Mapping of the natural and anthropic environments of Entre Rios (Argentina) using machine learning classification
DOI:
https://doi.org/10.4995/raet.2024.20831Keywords:
land cover dynamics, Google Earth Engine, Sentinel-2, supervised classificationAbstract
Entre Ríos presents a distinctive landscape with numerous contrasting environments. Mapping both natural and anthropic features is a common task facilitated using remote sensing technologies alongside geographic information systems. Knowing what, how much, and where they are located is essential for designing sustainable use and conservation strategies for natural resources in a territory. The free accessibility of data and the cloud processing capability for all this information are crucial for processing and classifying the vegetation of a specific area. The aim was to create an updated map that can be easily updated in the future, using the same method for the most representative natural and anthropic environments in the province of Entre Ríos. This involves determining the best time of the year to maximize the accuracy percentage of automatic algorithm classification for each environment. Employing automatic classification learning algorithms was useful in understanding the extent of natural and anthropic ecosystems across a vast territory. Google Earth Engine tools allowed for selecting the optimal time of year to maximize accuracy percentage and minimize the probability of error with low computational and operational costs. The results obtained are indispensable for planning precise and accurate public policies for productive activities, as well as for the conservation of natural resources.
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