Abstract

This paper presents a dataset of yearly land use and land cover classification maps for Mato Grosso State, Brazil, from 2001 to 2017. Mato Grosso is one of the world’s fast moving agricultural frontiers. To ensure multi-year compatibility, the work uses MODIS sensor analysis-ready products and an innovative method that applies machine learning techniques to classify satellite image time series. The maps provide information about crop and pasture expansion over natural vegetation, as well as spatially explicit estimates of increases in agricultural productivity and trade-offs between crop and pasture expansion. Therefore, the dataset provides new and relevant information to understand the impact of environmental policies on the expansion of tropical agriculture in Brazil. Using such results, researchers can make informed assessments of the interplay between production and protection within Amazon, Cerrado, and Pantanal biomes.

Measurement(s)

land • land use

Technology Type(s)

computational modeling technique

Factor Type(s)

year • geographic location • land use and cover class

Sample Characteristic - Environment

land

Sample Characteristic - Location

Mato Grosso State

Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.11440461

Details

Title
Land use and cover maps for Mato Grosso State in Brazil from 2001 to 2017
Author
Simoes Rolf 1   VIAFID ORCID Logo  ; Picoli Michelle C A 1   VIAFID ORCID Logo  ; Camara Gilberto 2 ; Maciel Adeline 1 ; Santos, Lorena 1 ; Andrade, Pedro R 1 ; Sánchez Alber 1 ; Ferreira Karine 1 ; Carvalho, Alexandre 3 

1  Brazil’s National Institute for Space Research (INPE), São José dos Campos, Brazil (GRID:grid.419222.e) (ISNI:0000 0001 2116 4512) 
2  Brazil’s National Institute for Space Research (INPE), São José dos Campos, Brazil (GRID:grid.419222.e) (ISNI:0000 0001 2116 4512); Group on Earth Observations (GEO), Geneva, Switzerland (GRID:grid.419222.e) 
3  Institute of Applied Economic Research (IPEA), Brasília, Brazil (GRID:grid.457041.3) (ISNI:0000 0001 2324 8955) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41597-020-0371-4
ProQuest document ID
2489906640
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.