Data/hora: 10/06/2024 14:16:24

Biblioteca(s): | Embrapa Cerrados.
Data corrente: | 14/12/2020
Data da última atualização: | 14/12/2020
Autoria: | CASSOL, H. L. G.; ARAI, E.; SANO, E. E.; DUTRA, A. C.; HOFFMANN, T. B.; SHIMABUKURO, Y. E.
Afiliação: | EDSON EYJI SANO, CPAC.
Título: | 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.
Ano de publicação: | 2020
Fonte/Imprenta: | Land, v. 9, n. 5, 2020.
Idioma: | Português

Conteúdo:

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.

Palavras-Chave: | Computação em nuvem; Desmistura espectral; Mato Grosso.
Thesagro: | Sensoriamento Remoto; Uso da Terra.
Categoria do assunto: | --
Registro original: | Embrapa Cerrados (CPAC)
Exemplares: | CPAC - (36853-1) - UPC - AP - DD - DIGITAL -  - DIGITAL - DIS