Height estimation for high-rise buildings based on InSAR analysis

Interferometric synthetic aperture radar (InSAR) analysis is a radar technique for generating large-area maps of ground deformation using differences in the phase of microwaves returning to a satellite. In recent years, high-resolution SAR sensors have been developed that enable small-scale slope deformation to be detected, such as the partial block movement of a landslide. The L-band SAR (PALSAR-2) is mounted on Advanced Land Observing Satellite-2 (ALOS-2), which was launched on 24 Mar. 2014. Its main improvements compared with ALOS are enhanced resolution of as high as 3 m with a high-frequency recurrence period (14 days). Owing to its high resolution and the use of the L-band, PALSAR-2 can obtain reflective data passing through a tree canopy surface, unlike the other synthetic aperture radars. Therefore, the coherence of InSAR in mountainous forest areas is less likely to decrease, making it advantageous for the extraction of slope movement. In this study, to verify the accuracy of InSAR analysis using PALSAR-2 data, we compared the results of InSAR analysis and the measurement of the displacement in a landslide by global navigation satellite system (GNSS) observation. It was found that the average difference between the displacements obtained by InSAR analysis and the field measurements by GNSS was only 15.1 mm in the slant range direction, indicating the high accuracy of InSAR analysis. Many of the areas detected by InSAR analysis corresponded to the locations of surface changes due to landslide activity. Additionally, in the areas detected by InSAR analysis using multiple datasets, the ground changes due to landslide movement were confirmed by site investigation.

Ground deformation is an important index for evaluating the stability and degradation of the permafrost. Due to limited accessibility, in-situ measurement of the ground deformation of permafrost area on the Tibetan Plateau is a challenge. Thus, the technique of time-series Interferometric Synthetic Aperture Radar (InSAR) is often adopted for measuring the ground deformation of the permafrost area, the effectiveness of which is however degraded in the areas with geometric distortions in Synthetic Aperture Radar (SAR) images. In this study, a method that integrates InSAR and random forest method is proposed for an improved permafrost stability mapping on the Tibetan Plateau; and, to demonstrate the application of the proposed method, the permafrost stability mapping in a small area located in the central region of the Tibetan Plateau is studied. First, the ground deformation in the concerned area is studied with InSAR, in which 67 Sentinel-1 scenes taken in the period from 2014 to 2020 are collected and analyzed. Second, the relationship between the environmental factors (i.e., topography, land cover, land surface temperature, and distance-to-road) and the permafrost stability is mapped with the random forest method, based on the high-quality data extracted from initial InSAR analysis. Third, the permafrost stability in the areas where the visibility of SAR images is poor or the InSAR analysis results are not available is mapped with the trained random forest model. Comparative analyses demonstrate that the integration of InSAR and random forest method yields a more effective permafrost stability mapping, compared to the sole application of InSAR analysis.

There is limited research focusing on Interferometric Synthetic Aperture Radar (InSAR) applications in the Great Lakes coastal wetlands with large water level fluctuations. In this study, we investigated the potential of using C-band SAR data to characterize marsh wetland and monitor water level changes along the coast of the Great Lakes. InSAR analysis was conducted using Radarsat-2 and Sentinel-1 data collected at Long Point, Ontario, Canada over the period of 2016–2018. Observations indicated that both backscattering coefficients and coherence from tall plants (e.g. cattail/Phragmites), short plants (e.g. grass), and water varied with different sensor modes (incidence angles and polarizations) in response to changes in phenology, disturbance, and water level. InSAR phase changes were closely related to fluctuations in water level and flow direction. We evaluated InSAR time series observations using measurements from water level loggers based on correlation and root mean square error (RMSE). It was found that correlation between InSAR measurements and water level changes in the field varied depending on the site, type of wetland vegetation, incidence angle and polarization. Although results from some sensor modes provided good correlation at a few locations, the low fringe rate and RMSE between 9 and 28 cm indicated that InSAR observations of water level changes were generally underestimated.

Interferometric synthetic aperture radar (InSAR) methods provide high-resolution maps of surface deformation applicable to many scientific, engineering, and management studies. Despite its utility, the specialized skills and computer resources required for InSAR analysis remain as barriers for truly widespread use of the technique. Reduction of radar scenes to maps of temporal deformation evolution requires not only detailed metadata describing the exact radar and surface acquisition geometries, but also a software package that can combine these for the specific scenes of interest. Furthermore, the range-Doppler reference frame and radar coordinate system itself are confusing, so that many users find it hard to incorporate even useful products in their customary analyses. Finally, the sheer data volume needed for interferogram time series makes InSAR analysis challenging for many analysis systems. We show here that it is possible to deliver radar data products to users that address all of these difficulties, so that the data acquired by large, modern satellite systems are ready to use in more natural coordinates, without requiring further processing, and in as small volume as possible.

Ground deformation is the direct manifestation of the earth-rock dam's hazard potential. Therefore, it is essential to monitor deformation for dam warning and security evaluation. The Liuduzhai Dam, a clay-core dam of a large reservoir in China, was reinforced with plastic concrete cut-off walls between 13 January 2009 and 29 May 2010, as it was subject to leakage and deformation. However, the deformation development and the mechanism of the dam are still unclear. In this study, the deformation fields before and after the reinforcement of the Liuduzhai Dam were yielded by using the Interferometric Synthetic Aperture Radar (InSAR) technique. Furthermore, a numerical simulation method was employed to obtain the dynamic seepage field of the dam during the InSAR observation period. The results indicated that the average deformation velocity and maximum deformation velocity are −11.7 mm/yr and −22.5 mm/yr, respectively, and the cumulative displacement exceeds 100 mm, which shows typical continuous growth characteristics in a time series. In contrast, the dam deformation tended to be stable after reinforcement, with the average deformation velocity and maximum deformation velocity being −0.4 mm/yr and −1.2 mm/yr, respectively, behaving as cyclical deformation time series. According to the results of InSAR and seepage analysis, it is shown that: (1) dynamic seepage was the main mechanism controlling dam deformation prior to reinforcement; (2) the concentrated load caused by construction and the rapid dissipation of pore water pressure caused by the sudden drop of the infiltration line were the reasons for the acceleration of deformation during and after construction; and (3) the plastic concrete cut-off walls effectively reduced the dynamic seepage field, while the water level fluctuations were the main driving factor of elastic deformation of the dam after reinforcement. This study provides a novel approach to investigating the deformation mechanism of earth-rock dams. Furthermore, it has been confirmed that InSAR can identify the seepage deformation of dams by detecting surface movements. It is recommended that InSAR deformation monitoring should be incorporated into future dam safety programs to provide detailed deformation signals. By analyzing the temporal and spatial characteristics of the deformation signal, we can identify areas where dam performance has degraded. This crucial information aids in conducting a comprehensive dam safety assessment.

Over the last decade, Fengcheng oil field rich in heavy oil geological reserves has had a broad prospect of exploration. The reservoirs are mainly composed of high porosity, high permeability sandstone and conglomerate in study area. The reservoir characteristics led to poor fluid and gas injection causing ground surface deformation with the injection of gas and fluid and threatened the safety of nearby construction facilities. Interferometric Synthetic Aperture Radar (InSAR) technology has been used to identify ground surface deformation with high precision, which is widely used in various fields. This paper adopted Small Baseline Subset InSAR (SBAS-InSAR) method to monitor oil field for the period of February 2007 to September 2010. In this study, the Fengcheng oil field in Xinjiang served as an example; we measured subsurface fluid and gas injection-induced ground surface deformation by SBAS-InSAR technique and investigated the causes of observed deformation. The results showed significant uplift over Fengcheng oil field and testified the feasibility of SBAS-InSAR method. The Phased Arrary L-band Synthetic Aperture Radar (PALSAR) datasets outcome revealed ground surface maximum cumulative uplift for 4 years was 40 cm in the line of sight (LOS) direction, and annual average deformation rates were ranging from − 80 to 120 mm/year. Further, the time series analysis based on monitoring results found that the causes of ground surface uplift were related to the increase in pore pressure under the reservoir, and the surface deformation area corresponded to the production well position of study area. Therefore, InSAR technique is an essential tool for the ground surface deformation monitoring due to fluid and gas injection in heavy oil field, ensuring the safety of personnel and property.

Accurate building height information is used for numerous urban applications, including infrastructure modeling, disaster risk assessment, and energy demand estimation. The U.S. Geological Survey collects airborne lidar data to support the three-dimensional elevation program (3DEP). The high point density and vertical accuracy of the lidar point clouds are optimal for 3D mapping. Few studies have systematically evaluated building height extraction from 3DEP lidar point clouds in urban areas. This study assesses the accuracy and consistency of building height estimates from two publicly available data sets by comparing them to the 3DEP lidar data. The public building height data sets are USA Structures and Microsoft Global Building Footprints (GBF), and they are evaluated across two contrasting urban sites. Building heights from USA Structures are also lidar-derived, whereas GBF estimates are based on stereo matching of optical imagery. Building heights from 3DEP were extracted by classifying lidar points using a deep learning model, isolating roof and ground returns, and computing the height as the median elevation difference between the roof and surrounding ground points. For both sites, 3DEP lidar-derived heights show strong agreement with USA Structures ( R ² = 0.87 and 0.86; MAE = 1.8 and 2.2 m), confirming the internal consistency and reliability of lidar-derived measurements. In contrast, comparisons with GBF height estimates reveal significantly lower correlations ( R ² = 0.54 and 0.60; MAE = 2.5 and 3.1 m), highlighting the limitations of imagery-based height reconstruction, particularly in dense or heterogeneous urban environments. These findings indicate that lidar can be used as an accurate source for vertical structure characterization and underscore the benefits of improved integration and bias correction when using imagery-derived building heights. The results provide support for hybrid approaches that combine the broad spatial coverage of optical data sets with the accuracy of lidar for scalable 3D urban mapping.

Smart monitoring of critical civil engineering infrastructures has become a priority nowadays as ageing of construction materials may have dramatic consequences on the community. The issue is exacerbated as it applies to many structures at the network level rather than to single structures or limited areas. To this effect, catalogues for assessment of decay conditions and identification of maintenance actions are crucial pieces of information for infrastructure management purposes. Within this context, innovative non-destructive methods, such as space-borne techniques, have been increasingly used for monitoring purposes in the past two decades. Among these, the Interferometric Synthetic Aperture Radar (InSAR) imagery technique is gaining momentum nowadays. This method is used to monitor ground and infrastructure displacements at the large scale with a millimeter resolution. It can compare radar satellite images over time, and it is capable to measure variations accurately using interferometry. An advantage of using InSAR techniques is that these are not affected by cloudiness as well as lighting conditions, since data can be also collected at night time. On the contrary, InSAR is computational-demanding as it requires to filter out unnecessary information from the entire captured area to obtain data on the target domain. Most common applications include landslide assessment and monitoring of surface deformations following major seismic events. This paper reports a methodology for the assessment of surface deformations of viaducts by reducing drastically computational time. To this purpose, a multi-stage automatic bridge monitoring protocol is developed at the network level by integration of information into Geographic Information System (GIS) catalogues and use of the InSAR imagery technique. The first stage locates the viaducts in the area of interest by querying open data and inputting results into a GIS catalogue. On a second stage, an InSAR analysis of the identified bridges is performed. This approach allows an estimate of surface displacements as well as an identification of bridge areas affected by millimeter-scale settlements. Fundamental theoretical and working principles of the two methodologies are first introduced in the paper. Advantages against drawbacks of each technique are then discussed. The last Section reports a case study and a discussion of the main results including conclusions and future prospects.

Interferometric SAR analysis provides an excellent opportunity to perform large-scale and rapid coseismic deformation mapping. Between December 28-30, 2020, three earthquakes with magnitudes greater than 4.3 occurred during the 2020 Petrinja Earthquake Sequence near Petrinja in Croatia, resulting in significant coseismic deformation. Considering both the available ascending and descending Sentinel-1A/B images preceding and following the Petrinja Earthquake Sequence, 2.5D differential interferometric analysis was performed to determine the resulting deformation field, which have significant importance in civil engineering related countermeasures and hazard assessment. With the applied methodology, the derived horizontal and vertical deformation fields can be characterized by a maximum of ±0.43 m local East-West, a maximum of 0.15 m local subsidence and a maximum of 0.19 m local vertical uplift near Petrinja.

Abstract. In recent years, Synthetic Aperture Radar (SAR) data have been widely used for scientific applications and several SAR missions were realized. The active sensor principle and the signal wavelength in the order of centimeters provide all-day and all-weather capabilities, respectively. The modern German TerraSAR-X (TSX) satellite provides high spatial resolution down to one meter. Based on such data SAR Interferometry may yield high quality digital surface models (DSMs), which includes points located on 3d objects such as vegetation, forest, and elevated man-made structures. By removing these points, digital elevation model (DEM) representing the bare ground of Earth is obtained. The primary objective of this paper is the validation of DEMs obtained from TSX SAR data covering Barcelona area, Spain, in the framework of a scientific project conducted by ISPRS Working Group VII/2 "SAR Interferometry" that aims the evaluation of DEM derived from data of modern SAR satellite sensors. Towards this purpose, a DSM was generated with 10 m grid spacing using TSX StripMap mode SAR data and converted to a DEM by filtering. The accuracy results have been presented referring the comparison with a more accurate (10 cm–1 m) digital terrain model (DTM) derived from large scale photogrammetry. The results showed that the TSX DEM is quite coherent with the topography and the accuracy is in between ±8–10 m. As another application, the persistent scatterer interferometry (PSI) was conducted using TSX data and the outcomes were compared with a 3d city model available in Google Earth, which is known to be very precise because it is based on LIDAR data. The results showed that PSI outcomes are quite coherent with reference data and the RMSZ of differences is around 2.5 m.

To compare the capability and the accuracy of Light Detection And Ranging (lidar) and Interferometric Synthetic Aperture Radar (INSAR) for the detection and measurement of individual tree heights and forest plot heights, one lidar dataset with nominal spacing of 3 m and one short-wavelength Ku-band INSAR with comparable ground resolution of 3 m were studied. Vegetation heights were based on the subtraction between the bare ground Digital Elevation Models (DEMs) and the canopy Digital Surface Models (DSMs). Two field measurement datasets of isolated individual trees and forest plots were used for validation. Results showed 78 percent and 17 percent of the individual trees was detected on lidar and INSAR data, respectively. General canopy height patterns could be successfully identified through a transect profile for both sensors, however, both sensors consistently underestimated vegetation height. Higher accuracy was obtained for both individual tree level and forest plot level with lidar than that from the Ku-band INSAR. These results indicate that lidar is much better than INSAR for the detection and estimation of tree and forest plot height.

Canopy penetration depth estimation with TanDEM-X and its compensation in temperate forests

A method is presented to characterize forest stand heights in a 110,000 km2 region in the eastern United States surrounding the Chesapeake Bay area, driven by a statistical fusion model solely based on remote sensing data. The predicted map was tested against ground survey data from the Forest Inventory and Analysis (FIA) plot network. Input data to the model were 2003 medium footprint lidar data from the Laser Vegetation Imaging Sensor (LVIS) sensor, interferometric radar data from the 2000 Shuttle Radar Topography Mission (SRTM), 1999–2001 Landsat ETM+ data, and ancillary data sets of land cover and canopy density developed for the 2001 National Land Cover Database. In the presented approach, the interferometric synthetic aperture radar (InSAR), optical, and ancillary data sets were masked to the forested areas of the study region and used to segment the raster data stack. The generated image objects closely represented quasi‐homogenous forest stands. For a small region in the study area covered by an LVIS acquisition, LVIS lidar data were then used within the established segments to extract lidar‐based mean forest stand heights. Subsequently these LVIS mean stand heights were used as the response variable to the statistical prediction model (randomForest) which had segment‐based metrics like mean InSAR height (derived from SRTM minus ground digital elevation model data from the National Elevation Data set), mean optical reflectance (derived from Landsat ETM+ Tassled Cap Data), and ancillary metrics as predictive variables. The model developed over the area where LVIS data were available was then applied to map the entire study region. Independent validation of the model was performed in two ways. First, splitting of the model data stack into training and independent testing populations, i.e., testing on LVIS data. This test was deemed to describe the model performance within the LVIS swath. Second, predicted heights were compared to plot height metrics derived from FIA data in the entire study region, thus testing the validity of the model across the larger study area. Results, which are somewhat tampered by the time disconnect between the various data collections, showed the validity and usefulness of this approach. Independent LVIS testing resulted in a correlation coefficient r = 0.83 with an RMSE of 3.0 m (9% error), independent FIA data tested with r = 0.71 with an RMSE of 4.4 m (13% error).

Glacier flow and slope instabilities in Alpine mountain areas represent a hazard issue. Sentinel-1 satellites provide regular Synthetic Aperture Radar (SAR) acquisitions that are potentially useful to monitor these areas, but they can be affected by temporal decorrelation due to rapid changes in the surface. The application of interferometric synthetic aperture radar (InSAR) therefore seems difficult due to loss of coherence. On the other hand, Corner Reflectors (CR) can be used as coherent targets in SAR images for accurate displacement measurement thanks to their strong backscattering property and temporal stability. The use of CRs in multi-temporal InSAR analysis in Alpine mountain areas can thus be beneficial. In this study, we present a comparison between triangular and rectangular CRs, based on Radar Cross Section (RCS) measurements in an anechoic chamber and on long-term experiments over the Argentière glacier and the surrounding slopes and moraine. The visibility in both summer and winter of 10 CRs installed on the test site was investigated. As this area is exposed to heavy precipitation including snow falls, two perforated CRs were tested. The amplitude stability and the phase error of each CR were estimated. A precise tracking of two CRs installed at the glacier surface was also able to measure the displacement of the Argentière glacier, giving results close to previous GPS measurements. Furthermore, a Persistent Scatterer Interferometry (PSI) study was conducted, using the most stable CR as reference point to estimate slope instabilities, which led to the identification of an area corresponding to a tectonic fault called “Faille de l’angle”. The precise absolute locations of the CRs were successfully estimated and PS heights were compared with a LiDAR-based (Light Detection And Ranging) digital elevation model (DEM) and GPS measurements.

High-rise buildings present substantial challenges for firefighting and rescue operations owing to their considerable height. The stack effect, which becomes more pronounced with increasing building height, accelerates smoke propagation and significantly increases the likelihood of casualties. This study identifies and analyzes the risks associated with fire incidents in high-rise residential buildings. A 49-story building was selected as the reference model, and population density was applied to estimate occupant numbers for the risk assessment. For the damage scenario, one disaster-vulnerable individual per household was assumed. The simulation results revealed that firefighters and vulnerable occupants were exposed to smoke within 541 s. The findings of this study indicate that the stack effect, amplified by building height, exacerbates fire and smoke spread, thereby increasing firefighting risks and potential casualties. These results highlight fire incidents in high-rise structures as a critical category of urban disaster. Furthermore, the study underscores the limitations of existing firefighting facilities in addressing such scenarios and emphasizes the urgent need for new paradigms in firefighting strategies and smoke control technologies to mitigate the risks associated with the stack effect.