Integration of Polarimetric PALSAR Attributes and Local Geomorphometric Variables Derived from SRTM for Forest Biomass Modeling in Central Amazonia

Bispo, Pitágoras C.; Santos, João Roberto dos; Valeriano, M. M.; Touzi, R.; Seifert, Frank Martin

doi:10.1080/07038992.2014.913477

Cited by 32 publications

(35 citation statements)

References 44 publications

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“…The estimation of AGB in the tropical SFs has often been performed using synthetic aperture radar (SAR) data due to the effects of high frequency of cloud cover in the tropical regions and to the sensitivity to biomass as the tree density increases in these complex structures [37][38][39][40]. Forest AGB is usually estimated through regression models based on empirical relationships between AGB and radar parameters, including backscattering intensity or polarimetric decompositions [37,38,41,42]. L-band SAR data is currently being used for AGB modeling, as the wavelength of its microwave pulse (23.5 cm) matches the dimensions of forest woody components, as twigs, secondary branches, and even the stems in the initial secondary successions.…”

Section: Introductionmentioning

confidence: 99%

“…However, the sensitivity of L-band SAR data for estimating AGB is efficient until 100-150 Mg ha −1 , with saturation of the signal observed above these values [39]. To surpass this limitation, the use of multiple variables obtained from polarimetric decomposition has been proposed to describe these structurally complex environments [37,[43][44][45][46]. Multiple linear regression (MLR) have achieved R 2 = 0.44 (RMSE = 54.32 Mg ha −1 ) for AGB prediction Mg ha −1 ) in the secondary and primary forests of the Tapajós National Forest, Brazilian Amazon, using multiple variables from L-band (ALOS PALSAR) [37].…”

Section: Introductionmentioning

confidence: 99%

“…To surpass this limitation, the use of multiple variables obtained from polarimetric decomposition has been proposed to describe these structurally complex environments [37,[43][44][45][46]. Multiple linear regression (MLR) have achieved R 2 = 0.44 (RMSE = 54.32 Mg ha −1 ) for AGB prediction Mg ha −1 ) in the secondary and primary forests of the Tapajós National Forest, Brazilian Amazon, using multiple variables from L-band (ALOS PALSAR) [37]. The best prediction variables were the Touzi helicity, the Freeman-Durden volumetric scattering, and the Cloude anisotropy, along with slope and elevation aspects from the digital elevation model [37].…”

Section: Introductionmentioning

confidence: 99%

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Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon

et al. 2018

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Secondary forests (SF) are important carbon sinks, removing CO2 from the atmosphere through the photosynthesis process and storing photosynthates in their aboveground live biomass (AGB). This process occurring at large-scales partially counteracts C emissions from land-use change, playing, hence, an important role in the global carbon cycle. The absorption rates of carbon in these forests depend on forest physiology, controlled by environmental and climatic conditions, as well as on the past land use, which is rarely considered for retrieving AGB from remotely sensed data. In this context, the main goal of this study is to evaluate the potential of polarimetric (quad-pol) ALOS-2 PALSAR-2 data for estimating AGB in a SF area. Land-use was assessed through Landsat time-series to extract the SF age, period of active land-use (PALU), and frequency of clear cuts (FC) to randomly select the SF plots. A chronosequence of 42 SF plots ranging 3–28 years (20 ha) near the Tapajós National Forest in Pará state was surveyed to quantifying AGB growth. The quad-pol data was explored by testing two regression methods, including non-linear (NL) and multiple linear regression models (MLR). We also evaluated the influence of the past land-use in the retrieving AGB through correlation analysis. The results showed that the biophysical variables were positively correlated with the volumetric scattering, meaning that SF areas presented greater volumetric scattering contribution with increasing forest age. Mean diameter, mean tree height, basal area, species density, and AGB were significant and had the highest Pearson coefficients with the Cloude decomposition (λ3), which in turn, refers to the volumetric contribution backscattering from cross-polarization (HV) (ρ = 0.57–0.66, p-value < 0.001). On the other hand, the historical use (PALU and FC) showed the highest correlation with angular decompositions, being the Touzi target phase angle the highest correlation (Φs) (ρ = 0.37 and ρ = 0.38, respectively). The combination of multiple prediction variables with MLR improved the AGB estimation by 70% comparing to the NL model (R2 adj. = 0.51; RMSE = 38.7 Mg ha−1) bias = 2.1 ± 37.9 Mg ha−1 by incorporate the angular decompositions, related to historical use, and the contribution volumetric scattering, related to forest structure, in the model. The MLR uses six variables, whose selected polarimetric attributes were strongly related with different structural parameters such as the mean forest diameter, basal area, and the mean forest tree height, and not with the AGB as was expected. The uncertainty was estimated to be 18.6% considered all methodological steps of the MLR model. This approach helped us to better understand the relationship between parameters derived from SAR data and the forest structure and its relation to the growth of the secondary forest after deforestation events.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon

et al. 2018

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“…: solo versus tronco), cujos atributos foram investigados por Liesenberg et al (2016) (Lee e Pottier, 2009). As informações sobre o processo de espalhamento podem ser extraídas por meio de técnicas polarimétricas, como a decomposição de alvos, que facilita a interpretação de imagens polarimétricas SAR no processo de classificação, pois possibilita separar mecanismos de espalhamento de diferentes naturezas (Bispo et al, 2014;Varghese et al, 2016). Vários são os teoremas de decomposição de alvos, dentre os quais citam-se os de A comparação entre as diferenças de fase de duas imagens de uma mesma região, obtidas a partir de antenas com ligeira diferença de geometria, torna possível encontrar as localizações dos pixels em três dimensões.…”

Section: Sínteseunclassified

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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“…Bispo et al 5 generated a predictive model for biomass estimation in a forested area of Central Amazonia based on the integration of incoherent target scattering decomposition polarimetric attributes, extracted from ALOS-PALSAR data, and geomorphometric variables derived from shuttle radar topography mission, thus improving biomass estimation.…”

Section: Introductionmentioning

confidence: 99%

Continuous monitoring of biophysicalEucalyptussp. parameters using interferometric synthetic aperture radar data in P and X bands

Gama

Santos

Mura

2016

J. Appl. Remote Sens

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Integration of Polarimetric PALSAR Attributes and Local Geomorphometric Variables Derived from SRTM for Forest Biomass Modeling in Central Amazonia

Cited by 32 publications

References 44 publications

Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon

Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon

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)

Continuous monitoring of biophysicalEucalyptussp. parameters using interferometric synthetic aperture radar data in P and X bands

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