VALIDATION OF SOIL USES AROUND RESERVOIRS IN THE SEMI-ARID THROUGH IMAGE CLASSIFICATION

• Autor(a) principal: Araújo, Efraim Martins
• Data de Publicação: 2021
• Outros Autores: Mamede, George Leite
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Caatinga
• Texto Completo: https://periodicos.ufersa.edu.br/caatinga/article/view/9613
• topic: Landsat 8. Hyperion. Índice de Kappa.; Landsat 8. Hyperion. Kappa index.

The work evaluated the potential for discrimination of land use and occupation around reservoirs, using spectral information obtained by multispectral, hyperspectral satellites and images obtained with an ARP (remotely piloted aircraft). The research analyzed the performance of different images classification techniques applied to multispectral and hyperspectral sensors for the detection and differentiation of soil classes around the Paus Brancos and Marengo reservoirs, located in Settlement 25 of Maio. The classes identified based on surveys in campaigns carried out in 2014 and 2015 around the reservoirs were: water, macrophytes, exposed soil, native vegetation, agriculture, thin and ebbing vegetation, in addition to the cloud and cloud shadow targets. The performance of the classifiers applied to the image of the Hyperion sensor was, in general, superior to those obtained in Landsat 8 image, which can be explained by the high spectral resolution of the first, which facilitates the differentiation of targets with similar spectral response. For validation of the supervised classification method of Maximum Likelihood, Landsat 8 (08/24/2015) and Hyperion (08/28/2015) images were used. The results of the application indicated a good performance of the classifier associated with the RGB composition of the chosen Hyperion image (bands R - 51, G - 161, B - 19) in the detection of the classes around this reservoir, producing a Kappa coefficient of 0.83. The availability of data from the Hyperion sensor is very restricted, which hinders the development of continued research, thus the use of images surpassed by RPA is extremely viable.