Application of spaceborne synthetic aperture radar data for extraction of soil moisture and its use in hydrological modelling at <scp>G</scp>ottleuba <scp>C</scp>atchment, <scp>S</scp>axony, <scp>G</scp>ermany

Elbialy, Samy; Mahmoud, Ali; Pradhan, Biswajeet; Buchroithner, Manfred

doi:10.1111/jfr3.12037

Cited by 18 publications

(10 citation statements)

References 43 publications

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“…), and other recent developments such as Synthetic Aperture Radars (Elbialy et al. ), hyperspectral aerial images (Pottier et al. ) and spatial modelling (Aalto et al.…”

Section: Discussionmentioning

confidence: 99%

“…Remote sensing combined with GIS provides ready-touse (coarse-scale) indices of moisture or wetness (e.g. the surface saturation degree of ASCAT soil wetness indices, see Brocca et al 2010;Lakshmi 2013;Wagner et al 2013), and other recent developments such as Synthetic Aperture Radars (Elbialy et al 2014), hyperspectral aerial images (Pottier et al 2014) and spatial modelling (Aalto et al 2013) show promise in estimating actual soil moisture at higher resolutions. To conclude, although often accounted for in SDMs with distal predictors, water-related variables could be improved through combined approaches, mixing refined field measures, GIS modelling and remote sensing.…”

Section: Watermentioning

confidence: 99%

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What we use is not what we know: environmental predictors in plant distribution models

Mod

Scherrer

Luoto

et al. 2016

J Vegetation Science

194

198

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Aims The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive accuracy and model realism, as highlighted in multiple earlier studies. Because variable selection is directly related to a model's capacity to capture important species' environmental requirements, one would expect an explicit prior consideration of all ecophysiologically meaningful variables. For plants, these include temperature, water, soil nutrients, light, and in some cases, disturbances and biotic interactions. However, the set of predictors used in published correlative plant SDM studies varies considerably. No comprehensive review exists of what environmental predictors are meaningful, available (or missing) and used in practice to predict plant distributions. Contributing to answer these questions is the aim of this review. Methods We carried out an extensive, systematic review of recently published plant SDM studies (years 2010–2015; n = 200) to determine the predictors used (and not used) in the models. We additionally conducted an in‐depth review of SDM studies in selected journals to identify temporal trends in the use of predictors (years 2000–2015; n = 40). Results A large majority of plant SDM studies neglected several ecophysiologically meaningful environmental variables, and the number of relevant predictors used in models has stagnated or even declined over the last 15 yr. Conclusions Neglecting ecophysiologically meaningful predictors can result in incomplete niche quantification and can thus limit the predictive power of plant SDMs. Some of these missing predictors are already available spatially or may soon become available (e.g. soil moisture). However, others are not yet easily obtainable across whole study extents (e.g. soil pH and nutrients), and their development should receive increased attention. We conclude that more effort should be made to build ecologically more sound plant SDMs. This requires a more thorough rationale for the choice of environmental predictors needed to meet the study goal, and the development of missing ones. The latter calls for increased collaborative effort between ecological and geo‐environmental sciences.

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“…), and other recent developments such as Synthetic Aperture Radars (Elbialy et al. ), hyperspectral aerial images (Pottier et al. ) and spatial modelling (Aalto et al.…”

Section: Discussionmentioning

confidence: 99%

Section: Watermentioning

confidence: 99%

What we use is not what we know: environmental predictors in plant distribution models

Mod

Scherrer

Luoto

et al. 2016

J Vegetation Science

194

198

View full text Add to dashboard Cite

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“…In recent years, radars have given a new dimension to the disaster management research by providing real-time and precise information (Pradhan et al 2009;Elbialy et al 2013). Deformation of the ground surface can occur through man-made or naturally induced factors.…”

Section: Introductionmentioning

confidence: 99%

Using ALOS PALSAR derived high-resolution DInSAR to detect slow-moving landslides in tropical forest: Cameron Highlands, Malaysia

Jebur

Pradhan

Tehrany

2013

Geomatics, Natural Hazards and Risk

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Landslide is one of the natural hazards that pose maximum threat for human lives and property in mountainous regions. Mitigation and prediction of this phenomenon can be done through the detection of landslide-susceptible areas. Therefore, an appropriate landslide analysis is needed in order to map and consequently understand the characteristic of this disaster. One of the recent popular remote sensing techniques in deformation analysis is the differential interferometric synthetic aperture radar which is popularly known as DInSAR. Due to the mass vegetation condition in Malaysia, a long-wavelength synthetic aperture radar ($24 cm) is required in order to be able to penetrate through the forests and reach the bare land. For that reason, ALOS PALSAR HH imagery was used in this study to derive a deformation map of the Gunung Pass area located in the tropical forest of the Cameron Highlands, Malaysia. In this study, the ascending orbit ALOS PALSAR images were acquired in September 2008, January 2009 and December 2009. Subsequently the displacement measurements of the study site (Gunung Pass) were calculated. The accuracy of the result was evaluated through its comparison with ground truth data using the R 2 and root mean square error (RMSE) methods. The resulted deformation map showed the landslide locations in the study area from interpretation of the results with 0.84 R 2 and 0.151 RMSE. The DInSAR precision was 11.8 cm which proved the efficiency of the proposed method in detecting landslides in a tropical country like Malaysia. It is highly recommended to use the proposed method for any other deformation studies.

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“…Other methods, such as the curve number (SCS-CN), concern the quantitative assumption of the surface runoff depth, based on a certain amount of rainfall. The SCS-CN method has widely been used in international studies by different authors: Kumar et al [8], Mack [9], Scozzafava and Tallini [10], Xiaoyong and Min-Lang [11], Duncan et al [12], Al-Hasan and Mattar [13], Mahmoud et al [14] but also in Romanian studies by: Haidu et al [15], Bilaşco [16], Minea [17], Gyory and Haidu [18],Domniţa [19], Costache [20], Elbialy et al [21]. The deployment of the curve number method was performed by the Natural Resources Conservation Service (NRCS).…”

Section: Introductionmentioning

confidence: 99%

“…The SCS-CN hydrological model consists in a methodology for transforming a certain amount of rainfall for a certain period of time into surface runoff, taking into consideration the land-use and the hydrological soil classes [16]. Apart from this method, there are also other models used in different studies, such as: KINEROS [22], LISEM [23], TOPMODEL [19], RHEM (Rangeland Hydrology and Erosion Model) [24], NAM rainfall-runoff model [25], HEC-HMS [21,26,27], Mike 11 [28] which offer quantitative simulations of the surface runoff depth based on a certain amount of rainfall. This study aims to highlight the changes in the surface runoff depth within Sǎrǎţel river basin during [1990][1991][1992][1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006] and to assess the influence of land use changes on this hydrological parameter.…”

Section: Introductionmentioning

confidence: 99%

Assessment of surface runoff depth changes in Sǎrǎţel River basin, Romania using GIS techniques

2014

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Sǎrǎţel River basin, which is located in Curvature Subcarpahian area, has been facing an obvious increase in frequency of hydrological risk phenomena, associated with torrential events, during the last years. This trend is highly related to the increase in frequency of the extreme climatic phenomena and to the land use changes. The present study is aimed to highlight the spatial and quantitative changes occurred in surface runoff depth in Sǎrǎţel catchment, between 1990-2006. This purpose was reached by estimating the surface runoff depth assignable to the average annual rainfall, by means of SCS-CN method, which was integrated into the GIS environment through the ArcCN-Runoff extension, for ArcGIS 10.1. In order to compute the surface runoff depth, by CN method, the land cover and the hydrological soil classes were introduced as vector (polygon data), while the curve number and the average annual rainfall were introduced as tables. After spatially modeling the surface runoff depth for the two years, the 1990 raster dataset was subtracted from the 2006 raster dataset, in order to highlight the changes in surface runoff depth.

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Application of spaceborne synthetic aperture radar data for extraction of soil moisture and its use in hydrological modelling at Gottleuba Catchment, Saxony, Germany

Cited by 18 publications

References 43 publications

What we use is not what we know: environmental predictors in plant distribution models

What we use is not what we know: environmental predictors in plant distribution models

Using ALOS PALSAR derived high-resolution DInSAR to detect slow-moving landslides in tropical forest: Cameron Highlands, Malaysia

Assessment of surface runoff depth changes in Sǎrǎţel River basin, Romania using GIS techniques

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