SILVA FILHO, R.; http://lattes.cnpq.br/0482060634968817; SILVA FILHO, Rivaildo da.
Résumé:
The Brazilian Caatinga is a biome that corresponds to one of the largest Seasonally Dry
Tropical Forests (SDTF) in the world and understanding the land cover in this area contributes
to studies aimed at environmental preservation and mitigation of environmental impacts. One
of the challenges in classifying land cover in this area is the distinction between different
vegetative patterns. In this context, Unmanned Aerial Vehicles (UAVs) emerge, capable of
obtaining data with high spatial resolution and overcoming this limitation. This research
evaluates the accuracy of land cover classification using data obtained from a UAV with an
onboard multispectral camera. Two approaches using the Random Forest (RF) classifier were
applied, the first based on the Red, Green, Nir and NDVI bands obtained from the
multispectral camera and the other based on the Red, Green, Blue bands and NGRDI obtained
from the RGB camera's native UAV in flights carried out at 120 meters high with clear sky
conditions and centered sun, in the Riacho do Frango Basin in Patos-PB. The collection period
was from 09/22/2023 to 10/22/2023, fully included in the dry season. The classification results
were evaluated based on model validation samples and indicate that the first approach
presented the best performance with Global Accuracy of 95.33% and Kappa Coefficient of
93.33%. The most important variable, according to the MDG index, was the NDVI, which
shows the importance of using vegetation indices to highlight vegetative characteristics. From
the results, it is concluded that the use of UAV with the onboard Mapir Survey 3w camera is
an efficient tool for classifying land cover in the Caatinga, with a high capacity for
distinguishing vegetative patterns, while the classification for the area under study made based
on RGB data alone does not provide good accuracy.
