Minimizing the residual topography effect on interferograms to improve DInSAR results: estimating land subsidence in Port-Said City, Egypt

The accurate detection of land subsidence rates in urban areas is important to identify damage-prone areas and provide decision-makers with useful information. Meanwhile, no precise measurements of land subsidence have been undertaken within the coastal Port-Said City in Egypt to evaluate its hazard in relationship to sea-level rise. In order to address this shortcoming, this work introduces and evaluates a methodology that substantially improves small subsidence rate estimations in an urban setting. Eight ALOS/PALSAR-1 scenes were used to estimate the land subsidence rates in Port-Said City, using the Small BAse line Subset (SBAS) DInSAR technique. A stereo pair of ALOS/PRISM was used to generate an accurate DEM to minimize the residual topography effect on the generated interferograms. A total of 347 well distributed ground control points (GCP) were collected in Port-Said City using the leveling instrument to calibrate the generated DEM. Moreover, the eight PALSAR scenes were co-registered using 50 well-distributed GCPs and used to generate 22 interferogram pairs. These PALSAR interferograms were subsequently filtered and used together with the coherence data to calculate the phase unwrapping. The phase-unwrapped interferogram-pairs were then evaluated to discard four interferograms that were affected by phase jumps and phase ramps. Results confirmed that using an accurate DEM (ALOS/PRISM) was essential for accurately detecting small deformations. The vertical displacement rate during the investigated period (2007–2010) was estimated to be −28 mm. The results further indicate that the northern area of Port-Said City has been subjected to higher land subsidence rates compared to the southern area. Such land subsidence rates might induce significant environmental changes with respect to sea-level rise

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

Sentinel-1 InSAR coherence to detect floodwater in urban areas: Houston and hurricane harvey as a test case

This paper presents an automatic algorithm for mapping floods. Its main characteristic is that it can detect not only inundated bare soils, but also floodwater in urban areas. The synthetic aperture radar (SAR) observations of the flood that hit the city of Houston (Texas) following the landfall of Hurricane Harvey in 2017 are used to apply and validate the algorithm. The latter consists of a two-step approach that first uses the SAR data to identify buildings and then takes advantage of the Interferometric SAR coherence feature to detect the presence of floodwater in urbanized areas. The preliminary detection of buildings is a pre-requisite for focusing the analysis on the most risk-prone areas. Data provided by the Sentinel-1 mission acquired in both Strip Map and Interferometric Wide Swath modes were used, with a geometric resolution of 5 m and 20 m, respectively. Furthermore, the coherence-based algorithm takes full advantage of the Sentinel-1 mission's six-day repeat cycle, thereby providing an unprecedented possibility to develop an automatic, high-frequency algorithm for detecting floodwater in urban areas. The results for the Houston case study have been qualitatively evaluated through very-high-resolution optical images acquired almost simultaneously with SAR, crowdsourcing points derived by photointerpretation from Digital Globe and Federal Emergency Management Agency's (FEMA) inundation model over the area. For the first time the comparison with independent data shows that the proposed approach can map flooded urban areas with high accuracy using SAR data from the Sentinel-1 satellite mission

Investigation of the ground displacement in Saint Petersburg, Russia, using multiple-track differential synthetic aperture radar interferometry

Abstract Global sea level rise and local land subsidence might exacerbate the risk of flooding in coastal plains. Among other cities, this is also the case for the high-latitude city of St. Petersburg, which has long been threatened by flood events. To protect the urban area from storm surges, the Union of Soviet Socialist Republics (USSR) in 1978 approved the construction of the 25 km long Flood Prevention Facility Complex (FPFC), which was completed in 2011. The risk of flooding in the city area of St. Petersburg is amplified by the fact that large sections of the coastal area have been reclaimed from the sea. In this study, we investigate the temporal evolution of the ground displacement in St. Petersburg. To this end, we perform an extended analysis based on the application of a simplified version of the differential interferometric synthetic aperture radar technique, known as the minimum acceleration (MinA) approach. The MinA algorithm is a multi-satellite/multi-track interferometric combination technique that allows working with multiple sets of SAR images. The method allowed generation of time series of two-dimensional (2-D) (i.e. East-West and Up-Down) deformation of the terrain by processing two sequences of Sentinel-1A/B (S-1A/B) SAR images acquired from 2016 to 2018, along the ascending and descending flight passes. The Small BAseline Subset (SBAS) algorithm was independently applied to the two sets of SAR data to generate the relevant Line-Of-Sight (LOS)-projected ground deformation time series. Subsequently, the LOS-projected deformation products were geocoded and jointly combined. The results indicate that the deformation in the city is predominantly vertical (i.e. it is subsiding) with a maximum subsidence rate of about 20 mm/year corresponding to the newly sea-reclaimed lands. Finally, the error budget of the retrieved 2-D deformation time series has also been addressed

Radarkaugseire rakendused metsaüleujutuste ja põllumajanduslike rohumaade jälgimiseks

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Käesolev doktoritöö keskendub radarkaugseire rakenduste arendamisele kahes keerukas looduskeskkonnas: üleujutatud metsas ja põllumajanduslikel rohumaadel. Uurimistöö viidi läbi Tartu Observatooriumis, Tartu Ülikoolis, Ventspilsi Kõrgkoolis ja Aalto Ülikoolis. Töö esimene osa käsitleb X-laineala polarimeetrilise radarisignaali käitumist regulaarselt üleujutatavas metsas Soomaa näitel ning teine osa põllumajanduslike rohumaade seisundi ja polarimeetriliste ning interferomeetriliste tehisava-radari parameetrite vahelisi seoseid. 2012 kevadel Soomaa testalal TerraSAR-X andmetega läbi viidud eksperiment näitas, et topelt-peegeldusele tundlik HH-VV polarimeetriline kanal pakub tõesti kontrastsemat tagasihajumisepõhist üleujutatud metsa eristust üleujutamata metsast kui traditsiooniline HH polarimeetriline kanal. HH-VV kanali eelis HH kanali ees on seda suurem, mida madalam on mets ning raagus tingimustes lehtmetsas oli HH-VV kanali eelis HH kanali ees suurem kui okasmetsas. Lisaks on üleujutusele tundlik HH ja VV kanali polarimeetriline faasivahe, mida on soovitatud ka varasemates töödes kasutada täiendava andmeallikana üleujutuste kaardistamisel. Käesolevas doktoritöös mõõdeti polarimeetrilise X-laineala tehisava-radari HH/VV faasivahe suurenemine üleujutuste tõttu erineva kõrgusega okas- ja lehtmetsas. 2013 a vegetatsiooniperioodil korraldati Rannu test-alal välimõõtmistega toetatud eksperiment uurimaks X- ja C-laineala polarimeetrilise ning X-laineala interferomeetrilise tehisava-radari parameetrite undlikkust rohumaade tingimuste muutustele. Ilmnes, et ühepäevase vahega kogutud X-laineala tehisava-radari interferomeetriliste paaride koherentsus korreleerus rohu kõrgusega. Koherentsus oli seda madalam, mida kõrgem oli rohi - leitud seost on võimalik potentsiaalselt rakendada niitmise tuvastamiseks. TerraSAR-X ja RADARSAT-2 polarimeetriliste aegridade analüüsi tulemusel leiti kaks niitmisele tundlikku parameetrit: HH/VV polarimeetriline koherentsus ja polarimeetriline entroopia. Niitmise järel langes HH/VV polarimeetriline koherentsus järsult ning polarimeetriline entroopia tõusis järsult. Rohu tagasikasvamise faasis hakkas HH/VV polarimeetriline koherentsus aeglaselt kasvama ning entroopia aeglaselt kahanema. Täheldatud efekt oli tugevam TerraSARX X-laineala aegridadel kui RADARSAT-2 C-riba tehisava-radari mõõtmistel ning seda selgemini nähtav mida rohkem biomassi niitmise järgselt maha jäi. Leitud HH/VV polarimeetrilise koherentsuse ja polarimeetrilise entroopia käitumine vastas taimkatte osakestepilve radarikiirguse tagasihajumismudelile. Mudeli järgi põhjus- 60 tas eelnimetatud parameetrite iseloomulikku muutust rohukõrte kui dipoolide orientatsiooni ja korrastatuse muut niitmise tõttu, mis on kooskõlas meie välimõõtmiste andmetega.This thesis presents research about the application of radar remote sensing for monitoring of complex natural environments, such as flooded forests and agricultural grasslands. The study was carried out in Tartu Observatory, University of Tartu, Ventspils University College, and Aalto University. The research consists of two distinctive parts devoted to polarimetric analysis of images from a seasonal flooding of wetlands, and to polarimetric and interferometric analysis of a summer-long campaign covering eleven agricultural grasslands. TerraSAR-X data from 2012 were used to assess the use of the double-bounce scattering mechanism for improving the mapping of flooded forest areas. The study confirmed that the HH–VV polarimetric channel that is sensitive to double-bounce scattering provides increased separation between flooded and unflooded forest areas when compared to the conventional HH channel. The increase in separation increases with decreasing forest height, and it is more pronounced for deciduous forests due to the leaf-off conditions during the study. The phase difference information provided by the HH–VV channel may provide additional information for delineating flooded and unflooded forest areas. Time series of X-band (TanDEM-X and COSMO-SkyMed) and C-band (RADARSAT-2) data from 2013 were analyzed in respect to vegetation parameters collected during a field survey. The one-day repeat-pass X-band interferometric coherence was shown to be correlated to the grassland vegetation height. The coherence was also found to be potentially useful for detecting mowing events. The polarimetric analysis of TanDEM-X and RADARSAT-2 data identified two parameters sensitive to mowing events - the HH/VV polarimetric coherence magnitude and the H2α entropy. Mowing of vegetation consistently caused the coherence magnitude to decrease and the entropy to increase. The effect was more pronounced in case of X-band data. Additionally, the effect was stronger with more vegetation left on the ground after mowing. The effect was explained using a vegetation particle scattering model. The changes in polarimetric variables was shown to be caused by the change of orientation and the randomness of the vegetation

Flood mapping in vegetated areas using an unsupervised clustering approach on Sentinel-1 and-2 imagery

The European Space Agency's Sentinel-1 constellation provides timely and freely available dual-polarized C-band Synthetic Aperture Radar (SAR) imagery. The launch of these and other SAR sensors has boosted the field of SAR-based flood mapping. However, flood mapping in vegetated areas remains a topic under investigation, as backscatter is the result of a complex mixture of backscattering mechanisms and strongly depends on the wave and vegetation characteristics. In this paper, we present an unsupervised object-based clustering framework capable of mapping flooding in the presence and absence of flooded vegetation based on freely and globally available data only. Based on a SAR image pair, the region of interest is segmented into objects, which are converted to a SAR-optical feature space and clustered using K-means. These clusters are then classified based on automatically determined thresholds, and the resulting classification is refined by means of several region growing post-processing steps. The final outcome discriminates between dry land, permanent water, open flooding, and flooded vegetation. Forested areas, which might hide flooding, are indicated as well. The framework is presented based on four case studies, of which two contain flooded vegetation. For the optimal parameter combination, three-class F1 scores between 0.76 and 0.91 are obtained depending on the case, and the pixel- and object-based thresholding benchmarks are outperformed. Furthermore, this framework allows an easy integration of additional data sources when these become available

Application potentials of synthetic aperture radar interferometry for land-cover mapping and crop-height estimation

Synthetic aperture radar (SAR) interferometry is widely used for applications like digital elevation map generation and studies related to surface movement. However, SAR interferometry can also be exploited in many other areas. Here a few of the potential applications of SAR interferometry have been demonstrated by exploring its use in delineation and density mapping of forested areas, delineation of surface water extent under adverse weather conditions, which is useful during flood-mapping; detection of human settlement and crop-height estimation. This has been achieved by exploiting interferometric coherence, which is inversely related to the magnitude of random dislocation of scatterers between the two passes. The study indicated that interferometric coherence decreases with increase in forest density or increase in crop height. It was also observed that interferometric coherence over stable targets like settlement is quite high compared to other land-cover classes. In contrast, interferometric coherence is always low for unstable surfaces like the water surface. The study suggested that interferometric coherence is a parameter that provides valuable information, which is completely different from that of SAR backscatter. It was also observed that synergic use of SAR backscatter with InSAR coherence enhances the application potential of a SAR system as a whole towards many land-cover features

Análise de principais componentes de imagens C-SAR para mapeamento de inundação, Província de Santa Fe, Argentina

Flood events are phenomena associated with heavy rainfall. In Argentina, floods have high economic and social costs, including loss of human life. In this paper, principal component analysis (PCA) is used to map flood-prone areas along the Paraná river in Santa Fe, Argentina. The Sentinel-1B (S1B) images, sensor C-SAR with VH polarisation Interferometric type (IW) Ground Range Detected (GRD) with spatial resolution of 10 m, from 2016, were referenced and the PCA method was used to extract the four first principal components. The flood-affected images make it possible to accurately define the flooded area. In targets with dense vegetation, however, there is no pixel backscatter pattern. PC2 better highlighted the threshold of pixel intensity, with an accuracy of 70%, and 93% of the mapped area was shown to be flood-prone. Procedures to map floods remotely are pivotal because they can quickly obtain precise data on flood areas that may not be accessible for fieldwork or that have not yet been mapped in great detail.Inundações são associadas a chuvas intensas. Na Argentina é o evento natural que causa mais perdas econômicas, sociais e de vidas humanas. O objetivo desse trabalho é mapear a área de inundação do rio Paraná, em Santa Fe, por Análise de Componentes Principais (ACP). As imagens Sentinel-1B, sensor C-SAR, polarização VH do tipo Interferométrico (IW) Ground Range Detected (GRD), pixel de 10 m, ano 2016, foram referenciadas, extraindo as quatro primeiras ACP.As imagens sob efeito de inundação permitiram delimitar com precisão a área inundada. No entanto, em áreas com densidade de vegetação não há um padrão de retroespalhamento dos pixéis. A PC2 destacou melhor o limiar de intensidade dos pixéis de inundação, com uma precisão de 70%, sendo que 93% da área mapeada é suscetível à inundação. A cartografia de risco de inundação obtida a partir de sensoriamento remoto revela-se essencial, pois possibilita a obtenção de resultados rápidos e precisos das áreas de inundação, em áreas cujo trabalho de campo não seja possível ou não se encontrem disponíveis mapas detalhados das áreas atingidas

Exploring Problems and Prospective of Satellite Interferometric Data for the Seismic Structural Health Monitoring of Existing Buildings and Architectural Heritage

Satellite interferometric data represent a promising source of information for the Structural Health Monitoring (SHM) of the existing built environment. This is especially true because they show differential temporal-spatial displacements of remotely monitored points, which can be easily interpreted with a visual inspection of their time-histories for different locations defined a priori. However, the interferometric information is commonly referred to extended territories (at the scale of city or region), thus several problems arise in the implementation of automatic SHM techniques for the damage detection, localization, and assessment of the built environment at a point level (scale of the building or lower). Despite a long list of challenges, interferometric data have also the potential to become a useful source to assess the health of a structure, especially for helping in define structural early warning methodologies. For this reason, in the paper, the authors summarize the main challenges in the use of satellite interferometric data for civil SHM, and rather than proposing remedial actions, try to critically analyze the challenges and perspectives for future applications