JOURNAL ARTICLE

Using multilayer perceptron and similarity‐weighted machine learning algorithms to reconstruct the past: A case study of the agricultural expansion on grasslands in the Uruguayan savannas.

  • Published In: Integrated Environmental Assessment & Management, 2024, v. 20, n. 4. P. 1140 1 of 3

  • Database: Environment Complete 2 of 3

  • Authored By: Kappes, Bruna Batista; Kuplich, Tatiana Mora; da Silva, Tatiana Silva; Weber, Eliseu José 3 of 3

Abstract

This article focuses on modeling past land use and land cover (LULC) scenarios in the Aristida spp. grasslands of the Uruguayan savannas, a region in southern Brazil experiencing significant native grassland loss due to agricultural expansion. Using transition analysis of LULC changes from 1985 to 2020, two machine learning algorithms—multilayer perceptron (MLP) and similarity weighted (SimWeight)—were applied to hindcast the LULC distribution around 1970. The SimWeight algorithm outperformed MLP in accuracy, particularly in distinguishing grassland and agriculture classes, estimating that over 60% of the native Aristida spp. grasslands were lost between 1970 and 2020. These findings provide valuable insights into the drivers of land conversion and offer a novel methodological approach for reconstructing historical LULC scenarios to inform conservation and land management policies.

Additional Information

  • Source:Integrated Environmental Assessment & Management. 2024/07, Vol. 20, Issue 4, p1140
  • Document Type:Article
  • Subject Area:Earth and Atmospheric Sciences
  • Publication Date:2024
  • ISSN:1551-3777
  • DOI:10.1002/ieam.4852
  • Accession Number:177961932
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