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

Caatinga, 2021-07, Vol.34 (3), p.670-681

2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/deed.pt (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;This work is licensed under a Creative Commons Attribution 4.0 International License. ;ISSN: 0100-316X ;ISSN: 1983-2125 ;EISSN: 1983-2125 ;DOI: 10.1590/1983-21252021v34n319rc

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  • Title:
    VALIDATION OF SOIL USES AROUND RESERVOIRS IN THE SEMI-ARID THROUGH IMAGE CLASSIFICATION
  • Author: ARAÚJO, EFRAIM MARTINS ; MAMEDE, GEORGE LEITE
  • Subjects: AGRICULTURE, DAIRY & ANIMAL SCIENCE ; AGRONOMY ; Aquatic plants ; Aridity ; Classification ; Classifiers ; Differentiation ; FISHERIES ; FOOD SCIENCE & TECHNOLOGY ; FORESTRY ; Image classification ; Land use ; Landsat ; Landsat satellites ; Macrophytes ; Remote sensing ; Remotely piloted aircraft ; Reservoirs ; Satellite imagery ; Sensors ; Soil water ; Soils ; Spectra ; Spectral resolution ; Spectral sensitivity ; Vegetation ; VETERINARY SCIENCES
  • Is Part Of: Caatinga, 2021-07, Vol.34 (3), p.670-681
  • Description: ABSTRACT 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. RESUMO O trabalho avaliou o potencial de discriminação dos usos e ocupação do solo no entorno de reservatórios, mediante informações espectrais obtidas por imagens de satélites multiespectrais, hiperespectrais e imagens obtidas com uma ARP (aeronave remotamente pilotada). A pesquisa analisou o desempenho de diferentes técnicas de classificação de imagens aplicadas a sensores multiespectrais e hiperespectrais para detecção e diferenciação das classes do solo no entorno dos reservatórios Paus Brancos e Marengo, situados no Assentamento 25 de Maio. As classes identificadas com base em levantamentos em campanhas realizadas em 2014 e 2015 no entorno dos reservatórios foram: água, macrófitas, solo exposto, vegetação nativa, agricultura, vegetação rala e vazante, além dos alvos nuvem e sombra de nuvem. O desempenho dos classificadores aplicados à imagem do sensor Hyperion foi, em geral, superior aos obtidos em imagem Landsat 8, o que pode ser explicado pela alta resolução espectral do primeiro, que facilita a diferenciação de alvos com reposta espectral similar. Para validação do método de classificação supervisionada de Máxima Verossimilhança, utilizaram-se imagens Landsat 8 (24/08/2015) e Hyperion (28/08/2015). Os resultados da aplicação indicaram um bom desempenho do classificador associado à composição RGB da imagem Hyperion escolhida (bandas R – 51, G – 161, B – 19) na detecção das classes no entorno deste reservatório, produzindo um coeficiente Kappa de 0,83. A disponibilidade de dados do sensor Hyperion é bem restrita, o que dificulta o desenvolvimento de pesquisas continuadas, dessa forma a utilização de imagens obtidas por RPA mostra-se extremamente viável.
  • Publisher: Mossoro: Universidade Federal Rural do Semiárido
  • Language: English;Portuguese
  • Identifier: ISSN: 0100-316X
    ISSN: 1983-2125
    EISSN: 1983-2125
    DOI: 10.1590/1983-21252021v34n319rc
  • Source: SciELO
    Alma/SFX Local Collection
    ProQuest Central
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