Radar Satellite Imagery Research Articles

ADVANCED DIFFERENTIAL INTERFEROMETRIC SAR TECHNIQUE A-DINSAR FOR THE STUDY OF LARGE LANDSLIDES

Large landslides are widespread both in the Alps and in Apennines; the observed movements are generally from extremely slow to slow with some occasional acceleration; the sudden and paroxysmal collapse of the whole mass may have catastrophic consequences. They are also difficult to characterize in their boundaries and state of activity and to monitor with traditional tools. The Advanced Differential interferometric SAR (Synthetic Aperture Radar) technique, A-DInSAR, is a remote sensing technique, which is based on satellite radar imagery for the ground motion measurement. It is based on large stacks of SAR images and enables the measure of small displacement of the ground with a millimetThe aim of this study is to apply the A-DInSAR technique in some large landslide in Alps (Brenvetto, Rosone and Alpe Baranca landslides) in order to study the effectiveness of the technique in their study and monitoring.

Accurately monitoring urbanization at global scale – the world settlement footprint

Reliably monitoring global urbanization is of key importance for supporting a variety of applications involving the analysis of human presence (e.g., socioeconomic development, population distribution, risks assessment, epidemiology, conservation areas protection, etc.). To this purpose, in order to accurately outline the actual settlement extent globally we generated the World Settlement Footprint (WSF) 2015, i.e. a 10m resolution binary mask derived by jointly exploiting multitemporal optical and radar satellite imagery, which outperforms all other existing similar layers. Furthermore, to characterize the urbanization occurred in the last three decades, we recently completed the WSF Evolution, i.e. a novel dataset outlining the growth of settlement extent globally at 30m spatial resolution on a yearly basis from 1985 to 2015.

Validation of flood risk maps using open source optical and radar satellite imagery

AbstractFlood risk maps delineate areas potentially at risk of flooding and are thus a crucial tool in flood risk management. In Spain, such maps are provided as open geospatial data. This article compares the flood‐prone areas according to those maps for floods of different return periods with the spatial extent of two floods that severely affected southwestern Spain using open source optical and radar satellite imagery. Results using the recall metric were found to be very good and are the most relevant to emergency preparedness services as this metric focuses on the correctly predicted flooded areas. It was also found that the 500‐year flood risk map was the one with the best precision and accuracy for both flood events. The results confirm the accuracy of the flood risk maps based on open source remote sensing imagery and hence demonstrate the potential of publicly available and freely distributed remote sensing data in their development.

Persistent Scatterer Interferometry of Sentinel-1 time series to detect ground subsidence in the city of Recife, Brazil

Persistent Scatterer Interferometry (PSI) analysis of Sentinel-1 time series was carried out to detect ground subsidence in the city of Recife, Brazil. The dataset consisted of sixty-eight Sentinel-1A Interferometric Wide (IW) Single Look Complex (SLC) images of the time period April 2017 – September 2019. The images were acquired in descending orbit in VV (vertical transmitting, vertical receiving) polarization. The results of the PSI analysis show that in the city of Recife occur several ground subsidence areas. The largest ground subsidence area occurs between the neighborhoods of Afogados, Torrŏes and Cordeiro. The subsidence rates in this area range from few mm/year up to -15 mm/year. This ground subsidence could be a result of groundwater extraction or of subsidence processes in urbanized reclaimed lands. Similar but smaller ground subsidence areas occur in several localities in Recife. In some cases, subsidence with rates of up to -25 mm/year is noted in small zones where new buildings have been constructed in the last decade. This should be due to ground settlement processes, taking a long time due to the particular soils and geology of the locality. This study can serve as a first contribution for further research on the ground subsidence hazard in the city of Recife and the surrounding areas by means of satellite radar imagery.

Mapping complex coastal wetland mosaics in Gabon for informed ecosystem management: use of object‐based classification

AbstractWetlands of coastal Gabon provide many ecosystems services including flood protection, water purification and wildlife habitat. Effective sustainable management of this coastal zone is hindered by a lack of accurate wetland maps. Here we describe a novel method used to map the wetland ecosystems of nearly 100 000 km2 of wetland and upland habitat mosaic in the delta of the Ogooué River using an object‐based classification of optical and radar satellite imagery based on training data collected from unmanned aerial vehicle and a post‐classification accuracy assessment using helicopter‐based video. We identified 15 land cover classes, of which nine were wetland. A stratified random sample accuracy assessment of the final classification yielded an overall accuracy of 0.80. Despite the important role that wetland habitats play for wildlife and ecosystem functioning across the region, our results indicate these wetlands cover only 22% of the project area. As expected, most of the wetland habitats are found close to major water bodies, including the Ogooué River, estuaries near the cities of Libreville and Port Gentil and coastal lagoons to the south of these cities. When considering the six Wetlands of International Importance designated under the Ramsar Convention within the project area, only 33% of mapped wetlands fall within the Ramsar site boundaries and only 10% of mapped wetlands fall within protected areas. Furthermore, within the Ramsar sites, only 31% of the land cover was classified as wetland. In order to better manage these wetland resources, more effective Ramsar boundaries would include the extensive wetland habitats found along the coast from Port Gentil south to Loango National Park. These data are now available for managers to improve wetland management within designated Ramsar sites and improving protection designations for vulnerable habitats, for example by protecting wetland connectivity and other ecosystem processes.

Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine

Monitoring the expansion of commodity crops in the tropics is crucial to safeguard forests for biodiversity and ecosystem services. Oil palm (Elaeis guineensis) is one such crop that is a major driver of deforestation in Southeast Asia. We evaluated the use of a semi-automated approach with random forest as a classifier and combined optical and radar datasets to classify oil palm land-cover in 2015 in Sumatra, Indonesia, using Google Earth Engine. We compared our map with two existing remotely-sensed oil palm land-cover products that utilized visual and semi-automated approaches for the same year. We evaluated the accuracy of oil palm land-cover classification from optical (Landsat), radar (synthetic aperture radar (SAR)), and combined optical and radar satellite imagery (Combined). Combining Landsat and SAR data resulted in the highest overall classification accuracy (84%) and highest producer’s and user’s accuracy for oil palm classification (84% and 90%, respectively). The amount of oil palm land-cover in our Combined map was closer to official government statistics than the two existing land-cover products that used visual interpretation techniques. Our analysis of the extents of disagreement in oil palm land-cover indicated that our map had comparable accuracy to one of them and higher accuracy than the other. Our results demonstrate that a combination of optical and radar data outperforms the use of optical-only or radar-only datasets for oil palm classification and that our technique of preprocessing and classifying combined optical and radar data in the Google Earth Engine can be applied to accurately monitor oil-palm land-cover in Southeast Asia.

Mapping Kenyan Grassland Heights Across Large Spatial Scales with Combined Optical and Radar Satellite Imagery

Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user’s and producer’s accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user’s accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve.

Impact of Hurricane Maria on Beach Erosion in Puerto Rico: Remote Sensing and Causal Inference

AbstractBeach erosion due to large storms critically affects coastal vulnerability, but is challenging to monitor and quantify. Attributing erosion to a specific storm requires a reliable counterfactual scenario: hypothetical beach conditions, absent the storm. Calibrating models to construct counterfactuals requires numerous observations that are rarely available. Storm paths are unpredictable, making long‐term instrumentation of specific beaches costly. Optical remote sensing is hampered by persistent cloud cover. We use Sentinel‐1 satellite radar imagery to monitor shoreline changes through clouds and propose regression discontinuity as a strategy to estimate the causal effect of large storms on beach erosion. Applied to 75 beaches across Puerto Rico, the approach detects shoreline changes with a root‐mean‐square error comparable to the resolution of the imagery. Hurricane Maria caused an erosion of 3 to 5 m along its path, up to 40 m at particular beaches. Results reveal strong local disparities that are consistent with simulated nearshore hydrodynamic conditions.

Satellite Radar Imagery for Detection and Monitoring of Geohazards

This paper presents three case studies that were part of a 1-year study that explores the feasibility of using commercially available satellite and other aerial imagery to monitor the right of way of railroads for effects and conditions that could potentially trigger landslides and other geohazards. Two satellite image processing techniques in the Interferometric Synthetic Aperture Radar (InSAR) family have been studied and employed, that is, the Differential Interferometric Synthetic Aperture Radar (DInSAR) and the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR). All satellite images used in this work are in the public domain and the software is open source. Showcase studies have demonstrated that the current satellite technology makes it feasible to monitor the railway right of way for large- and small-scale deformations and changes in the ground moisture content in adequate resolution. The frequency of acquisition of satellite images is adequate for the long-term monitoring of the infrastructure. The satellite analysis results can be superimposed to visual imagery for ease of visual inspection and evaluation. Future work for the development of a monitoring system of the railway right of way needs to focus on verifying the accuracy of the techniques with in situ measurements through conventional means and quantifying the changes of the moisture content.

Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series

Abstract The recent availability of high spatial and temporal resolution optical and radar satellite imagery has dramatically increased opportunities for mapping land cover at fine scales. Fusion of optical and radar images has been found useful in tropical areas affected by cloud cover because of their complementarity. However, the multitemporal dimension these data now offer is often neglected because these areas are primarily characterized by relatively low levels of seasonality and because the consideration of multitemporal data requires more processing time. Hence, land cover mapping in these regions is often based on imagery acquired for a single date or on an average of multiple dates. The aim of this work is to assess the added value brought by the temporal dimension of optical and radar time series when mapping land cover in tropical environments. Specifically, we compared the accuracies of classifications based on (a) optical time series, (b) their temporal average, (c) radar time series, (d) their temporal average, (e) a combination of optical and radar time series and (f) a combination of their temporal averages for mapping land cover in Jambi province, Indonesia, using Sentinel‐1 and Sentinel‐2 imagery. Using the full information contained in the time series resulted in significantly higher classification accuracies than using temporal averages (+14.7% for Sentinel‐1, +2.5% for Sentinel‐2 and +2% combining Sentinel‐1 and Sentinel‐2). Overall, combining Sentinel‐2 and Sentinel‐1 time series provided the highest accuracies (Kappa = 88.5%). Our study demonstrates that preserving the temporal information provided by satellite image time series can significantly improve land cover classifications in tropical biodiversity hotspots, improving our capacity to monitor ecosystems of high conservation relevance such as peatlands. The proposed method is reproducible, automated and based on open‐source tools satellite imagery.

Evolution of Supraglacial Lakes on the Larsen B Ice Shelf in the Decades Before it Collapsed

AbstractThe Larsen B ice shelf collapsed in 2002 losing an area twice the size of Greater London to the sea (3,000 km2), in an event associated with widespread supraglacial lake drainage. Here we use optical and radar satellite imagery to investigate the evolution of the ice shelf's lakes in the decades preceding collapse. We find (1) that lakes spread southward in the preceding decades at a rate commensurate with meltwater saturation of the shelf surface; (2) no trend in lake size, suggesting an active supraglacial drainage network which evacuated excess water off the shelf; and (3) lakes mostly refreeze in winter but the few lakes that do drain are associated with ice breakup 2–4 years later. Given the relative scale of lake drainage and shelf breakup, however, it is not clear from our data whether lake drainage is more likely a cause, or an effect, of ice shelf collapse.

Yarı kurak bölgelerde sentetik açıklıklı radar (mikrodalga) görüntüleri ile toprak neminin tahmini

Spatial and temporal distribution of soil moisture is a key parameter for agricultural applications at watershed level such as drought monitoring, crop irrigation scheduling, and yield estimations in arid and semi-arid regions. Moreover, radar satellite imagery systems have been used to figure out soil and vegetation distributions spatially and temporally for various regions. Estimation of surface soil moisture using active microwave sensor systems is among useful information for researchers, conservation planners, and decision makers pursuing sustainable use of natural resources. This study was carried out at the soils of selected sixty-four square kilometers test site in Altinova State Farm. It was aimed to determine the relationship between the surface soil moisture and the backscatter values of SAR images (Radarsat-2) obtained four different times. To that end, four SAR images (4 Radarsat-2 images) from Altinova State Farm were used. Surface soil samples were collected simultaneously from 0-20 cm depth at 730 different points with 250 m-intervals, and soil moisture was determined using gravimetric method throughout the study area. In regards to each sampling period, surface soil moisture distribution maps were produced using ordinary kriging method. Considering the soil moisture distribution maps the data obtained in August indicated the most alterations in the surface soil moisture throughout the study area in comparison to the other sampling periods. Therefore, it was revealed that the relationship between backscattering (obtained from Radarsat-2 data in August, 2012) and soil moisture content was better than the other SAR data results (r=0.506, p<0.05).

Spaceborne radar imagery – An under-utilized source of information for humanitarian relief

This practical paper gives an overview about the widely unused potential of radar satellite imagery to assist humanitarian action. It briefly introduces the basic differences between optical and radar images and demonstrates the practical use of the latter in different settings based on their information content and their potential for multi-temporal analyses. It gives recommendations on further reading and closes with suggestions on the practical integration of radar data into humanitarian work.

Analysis of surfactant-associated bacteria in the sea surface microlayer using deoxyribonucleic acid sequencing and synthetic aperture radar

ABSTRACTThe sea surface microlayer (SML) is the upper 1 mm of the ocean, where Earth’s biogeochemical processes occur between the ocean and atmosphere. It is physicochemically distinct from the water below and highly variable in space and time due to changing physical conditions. Some microorganisms influence the composition of the SML by producing surfactants for biological functions that accumulate on the surface, decrease surface tension, and create slicks. Slicks can be visible to the eye and in synthetic aperture radar (SAR) satellite imagery. This study focuses on surfactant-associated bacteria in the near-surface layer and their role in slick formation where oil is present.

Авария танкера Sanchi в Восточно-Китайском море: обнаружение и мониторинг нефтяного загрязнения по радиолокационным и оптическим спутниковым изображениям

Авария танкера Sanchi в Восточно-Китайском море: обнаружение и мониторинг нефтяного загрязнения по радиолокационным и оптическим спутниковым изображениям

Reativação Tectônica Quaternária no Domínio Sul da Província Borborema, NE do Brasil: Integração de Dados Morfométricos, Geológicos e Geofísicos da Bacia do Rio Una

An integration of data in different scales was built aiming to identify possible effects of tectonic reactivations of Cenozoic origin (post-rift), related to the continental basement adjacent to the coastal zone of Pernambuco marginal basin, over which was formed the hydrographic basin of the Una River. The study was based in the integration of morphometric parameters of the Una River channel modeled with satellite radar imagery, geological data, and 2D multichannel shallow seismic data acquired over the river channel in the proximities of Barreiros and Palmares cities. Morphometric analysis comprised the calculation of the length-gradient index along the longitudinal profile of the Una River. Geological data were collected from basement outcrops near the cities of Barreiros and Palmares. Barreiras Formation outcrops located in the coastal basin, near the city of Sao Jose da Coroa Grande were also investigated. Analysis of longitudinal profile showed that the Una River channel was established over three planed surfaces, created by successive events of uplift and erosion of Borborema Plateau which controlled the compartmentation of this hydrographic basin. The calculation of length-gradient index showed the occurrence of first order anomalies in the middle course, and second order anomalies in the middle and low course of the river profile. Anomalies of first and second order are located over the main shear zones of the Pre-Cambrian basement, trending NE-SW. These anomalies are also related to younger fractures and faults trending NW-SE. Interpretation of seismic sections showed a series of high angle faults within the basement which affected Quaternary and holocenic deposits. The investigation also found faults in deposits of Barreiras Formation (Miocene) located in the coastal zone near the border of the Pernambuco Basin. Faults in basement rocks and in sedimentary deposits formed flower-like structures, which indicates the actuation of a transcurrent regime in the continental margin, with a maximum compression trending E-W and extension trending N-S. This regime affected the morphology of the Una River profile and the sedimentary deposits formed in its drainage basin.

Six Decades (1958–2018) of Geodetic Glacier Mass Balance in Monte San Lorenzo, Patagonian Andes

A full understanding of glacier changes in the Patagonian Andes over decadal to century time-scales is presently limited by a lack of detailed and appropriate long-term observations. Here, we present geodetic mass and area changes of three valley glaciers from Monte San Lorenzo derived from stereo aerial photos, the Shuttle Radar Topography Mission (SRTM) and satellite imagery (SPOT5 and Pleiades) spanning four periods from 1958 to 2018. Our results indicate that net mass balance was negative throughout the six decades, with a mean mass loss of -1.35 ±0.08 m w.e. a-1 and a total glacier area loss of 14.2 ±0.7 km2 (23 ±1 % or 0.40 ±0.02 % a-1). The period 1981-2000 had the most negative mass budget, with an area-averaged mass loss of 1.67 ±0.11 m w.e. a-1 and a maximum loss of -2.23 ±0.07 m w.e.a−1 at San Lorenzo Sur glacier. Over the periods of 2000-2012 and 2012-2018, the mass budget of these three glaciers remained virtually unchanged at -1.37 ±0.06 m w.e. a-1 and –1.36 ±0.17 m w.e. a-1, respectively. To place these results into a broader geographical context, the mass balance of a further 15 glaciers from around the Monte San Lorenzo massif was determined from 2000 onwards. This wider analysis reveals a period of reduced mass loss of -0.13 ±0.21 m w.e.a−1 from 2012 to 2018 after a period of enhanced mass loss of -0.31 ±0.16 m w.e.a−1 between 2000 and 2012. We find that increasing air temperatures coupled with diminishing precipitation across the region explains the observed patterns and are the main drivers of the negative mass budget. Furthermore, increased calving and melting into recently formed proglacial lakes has further enhanced mass loss at some lake-terminating glaciers.

Evaluation of Approaches for Mapping Tidal Wetlands of the Chesapeake and Delaware Bays

The spatial extent and vegetation characteristics of tidal wetlands and their change are among the biggest unknowns and largest sources of uncertainty in modeling ecosystem processes and services at the land-ocean interface. Using a combination of moderate-high spatial resolution (≤30 meters) optical and synthetic aperture radar (SAR) satellite imagery, we evaluated several approaches for mapping and characterization of wetlands of the Chesapeake and Delaware Bays. Sentinel-1A, Phased Array type L-band Synthetic Aperture Radar (PALSAR), PALSAR-2, Sentinel-2A, and Landsat 8 imagery were used to map wetlands, with an emphasis on mapping tidal marshes, inundation extents, and functional vegetation classes (persistent vs. non-persistent). We performed initial characterizations at three target wetlands study sites with distinct geomorphologies, hydrologic characteristics, and vegetation communities. We used findings from these target wetlands study sites to inform the selection of timeseries satellite imagery for a regional scale random forest-based classification of wetlands in the Chesapeake and Delaware Bays. Acquisition of satellite imagery, raster manipulations, and timeseries analyses were performed using Google Earth Engine. Random forest classifications were performed using the R programming language. In our regional scale classification, estuarine emergent wetlands were mapped with a producer’s accuracy greater than 88% and a user’s accuracy greater than 83%. Within target wetland sites, functional classes of vegetation were mapped with over 90% user’s and producer’s accuracy for all classes, and greater than 95% accuracy overall. The use of multitemporal SAR and multitemporal optical imagery discussed here provides a straightforward yet powerful approach for accurately mapping tidal freshwater wetlands through identification of non-persistent vegetation, as well as for mapping estuarine emergent wetlands, with direct applications to the improved management of coastal wetlands.

Wastewater leakage in West Texas revealed by satellite radar imagery and numerical modeling

Wastewater, a byproduct of oil and gas production, is injected into disposal wells. Using Interferometric Synthetic Aperture Radar (InSAR) to observe ground deformation in the Ken Regan field, West Texas, we detected surface uplift that occurred near a wastewater disposal well from 2007 to 2011. High correlation between the observed deformation and the injection volume suggests that the uplift was caused by wastewater disposal in the well. Inverse elastic models were first used to calculate the injection depth and volume. Given the initial estimates of wastewater injection, forward poroelastic finite element models were applied to simulate stress/strain and displacement fields and to estimate the effective injection volume and depth, so as to ultimately understand the subsurface geomechanical processes and provide insight into the local hydrologic properties of the strata in the well location. Results from both elastic and poroelastic models indicate that the effective injection depth is much shallower than the depth reported to the Texas Railroad Commission (RRC). The most reasonable explanation is that the well was experiencing leakage due to casing failures and/or sealing problem(s). The Rustler Aquifer, within the zone of the effective injection depth, has been used as a source of freshwater for irrigation and livestock; wastewater leaked into this aquifer may possibly contaminate that freshwater. Our analysis that exploits remote sensing data and numerical models provides a clue as to understanding the subsurface hydrogeological process responding to the oil and gas activities and an indirect leakage monitoring method to supplement current infrequent leakage detection.

Geomorphological Transition Research for Affecting the Coastal Environment due to the Volcanic Eruption of Anak Krakatau by Satellite Imagery

Achmad, A.R.; Syifa, M.; Park, S.J.; and Lee, C.W., 2019. Geomorphological transition research for affecting the coastal environment due to the volcanic eruption of Anak Krakatau by satellite imagery. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 214-220. Coconut Creek (Florida), ISSN 0749-0208.Volcano eruptions of Anak Krakatau had a great impact on the coastal area at the western part of Java island in Indonesia. In the middle of 2018, the volcanic activity at Anak Krakatau in Indonesia increased. Since June 2018, the eruption begun, accompanied by volcanic earthquakes, emission, and tremor vibration. However, on December 22, 2018, the eruption of Anak Krakatau caused a landslide on the southwest part of the volcano. The landslide also caused a tsunami in coastal area and caused many people to become victims in Banten province. There were significant geomorphological changes at Anak Krakatau Island where the volcano is situated. In this study, we monitored the geomorphological change of the Anak Krakatau Island and calculated the total area of the island for pre-eruption, post-eruption, and recent times. By using SNAP 6.0 program and ArcMap 10.4, Sentinel-1 data were processed to generate maps and calculate the extent of the island. The geomorphological changes were successfully detected and quantified. This kind study using all-weather satellite radar imagery can be used to detect and calculate the total area of any geomorphological change from future volcanic activity.