Next Article in Journal
How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?
Next Article in Special Issue
Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach
Previous Article in Journal
Deep Relation Network for Hyperspectral Image Few-Shot Classification
Previous Article in Special Issue
Monitoring Grass Phenology and Hydrological Dynamics of an Oak–Grass Savanna Ecosystem Using Sentinel-2 and Terrestrial Photography
Open AccessEditor’s ChoiceArticle

Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform

1
Amazon Environmental Research Institute (IPAM), SCN 211, Bloco B, Sala 201, Brasília 70836-520, Brazil
2
Programa de Pós-Graduação em Geografia Física, Faculdade de Filosofia, Letras e Ciências Humanas, Universidade de São Paulo, São Paulo 05508-080, Brazil
3
Stockholm Environment Institute (SEI), Linnégatan 87D, 115 23 Stockholm, Sweden
4
Departamento de Ecologia, Universidade de Brasília, Campus Darcy Ribeiro, Brasilia 70910-900, Brazil
5
Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis (IBAMA), SCEN Trecho 2, Edifício Sede, Brasília 70818-900, Brazil
6
The Nature Conservancy Brasil (TNC), SCN Quadra 05 Bloco A Sala 1407—Torre Sul, Brasilia 70715-900, Brazil
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 924; https://doi.org/10.3390/rs12060924
Received: 11 February 2020 / Revised: 9 March 2020 / Accepted: 10 March 2020 / Published: 13 March 2020
(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)
Widespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985–2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr−1), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other land uses but is now in some level of secondary NV. Our results were fundamental in indicating areas with higher rates of change in a long time series in the Brazilian Cerrado and to highlight the challenges of mapping distinct NV types in a highly seasonal and heterogeneous savanna biome. View Full-Text
Keywords: Cerrado; land cover; grasslands; forests; monitoring; random forest; spectral indexes; vegetation seasonality Cerrado; land cover; grasslands; forests; monitoring; random forest; spectral indexes; vegetation seasonality
Show Figures

Figure 1

MDPI and ACS Style

Alencar, A.; Z. Shimbo, J.; Lenti, F.; Balzani Marques, C.; Zimbres, B.; Rosa, M.; Arruda, V.; Castro, I.; Fernandes Márcico Ribeiro, J.P.; Varela, V.; Alencar, I.; Piontekowski, V.; Ribeiro, V.; M. C. Bustamante, M.; Eyji Sano, E.; Barroso, M. Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform. Remote Sens. 2020, 12, 924. https://doi.org/10.3390/rs12060924

AMA Style

Alencar A, Z. Shimbo J, Lenti F, Balzani Marques C, Zimbres B, Rosa M, Arruda V, Castro I, Fernandes Márcico Ribeiro JP, Varela V, Alencar I, Piontekowski V, Ribeiro V, M. C. Bustamante M, Eyji Sano E, Barroso M. Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform. Remote Sensing. 2020; 12(6):924. https://doi.org/10.3390/rs12060924

Chicago/Turabian Style

Alencar, Ane; Z. Shimbo, Julia; Lenti, Felipe; Balzani Marques, Camila; Zimbres, Bárbara; Rosa, Marcos; Arruda, Vera; Castro, Isabel; Fernandes Márcico Ribeiro, João P.; Varela, Victória; Alencar, Isa; Piontekowski, Valderli; Ribeiro, Vivian; M. C. Bustamante, Mercedes; Eyji Sano, Edson; Barroso, Mario. 2020. "Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform" Remote Sens. 12, no. 6: 924. https://doi.org/10.3390/rs12060924

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop