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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.)

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

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Godinho Cassol, Henrique Luis [1] ; Arai, Egidio [1] ; Eyji Sano, Edson [2] ; Dutra, Andeise Cerqueira [1] ; Hoffmann, Tania Beatriz [1] ; Shimabukuro, Yosio Edemir [1]
Total Authors: 6
[1] Natl Inst Space Res INPE, Av Astronautas 1758, BR-12227010 Sao Jose Dos Campos, SP - Brazil
[2] Embrapa Cerrados, BR-020 Km 18, BR-73301970 Planaltina, DF - Brazil
Total Affiliations: 2
Document type: Journal article
Source: LAND; v. 9, n. 5 MAY 2020.
Web of Science Citations: 0

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 (similar to 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. (AU)

FAPESP's process: 16/19806-3 - Mapping and monitoring forest degradation using remote sensing data with medium and moderate spatial resolution
Grantee:Yosio Edemir Shimabukuro
Support Opportunities: Regular Research Grants
FAPESP's process: 18/14423-4 - Modeling a decade of carbon gross emissions from forest fires in the Amazon: Conciliating the bottom-up and top-down views of the problem
Grantee:Henrique Luis Godinho Cassol
Support Opportunities: Scholarships in Brazil - Post-Doctoral