural lands of each administrative unit, with the exception of planted to natural pastures ratio and pasture lands extent. Brazil and Peru in most cases have significant correlations for all ratios analyzed even for specific census and survey dates. Results suggested improvements, and potential applications of the database for carbon, water, climate, and land use change studies are discussed. The database presented here provides an Amazon-wide improved data set on agricultural dynamics with expanded temporal and spatial coverage.