Approaches for classifying successional forest stages in são joaquim national park using landsat-8 and rapideye images Abordagens para classificação do estádio sucessional da vegetação do parque nacional de são joaquim empregando imagens landsat-8 e rapideye

• Main Author: Sothe C.
• Publication Date: 2017
• Other Authors: De Almeida C.M., Liesenberg, Veraldo, Schimalski, Marcos Benedito
• Format: Article
• Language: eng
• Source: Repositório Institucional da Udesc
• dARK ID: ark:/33523/001300000p5xp
• Download full: https://repositorio.udesc.br/handle/UDESC/6921

© 2017, Universidade Federal do Parana. All rights reserved.The remote classification of the different vegetation successional stages still represents a challenging task in face of the similar spectral response of such classes. This paper is committed to evaluate the performance of Landsat-8 and RapidEye images in the classification of successional stages within a patch of Mixed Ombrophilous Forest located in São Joaquim National Park, Santa Catarina State, south of Brazil. Three variables dataset extracted from each image were analyzed, namely; (1) one solely consisting of the spectral bands themselves; (2) a second one comprising GLCM-based texture measures derived from the spectral bands; and (3) a third one containing these two datasets and additionally two vegetation indices obtained from the Landsat 8 image and three vegetation indices from the RapidEye image. Each dataset was subject to three classifiers: random forest (RF), support vector machine (SVM), and maximum likelihood estimation (MLE or maxver). All conducted experiments achieved satisfactory results, with Kappa coefficients ranging from 0.66 to 0.88, and both user’s and producer’s accuracies lying over 50%. The best result was attained with the Landsat 8 image using the third dataset and the RF classifier. The analysis of the variables relevance with this classifier showed that the texture measures mean, contrast and dissimilarity were decisive for the successful classification of both images.