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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Big earth observation time series analysis for monitoring Brazilian agriculture

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Author(s):
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Araujo Picoli, Michelle Cristina [1] ; Camara, Gilberto [1] ; Sanches, Ieda [1] ; Simoes, Rolf [1] ; Carvalho, Alexandre [2] ; Maciel, Adeline [1] ; Coutinho, Alexandre [3] ; Esquerdo, Julio [3] ; Antunes, Joao [3] ; Begotti, Rodrigo Anzolin [1] ; Arvor, Damien [4] ; Almeida, Claudio [1]
Total Authors: 12
Affiliation:
[1] Natl Inst Space Res INPE, Sao Jose Dos Campos - Brazil
[2] Inst Appl Econ Res IPEA, Brasilia, DF - Brazil
[3] Brazilian Agr Res Corp EMBRAPA, Embrapa Agr Informat, Campinas, SP - Brazil
[4] Univ Rennes, CNRS, LETG UMR 6554, F-35000 Rennes - France
Total Affiliations: 4
Document type: Journal article
Source: ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING; v. 145, n. B, p. 328-339, NOV 2018.
Web of Science Citations: 13
Abstract

This paper presents innovative methods for using satellite image time series to produce land use and land cover classification over large areas in Brazil from 2001 to 2016. We used Moderate Resolution Imaging Spectroradiometer (MODIS) time series data to classify natural and human-transformed land areas in the state of Mato Grosso, Brazil's agricultural frontier. Our hypothesis is that building high-dimensional spaces using all values of the time series, coupled with advanced statistical learning methods, is a robust and efficient approach for land cover classification of large data sets. We used the full depth of satellite image time series to create large dimensional spaces for statistical classification. The data consist of MODIS MOD13Q1 time series with 23 samples per year per pixel, and 4 bands (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), near-infrared (nir) and mid-infrared (mir)). By taking a series of labelled time series, we fed a 92 dimensional attribute space into a support vector machine model. Using a 5-fold cross validation, we obtained an overall accuracy of 94% for discriminating among nine land cover classes: forest, cerrado, pasture, soybean fallow, fallow-cotton, soybean-cotton, soybean-corn, soybean-millet, and soybean-sunflower. Producer and user accuracies for all classes were close to or better than 90%. The results highlight important trends in agricultural intensification in Mato Grosso. Double crop systems are now the most common production system in the state, sparing land from agricultural production. Pasture expansion and intensification has been less studied than crop expansion, although it has a stronger impact on deforestation and greenhouse gas (GHG) emissions. Our results point to a significant increase in the stocking rate in Mato Grosso and to the possible abandonment of pasture areas opened in the state's frontier. The detailed land cover maps contribute to an assessment of the interplay between production and protection in the Brazilian Amazon and Cerrado biomes. (AU)

FAPESP's process: 16/16968-2 - Using time series satellite imagery to monitor deforestation and degradation in the Amazon rainforest
Grantee:Rodrigo Anzolin Begotti
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 14/08398-6 - E-Sensing: big earth observation data analytics for land use and land cover change information
Grantee:Gilberto Camara Neto
Support type: Research Grants - eScience and Data Science Program - Thematic Grants
FAPESP's process: 16/23750-3 - Use of remote sensing images time series for monitoring Brazilian Agriculture
Grantee:Michelle Cristina Araujo Picoli
Support type: Scholarships in Brazil - Post-Doctorate