Biophysical properties and mapping of aquatic vegetation during the hydrological cycle of the Amazon floodplain using JERS-1 and Radarsat

Costa, Maycira; Niemann, O.; Novo, Evlyn Márcia Leão de Moraes; Ahern, F.J.

doi:10.1080/01431160110092957

Cited by 63 publications

(43 citation statements)

References 26 publications

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“…Generally, we see that higher VCF results in higher saturation height. In fact, this positive correlation is consistent with the conclusion drawn from [46,47] which used above-water biomass and above-water canopy height. The positive correlation indicates that higher VCF area would need higher water level to reach the saturation biomass and hence higher saturation height.…”

Section: Distinguishing Forested and Non-forested Landssupporting

confidence: 80%

“…Here, we have separated the σ response to water level changes with respect to the water level increase which yields the highest σ . Saturation point of σ , which is described in [46,47] is generally determined by above-water biomass and above-water canopy height. Figure 14 shows the relationship between the water increase yielding the highest σ (or "saturation height") and the corresponding VCF value in the non-forested land (between 10% and 20%).…”

Section: Distinguishing Forested and Non-forested Landsmentioning

confidence: 99%

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Toward Estimating Wetland Water Level Changes Based on Hydrological Sensitivity Analysis of PALSAR Backscattering Coefficients over Different Vegetation Fields

2015

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Synthetic Aperture Radar (SAR) has been successfully used to map wetland's inundation extents and types of vegetation based on the fact that the SAR backscatter signal from the wetland is mainly controlled by the wetland vegetation type and water level changes. This study describes the relation between L-band PALSAR σ and seasonal water level changes obtained from Envisat altimetry over the island of Île Mbamou in the Congo Basin where two distinctly different vegetation types are found. We found positive correlations between σ and water level changes over the forested southern Île Mbamou whereas both positive and negative correlations were observed over the non-forested northern Île Mbamou depending on the amount of water level increase. Based on the analysis of σ sensitivity, we found that denser vegetation canopy leads to less sensitive σ variation with respect to the water level changes regardless of forested or non-forested canopy. Furthermore, we attempted to estimate water level changes which were then compared with the Envisat altimetry and InSAR results. Our results demonstrated a potential to generate two-dimensional maps of water level changes over the wetlands, and thus may have substantial synergy with the planned Surface Water and Ocean Topography (SWOT) mission. OPEN ACCESSRemote Sens. 2015, 7 3154

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Section: Distinguishing Forested and Non-forested Landssupporting

confidence: 80%

Section: Distinguishing Forested and Non-forested Landsmentioning

confidence: 99%

Toward Estimating Wetland Water Level Changes Based on Hydrological Sensitivity Analysis of PALSAR Backscattering Coefficients over Different Vegetation Fields

2015

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“…Regarding the omission errors (Table S1), some of the pixels classified as open water in marsh areas might actually correspond to flooded vegetation. In areas of the Amazonas floodplain dominated by macrophytes, mirror reflection has been reported for emergent biomass (above water level) up to 200 g/m 2 , from which volume scattering starts increasing [13]. Thus, some the flooded areas of PFT D and E could be underestimated and assigned to the water class.…”

Section: Scenementioning

confidence: 99%

Mapping Plant Functional Types in Floodplain Wetlands: An Analysis of C-Band Polarimetric SAR Data from RADARSAT-2

et al. 2016

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Abstract:The inclusion of functional approaches on wetland characterizations and on biodiversity assessments improves our understanding of ecosystem functioning. In the Lower Paraná River floodplain, we assessed the ability of C-band polarimetric SAR data of contrasting incidence angles to discriminate wetland areas dominated by different plant functional types (PFTs). Unsupervised H/α and H/A/α Wishart classifications were implemented on two RADARSAT-2 images differing in their incidence angles (FQ24 and FQ08). Obtained classes were assigned to the information classes (open water, bare soil and PFTs) by a priori labeling criteria that involved the expected interaction mechanisms between SAR signal and PFTs as well as the relative values of H and α. The product obtained with the shallow incidence angle scene had a higher accuracy than the one obtained with the steep incidence angle product (61.5% vs. 46.2%). We show how a systematic analysis of the H/A/α space can be used to improve the knowledge about the radar polarimetric response of herbaceous vegetation. The map obtained provides novel ecologically relevant information about plant strategies dominating the floodplain. Since the obtained classes can be interpreted in terms of their functional features, the approach is a valuable tool for predicting vegetation response to floods, anthropic impacts and climate change.

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“…For example, wind or heavy rainfall can roughen the water surface and reduce or even erase the typical decrease in backscatter values during the flood event [4,9]. The increase in backscatter at the date of the flood can also be strongly influenced by the interaction between sensor characteristics (wavelength, angle of incidence, and polarisation) [6] and environmental conditions, such as aboveground biomass [72][73][74] or the relation between water level and plant height [10]. Such external environmental conditions can have a limiting impact on the methodology presented here.…”

Section: Multi-temporal Characteristics and Time Series Featuresmentioning

confidence: 99%

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

et al. 2018

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Abstract:The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth's surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.

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Biophysical properties and mapping of aquatic vegetation during the hydrological cycle of the Amazon floodplain using JERS-1 and Radarsat

Cited by 63 publications

References 26 publications

Toward Estimating Wetland Water Level Changes Based on Hydrological Sensitivity Analysis of PALSAR Backscattering Coefficients over Different Vegetation Fields

Toward Estimating Wetland Water Level Changes Based on Hydrological Sensitivity Analysis of PALSAR Backscattering Coefficients over Different Vegetation Fields

Mapping Plant Functional Types in Floodplain Wetlands: An Analysis of C-Band Polarimetric SAR Data from RADARSAT-2

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

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