Land use and cover change caused by the expansion of soybean production in Sorriso - MT, Brazil (1990-2020)

Authors

  • Rodrigo Zimmerle dos Santos Rodrigues Programa de Pós-Graduação em Geografia da Universidade Federal de Pernambuco, Recife, Pernambuco, 50670901, Brasil
  • Rodrigo de Queiroga Miranda Laboratório de Sensoriamento Remoto e Geoprocessamento, Universidade Federal de Pernambuco, Recife, Pernambuco, 50670901, Brasil

DOI:

https://doi.org/10.29150/2237-2202.2021.252394

Keywords:

biodiversity, environmental conservation, remote sensing

Abstract

The process of agricultural expansion has been a risk to native forests around the world. Brazil is the largest producer of soybeans in the world. Sorriso, a municipality in the state of Mato Grosso (Midwest), is known as the capital of Brazilian agribusiness and is located between the Amazon and Cerrado biomes. In view of this, the study aims to analyze the relationship between the growth of soybean cultivation and forest degradation. For this purpose, Landsat images reclassified by the MapBiomas project were obtained, which identify the class of land use and land cover in the years 1990, 2000, 2010 and 2020. The results of land use and land cover changes designate an increase in the presence of soybean, which in 1990 was 11.7%, while in 2020 was 61.8%. Still, when analyzing the changes in the forest, it is identified that the presence of 71.3% in 1990, goes to 30.3% in 2020. The correlation coefficient indicated strong inversely proportional relationship, between forest and soybean classes. The results of this study are important for monitoring biodiversity conservation.
Keywords: biodiversity, environmental conservation, remote sensing.

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Published

2021-12-20

How to Cite

Rodrigues, R. Z. dos S., & Miranda, R. de Q. (2021). Land use and cover change caused by the expansion of soybean production in Sorriso - MT, Brazil (1990-2020). Journal of Hyperspectral Remote Sensing, 11(3), 189–195. https://doi.org/10.29150/2237-2202.2021.252394

Issue

Vol. 11 No. 3 (2021): Journal of Hyperspectral Remote Sensing

Section

Agricultural Applications

License

Copyright (c) 2021 Journal of Hyperspectral Remote Sensing

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