ALOS/PALSAR Data Evaluation for Land Use and Land Cover Mapping in the Amazon Region

Pereira, Luciana Oliveira; Freitas, Corina da Costa; Sant’Anna, Sidnei J. S.; Reis, Mariane Souza

doi:10.1109/jstars.2016.2622481

Cited by 13 publications

(10 citation statements)

References 26 publications

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“…The results obtained and presented in this research do not exhaust the thematic classes discrimination by the MAXVER-ICM classifier from radar data processed by the target decomposition techniques. Recent investigations have aimed the integration between radar and optical data (Pereira et al 2013(Pereira et al , 2016Otukei et al 2015), in which data processing is directed towards the wavelet transforms or hybrid classifier approaches, such as cited by Lu et al (2013). However, such actions need to take into consideration the inter and intra-pixel complexity in tropical landscapes, the loss of variables in the classifier models by attribute selection or by radiometric transformations intrinsic to the fusion of multi-source or multi-data.…”

Section: Discussionmentioning

confidence: 99%

“…The images derived from the target decomposition techniques and from the ratios that express the physical characteristics of the backscattering process were classified by the MAXVER pixel-based classifier, integrated with the Interated Conditional Modes (ICM) contextual algorithm. The ICM algorithm consists of a classification technique that incorporates spatial dependence to refine the initial classification (Freitas et al 2007;Nery et al 2013;Pereira et al 2016). The results of all classifications were used to generate the confusion matrices in order to determine the accuracy of the classification that was based on Kappa index and Z-test.…”

Section: Radar Images Processing and Classification Proceduresmentioning

confidence: 99%

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Analysis of the Target Decomposition Technique Attributes and Polarimetric Ratios to Discriminate Land Use and Land Cover Classes of the Tapajós Region

et al. 2019

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This study aims to analyze the capability of the target decomposition techniques and the polarimetric ratios applied to the ALOS/PALSAR-2 satellite polarimetric images to discriminate the land use and land cover classes in the Tapajós National Forest region, Pará State. Three full polarimetric ALOS/PALSAR-2, level 1 single look complex scenes were selected to generate the coherence and the covariance matrices to derive the Cloude-Pottier and the Freeman-Durden target decomposition attributes. From the radiometrically calibrated PALSAR-2 images, we generated the backscatter coefficients, the cross polarized ratio (RC; HV/HH), the parallel polarized ratio (RP; VV/HH) and the Radar Forest Degradation Index (RFDI). The images resulting from these polarimetric attributes were processed by the Maximum Likelihood (MAXVER) classifier coupled with the Iterated Conditional Modes (ICM) contextual algorithm. We found that the classifications derived from the target decomposition attributes, mainly from the Cloude-Pottier technique, with a Kappa index of 0.75, presented a significant higher performance than those derived from the RC ratio, RP ratio, and RFDI.

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Section: Discussionmentioning

confidence: 99%

Section: Radar Images Processing and Classification Proceduresmentioning

confidence: 99%

Analysis of the Target Decomposition Technique Attributes and Polarimetric Ratios to Discriminate Land Use and Land Cover Classes of the Tapajós Region

et al. 2019

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“…O aumento da pressão antrópica sobre as paisagens tropicais e a crescente disponibilidade de dados de Radar de Abertura Sintética (SAR, sigla no inglês Synthetic Aperture Radar) têm motivado o desenvolvimento de novas metodolodias para o tratamento da informação adquirida, para elevar a precisão do processo de identificação dos processos antrópicos de mudanças da paisagem (Pereira et al, 2016). Neste estudo, esteve sob investigação um dado SAR polarimétrico obtido pelo sensor Phased Array L-band Synthetic Aperture Radar (PALSAR) em banda L, a bordo do Advanced Land Observing Satellite-1 (ALOS-1) da Agência Espacial Japonesa -JAXA e um dado de coerência interferométrica gerado a partir de imagens SAR em banda X, adquiridas pela missão TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) composta pelos satélites TerraSAR-X (TSX-1) e TanDEM-X (TDX-1) do Centro Aeroespacial Alemão (Deutsches Zentrum für Luft-und Raumfahrt -DLR).…”

Section: Introductionunclassified

Análise de uso e cobertura da terra na região do tapajós, Amazônia central, a partir de dado polarimétrico PALSAR/ALOS-1 e coerência interferométrica TanDEM-X (Land use and forest cover analysis in central Amazon, from PALSAR/ALOS polarimetric data and TANDEM-X interferometric coherence)

Pôssa

Gama

Santos

et al. 2019

Rev. Bras. Geog. Fis.

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O objetivo do presente trabalho é verificar o potencial de atributos polarimétricos extraídos de imagens-radar PALSAR/ALOS conjugados ao atributo de coerência interferométrica dos dados TanDEM-X/TerraSAR-X para o mapeamento de uso e cobertura da terra na Amazônia brasileira. Na análise dessas imagens-radar uma série de atributos polarimétricos foi extraída a partir da informação de fase utilizando teoremas de decomposições de alvos formulados por Cloude-Pottier, Touzi, Freeman-Durden e Yamaguchi. Esse procedimento resultou em quatro grupos de atributos, os quais foram classificados individualmente e/ou associados à coerência interferométrica, através do algoritmo MAXVER-ICM. Empregando informações de campo, a validação temática foi realizada por matrizes de confusão, índice Kappa e exatidão global. No cenário investigado nove classes temáticas (floresta primária; solo em pousio ou solo preparado; sucessões secundárias avançada, intermediária e inicial; área cultivada; pasto sujo; pasto limpo; e corpo d’água) foram identificadas no processo de mapeamento deste ambiente tropical. Atributos polarimétricos derivados do teorema de decomposição de Cloude-Pottier (H/α/A) associados ao atributo de coerência interferométrica (γ) mostraram melhor desempenho classificatório (Kappa = 0,72 e exatidão global = 78,79%) comparado aos demais. A B S T R A C TThe aim of this study is to verify the potential of polarimetric attributes extracted from PALSAR/ALOS-1 radar images combined to the interferometric coherence from TanDEM-X/TerraSAR-X data for land use and land cover mapping in the Brazilian Amazon. From the phase informationIn of these radar images, polarimetric attributes were extracted by target decomposition theorems formulated by Cloude-Pottier, Touzi, Freeman-Durden and Yamaguchi. This procedure resulted in four groups of attributes, which were classified individually and/or associated with interferometric coherence descriptors through the MAXVER-ICM algorithm. Using field information the method was validated based on the confusion matrix, Kappa and overall accuracy. A total of nine thematic classes were identified in the mapping process of this tropical environment: primary forest; advanced, intermediate and initial secondary succession; bare soil; agricultural area; two classes of pasture and water body. Derived attributes from Cloude-Pottier decomposition theorem (H/α/A) associated with interferometric coherence attribute (γ) showed the best classification performance (Kappa = 0.72 and an overall accuracy = 78.79%) compared to the other methods.Keywords: land use and land cover; Amazon. Remote Sensing; SAR; Target decomposition; interferometric coherence

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“…The most obvious one is that studies with similar study area and objectives can be carried out with different legends because they are based on different sets of data, or they use the same type of data in distinct times or because of differences in field data collection, processing methodologies, or the expected quality of the final mapping. It is the case of the works carried out by [6,11,12,[33][34][35][36][37][38][39][40] within an area in the Lower Tapajós region, Pará state, within the Brazilian Amazon. The resultant maps are rarely directly comparable and their usefulness for studies with other types of data is diminished.…”

Section: Introductionmentioning

confidence: 99%

Towards a Reproducible LULC Hierarchical Class Legend for Use in the Southwest of Pará State, Brazil: A Comparison with Remote Sensing Data-Driven Hierarchies

Reis

Escada

Dutra

et al. 2018

Land

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Land Use and Land Cover (LULC) classes defined by subjective criteria can diminish the significance of a study, hindering the reproducibility and the comparison of results with other studies. Having a standard legend for a given study area and objective could benefit a group of researchers focused on long-term or multidisciplinary studies in a given area, in the sense that they would be able to maintain class definition among different works, done by different teams. To allow for reproducibility, it is important that the classes in this legend are described using quantifiable elements of land cover, which can be measured on the ground, as is recommended by Land Cover Meta Language (LCML). The present study aims to propose LCML formalized hierarchical legends for LULC classes, focusing on the southwest of Pará state, within the Brazilian Amazon. In order to illustrate the potential of these legends, a secondary objective of the current study is to analyze classification results using legends derived from a particular Remote Sensing dataset and compare these results with the classification obtained using the LCML hierarchical legend proposed. To perform this analysis, firstly, we proposed a conceptual class model based on existing classification systems for the upland Brazilian Amazon Biome. From this model, 16 LULC classes were described in LCML, using quantifiable and easily recognizable physiognomic characteristics of land cover classes measured on the lower Tapajós river, in Pará state. These classes were grouped into legends with different levels of detail (number of classes), based on our model or on the image and clustering algorithms. All legends were used in supervised classification of a Landsat5/TM image. Results indicate that it is necessary to incorporate multi-temporal knowledge for class definition as well as the proposed thresholds (height and cover proportion of soil, litter, herbaceous vegetation, shrubs, and trees) in order to properly describe classes. However, the thresholds are useful to delimit classes that happen in a successive way. Classification results revealed that classes formed by the same elements of land cover with similar thresholds present high confusion. Additionally, classifications obtained using legends based on the class separability in a given Remote Sensing image tend to be more accurate but not always useful because they can hide or mix important classes. It was observed that the more generalized the legend (those with few details and number of classes), the more accurate the classifications results are for all types of legends.

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ALOS/PALSAR Data Evaluation for Land Use and Land Cover Mapping in the Amazon Region

Cited by 13 publications

References 26 publications

Analysis of the Target Decomposition Technique Attributes and Polarimetric Ratios to Discriminate Land Use and Land Cover Classes of the Tapajós Region

Analysis of the Target Decomposition Technique Attributes and Polarimetric Ratios to Discriminate Land Use and Land Cover Classes of the Tapajós Region

Análise de uso e cobertura da terra na região do tapajós, Amazônia central, a partir de dado polarimétrico PALSAR/ALOS-1 e coerência interferométrica TanDEM-X (Land use and forest cover analysis in central Amazon, from PALSAR/ALOS polarimetric data and TANDEM-X interferometric coherence)

Towards a Reproducible LULC Hierarchical Class Legend for Use in the Southwest of Pará State, Brazil: A Comparison with Remote Sensing Data-Driven Hierarchies

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