An explanation of enhanced radar backscattering from flooded forests

Richards, John A.; Woodgate, Peter; Skidmore, Andrew K.

doi:10.1080/01431168708954756

Cited by 171 publications

(80 citation statements)

References 2 publications

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“…This negative change is caused by the double/multiple bounce effect of partially flooded vegetation in figure 3a. If the flood water is not above the total vegetation, the water surface and lower sections of vegetation form a corner reflector and result in a strong signal return (Richards et al, 1987). The proposed method provides an effective algorithm to extract double/multiple bounced areas within a flood region.…”

Section: Resultsmentioning

confidence: 99%

SAR-based change detection using hypothesis testing and Markov random field modelling

Cao

Martinis

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The objective of this study is to automatically detect changed areas caused by natural disasters from bi-temporal co-registered and calibrated TerraSAR-X data. The technique in this paper consists of two steps: Firstly, an automatic coarse detection step is applied based on a statistical hypothesis test for initializing the classification. The original analytical formula as proposed in the constant false alarm rate (CFAR) edge detector is reviewed and rewritten in a compact form of the incomplete beta function, which is a builtin routine in commercial scientific software such as MATLAB and IDL. Secondly, a post-classification step is introduced to optimize the noisy classification result in the previous step. Generally, an optimization problem can be formulated as a Markov random field (MRF) on which the quality of a classification is measured by an energy function. The optimal classification based on the MRF is related to the lowest energy value. Previous studies provide methods for the optimization problem using MRFs, such as the iterated conditional modes (ICM) algorithm. Recently, a novel algorithm was presented based on graph-cut theory. This method transforms a MRF to an equivalent graph and solves the optimization problem by a max-flow/min-cut algorithm on the graph. In this study this graph-cut algorithm is applied iteratively to improve the coarse classification. At each iteration the parameters of the energy function for the current classification are set by the logarithmic probability density function (PDF). The relevant parameters are estimated by the method of logarithmic cumulants (MoLC). Experiments are performed using two flood events in Germany and Australia in 2011 and a forest fire on La Palma in 2009 using pre-and post-event TerraSAR-X data. The results show convincing coarse classifications and considerable improvement by the graph-cut post-classification step.

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Section: Resultsmentioning

confidence: 99%

SAR-based change detection using hypothesis testing and Markov random field modelling

Cao

Martinis

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The method works, because the radar pulse is backscattered twice ("double-bounce" - Richards et al, 1987) from the water surface and vegetation. Although most vegetation scattering theories suggest that the short wavelength radar signal (X-and C-band), as well as the cross-polarization signal, interacts mainly with upper levels of the vegetation and, hence, should be suitable for repeat pass interferometry, the analysis of all data types with different polarizations have indicated the suitability of all inSAR data to the wetland application (Hong et al, 2010a;Hong and Wdowinski, 2011).…”

Section: Wetland Mentoring By Insarmentioning

confidence: 99%

Observing and understanding the Earth system variations from space geodesy

Jin

Dam

Wdowinski

2013

Journal of Geodynamics

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a b s t r a c tThe interaction and coupling of the Earth system components that include the atmosphere, hydrosphere, cryosphere, lithosphere, and other fluids in Earth's interior, influence the Earth's shape, gravity field and its rotation (the three pillars of geodesy). The effects of global climate change, such as sea level rise, glacier melting, and geoharzards, also affect these observables. However, observations and models of Earth's system change have large uncertainties due to the lack of direct high temporal-spatial measurements. Nowadays, space geodetic techniques, particularly GNSS, VLBI, SLR, DORIS, InSAR, satellite gravimetry and altimetry provide a unique opportunity to monitor and, therefore, understand the processes and feedback mechanisms of the Earth system with high resolution and precision. In this paper, the status of current space geodetic techniques, some recent observations, and interpretations of those observations in terms of the Earth system are presented. These results include the role of space geodetic techniques, atmospheric-ionospheric sounding, ocean monitoring, hydrologic sensing, cryosphere mapping, crustal deformation and loading displacements, gravity field, geocenter motion, Earth's oblateness variations, Earth rotation and atmospheric-solid earth coupling, etc. The remaining questions and challenges regarding observing and understanding the Earth system are discussed.

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“…InSAR is regularly used to measure displacements of solid surfaces. However, in the case of wetlands, coherent interferometric signal occurs due to double bounce scattering, from the water surface and vegetation, to measure water level changes [25,26]. Data was processed using the ROI_PAC package [27], with 4-8 looks in order to co-register pairs of images.…”

Section: Data and Data Processingmentioning

confidence: 99%

InSAR-Based Mapping of Tidal Inundation Extent and Amplitude in Louisiana Coastal Wetlands

Oliver-Cabrera

Wdowinski

2016

Remote Sensing

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Abstract:The Louisiana coast is among the most productive coastal areas in the US and home to the largest coastal wetland area in the nation. However, Louisiana coastal wetlands have been disappearing at an alarming rate due to natural and anthropogenic processes, including sea level rise, land subsidence and infrastructure development. Wetland loss occurs mainly along the tidal zone, which varies in width and morphology along the Louisiana shoreline. In this study, we use Interferometric Synthetic Aperture Radar (InSAR) observations to detect the extent of the tidal inundation zone and evaluate the interaction between tidal currents and coastal wetlands. Our data consist of ALOS and Radarsat-1 observations acquired between 2006-2011 and 2003-2008, respectively. Interferometric processing of the data provides detailed maps of water level changes in the tidal zone, which are validated using sea level data from a tide gauge station. Our results indicate vertical tidal changes up to 30 cm and horizontal tidal flow limited to 5-15 km from open waters. The results also show that the tidal inundation is disrupted by various man-made structures, such as canals and roads, which change the natural tidal flow interaction with the coast.

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An explanation of enhanced radar backscattering from flooded forests

Cited by 171 publications

References 2 publications

SAR-based change detection using hypothesis testing and Markov random field modelling

SAR-based change detection using hypothesis testing and Markov random field modelling

Observing and understanding the Earth system variations from space geodesy

InSAR-Based Mapping of Tidal Inundation Extent and Amplitude in Louisiana Coastal Wetlands

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