SRTM vs ASTER elevation products. Comparison for two regions in Crete,Greece

The Shuttle Radar Topography Mission (SRTM) collected elevation data over 80% of earth's land area during an 11‐day Space Shuttle mission. With a horizontal resolution of 3 arc sec, SRTM represents the best quality, freely available digital elevation models (DEMs) worldwide. Since the SRTM elevation data are unedited, they contain occasional voids, or gaps, where the terrain lay in the radar beam's shadow or in areas of extremely low radar backscatter, such as sea, dams, lakes and virtually any water‐covered surface. In contrast to the short duration of the SRTM mission, the ongoing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is continuously collecting elevation information with a horizontal resolution of 15 m. In this paper we compared DEM products created from SRTM data with respective products created from ASTER stereo‐pairs. The study areas were located in Crete, Greece. Absolute DEMs produced photogrammetricaly from ASTER using differentially corrected GPS measurements provided the benchmark to infer vertical and planimetric accuracy of the 3 arc sec finished SRTM product. Spatial filters were used to detect and remove the voids, as well as to interpolate the missing values in DEMs. Comparison between SRTM‐ and ASTER‐derived DEMs allowed a qualitative assessment of the horizontal and vertical component of the error, while statistical measures were used to estimate their vertical accuracy. Elevation difference between SRTM and ASTER products was evaluated using the root mean square error (RMSE), which was found to be less than 50 m.     

NASA EOS Terra ASTER: Volcanic topographic mapping and capability

Up-to-date, accurate topographic data are a crucial resource for volcanic research and risk mitigation efforts, in particular, for modeling volcanic flow processes at a detailed spatial resolution. In this paper, we examine the utility of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument currently operating on the NASA Terra satellite, which provides near infrared (VNIR) stereo imaging from which topography can be derived. We wrote software to generate digital elevation models (DEMs) from the ASTER level 1A product, which employs an automated stereo matching technique to calculate the parallax offsets between the images acquired by the nadir- and aft-looking sensors. Comparison of ASTER DEMs with DEMs derived from other sources (digitized 1:50 K topographic maps and aerial interferometric radar) at Ruapehu volcano reveal an RMS error of about 10 m for the ASTER DEM, in the absence of significant atmospheric water vapor. A qualitative assessment of surface features showed that the ASTER DEM is superior to the interpolated 1:50 K map product but falls short of the detail provided by aerial interferometric radar, especially in terms of stream channel preservation. A second ASTER DEM was generated for Taranaki volcano, where previously only 1:50 K topographic map data were available. Although the 2000 Space Shuttle radar topography mission (SRTM) will largely remedy the previous global paucity of adequate topographic data at volcanoes, such as Taranaki, we anticipate the problem that at active volcanoes, the topography may change significantly following activity, rendering the SRTM data inaccurate. With the high temporal coverage of the dataset, ASTER not only provides a means to update significant (>10 m) topographic measurements at active volcanoes via a time-series of DEMs, but also provides a simultaneous means to map surface cover and localized land-use via the near infrared sensors. Thus we demonstrate the potential for up-to-date volcanic economic risk assessment using geographic information systems (GIS) analysis.     

低山丘陵区多源数字高程模型误差分析北大核心CSCD

作为多学科交叉与渗透产物的数字高程模型(DEM)已在诸多学科和领域及实际应用中发挥了重要作用,但目前能够免费获取的高分辨全球DEM在不同区域仍存在很大的不确定性,应用之前进行质量评估至关重要。以烟台市为实验区,以大比例尺地形图(1∶10 000)生成的DEM为参照,结合坡度、坡向和土地覆被类型等地学因子,定量分析了目前广泛应用的两个版本ASTER GDEM(先进星载热辐射和反射辐射计全球数字高程模型)ASTETR 1和ASTER 2及不同空间分辨率SRTM DEM(航天飞机雷达地形测绘任务)(SRTM 1:～30m和SRTM 3:～90m)在低山丘陵区高程、坡度及坡向误差。结果表明:在研究区域内,ASTER 1、ASTER 2、SRTM 3、SRTM 1总体高程均方根误差分别为8.7m、6.3m、3.7m和2.9m。ASTER与SRTM的高程精度不同程度地受坡度、坡向以及土地覆被类型等地学因子的影响,DEM误差随坡度增加而增大,其中SRTM 3精度对该因子最敏感。尽管坡向对DEM精度影响不明显(4种DEM在不同坡向上的均方根误差波动范围均不超过2m),但是不同土地覆被类型下这4种DEM精度差异显著。此外,分析4种DEM提取的坡度可知,SRTM 1的均方根坡度误差最低(2.5°)、ASTER 1与ASTER 2的坡度的均方根误差大致相同(3.6°、3.9°)、SRTM 3的坡度均方根误差最高(4.3°)。坡向的精度SRTM 1最高,ASTER 1与ASTER 2次之,SRTM 3最低。研究结果对我国低山丘陵区ASTER GDEM与SRTM DEM的应用与精度评估具有一定的借鉴作用。     

SRTM DEM accuracy assessment over vegetated areas in Norway

Results from the Shuttle Radar Topography Mission (SRTM) are presented. The SRTM C‐band and X‐band digital elevation models (DEMs) are evaluated with regard to elevation accuracies over agricultural fields, forest areas and man‐made features in Norway. High‐resolution digital maps and satellite images are used as background data. In general, many terrain details can be observed in the SRTM elevation datasets. The elevation accuracy (90% confidence level) of the two SRTM systems is estimated to less than 6.5 m for open agricultural fields and less than 11 m considering all land surface covers. This is better than specifications. Analysis of dense Norwegian forest stands shows that the SRTM system will produce elevation data that are as much as 15 m higher than the ground surface. The SRTM DEM products will therefore partly indicate canopy elevations in forested areas. We also show that SRTM data can be used to update older DEMs obtained from other sources, as well as estimating the volume of rock removed from large man‐made gravel pits.     

A mutual assessment of the uncertainties of digital elevation models using the triple collocation technique

This study investigates the uncertainties of digital elevation models (DEMs) using the triple collocation (TC) method. DEMs from satellite missions are important for many geoscience disciplines and for economic benefits and are freely available. Validating DEMs is necessary to select an appropriate model for a given region and application. Provided certain assumptions about the error structure of any three data sets – measuring the same phenomenon – can be made, the TC approach can be used to provide an unbiased and scaled estimate of the error variances of the data sets, without requiring a reference data. We compared the TC approach to the traditional approach of using a reference data set using the Shuttle Radar Topography Mission version 4.1 (SRTM v4.1) DEM, ASTER (the Advanced Spaceborne Thermal Emission and Reflection Radiometer) GDEM (Global DEM) version 2, the 1 arc-minute global relief model (ETOPO1), a DEM compiled by the Survey and Mapping Division of Ghana (SMD DEM), and 545 ground control stations (GCSs). The error estimates for the DEMs via TC were considerably smaller than those obtained from the reference-based approach. As an example, the best performing DEM (SRTM v4.1) recorded a root-mean-square error (RMSE) of 15.601 m using the GCSs as reference, while its TC-derived accuracy was 6.517 m. We note that based on the results of the TC, the estimated error of the GCSs is approximately 14 m. Using a data set with an error of 14 m to validate other data sets is certainly bound to result in unfavorable results. Thus, we have demonstrated in this work that the TC approach is able to provide an unbiased error of DEMs. The approach is important even for regions where GCSs are highly accurate, but more so for regions with low-quality GCSs.     

Evaluating error associated with lidar-derived DEM interpolation

Light detection and ranging (lidar) technology is capable of precisely measuring a variety of vegetation metrics, the estimates of which are usually based on relative heights above a digital elevation model (DEM). As a result, the development of these elevation models is a critical step when processing lidar observations. A number of different algorithms exist to interpolate lidar ground hits into a terrain surface. We tested seven interpolation routines, using small footprint lidar data, collected over a range of vegetation classes on Vancouver Island, British Columbia, Canada. The lidar data were randomly subsetted into a prediction dataset and a validation dataset. A suite of DEMs were then generated using linear, quintic, natural neighbour, regularized spline, spline with tension, a finite difference approach (ANUDEM), and inverse distance weighted interpolation routines, at spatial resolutions of 0.5, 1.0 and 1.5 m. In order to examine the effects of terrain and ground cover on interpolation accuracies, the study area was stratified by terrain slope, vegetation structural class, lidar ground return density, and normalized difference vegetation indices (NDVI) derived from Quickbird and Landsat7 ETM+ imagery. The root mean square (RMS) and mean absolute errors of the residuals between the surfaces and the validation points indicated that the 0.5 m DEMs were the most accurate. Of the tested approaches, the regularized spline and IDW algorithms produced the most extreme outliers, sometimes in excess of ±6 m in sloping terrain. Overall, the natural neighbour algorithm provided the best results with a minimum of effort. Finally, a method to create prediction uncertainty maps using classification and regression tree (CART) analysis is proposed.     

Evaluation of data sources for mapping glacial meltwater features

The meltwater system of disintegrating ice sheets provides an important source of information for the reconstruction of ice-retreat patterns during deglaciation. Recent method development in glacial geomorphology, using satellite imagery and digital elevation models (DEMs) for glacial landform mapping, has predominantly been focused on the identification of lineation and other large-scale accumulation features. Landforms created by meltwater have often been neglected in these efforts. Meltwater features such as channels, deltas and fossil shorelines were traditionally mapped using stereo interpretation of aerial photographs. However, during the transition into the digital era, driven by a wish to cover large areas more economically, meltwater features were lost in most mapping surveys. We have evaluated different sets of satellite images and DEMs for their suitability to map glacial meltwater features (lateral meltwater channels, eskers, deltas, ice-dammed lake drainage channels and fossil shorelines) in comparison with the traditional mapping from aerial photographs. Several sets of satellite images and DEMs were employed to map the landform record of three reference areas, located in northwestern Scotland, northeastern Finland and western Sweden. The employed satellite imagery consisted of Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Satellite Pour l'Observation de la Terre (SPOT) 5 and Indian Remote Sensing (IRS) 1C, and the DEMs used were from NEXTMap Britain, Panorama, National elevation data set of Sweden and National Land Survey of Finland. ASTER images yielded better results than the panchromatic band of Landsat 7 ETM+?in all three regions, despite the same spatial resolution of the data. In agreement with previous studies, this study shows that DEMs display accumulation features such as eskers suitably well. Satellite images are shown to be insufficiently detailed for the interpretation of smaller features such as meltwater channels. Hence, satellite imagery and DEMs of intermediate resolution contain meltwater system information only at a general level that allows for the identification of landforms of medium to large sizes. It is therefore pertinent that data with an appropriate spatial and spectral resolution are accessed to fulfil the need of a particular mapping effort. Stereo interpretation of aerial photographs continues to be an advisable method for local meltwater system reconstructions; alternatively, it can be replaced by mapping from high-resolution DEMs such as NEXTMap Britain. For regional to sub-continental reconstructions, the use of ASTER satellite imagery is recommended, because it provides both spectral and spatial resolutions suitable for the identification of meltwater features on a medium to large scale.     

Terrain Super‐resolution through Aerial Imagery and Fully Convolutional Networks

Despite recent advances in surveying techniques, publicly available Digital Elevation Models (DEMs) of terrains are low‐resolution except for selected places on Earth. In this paper we present a new method to turn low‐resolution DEMs into plausible and faithful high‐resolution terrains. Unlike other approaches for terrain synthesis/amplification (fractal noise, hydraulic and thermal erosion, multi‐resolution dictionaries), we benefit from high‐resolution aerial images to produce highly‐detailed DEMs mimicking the features of the real terrain. We explore different architectures for Fully Convolutional Neural Networks to learn upsampling patterns for DEMs from detailed training sets (high‐resolution DEMs and orthophotos), yielding up to one order of magnitude more resolution. Our comparative results show that our method outperforms competing data amplification approaches in terms of elevation accuracy and terrain plausibility.     

Spatial accuracy of orthorectified IKONOS imagery and historical aerial photographs across five sites in China

High‐resolution (?1?m) satellite imagery and archival World War II era (WW2) aerial photographs are currently available to support high‐resolution long‐term change measurements at sites across China. A major limitation to these measurements is the spatial accuracy with which this imagery can be orthorectified and co‐registered. We orthorectified IKONOS 1?m resolution GEO‐format imagery and WW2 aerial photographs across five 100?km² rural sites in China with terrain ranging from flat to hilly to mountainous. Ground control points (GCPs) were collected uniformly across 100?km² IKONOS scenes using a differential Global Positioning Systems (GPS) field campaign. WW2 aerial photos were co‐registered to orthorectified IKONOS imagery using bundle block adjustment and rational function models. GCP precision, terrain relief and the number and distribution of GCPs significantly influenced image orthorectification accuracy. Root mean square errors (RMSEs) at GCPs for IKONOS imagery were <2.0?m (0.9–2.0?m) for all sites except the most heterogeneous site (Sichuan Province, 2.6?m), meeting 1:12?000 to 1:4800 US National Map Accuracy Standards and equalling IKONOS Precision and Pro format accuracy standards. RMSEs for WW2 aerial photos ranged from 0.2 to 3.5?m at GCPs and from 4.4 to 6.2?m at independent checkpoints (ICPs), meeting minimum requirements for high‐resolution change detection.     

Using satellite remote sensing for DEM extraction in complex mountainous terrain: Landscape analysis of the Makalu Barun National Park of eastern Nepal

The design and management of national parks and other protected areas requires a broad base of physiographic and geo-ecological information about the landscape. This paper evaluates the effectiveness of satellite remote sensing for photogrammetric stereo-mapping and digital elevation model (DEM) extraction within remote mountainous terrain. As a case study, a landscape analysis of the Makalu Barun National Park and Conservation Area of east Nepal (27.5° N, 87.0° E) was examined. The study area is a highly complex and rugged mountain landscape, with extreme topographic relief and an elevation gradient spanning more than 8300 m. A DEM extracted from stereo SPOT imagery resulted in a median disagreement of 58 m when compared to a DEM generated from a conventionally digitized GIS dataset of topographic contours (scale=1:250 000). Visual comparison of the two DEMs showed substantial agreement at the landscape scale, while larger scale comparison of 100 m contours revealed some localized differences. The SPOT extracted DEM provided equal or better basis for orthorectification of satellite imagery when compared to the conventional DEM. Derivative landscape analysis outputs, such as hydrological modelling, drainage networks and watershed boundaries, compared well with results based upon the conventional dataset. Intermediate map products useful for field research and mapping included production of an orthorectified satellite base-map image. Additionally, a fused multisensor high resolution image of the study area, combining Landsat Thematic Mapper (TM) and SPOT imagery at 10 m resolution, was orthorectified to produce a false-colour satellite image map highlighting the spectral discrimination between land cover classes.     

ASTER DEMs for geomatic and geoscientific applications: a review

Most geoscientific applications using georeferenced cartographic/geospatial data require good knowledge and visualization of the topography of the Earth's surface. For example, mapping of geomorphological features is hardly feasible from a single image; three‐dimensional (3D) information has to be generated or added for a better interpretation of the two‐dimensional data. Since the early emergence of earth observation satellites, researchers have investigated different methods of extracting 3D information using satellite data. Since the early experiments with the Earth Terrain Camera flown onboard SkyLab in 1973 to 1974, various analogue or digital sensors in the visible or microwave spectrum have been flown to provide researchers and geoscientists with spatial data for extracting and interpreting 3D information of the Earth's surface. Stereo viewing using digital scanner images, such as with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) along‐track sensors, was, and still is, the most common method used by the mapping, geomatic, and geoscientific communities for generating digital elevation models (DEMs). This paper will review the basic characteristics of stereoscopy and its application to the ASTER system for DEM generation. It will thus address the methods, algorithms and commercial software to extract absolute or relative elevation and assess their performance using the results from various research and commercial organizations. It will finally discuss the use of stereo ASTER DEMs for different geomatic and geoscientific applications.     

Geomorphological changes associated with underground coal mining in the Fushun area,northeast China revealed by multitemporal satellite remote sensing data

Fushun is a famous coal-mining city in northeastern China with more than 100 years of history. Long-term underground coal mining has caused serious surface subsidence in the eastern part of the city. In this study, multitemporal and multisource satellite remote sensing data were used to detect subsidence and geomorphological changes associated with underground coal mining over a 10-year period (1996–2006). A digital elevation model (DEM) was generated through Synthetic Aperture Radar (SAR) interferometry processing using data from a pair of European Remote Sensing Satellite (ERS) SAR images acquired in 1996. In addition, a Shuttle Radar Topography Mission (SRTM) DEM obtained from data in 2000 and an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM from 2006 were used for this study. The multitemporal DEMs indicated that the maximum vertical displacement due to subsidence was around 13 m from 1996 to 2006. Multitemporal ASTER images showed that the flooded water area associated with subsidence had increased by 1.73 km² over the same time period. Field investigations and ground level measurements confirmed that the results obtained from the multitemporal remote sensing data agreed well with ground truth data. This study demonstrates that DEMs derived from multisource satellite remote sensing data can provide a powerful tool to map geomorphological changes associated with underground mining activities.     

Validation of operational AVHRR subpixel snow retrievals over the European Alps based on ASTER data

Snow is of great economic and social importance for the European Alps. Accurate monitoring of the alpine snow cover is a key component in studying regional climate change as well as in daily weather forecasting and snowmelt run‐off modelling. These applications require snow cover information on a high temporal resolution in near‐real time. For the European Alps, operational snow cover fraction maps are generated on a daily basis using data from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) platforms. Snow cover distribution is inherently discontinuous and heterogeneous in this mountainous region. We have therefore implemented a straightforward multiple endmember unmixing approach to estimate fractional snow cover. Subpixel proportions are difficult to validate because similar products are not available and appropriate ground‐based observations do not exist. In this study, we validate AVHRR subpixel snow retrievals using binary classified data sets from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to establish absolute errors of our operational approach at three test sites. Our analysis indicates that the AVHRR subpixel maps compare well with the aggregated ASTER data, showing an overall correlation of 0.78 and providing subpixel estimates with a mean absolute error of 10.4% fractional snow cover. Discrepancies between AVHRR and ASTER snow fraction maps can be attributed to varying snow conditions, terrain effects and density in forest cover.     

Glacier-surface velocities in alpine terrain from optical satellite imagery—Accuracy improvement and quality assessment

The worldwide retreat of mountain glaciers has important consequences for the water, food, and power supply of large and densely populated areas in South and Central Asia. Successful mitigation of the hydrological impacts on societies as well as assessing glacier-related hazards require large-scale monitoring of glacier dynamics. However, detailed glaciological data from the Asian highlands are lacking, due to its size and difficult accessibility. We have applied a novel technique for precise orthorectification, co-registration, and sub-pixel correlation of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery to derive surface velocities of Himalayan glaciers. Our approach allows for the correction of offsets due to attitude effects and sensor distortions, as well as elevation errors if a digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) was used for orthorectification. After post-processing, the error on the displacements is on the order of 2–4 m per correlation. Translated into annual velocities, this error is reduced (increased) when the correlated images are more (less) than a year apart. Through application of a filtering procedure and several quality tests, the consistency of the results is validated to provide confidence in the remotely sensed velocity measurements, despite the lack of ground control. This novel approach allows fast, easy, and economically viable acquisition of detailed glaciological data in areas of difficult access and provides a means for large-scale monitoring of glaciers in high mountainous terrain.     

Estimation of average DEM accuracy under linear interpolation considering random error at the nodes of TIN model

The concept of a digital elevation model (DEM) can be used for a digital representation of any single‐valued surface such as a terrain relief model (digital terrain model, DTM). DEMs are widely used in remote sensing, geographical information systems (GIS), and virtual reality. Estimating the accuracy of a DEM is an essential issue in the acquisition of spatial data, particularly for applications that require a highly accurate DEM, such as engineering applications. The accuracy of a DEM is subject to many factors such as the number of sampling points, the spatial distributions of the sampling points, the methods used for interpolating surface elevations, the propagated error from the source data, and other factors. Of these factors, this study will focus on estimating the mean elevation error in a DEM surface that is caused by errors of component nodes in a triangulated irregular network (TIN). This paper will present a newly derived mathematical formula, with the details of the procedure used to derive this formula, to study the relationship between the errors at the TIN nodes and the propagated mean elevation error of a DEM surface that is linearly constructed from the TIN. We have verified the analytical formula by numerical simulation. The experimental results confirm the theoretical derivation of the formula.     

Advances in the data compression of digital elevation models

The maintenance and dissemination of spatial databases requires efficient strategies for handling the large volumes of data that are now publicly available. In particular, satellite and aerial imagery, radar, LiDAR, and digital elevation models (DEMs) are being utilised by a sizeable user-base, for predominantly environmental applications. The efficient dissemination of such datasets has become a key issue in the development of web-based and distributed computing environments. However, the physical size of these datasets is a major bottleneck in their storage and transmission. The problem is often exaggerated when the data is supplied in less efficient, proprietary or national data formats.This paper presents a methodology for the lossless compression of DEMs, based on the statistical correlation of terrain data in local neighbourhoods. Most data and image compression algorithms fail to capitalise fully on the inherent redundancy in spatial data. At the same time, users often prefer a uniform solution to all their data compression requirements, but these solutions may be far from optimal. The approach presented here can be thought of as a simple pre-processing of the elevation data before the use of traditional data compression software frequently applied to spatial data sets, such as GZIP. Identification and removal of the spatial redundancy in terrain data, with the use of optimal predictors for DEMs and optimal statistical encoders such as Arithmetic Coding, gives even higher compression ratios. Both GZIP and our earlier approach of combining a simple linear prediction algorithm with Huffman Coding are shown to be far from optimal in identifying and removing the spatial redundancy in DEMs. The new approaches presented here typically halve the file sizes of our earlier approach, and give a 40–62% improvement on GZIP-compressed DEMs.     

Combination of stereo SAR and InSAR for DEM generation using TanDEM-X spotlight data

The objective of this work is to generate accurate digital elevation models (DEMs) from synthetic aperture radar (SAR) interferograms using the spotlight and staring spotlight modes during the TanDEM-X (TDX) science phase. We use stereo SAR with TDX or TerraSAR-X (TSX) to estimate absolute heights of natural or man-made reflectors for input to the unwrapped interferograms. The accuracy of the absolute heights in the DEMs was a few decimetres in flat regions and a few metres in the hilly and rugged terrain.     

An evaluation of LIDAR- and IFSAR-derived digital elevation models in leaf-on conditions with USGS Level 1 and Level 2 DEMs

An assessment of four different remote sensing based methods for deriving digital elevation models (DEMs) was conducted in a flood-prone watershed in North Carolina. New airborne LIDAR (light detecting and ranging) and IFSAR (interferometric synthetic aperture radar (SAR)) data were collected and corresponding DEMs created. These new sources were compared to two methods: Gestalt Photomapper (GPM) and contour-to-grid, used by the U.S. Geological Survey (USGS) for creating DEMs. Survey-grade points (1470) for five different land cover classes were used as reference points. One unique aspect of this study was the LIDAR and IFSAR data were collected during leaf-on conditions. Analyses of absolute elevation accuracy and terrain slope were conducted. The LIDAR- and contour-to-grid derived DEMs exhibited the highest overall absolute elevation accuracies. Elevation accuracy was found to vary with land cover categories. Elevation accuracy also decreased with increasing slopes—but only for the scrub/shrub land cover category. Appreciable terrain slope errors for the reference points were found with all methods.     

多源外部DEM对两轨DINSAR测量结果影响分析

在总结两轨差分中参考DEM影响的最新研究成果基础上,以青藏高原上典型平地和山地作为研究区,利用理论上没有形变的ERS Tandem像对以及3种常用外部参考DEM（SRTM,ASTER GDEM,1∶5万DEM）,使用ROI_PAC软件进行两轨差分干涉试验。实例证明：SRTM更适合作为两轨差分中的外部参考DEM,并对此试验结果予以解释分析,即多源DEM数据质量的差异导致干涉图与DEM配准精度的不同,并最终反映在差分干涉相位误差中。本文研究结论对提高DInSAR处理精度有参考价值。     

Validation and comparison of different techniques for the derivation of digital elevation models and volcanic monitoring (Vulcano Island,Italy)

High accurate digital elevation models (DEM) acquired periodically over a volcanic area can be used for monitoring crustal deformations. Airborne stereoscopic photography is a powerful tool for the derivation of high resolution DEM, especially when combined with Global Positioning System (GPS). We analyse data acquired on Vulcano Island (Italy) to assess the performance of two photogrammetry methods for DEM generation. The first method is based on automatic digital processing of scanned airborne stereo images from a film camera (Wild RC20). In the second method digital stereo data from the multi-spectral High Resolution Stereo Camera-Airborne (HRSC-A) are used. Accuracy assessment through comparison with kinematic GPS height profiles shows that both DEMs have accuracy on the order of few decimetres. Direct comparison of the two DEMs on the La Fossa volcanic cone provides a standard deviation of the residuals of 78 cm. Residuals greater than two metres between the two DEMs acquired at one year interval are locally evidenced in unstable areas with uneven morphology. The application of photogrammetric DEMs is also discussed within a SAR interferometry study carried out on Vulcano Island to evaluate the potentialities of such techniques for ground deformation monitoring. Although accuracy better than 1 m or 2 m is not required for satellite SAR interferometry, we show how the precise photogrammetric DEMs could still significantly improve SAR interferograms of Vulcano Island.