PALSAR-2/ALOS-2 AND OLI/LANDSAT-8 DATA INTEGRATION FOR LAND USE AND LAND COVER MAPPING IN NORTHERN BRAZILIAN AMAZON

10.1590/S1982-21702018000200017 ARTICLE PALSAR-2/ALOS-2 AND OLI/LANDSAT-8 DATA INTEGRATION FOR LAND USE AND LAND COVER MAPPING IN NORTHERN BRAZILIAN AMAZON Integração de dados PALSAR-2/ALOS-2 e OLI/LANDSAT-8 para mapeamento de uso e cobertura da terra no Norte da Amazônia brasileira João Arthur Pompeu Pavanelli1 – ORCID: 0000-0001-9126-8229 João Roberto dos Santos1 – ORCID: 0000-0002-1139-9577 Lênio Soares Galvão1 – ORCID: 0000-0002-8313-0497 Maristela Ramalho Xaud2 – ORCID: 0000-0001-5536-2736 Haron Abrahim Magalhães Xaud2 – ORCID: 0000-0002-5195-3966 1 Instituto Nacional de Pesquisas Espaciais, Divisão de Sensoriamento Remoto, São José dos Campos – São Paulo, Brasil. E-mail: ; ; 2 Empresa Brasileira de Pesquisa Agropecuária, EMBRAPA Roraima, Boa Vista – Roraima, Brasil. E-mail: ; Received in December 13th, 2017 Accepted in April 05th, 2018 Abstract: In northern Brazilian Amazon, the crops, savannahs and rainforests form a complex landscape where land use and land cover (LULC) mapping is difficult. Here, data from the Operational Land Imager (OLI)/Landsat-8 and Phased Array type L-band Synthetic Aperture Radar (PALSAR-2)/ALOS2 were combined for mapping 17 LULC classes using Random Forest (RF) during the dry season. The potential thematic accuracy of each dataset was assessed and compared with results of the hybrid classification from both datasets. The results showed that the combination of PALSAR-2 HH/HV amplitudes with the reflectance of the six OLI bands produced an overall accuracy of 83% and a Kappa of 0.81, which represented an improvement of 6% in relation to the RF classification derived solely from OLI data. The RF models using OLI multispectral metrics performed better than RF models using PALSAR-2 L-band dual polarization attributes. However, the major contribution of PALSAR-2 in the savannahs was to discriminate low biomass classes such as savannah grassland and wooded savannah. Keywords: Random Forest, LULC, hybrid classification. How to cite this article: Pavanelli,J.A.P, et al. PALSAR-2/ALOS-2 and OLI/LANDSAT-8 data integration for land use and land cover mapping in northern brazilian amazon. Bulletin of Geodetic Sciences, Vol. 24, issue 2, 250-269, Apr-Jun, 2018. This content is licensed under a Creative Commons Attribution 4.0 International License. 251 Pavanelli,J.A.P, et al. Resumo: Na porção norte da Amazônia brasileira, as savanas, florestas estacionais e terras agropecuárias formam uma paisagem complexa, onde o mapeamento de uso e cobertura da terra é uma tarefa desafiadora. Nesse trabalho, dados Landsat-8/OLI e ALOS-2/PALSAR-2 foram combinados para mapeamento de 17 classes de uso e cobertura da terra usando o algoritmo Random Forest. O potencial de cada conjunto de dados foi analisado separadamente e em comparação ao modelo híbrido. Os resultados mostraram que o modelo híbrido com as polarizações PALSAR-2 HH/HV e seis bandas de reflectância do OLI produziu os melhores resultados, com acurácia global de 83% e Kappa de 0,81. Isto representou um aumento de 6% em relação à classificação das bandas do OLI somente. Os modelos usando os dados ópticos produziram resultados melhores do que os do SAR. Entretanto, a maior contribuição do PALSAR-2 foi melhorar a discriminação de classes de savana com menor biomassa, como os campos limpos e campos cerrados. Palavras-chave: Random Forest, uso e cobertura da terra, classificação híbrida. 1. Introduction Optical remote sensing has been generally used to map land use and land cover (LULC) changes (Silva et al. 2014). Recent global and regional LULC mapping programs based on remote sensing imagery have emerged in the scientific literature. For instance, the Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) (Gong et al. 2013) and the GlobeLand30 (Chen et al. 2015) are examples of high-resolution global LULC projects. However, they had limiting results in tropical landscapes, especially in the Amazon, with Kappa values of 0.262 and 0.677, respectively. Another example is the TerraClass project in Brazil, which mapped the entire Legal Amazon with a Kappa of 0.67 and an overall accuracy of 76.64% (Almeida et al. 2016). Thus, depending on the method and type of satellite data used in the analysis, there are uncertainties to detect the magnitude and extension of the LULC changes and to classify correctly the classes in tropical areas. In the Brazilian Amazon, most of the uncertainties are related to the difficulties inherent to optical remote sensing in persistent cloud-covered regions (Lu et al. 2007). The fragmentation of tropical landscapes and the subtle transitions between the vegetation types are also sources of uncertainties for LULC mapping using optical remote sensing (Laurin et al. 2013). In this context, orbital Synthetic Aperture Radar (SAR) sensors have become increasingly important in LULC studies. Furthermore, they are sensitive to the geometry of the surface and vegetation canopy structure (Lu et al. 2007). For instance, L-band SAR data have been used to detect deforested sites in the Brazilian Amazon (Santos et al. 2008). In addition, SAR data can be integrated to optical data whenever possible to obtain information not only associated with the biophysical attributes of vegetation, but also with the structural characteristics of the surface (Lu et al. 2011). Among the several approaches for integrating SAR and optical data, two strategies are commonly used: image fusion and hybrid approaches combining more than one method. Image fusion is often employed by means of Principal Component Analysis or Wavelet Transformations (Pereira et al. 2013; Otukei et al. 2015). Hybrid approaches generally require feature selection or radiometric transformations that may affect the quality of the information retrieved from data integration or the interpretation of results (Lu et al. 2011; Hong et al. 2014). Bulletin of Geodetic Sciences, 24(2): 250-269, Apr-Jun,2018 252 PALSAR-2/ALOS-2 and OLI/LANDSAT-8 … Another emerging method to integrate SAR and optical data for LULC mapping is the Random Forest (RF) algorithm due to its robustness and capacity of handling a great number of variables (Jhonnerie et al. 2015). RF ranks the variables according to their importance for classification (Breiman 2001). The use of RF for SAR and optical data integration provides more accurate maps than the other classifiers (Forkuor et al. 2014). Furthermore, RF is a non-parametric classifier, which confirms its suitability for classification and integration of both datasets (van Beijma et al. 2014). Compared to support vector machine, RF presents better classification accuracy, requiring less user-defined parameters, as shown in previous studies using Enhanced Thematic Mapper Plus (ETM+) and RapidEye data (Adam et al. 2014). Two orbital sensors that represent the state-of-the-art of the new generation of SAR and optical instruments are the PALSAR-2/ALOS-2 and the Operational Land Imager (OLI)/L (...truncated)