Maximum Fraction Images Derived from Year-Based Project for On-Board Autonomy-Vegetation (PROBA-V) Data for the Rapid Assessment of Land Use and Land Cover Areas in Mato Grosso State, Brazil

Henrique Luis Godinho Cassol, Egidio Arai, Edson Eyji Sano, Andeise Cerqueira Dutra, Tânia Beatriz Hoffmann and Yosio Edemir Shimabukuro
Additional contact information
Henrique Luis Godinho Cassol: National Institute for Space Research (INPE), Av. dos Astronautas, 1758, CEP: 12.227-010 São José dos Campos, SP, Brazil
Egidio Arai: National Institute for Space Research (INPE), Av. dos Astronautas, 1758, CEP: 12.227-010 São José dos Campos, SP, Brazil
Edson Eyji Sano: Embrapa Cerrados, BR-020, km 18, CEP: 73301-970 Planaltina, DF, Brazil
Andeise Cerqueira Dutra: National Institute for Space Research (INPE), Av. dos Astronautas, 1758, CEP: 12.227-010 São José dos Campos, SP, Brazil
Tânia Beatriz Hoffmann: National Institute for Space Research (INPE), Av. dos Astronautas, 1758, CEP: 12.227-010 São José dos Campos, SP, Brazil
Yosio Edemir Shimabukuro: National Institute for Space Research (INPE), Av. dos Astronautas, 1758, CEP: 12.227-010 São José dos Campos, SP, Brazil

Land, 2020, vol. 9, issue 5, 1-20

Abstract: This paper presents a new approach for rapidly assessing the extent of land use and land cover (LULC) areas in Mato Grosso state, Brazil. The novel idea is the use of an annual time series of fraction images derived from the linear spectral mixing model (LSMM) instead of original bands. The LSMM was applied to the Project for On-Board Autonomy-Vegetation (PROBA-V) 100-m data composites from 2015 (~73 scenes/year, cloud-free images, in theory), generating vegetation, soil, and shade fraction images. These fraction images highlight the LULC components inside the pixels. The other new idea is to reduce these time series to only six single bands representing the maximum and standard deviation values of these fraction images in an annual composite, reducing the volume of data to classify the main LULC classes. The whole image classification process was conducted in the Google Earth Engine platform using the pixel-based random forest algorithm. A set of 622 samples of each LULC class was collected by visual inspection of PROBA-V and Landsat-8 Operational Land Imager (OLI) images and divided into training and validation datasets. The performance of the method was evaluated by the overall accuracy and confusion matrix. The overall accuracy was 92.4%, with the lowest misclassification found for cropland and forestland (<9% error). The same validation data set showed 88% agreement with the LULC map made available by the Landsat-based MapBiomas project. This proposed method has the potential to be used operationally to accurately map the main LULC areas and to rapidly use the PROBA-V dataset at regional or national levels.

Keywords: spectral unmixing; machine learning; fraction images; cloud computing (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2020
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