Banca de DEFESA: RAUL PIO DE AZEVEDO

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
DISCENTE : RAUL PIO DE AZEVEDO
DATA : 28/02/2023
HORA: 15:00
LOCAL: Google Meet (videoconferência)
TÍTULO:

REMOTELY SENSING IMAGES AND MACHINE LEARNING IN SESAME CULTURE MAPPING

PALAVRAS-CHAVES:

Sesame, Landsat 8/OLI, Machine Learning, Mapping, Random Forest, SVM.

PÁGINAS: 82
GRANDE ÁREA: Ciências Exatas e da Terra
ÁREA: Geociências
SUBÁREA: Geofísica
ESPECIALIDADE: Sensoriamento Remoto
RESUMO:

The changes in landscapes have been followed more intensely in recent decades, thanks to scientific advances, both in the field of technological improvement of satellites and in remote sensing techniques. Advanced and efficient machine learning techniques have helped remote sensing professionals to determine these changes, from the simplest to the most complex landscapes, allowing the identification of the most varied land uses and occupation, as well as the estimation of the areas that these uses occupy, allowing a more dynamic management of natural resources, especially in agricultural exploitation, providing reliable information to decision makers. Thus, the objective of this work is, through machine learning techniques, to estimate the area of sesame (Sesamum indicum) cultivation in the agricultural year 2021/2022, in the municipality of Canarana, in the state of Mato Grosso, comparing the performance of the Random Forest and Support Vector Machine (SVM) classifiers, using images from the Landsat 8/OLI satellite. For this, control points in geographic coordinates were collected in the study area, for the identification of areas cultivated with sesame, as a source of information for the supervised classification. For the elaboration of thematic maps, the vegetation indices NDVI, EVI, NDBI, PVI and SAVI were used along with the Landsat 8/OLI images. In the evaluation of the thematic maps, the Overall Accuracy and Kappa index were used as a rule, compared by the Z test, with significance of α=0.05. The test revealed that the Random Forest classifier showed better performance in identifying the sesame cultivated areas, with Global Accuracy of 0.95 and Kappa of 0.90, when compared to the SVM. Thus, the use of machine learning techniques in Landsat 8/OLI images proved satisfactory in the estimation of areas cultivated with sesame in the municipality of Canarana-MT, demonstrating confidence in the mapping.

 

MEMBROS DA BANCA:
Presidente - 265126001 - CARLOS ANTONIO DA SILVA JUNIOR
Interno - 131916001 - RIVANILDO DALLACORT
Externo à Instituição - CÁCIO LUIZ BOECHAT - UFPI