TerraSAR-X and Wetlands: A Review

Wohlfart, Christian; Winkler, Karina; Wendleder, Anna; Roth, Achim

doi:10.3390/rs10060916

Cited by 34 publications

(27 citation statements)

References 78 publications

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“…7. These changes are consistent with those found by Yang et al (2017) for the MYNN scheme. Although above ∼ 100 m the simulation using the modified WRFV3.8.1 gives lower mean wind speeds than WRFV3.6.1 at all sites, we nonetheless consider that this difference is less relevant, and based on the improvements of the scheme "WRFV3.8.1_MOD", this setup was selected as the baseline, named "BASE" from hereon.…”

Section: Sensitivity To Properties Of the Mynn Schemesupporting

confidence: 91%

The making of the New European Wind Atlas – Part 1: Model sensitivity

et al. 2020

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Abstract. This is the first of two papers that document the creation of the New European Wind Atlas (NEWA). It describes the sensitivity analysis and evaluation procedures that formed the basis for choosing the final setup of the mesoscale model simulations of the wind atlas. The suitable combination of model setup and parameterizations, bound by practical constraints, was found for simulating the climatology of the wind field at turbine-relevant heights with the Weather Research and Forecasting (WRF) model. Initial WRF model sensitivity experiments compared the wind climate generated by using two commonly used planetary boundary layer schemes and were carried out over several regions in Europe. They confirmed that the most significant differences in annual mean wind speed at 100 m a.g.l. (above ground level) mostly coincide with areas of high surface roughness length and not with the location of the domains or maximum wind speed. Then an ensemble of more than 50 simulations with different setups for a single year was carried out for one domain covering northern Europe for which tall mast observations were available. We varied many different parameters across the simulations, e.g. model version, forcing data, various physical parameterizations, and the size of the model domain. These simulations showed that although virtually every parameter change affects the results in some way, significant changes in the wind climate in the boundary layer are mostly due to using different physical parameterizations, especially the planetary boundary layer scheme, the representation of the land surface, and the prescribed surface roughness length. Also, the setup of the simulations, such as the integration length and the domain size, can considerably influence the results. We assessed the degree of similarity between winds simulated by the WRF ensemble members and the observations using a suite of metrics, including the Earth Mover's Distance (EMD), a statistic that measures the distance between two probability distributions. The EMD was used to diagnose the performance of each ensemble member using the full wind speed and direction distribution, which is essential for wind resource assessment. We identified the most realistic ensemble members to determine the most suitable configuration to be used in the final production run, which is fully described and evaluated in the second part of this study (Dörenkämper et al., 2020).

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Section: Sensitivity To Properties Of the Mynn Schemesupporting

confidence: 91%

The making of the New European Wind Atlas – Part 1: Model sensitivity

et al. 2020

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“…In the context of flood mapping, Feng et al [41] used the RF classifier with medium resolution optical imagery. The use of the Random Forest algorithm for TerraSAR-X data classification has already proved successful by Wohlfart et al [42], a study that reviewed most articles published in the past decade, incorporating TerraSAR-X information into wetland.…”

Section: Random Forest Classification Approachmentioning

confidence: 99%

Flood Mapping in a Complex Environment Using Bistatic TanDEM-X/TerraSAR-X InSAR Coherence

et al. 2018

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In this paper, we assess the flood mapping capabilities of the X-band Synthetic Aperture Radar (SAR) imagery acquired by the bistatic pair TanDEM-X/TerraSAR-X (TDX/TSX). The main objective is to investigate the added value of the bistatic TDX/TSX Interferometric Synthetic Aperture Radar (InSAR) coherence in addition to the SAR backscatter in the context of inundation mapping. As a classifier, we consider a Random Forest (RF) classification scheme using TDX/TSX SAR intensities and their bistatic InSAR coherence to extract the flood extent map. To evaluate the classification results and as no "ground truth" was available at the SAR data acquisition time, we set up a LISFLOOD-FP hydraulic model for simulating the temporal evolution of the flood water. The flood map simulated by the model shows good performances with an Overall Accuracy (OA) of 97.92% and a Critical Success Index (CSI) of 94.01%. The SAR-derived flood map is then compared to the LISFLOOD-FP extent map simulated at the SAR data acquisition time. As a test case, we consider the flooding event of the Richelieu River that occurred in the Montérégie region of Quebec (Canada) from April to June 2011. Experimental results highlight the potential of the bistatic InSAR coherence for more accurate flood mapping in a complex landscape with urban and vegetation areas. The classification results of the SAR-derived flood map with respect to the LISFLOOD-FP flood map reach an OA of 78.65% and a Precision of 82.08% when integrating the bistatic InSAR coherence. These classification OA and Precision values are 69.63% and 64.52%, respectively, using only the TDX/TSX SAR intensity.

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“…To capture specific events such as floods or specific features such as peak season it is most beneficial to take into account all available information. Although various approaches to assess wetlands were published recently [15,16] their analysis is often limited to the actual wetland area and usually a buffer around it. Catchment-wide studies are rare, in particular in East Africa.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Land Use/Land Cover Change Assessment of the Kilombero Catchment in Tanzania Using Random Forest Classification and Robust Change Vector Analysis

Thonfeld

Steinbach²,

Muro

et al. 2020

Remote Sensing

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Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In this study, we assess LULC changes in the Kilombero catchment in Tanzania, an important area of recent development in East Africa. LULC change is assessed in two ways: first, post-classification comparison (PCC) which allows us to directly assess changes from one LULC class to another, and second, spectral change detection. We perform LULC classification by applying random forests (RF) on sets of multitemporal metrics that account for seasonal within-class dynamics. For the spectral change detection, we make use of the robust change vector analysis (RCVA) and determine those changes that do not necessarily lead to another class. The combination of the two approaches enables us to distinguish areas that show (a) only PCC changes, (b) only spectral changes that do not affect the classification of a pixel, (c) both types of change, or (d) no changes at all. Our results reveal that only one-quarter of the catchment has not experienced any change. One-third shows both, spectral changes and LULC conversion. Changes detected with both methods predominantly occur in two major regions, one in the West of the catchment, one in the Kilombero floodplain. Both regions are important areas of food production and economic development in Tanzania. The Kilombero floodplain is a Ramsar protected area, half of which was converted to agricultural land in the past decades. Therefore, LULC monitoring is required to support sustainable land management. Relatively poor classification performances revealed several challenges during the classification process. The combined approach of PCC and RCVA allows us to detect spatial patterns of LULC change at distinct dimensions and intensities. With the assessment of additional classifier output, namely class-specific per-pixel classification probabilities and derived parameters, we account for classification uncertainty across space. We overlay the LULC change results and the spatial assessment of classification reliability to provide a thorough picture of the LULC changes taking place in the Kilombero catchment.

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TerraSAR-X and Wetlands: A Review

Cited by 34 publications

References 78 publications

The making of the New European Wind Atlas – Part 1: Model sensitivity

The making of the New European Wind Atlas – Part 1: Model sensitivity

Flood Mapping in a Complex Environment Using Bistatic TanDEM-X/TerraSAR-X InSAR Coherence

Long-Term Land Use/Land Cover Change Assessment of the Kilombero Catchment in Tanzania Using Random Forest Classification and Robust Change Vector Analysis

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