A Fuzzy Logic-Based Approach for the Detection of Flooded Vegetation by Means of Synthetic Aperture Radar Data

Tsyganskaya, V.; Martinis, Sandro; Twele, André; Cao, W.; Schmitt, Andreas; Marzahn, Philip; Ludwig, R.

doi:10.5194/isprsarchives-xli-b7-371-2016

Cited by 10 publications

(2 citation statements)

References 13 publications

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“…For subregions where bimodality is satisfied, Otsu thresholding is applied to classify pixels into foreground and background (or flooded and dry). We then applied a few post-processing steps to remove false-positive (pixels that are classified as flooded but should be dry) pixels with some commonly used geo-topo indexes in RS-based flood mapping applications (Tsyganskaya et al, 2016(Tsyganskaya et al, , 2018. We recommend moderate adjustment of those indexes in order to get satisfying results for different regions.…”

Section: Rs-based Flood Mapping With Qfr Proceduresmentioning

confidence: 99%

Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation

Li¹,

Demir²

2024

Preprint

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Remote sensing (RS) imagery has become more and more popular in surface water extent extraction applications with the help of increasing availability of RS data and advancements in image processing algorithms, software, and hardware. Many studies have demonstrated that RS imagery has the potential to work independently or along with other well-documented approaches in identifying flood extent. However, due to the insufficiency of images from single-sourced RS and independent references for validation, most existing studies either focused on mapping a single scene or failed to support their results with adequate non-RS validation when multi-temporal/multi-spatiotemporal images were involved. Because of that, hydrosimulations still dominate flood series mapping despite requiring huge data and computational resources. To close these gaps, this study investigated the efficacy of RS-based multi-spatiotemporal flood inundation mapping using multimodal RS imageries to take advantage of improved data availability and complementary image properties. This study also proposed a Quantile-based Filling & Refining (QFR) workflow to resolve the blocking effects of dense vegetation that occurs in study areas. We tested the workflow in four lock and dam sites on the Mississippi River, downstream to the Quad City area, by comparing the RS-based flood maps with HEC-RAS simulations. Compared to the original flood extent that only went through basic post-processing, QFR maps were noticeably more consistent with HEC-RAS maps. Results also showed that all steps in QFR contributed to performance improvements. Despite all being necessary in our case, some should be adjusted in different study regions, such as the levee step. Our findings showcased the efficacy of the multimodal RS flood mapping with QFR post-processing. Due to its simple structure, the proposed workflow has potential to be fully automated and can benefit near-real-time and real-time applications.

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Section: Rs-based Flood Mapping With Qfr Proceduresmentioning

confidence: 99%

Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation

Li¹,

Demir²

2024

Preprint

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“…Although the natural breaks can handle volumes of spatial data, this method required predefined numbers of intervals before the discretization process. There are numerous studies including works by various researchers (Chang & Tsai, 2016;Güçlü & Şen, 2016;Lohani, Kumar, & Singh, 2012;Pulvirenti, Pierdicca, Chini, & Guerriero, 2011;Tsyganskaya et al, 2016) that exploit the concept of fuzzy logic in model development for disaster management analysis. It has been shown that fuzzy logic is extensively applied to analyze complex patterns with high accuracy.…”

Section: Discretization Of Continuous Flood Inducing Factorsmentioning

confidence: 99%

Fuzzy Discretization Technique for Bayesian Flood Disaster Model

Ahmad-Azami

Yusoff

Ku-Mahamud

2018

JICT

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The use of Bayesian Networks in the domain of disaster management has proven its efficiency in developing the disaster model and has been widely used to represent the logical relationships between variables. Prior to modelling the correlation between the flood factors, it was necessary to discretize the continuous data due to the weakness of the Bayesian Network to handle such variables. Therefore, this paper aimed to propose a data discretization technique and compare the existing discretization techniques to produce a spatial correlation model. In particular, the main contribution of this paper was to propose a fuzzy discretization method for the Bayesian-based flood model. The performance of the model is based on precision, recall, F-measure, and the receiver operating characteristic area. The experimental results demonstrated that the fuzzy discretization method provided the best measurements for the correlation model. Consequently, the proposed fuzzy discretization technique facilitated the data input for the flood model and was able to help the researchers in developing effective early warning systems in

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Methods, Techniques and Sensors for Precision Flood Monitoring Through Remote Sensing

Refice

D’Addabbo

Capolongo

2017

Springer Remote Sensing/Photogrammetry

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A Fuzzy Logic-Based Approach for the Detection of Flooded Vegetation by Means of Synthetic Aperture Radar Data

Cited by 10 publications

References 13 publications

Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation

Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation

Fuzzy Discretization Technique for Bayesian Flood Disaster Model

Methods, Techniques and Sensors for Precision Flood Monitoring Through Remote Sensing

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