Satellite Digital Elevation Models Research Articles

Impacts of Digital Elevation Model Elevation Error on Terrain Gravity Field Calculations: A Case Study in the Wudalianchi Airborne Gravity Gradiometer Test Site, China

Accurate Digital Elevation Models (DEMs) are essential for precise terrain gravity field calculations, which are critical in gravity field modeling, airborne gravimeter and gradiometer calibration, and geophysical inversion. This study evaluates the accuracy of various satellite DEMs by comparing them with a LiDAR DEM at the Wudalianchi test site, a location requiring ultra-accurate terrain gravity fields. Major DEM error sources, particularly those related to vegetation, were identified and corrected using a least squares method that integrates canopy height, vegetation cover, NDVI, and airborne LiDAR DEM data. The impact of DEM vegetation errors on terrain gravity anomalies and gravity gradients was quantified using a partitioned adaptive gravity forward-modeling method at different measurement heights. The results indicate that the TanDEM-X DEM and AW3D30 DEM exhibit the highest vertical accuracy among the satellite DEMs evaluated in the Wudalianchi area. Vegetation significantly affects DEM accuracy, with vegetation-related errors causing an impact of approximately 0.17 mGal (RMS) on surface gravity anomalies. This effect is more pronounced in densely vegetated and volcanic regions. At 100 m above the surface and at an altitude of 1 km, vegetation height affects gravity anomalies by approximately 0.12 mGal and 0.07 mGal, respectively. Additionally, vegetation height impacts the vertical gravity gradient at 100 m above the surface by approximately 4.20 E (RMS), with errors up to 48.84 E over vegetation covered areas. The findings underscore the critical importance of using DEMs with vegetation errors removed for high-precision terrain gravity and gravity gradient modeling, particularly in applications such as airborne gravimeter and gradiometer calibration.

Lake water storage changes and their cause analysis in Mongolia

Lake is an important water resources in Mongolia, which has undergone a large variation in past decades. However, it is still challenging to monitor long-term changes in lake water storage (LWS) due to the lack of lake level monitoring and long-term satellite altimetry data for Mongolian lakes. According to the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM) and the Joint Research Centre (JRC) dataset, we estimated the LWS changes of 55 Mongolian lakes (> 10 km2) from 1991 to 2020. The results showed that the LWS increased by 40.24 km3 from 1991 to 1997, especially for northwestern Mongolia with 31.47 km3. However, the LWS decreased by 32.44 km3 from 1998 to 2010, and the lakes in the northwestern and southern decreased by 20.24 km3 (62%) and 7.38 km3 (23%), respectively, and then the LWS continued to decrease by 10.22 km3 from 2011 to 2020. The precipitation was the primary cause of lake change, which could explain 45.13% of LWS change based on Generalized Linear Model, followed by temperature (26.33%) and irrigated area (16.72%). Analyzing the changing characteristic and driving mechanisms of LWS can provide a scientific basis for local water resources management and planning.

New approach for satellite DEM accuracy enhancement by combing machine learning, fuzzy majority voting, and weighted interpolation techniques

The digital elevation model (DEM) is crucial in many global and regional scientific studies in civilian and military applications. The aim of this research is to develop and test a new DEM approach for correcting the various errors in the Shuttle Radar Topography Mission (SRTM) digital elevation model. Firstly, the DEMs with the feature attributes from Sentinel-2 multispectral imagery are generated. Secondly, SRTM DEM with one band and attributes of a sentinel-2 image with eight bands are used as input data in supervised max-like hood, an artificial neural network (ANN), and support vector machine (SVM) classification models. Thirdly, ANN, supervised max-like hood, and SVM classification models, which have various properties, are fused by fuzzy majority voting (probability fusion). Finally, the fused probability is assigned for each pixel of the image, which has 12 fixed ground control points (GCPs), which is considered new input data for the inverse probability weighted interpolation (IPWI) approach to create the corrected SRTM elevations. The results were contrasted with a reference DEM (RD) created by image matching with Worldview-1 stereo satellite images, which had a 1-m vertical accuracy. The results of this study demonstrated that the RMSE of the original SRTM DEM was 5.95. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 1.98 compared with that of the MLR method (3.01). The study shows a series of significant improvements in the SRTM when assessed with the reference DEM, with an RMSE reduction of (66.72%) when compared to the widely utilized multiple linear regression (MLR) method. It can be concluded that the elevation error of the original SRTM DEM is clearly reduced by the suggested approach.

Analysis of Topographic Feature Parameters of Dinosaur Valley Ring Tectonic Geomorphology Based on the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM)

Geomorphological surveys and terrain analysis are essential for geomorphology, hydrology, and geographic information systems (GIS). Terrain characterization parameters are fundamental for comprehending geomorphological processes, delineating landforms, and evaluating geohazards. Hence, this study relies on data from the Gaofen-2 satellite (GF-2) and the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM). Focusing on the ring tectonics landform of Dinosaur Valley, the research utilizes visual interpretation, GIS terrain parameter extraction techniques, and visual mapping to identify, measure, and analyze terrain features. The results indicate that ALOS DEM can offer accurate geomorphic feature information. The optimal threshold for extracting the surface water system network is 150, and the optimal analysis window for extracting terrain feature parameters is 16 × 16 (0.04 km2), determined through statistical methods. This study reveals distinct double-ring linear features and prominent ring-shaped terrain characteristics by extracting terrain feature parameters and utilizing mathematical and statistical analysis. The topography gradually ascends from the center to the ridgeline and then descends to the valley line, resembling a bowl shape. This study highlights the potential application of ALOS DEM for accurately identifying topographic features in the intricate mountains of the Yunnan Plateau. It establishes a practical research framework for high-precision topographic datasets and geomorphological characterization, facilitating further analysis of the evolution of topographic development and the assessment of tectonic activity.

Using a scenario-neutral approach to assess the impacts of climate change on flooding in the Ba River Basin, Viet Nam

Abstract Due to the hydrologic non-stationarity and uncertainty related to the probability assignment of flood peaks under climate change, the use of flood statistics may no longer be applicable. Therefore, a sensitivity analysis (i.e., a scenario-neutral approach) is used to examine the impacts of climate change on flooding in the Ba River Basin. A Delphi method with a set of KAMET rules was used to obtain a representative and a threshold flood event. These inputs are used for hydraulic simulation using a MIKE FLOOD model package. Flood simulations were performed using parametrically varied rainfall and temperature conditions. In total, 22 conditions were explored and are in line with CMIP5 and CMIP6. The results obtained have several implications. Firstly, rainfall change is the primary factor affecting flood impact in the Ba River Basin. Secondly, the flood peak in the Ba River Basin is highly sensitive to an increase in rainfall by up to 10%. Thirdly, the flooded threshold is reached when rainfall increases beyond 20%. Fourthly, the flood extent and depth are expected to increase as rainfall increases. Further research could improve the study using satellite rainfall data, satellite digital elevation models, and stochastic weather generators.

A novel multi-strategy hydrological feature extraction (MHFE) method to improve urban waterlogging risk prediction, a case study of Fuzhou City in China

Reliable hydrological data ensure the precision of the urban waterlogging simulation. To reduce the simulation error caused by insufficient basic data, a multi-strategy method (MHFE) for extracting hydrological features is proposed, which includes land use/land cover (LULC) extraction (LE) and digital elevation model (DEM) reconstruction (DR). First, the high-resolution remote image, satellite DEM, precipitation, flood points and depth, and planned LULC were collected. Second, the buildings, roads, and other areas of the satellite image were segmented using the U-Net model, and the LULC data with drainage features were extracted by combining the segmentation result with the planned LULC and drainage data. Then, the terrain features of the road were enhanced to construct high-precision DEM based on the fusion of multi-source data, such as elevation points, LULC, and satellite DEM. Finally, the waterlogging model was implemented under different return periods of rainfalls and typhoon rainfall to obtain the waterlogging distribution and water depth. The simulation results were compared with historical waterlogging event data and water depth observations. The results indicated that the proposed method significantly improved the accuracy of the simulation. In terms of identifying the waterlogging points, the average F1 score increased by 0.36, 0.20, and 0.07 compared to the raw model and the single LE and DR methods, respectively. In terms of water depth simulation, the average Nash-Sutcliffe efficiency (NSE) was increased from −0.24 to 0.86, with DR and LE contributing to 79.1 % and 20.9 %, respectively. The principal contribution and novelty of this paper is to explore the generic method that enhance the hydrological data, and the findings of this study improved the performance of urban waterlogging simulation.

Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran)

Abstract Satellite digital elevation models (DEMs) are used for decision-making in various fields. Therefore, evaluating and improving vertical accuracy of DEM can increase the quality of end products. This article aimed to increase the vertical accuracy of most popular satellite DEMs (i.e., the ASTER, Shuttle Radar Topography Mission [SRTM], Forest And Buildings removed Copernicus DEM [FABDEM], and Multi-Error-Removed Improved-Terrain [MERIT]) using the particle swarm optimization (PSO) algorithm. For this purpose, at first, the vertical error of DEMs was estimated via ground truth data. Next, a second-order polynomial was applied to model the vertical error in the study area. To select the polynomial with the highest accuracy, employed for vertical error modeling, the coefficients of the polynomial have been optimized using the PSO algorithm. Finally, the efficiency of the proposed algorithm has been evaluated by other ground truth data and in situ observations. The results show that the mean absolute error (MAE) of SRTM DEM is 4.83 m while this factor for ASTER DEM is 5.35 m, for FABDEM is 4.28, and for MERIT is 3.87. The obtained results indicated that the proposed model could improve the MAE of vertical accuracy of SRTM, ASTER, FABDEM, and MERIT DEMs to 0.83, 0.51, 0.37, and 0.29 m, respectively.

Quantifying the impacts of DEM uncertainty on clear-sky surface shortwave radiation estimation in typical mountainous areas

Accurate estimation of shortwave radiation in mountains will advance our knowledge of climate change effects, especially on mountain ecosystems. Recently, some approaches have been developed to estimate shortwave radiation parameters in mountains with satellite data, but few attempts were made to understand the impacts of digital elevation model (DEM) uncertainty on estimates. Our study investigates such impacts quantitatively in clear-sky conditions at multiple spatial and temporal scales (30–3000 m, instantaneous to daily). We employed a retrieval algorithm to estimate instantaneous and daily mean clear-sky downward shortwave radiation (DSR) and net shortwave radiation (NSR), as a proxy for our evaluation. The accuracy of our method based on accurate terrain data was verified against in-situ measurements with root-mean-square errors (RMSEs) of 65.9 W/m2 and 65.1 W/m2 for instantaneous DSR and NSR, and 21.2 W/m2 and 22.5 W/m2 for daily mean values, respectively. When using satellite DEM products, the DSR estimation uncertainty could increase by 64.0% for instantaneous values and 46.2% for daily mean values. Using AW3D30 and SRTM DEMs for DSR estimation led to a maximum difference of 16.8% (103.6 W/m2) and 13.0% (25.8W/m2) on instantaneous and daily mean values, respectively. That estimation difference of shortwave radiation decreased with an increase in spatial scale, with RMS deviation lower than 2% for spatial resolution beyond 3000 m. In addition, the evaluation of introducing random errors into AW3D30 DEM showed that the shortwave radiation uncertainty caused by DEM may exceed the algorithm uncertainty itself with DEM mean absolute error (MAE) equaling about 5.0 m. Considering the current DEM accuracy, the impacts of DEM errors on shortwave radiation in mountains cannot be ignored. This study emphasizes the potential impacts of DEM uncertainty on surface shortwave radiation estimation, which is crucial in using satellite-derived datasets for energy balance calculation and climate change applications in mountains.

Land surface temperature downscaling in the karst mountain urban area considering the topographic characteristics

To obtain high-spatial-resolution land surface temperature (LST) in karst areas, it is necessary to select a downscale regression model with a better simulation effect and the scale factors that can best represent the topographic characteristics of karst mountainous areas. In Guiyang, a typical karst mountain city, two areas are selected as the study area, which is dominated by natural surface and construction land. Based on the data of Landsat-8 Thermal Infrared Sensor (TIRS), Sentinel-2, Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM), and meteorological stations, the scale factors representing bare land: bare soil index and topographic relief: mountain shadow (hillshade), relief degree of land surface (RDLS), solar incident angle, and sky view factor (SVF) are added on the basis of the conventional factors. At the same time, random forest (RF) and extreme gradient boosting (XGBoost) models are used to construct an LST downscaling method that is more suitable for karst mountain cities. After the above steps, the LST product with a spatial resolution of 10 m is finally estimated. The results show that, due to the characteristics of large elevation variation, fragmentation, and high heterogeneity of surface landscape in karst areas, digital elevation model (DEM), RDLS, and SVF factors need to be considered in the downscaling of surface temperature, and the contribution rates of these factors are all more than 6% in the model. In terms of accuracy evaluation of ground temperature, XGBoost model has the highest accuracy with an average absolute error of 1.67K, RF model has an average error of 1.90K, and thermal image sharpening has the worst accuracy with an average error of 2.41K. In terms of accuracy evaluation of ascending scale, the XGBoost model also shows higher accuracy and richer texture details. The research results can provide basic data for the acquisition of high-resolution LST and its intermediate parameters in this area and also provide a method reference for the reduction of high-resolution LST in similar areas.

SATELLITE-BASED LAND/SEA CONTINUUM: AN APPLICATION TO MONITOR THE SAINT LOUIS COAST (SENEGAL, WEST AFRICA)

Abstract. The historical city of Saint Louis in Senegal is situated on the Langue de Barbarie sand spit and is particularly prone to erosion: buildings have collapsed and population relocated due to shoreline retreat. At Saint Louis understanding the beach-morphodynamics is essential, and relies on the monitoring of nearshore topography and bathymetry. Remote sensing techniques relying on very high resolution (sub metric) satellites such as the Pleiades constellation and Planet now offer new perspectives in coastal monitoring and engineering. Digital Elevation Models (DEMs) of the emerged part of the beach, topography, and the submerged nearshore bathymetry can be obtained using respectively (tri-)stereogrammetry, and depth inversion through wave kinematics or/and colour based methods. These methods offered promising results (Almeida et al., 2019, Taveneau et al., 2021, Almar et al., 2019a) in previous studies when applied to Pleiades satellite. This work showcases a DEM derived from a 3-images (dT ≃ 9.5s) Pleiades acquisition in March 2020 and covering the key topography/bathymetry morphological continuum. Root mean squared errors of about 5 m for bathymetry and 0.9 m for topography are obtained. To limit erosion at Saint Louis urban beaches, the construction of a protection structure has begun in late 2020. Here, satellite DEM is used to monitor the efficiency of the structure and its downstream impact along the sand spit.

Glacier Mass Balance Based on Two Digital Elevation Models and Ground Observation Records for the Baishui River Glacier No. 1 in Yulong Snow Mountain, Southeastern Qinghai–Tibet Plateau

Glacier mass balance is a key idea in understanding the relationship between glaciers and climate. In this study, Landsat images, Shuttle Radar Topography Mission, and Advance Land Operation Satellite digital elevation models, and ground observation records for 2000–2010 and 2019–2020 were used to analyze a mass balance of the Baishui River Glacier No. 1 (BRG1) in Yulong Snow Mountain, southeastern Qinghai–Tibet Plateau. Both glaciological and geodetic methods were used to understand the spatial pattern of the mass balance. A mass balance contour line map of the glacier was created for the first time. From 2000 to 2010, the mean annual mass balance of the BRG1 was calculated to be -0.99 m w.e., -1.01 m w.e., and −1.18 m w.e. using the contour line, profile curve, and geodetic methods, respectively. In addition, the analysis revealed that strong melting of the glacier occurred from July 27 to 15 September 2019, with an averaged glacier mass balance of -1.75 m w.e. and a mean daily melting rate of 35 mm w.e. The annual mass balance was assessed to be −1.31 m w.e. during 2019/20 using the contour line method. In addition, the spatial patterns on the mass balance contour maps for 2000–2010 and the summer of 2019, and the climate change in this region were analyzed. The results indicate that the increased air temperature was the main cause of the accelerated glacial melting.

Significant Seismic Risk Potential From Buried Faults Beneath Almaty City, Kazakhstan, Revealed From High‐Resolution Satellite DEMs

AbstractMajor faults of the Tien Shan, Central Asia, have long repeat times, but fail in large (7+) earthquakes. In addition, there may be smaller, buried faults off the major faults which are not properly characterized or even recognized as active. These all pose hazard to cities along the mountain range front such as Almaty, Kazakhstan. Here, we explore the seismic hazard and risk for Almaty from specific earthquake scenarios. We run three historical‐based earthquake scenarios (1887 Verny7.3, 1889 Chilik8.0 and 1911 Chon‐Kemin8.0) on the current population and four hypothetical scenarios for near‐field faulting. By making high‐resolution Digital Elevation Models (DEMs) from SPOT and Pleiades stereo optical satellite imagery, we identify fault splays near and under Almaty. We assess the feasibility of using DEMs to estimate city building heights, aiming to better constrain future exposure datasets. Both Pleiades and SPOT‐derived DEMs find accurate building heights of the majority of sampled buildings within error; Pleiades tri‐stereo estimates 80% of 15 building heights within one sigma and has the smallest average percentage difference to field‐measured heights (14%). A moderately sized6.5 earthquake rupture occurring on a blind thrust fault, under folding north of Almaty is the most damaging scenario explored here due to the modeled fault stretching under Almaty, with estimated 12,3005,000 completely damaged buildings, 4,1003,500 fatalities and an economic cost of 4,7002,700 Million US dollars (one sigma uncertainty). This highlights the importance of characterizing location, extent, geometry, and activity of small faults beneath cities.

Regional-Scale Systematic Mapping of Archaeological Mounds and Detection of Looting Using COSMO-SkyMed High Resolution DEM and Satellite Imagery

“Tells” are archaeological mounds formed by deposition of large amounts of anthropogenic material and sediments over thousands of years and are the most important and prominent features in Near and Middle Eastern archaeological landscapes. In the last decade, archaeologists have exploited free-access global digital elevation model (DEM) datasets at medium resolution (i.e., up to 30 m) to map tells on a supra-regional scale and pinpoint tentative tell sites. Instead, the potential of satellite DEMs at higher resolution for this task was yet to be demonstrated. To this purpose, the 3 m resolution imaging capability allowed by the Italian Space Agency’s COSMO-SkyMed Synthetic Aperture Radar (SAR) constellation in StripMap HIMAGE mode was used in this study to generate DEM products of enhanced resolution to undertake, for the first time, a systematic mapping of tells and archaeological deposits. The demonstration is run at regional scale in the Governorate of Wasit in central Iraq, where the literature suggested a high density of sites, despite knowledge gaps about their location and spatial distribution. Accuracy assessment of the COSMO-SkyMed DEM is provided with respect to the most commonly used SRTM and ALOS World 3D DEMs. Owing to the 10 m posting and the consequent enhanced observation capability, the COSMO-SkyMed DEM proves capable to detect both well preserved and levelled or disturbed tells, standing out for more than 4 m from the surrounding landscape. Through the integration with CORONA KH-4B tiles, 1950s Soviet maps and recent Sentinel-2 multispectral images, the expert-led visual identification and manual mapping in the GIS environment led to localization of tens of sites that were not previously mapped, alongside the computation of a figure as up-to-date as February 2019 of the survived tells, with those affected by looting. Finally, this evidence is used to recognize hot-spot areas of potential concern for the conservation of tells. To this purpose, we upgraded the spatial resolution of the observations up to 1 m by using the Enhanced Spotlight mode to collect a bespoke time series. The change detection tests undertaken on selected clusters of disturbed tells prove how a dedicated monitoring activity may allow a regular observation of the impacts due to anthropogenic disturbance (e.g., road and canal constructions or ploughing).

Geomorphology of the Mirador-Calakmul Karst Basin: A GIS-based approach to hydrogeologic mapping

This paper classifies the karst landscapes of the Petén Plateau and defines the Mirador-Calakmul Karst Basin by illustrating the distribution of its karst hydrologic features. Archaeological and spatial research of the Mirador-Calakmul area of Guatemala and Mexico has shown it to be a karst basin with geopolitical implications. Current research characterizes the karst landscapes of the Petén Plateau, maps the distribution of karst hydrologic features, and delineates the basin in geomorphological terms. To further this aim, multiple forms of remote sensing data including orthophotographs, a satellite Digital Elevation Model, satellite multispectral images, and Light Detection and Ranging (LiDAR) data have been integrated to interpret the karst features in the study area. Outcrop study and thin section analysis of the upper Buena Vista Formation document that the dominant lithologies are a shallow water algal boundstone interbedded with terrestrial caliche. Karst landforms have been mapped over the Petén Plateau and we identify five karst landscapes, the largest of which is a fluviokarst landscape dominated by karst valleys. We further map karst hydrologic features including seasonal swamps, dolines, intermittent lakes, intermittent streams, solution-enhanced fractures, and springs all of which are characteristic of drainage basins. Boundaries of the karst basin are mapped from multiple lines of evidence including distribution of the karst valleys, a line of springs along the western boundary of the fluviokarst landscape, and a surface drainage analysis. We capture and classify hydrologic data points and develop a regional groundwater map that indicates subsurface flow from east to west within the basin. A drainage map illustrates the extensive system of karst valleys, boundaries, and inferred groundwater flow paths of the Mirador-Calakmul Karst Basin. It was within this geomorphological setting that the ancient Maya developed an extensive civilization during the Middle and Late Preclassic periods (1000 BCE-150 CE).

A High-Resolution Satellite DEM Filtering Method Assisted with Building Segmentation

Digital elevation model (DEM) filtering is critical in DEM production, and large-area meter-level resolution DEM is mainly generated from high-resolution satellite images. However, the current DEM filtering methods are mostly aimed at laser scanning data and tend to excessively remove ground points when processing a satellite digital surface model (DSM). To accurately filter out buildings and preserve terrain, we propose a DEM filtering algorithm using building segmentation results of orthophoto. Based on morphological filtering, our method estimates the probability of being a built-up area or mountains for DSM, and according to this probability the filtering parameters are adaptively adjusted. For robustness, our method performs the above filtering operation on DSM through a sliding-window approach, and finally the nonground points are determined by the votes of multiple filtering. Experiments against six representative data sets have shown that our method achieved superior perfor- mance than classical algorithms and commercial software.

Satellite DEM Improvement Using Multispectral Imagery and an Artificial Neural Network

The digital elevation model (DEM) is crucial for various applications, such as land management and flood planning, as it reflects the actual topographic characteristic on the Earth’s surface. However, it is quite a challenge to acquire the high-quality DEM, as it is very time-consuming, costly, and often confidential. This paper explores a DEM improvement scheme using an artificial neural network (ANN) that could improve the German Aerospace’s TanDEM-X (12 m resolution). The ANN was first trained in Nice, France, with a high spatial resolution surveyed DEM (1 m) and then applied on a faraway city, Singapore, for validation. In the ANN training, Sentinel-2 and TanDEM-X data of the Nice area were used as the input data, while the ground truth observation data of Nice were used as the target data. The applicability of iTanDEM-X was finally conducted at a different site in Singapore. The trained iTanDEM-X shows a significant reduction in the root mean square error of 43.6% in Singapore. It was also found that the improvement for different land covers (e.g., vegetation and built-up areas) ranges from 20 to 65%. The paper also demonstrated the application of the trained ANN on Ho Chi Minh City, Vietnam, where the ground truth data are not available; for cases such as this, a visual comparison with Google satellite imagery was then utilized. The DEM from iTanDEM-X with 10 m resolution categorically shows much clearer land shapes (particularly the roads and buildings).

Towards a satellite-based approach to measure eruptive volumes at Mt. Etna using Pleiades datasets

Only a few high precision studies of lava and tephra during simultaneous explosive and effusive activity have so far been undertaken. We carried out such measurements by analysis of a unique and homogeneous multi-temporal dataset of high-spatial resolution satellite optical images. Digital Elevation Models (DEMs) and orthophotos (with 1- and 0.5-m-pixel resolutions respectively) were extracted from six specifically tasked Pleiades tri-stereo pairs of Mt. Etna volcano, between 2011 and 2016. During this period, frequent effusive and explosive events formed lava flow fields and built up the new south-east crater pyroclastic cone. The volumes of lava fields and proximal pyroclastic deposits were measured by comparing the Pleiades DEMs with an aerial photogrammetric DEM updated in 2007. The volumes of all distal deposits were estimated using lava and tephra partitioning from the literature for an Etnean lava fountain. The dense rock equivalent volume of lava and tephra, calculated to be 248.4 ± 2.1 × 106 m3 in total, corresponds to an average output rate of 0.98 m3/s over the analysed 8-year period (May 2008–May 2016) and to a multi-event eruption rate of 5.53 m3/s for 520 days of activity. The multi-temporal analysis of high-spatial resolution satellite DEMs, here successfully applied to the well-monitored Etna volcano, demonstrated that the tasking of high-spatial resolution satellite images is crucial for fast and effective monitoring during intense volcanic activity (frequent and overlapping eruptive events). This methodology could be used for the monitoring of remote or hazardous volcanoes that are difficult to study by means of repeated field surveys.

Validation of Remote Sensing Data and Direct Field Observation in Mapping of Gully Erosion for Accuracy with Respect To Terrain Variation

Many Digital Elevation Model (DEM)-based methods have been proposed to enable automated gully mapping. However, the accuracy of a gully map derived from a DEM is inevitably affected by the DEM resolution. Related literature evaluated the accuracy of DEMs generated from stereo imagery of different satellite sensors; however, limited work has been done on quantitative analysis concerning the capability and accuracy of satellite DEMs in Gully erosion mapping. Therefore, this study was centered to map gully erosion and to investigate the accuracy of measured heights of gully erosion using field acquired data (RTK GNSS) and Global Satellite DEMs obtained from ALOS World 3D 30m and 30m ASTER GDEM. The Etim Umana Gully Erosion site in Uyo Local Government Area of Akwa Ibom State, Nigeria was used as the test site for this study. These datasets were referenced to orthometric height, therefore were converted to ellipsoidal height using the Earth Gravitational Model (EGM) 2008. Digital Elevation Model of the field data were generated using various kriging method (simple, ordinary, universal) of which the ordinary method assumed the best with the least Root-Mean-Square Error(RMSE) of 1.326199. Ground Control Points (GCP) (32) values were used to validate the accuracy of the field acquired data and obtained a RMSE of 1.125463 and a Mean Absolute Error (MAE) of 0.8. The spot height values obtained from field were used to validate the satellite DEMs by calculating their RMSE from the difference between the measured values and the predicted values. The ALOS had a RMSE of 10.89154 when compared to the ASTER which had the least RMSE of 10.71999 which indicates the propagation of error. The Mean-Absolute-Error was calculated recoding MAE of 8.539147 for ALOS and ASTER 9.346781 and a Standard deviation of 9.176663 for the ALOS and ASTER 10.53712. ALOS had a mean error of -8.5174 and ASTER 7.706339. The result from this research indicates that they are similar levels of error propagation between the ALOS and ASTER and this can further be ascertained by carrying out validation on different terrain surfaces. These results indicate that it can be map to scale with contours based on the specifications of the National Mapping Accuracy Standards (NMAS)

Volume loss of the Greenland ice sheet revealed by SARAL/AltiKa repeat passes radar altimetry

The Greenland ice sheet (GrIS) is one of the drivers of global sea level rise and plays a crucial role in understanding the global climate changes. Here, we have estimated and analysed the decadal (between 2013–2016 and 2003–2005) and annual (2014–2015, 2015–2016) volume discharge of ice from the entire GrIS. The 40 Hz Geophysical Data Record product of the unique Ka band (AltiKa) radar altimeter were utilised to derive the elevation, elevation changes and volume changes over the GrIS. To test the first-level accuracy of the result, AltiKa and NASA’s ice, cloud and land elevation satellite digital elevation model (ICESat DEM)-derived elevation were compared, which yielded a correlation value of 0.95. Thereafter, decadal volume changes obtained over the entire GrIS, from the differencing of the AltiKa and ICESat DEM elevation revealed a decreasing rate of 247 km3/year. Moreover, basin-wise analysis indicated the maximum decrease in elevation of basin located in the north and north-west region of GrIS. Annual changes obtained by differencing the AltiKa cycle of the same month (so that the surface condition will remain same) between the two consecutive years, specifically during 2014–2015 and 2015–2016 over the entire GrIS contributed volume loss of 187 and 210 km3, respectively, indicating an enhanced decrease for a later period.

Impact of Cartosat-1 orography on weather prediction in a high-resolution NCMRWF unified model

The current study reports for the first time an application of orography from the Cartosat-1 satellite digital elevation model (DEM) generated at a source resolution of 30 m in a convection-permitting numerical weather prediction model. The effects of improvements in the representation of orography have been examined in the high-resolution regional National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model predictions for a heavy rainfall event over the city of Chennai. A time-lagged ensemble method is employed to account for the uncertainties associated with the initial conditions, which can better forecast extreme weather events than single forecasts. The simulations reveal that the predictions based on Cartosat-1 DEM capture the local details of the rainfall distribution better than the National Aeronautics and Space Administration shuttle radar topography mission DEM-based predictions, and better represent the orographic and thermal uplifting. The spatio-temporal patterns of the simulated rainfall over Chennai are superior in Cartosat-1 DEM-based simulations mainly due to the enhanced wind convergence and moisture transport. The present study reveals the role of mountains in the enhancement of heavy rainfall events over coastal cities and highlights the potential use of high-resolution orography in the improvement of the operational weather forecasting skill of the NCMRWF Unified Model.