Using ALOS/PALSAR and RADARSAT-2 to Map Land Cover and Seasonal Inundation in the Brazilian Pantanal

Evans, Teresa; Costa, Maycira; Telmer, Kevin; Silva, Thiago Sanna Freire

doi:10.1109/jstars.2010.2089042

Cited by 108 publications

(63 citation statements)

References 34 publications

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“…Although the pixel-based approach is the most commonly used method for the extraction of TFV [6], object-based approaches are emerging as an effective method for complex image classification [18,30,38,39]. On the one hand side, the objects are suitable to map heterogeneous classes, such as TFV, if the spatial resolution of the SAR data is higher than the objects on the ground [6].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…Therefore, the actual extent of flood disasters can be either under-or over-estimated. Remote sensing technology appears to be one of the most effective and fastest ways for observing and providing information on flood inundation extent and damage assessment over a wide area in a cost-effective manner, especially, radar remote sensing sensors, such as Synthetic Aperture Radar (SAR), which have the advantages of penetrating clouds and, to some extent, rainfall of microwave electromagnetic signals, and can capture and provide users with clear views of the inundation extent [4,18,19]. However, the number of radar remote sensing satellites is limited, and their images are commonly expensive and unavailable for public use.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Dao

Liou

2015

Remote Sensing

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Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by an overwhelming concentration of rainfall water over the Tonle Sap Lake's and Mekong River's banks during monsoon seasons. Flood damage assessment in the rice ecosystem plays an important role in this region as local residents rely heavily on agricultural production. This study introduced an object-based approach to flood mapping and affected rice field estimation in central Cambodia. In this approach, image segmentation processing was conducted with optimal scale parameter estimation based on the variation of objects' local variances. The inundated area was identified by using Landsat 8 images with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products were utilized to identify the paddy rice field based on seasonal inter-variation between vegetation and water index during the transplanting stage. The rice classification result was well correlated with the statistical data at a commune level (R 2 = 0.675). The flood mapping and affected rice estimation results are useful to provide local governments with valuable information for flooding mitigation and post-flooding compensation and restoration.

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“…It is located in South America, mostly in Brazil with smaller parts in Bolivia and Paraguay. Several interesting studies exist on this inundation area, mainly dealing with the extent of water areas and consequences for the ecosystem, among them Hamilton et al [27], Hamilton et al [28], Evans et al [29], Evans and Costa [30], Girard et al [31], and Padovani [32]. All these studies rely on sparse in situ gauging stations to derive water level time series.…”

Section: Introductionmentioning

confidence: 99%

Potential of ENVISAT Radar Altimetry for Water Level Monitoring in the Pantanal Wetland

Dettmering¹,

Schwatke²,

Boergens³

et al. 2016

Remote Sensing

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Wetlands are important ecosystems playing an essential role for continental water regulation and the hydrologic cycle. Moreover, they are sensitive to climate changes as well as anthropogenic influences, such as land-use or dams. However, the monitoring of these regions is challenging as they are normally located in remote areas without in situ measurement stations. Radar altimetry provides important measurements for monitoring and analyzing water level variations in wetlands and flooded areas. Using the example of the Pantanal region in South America, this study demonstrates the capability and limitations of ENVISAT radar altimeter for monitoring water levels in inundation areas. By applying an innovative processing method consisting of a rigorous data screening by means of radar echo classification as well as an optimized waveform retracking, water level time series with respect to a global reference and with a temporal resolution of about one month are derived. A comparison between altimetry-derived height variations and six in situ time series reveals accuracies of 30 to 50 cm RMS. The derived water level time series document seasonal height variations of up to 1.5 m amplitude with maximum water levels between January and June. Large scale geographical pattern of water heights are visible within the wetland. However, some regions of the Pantanal show water level variations less than a few decimeter, which is below the accuracies of the method. These areas cannot be reliably monitored by ENVISAT.

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Using ALOS/PALSAR and RADARSAT-2 to Map Land Cover and Seasonal Inundation in the Brazilian Pantanal

Cited by 108 publications

References 34 publications

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

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

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Potential of ENVISAT Radar Altimetry for Water Level Monitoring in the Pantanal Wetland

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