Accuracy Assessment of DEMs Derived from Multiple SAR Data Using the InSAR Technique

Karabörk, Hakan; Makineci, Hasan Bilgehan; Orhan, Osman; Karakuş, Pınar

doi:10.1007/s13369-020-05128-8

Cited by 12 publications

(8 citation statements)

References 40 publications

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“…The accuracy of INSAR-DEMs can be significantly offset from conventional DEMs such as SRTM products. Several studies have found that INSAR-DEMs are suitable for plain and lowland regions such as our study area, but are not recommended for mountainous regions [30][31][32]. This technique uses the phase difference between two complex SAR images taken from slightly different positions of the sensor and which must meet certain conditions (the date of acquisition must be close; it is also preferable to have images in the summer period to ensure obtaining the minimum vegetation cover; and a minimum baseline of 200 m).…”

Section: Geo-environmental Factorsmentioning

confidence: 97%

Multivariate Analysis and Machine Learning Approach for Mapping the Variability and Vulnerability of Urban Flooding: The Case of Tangier City, Morocco

Bouramtane

Kacimi

Bouramtane³

et al. 2021

Hydrology

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Urban flooding is a complex natural hazard, driven by the interaction between several parameters related to urban development in a context of climate change, which makes it highly variable in space and time and challenging to predict. In this study, we apply a multivariate analysis method (PCA) and four machine learning algorithms to investigate and map the variability and vulnerability of urban floods in the city of Tangier, northern Morocco. Thirteen parameters that could potentially affect urban flooding were selected and divided into two categories: geo-environmental parameters and socio-economic parameters. PCA processing allowed identifying and classifying six principal components (PCs), totaling 73% of the initial information. The scores of the parameters on the PCs and the spatial distribution of the PCs allow to highlight the interconnection between the topographic properties and urban characteristics (population density and building density) as the main source of variability of flooding, followed by the relationship between the drainage (drainage density and distance to channels) and urban properties. All four machine learning algorithms show excellent performance in predicting urban flood vulnerability (ROC curve > 0.9). The Classifications and Regression Tree and Support Vector Machine models show the best prediction performance (ACC = 91.6%). Urban flood vulnerability maps highlight, on the one hand, low lands with a high drainage density and recent buildings, and on the other, higher, steep-sloping areas with old buildings and a high population density, as areas of high to very-high vulnerability.

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Section: Geo-environmental Factorsmentioning

confidence: 97%

Multivariate Analysis and Machine Learning Approach for Mapping the Variability and Vulnerability of Urban Flooding: The Case of Tangier City, Morocco

Bouramtane

Kacimi

Bouramtane³

et al. 2021

Hydrology

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“…Dessa forma, os erros encontrados para as classes de Ambiente Natural e Ambiente Construído, neste estudo, está relacionado a variabilidade da cobertura do solo. Ou seja, a elevação medida em áreas abertas será a altura do solo, enquanto as elevações medidas em áreas com vegetação serão o topo do dossel (CHU,2017;KARABÖRK et al 2021).…”

Section: Resultados E Discurssõesunclassified

Análise de acurácia altimétrica para dados ALOS PALSAR e ASTER GDEM para o município do Recife-PE

et al. 2022

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Modelagens geomorfológicas da superfície terrestre tem se beneficiado do aprimoramento das geotecnologias, a exemplo do Modelo Digital de Elevação (MDE) derivado dos dados remotos por proporcionarem uma disponibilidade de informações altimétricas com cobertura em nível global. Entretanto, tais vantagens são expressas pelo grau de qualidade dos dados. O objetivo deste estudo foi analisar a acurácia altimétrica dos MDEs derivados dos dados ALOS PALSAR (RTC) e o ASTER (GDEM) Versão 3 para o mapeamento de uso do solo do município de Recife-PE, por meio da normativa de Precisão Cartográfica para produtos digitais (PEC-PCD), nas escalas de 1:250.000; 1:50.000 e 1:100.000. A metodologia consistiu em analisar as discrepâncias entre os MDEs e as altitudes ortométricas fornecidas por pontos de referência do Sistema Geodésico Brasileiro. Os dados foram testados por meio do RMSE e regressões lineares baseadas no R² e RMS. Como resultado, o ALOS PALSAR apresentou menores valores de RMSE e desvio padrão, indicando que apresenta maior acurácia em relação ao ASTER. As áreas urbanas apresentaram variações de discrepância média entre 0,1 e 3m para os dados ALOS PALSAR enquanto o ASTER foram entre 6 e 8 m, porém em áreas de vegetação densa, ambos os dados apresentaram discrepâncias acima de 10 m.

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“…Sentinel-1A data usually performs well on localized growth map production with C-B and wavelength. In addition to this, Sentinel-1A offers a unique possibility to develop a service for detecting BAs at a global scale with its freely available data [35]. Sentinel-1 time series data have been used in several kinds of research with hopeful outcomes for change detection [36,37].…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Change Detection of Built-Up Areas with Sentinel-1 SAR Data Using Random Forest Classification Arnavutköy Istanbul

Makineci

2023

NÖHÜ Müh. Bilim. Derg.

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As one of the most populated cities in Turkiye and the world, the Istanbul metropolis has always attracted the masses. Arnavutköy Town has become one of the critical points of Istanbul City with increasing built-up areas (BAs). The spatial-temporal change detection of the expansion of the BA of this district is essential data on behalf of Istanbul City. This research aims to determine urban areas expansion zones, also defined as the BA footprint, from Sentinel-1 radar data. The determination of Sentinel-1A data of the urban area change detection encountered in Arnavutköy Town between 2018-2021 with Random Forest (RF) classification machine learning algorithm is investigated in this study. The changes experienced with the spatial-temporal data were determined, and causes and effects were investigated. In order to visually compare the Normalized Difference Built-up Index (NDBI) and optical Sentinel-2A's false color urban RGB composite, which is a distinct data format, the processes have been proved. SAR satellite data was found to be more appropriate than optical satellite data since not being affected by atmospheric conditions for extracting BAs with remotely sensed data.

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Accuracy Assessment of DEMs Derived from Multiple SAR Data Using the InSAR Technique

Cited by 12 publications

References 40 publications

Multivariate Analysis and Machine Learning Approach for Mapping the Variability and Vulnerability of Urban Flooding: The Case of Tangier City, Morocco

Multivariate Analysis and Machine Learning Approach for Mapping the Variability and Vulnerability of Urban Flooding: The Case of Tangier City, Morocco

Análise de acurácia altimétrica para dados ALOS PALSAR e ASTER GDEM para o município do Recife-PE

Spatio-temporal Change Detection of Built-Up Areas with Sentinel-1 SAR Data Using Random Forest Classification Arnavutköy Istanbul

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