Global Shuttle Radar Topography Mission Research Articles

An adaptive low‐rank group sparse model based on edge‐preserving for eliminating mixed noise in SRTM

AbstractThe Shuttle Radar Topography Mission (SRTM) is a digital representation of the terrain surface morphology that contains rich terrain information and is widely used in environmental analyses. However, SRTM is adversely affected by mixed noise, which typically include random and stripe noise. Mixed noise results in the significant loss of topographic information, which reduce the validity of related research. To eliminate mixed noise in SRTM data, we propose an adaptive low‐rank group sparse model based on edge preservation (ALGS_EP) to remove mixed noise from datasets. The method relies on a low‐rank group sparse model that considers the gradient features of the terrain. It calculates a terrain factor to adapt the noise elimination model to terrain changes. Additionally, it integrates with the edge structure of elevation data and applies a double‐gradient constraint to preserve the structural details of the elevation data. The proposed model, built upon the alternating direction multiplier method framework, enhances the traditional weighted kernel paradigm minimization algorithm by introducing variable weights that adjust according to the gradient of elevation data during iterations. Additionally, it incorporates the correlation between strip noise and residual data blocks when computing the iteration count, ensuring an iterative solution approach that converges to the optimal solution. We used ALGS_EP to process global SRTM 1 data and published a higher‐quality and higher‐precision elevation dataset. The elevation data noise before and after noise elimination were statistically analyzed. Simulated and empirical results show that the model is highly robust and more effective than existing methods in both visual and quantitative evaluations. The noise elimination rate was 97.6%, compared to the original data. Therefore, this research was valuable for applications that use digital elevation model as an important data layer.

High-altitude exposure decreases bone mineral density and its relationship with gut microbiota: Results from the China multi-ethnic cohort (CMEC) study

BackgroundGeographic altitude is a potent environmental factor for human microbiota and bone mineral density. However, little evidence exists in population-based studies with altitude diversity ranges across more than 3000 m. This study assessed the associations between a wide range of altitudes and bone mineral density, as well as the potential mediating role of microbiota in this relationship. MethodsA total of 99,556 participants from the China Multi-Ethnic Cohort (CMEC) study were enrolled. The altitude of each participant was extracted from global Shuttle Radar Topography Mission (SRTM) 4 data. Bone mineral density was measured by calcaneus quantitative ultrasound index (QUI). Stool samples were collected for 16S rRNA gene sequencing (n = 1384). The metabolites of gut microbiota, seven kinds of short-chain fatty acids (SCFAs), were detected by gas chromatography–mass spectrometry (GC-MS, n = 128). After screening, 73,974 participants were selected for the “altitude-QUI” analysis and they were placed into the low-altitude (LA) and high-altitude (HA) groups. Additionally, a subgroup (n = 1384) was further selected for the “altitude-microbiota-QUI” analysis. Multivariate linear regression models and mediation analyses were conducted among participants. ResultsA significant negative association between high-altitude and QUI was obtained (mean difference = −0.373 standard deviation [SD], 95% confidence interval [CI]: −0.389, −0.358, n = 73,974). The same negative association was also observed in the population with microbiota data (mean difference = −0.185 SD, 95%CI: −0.360, −0.010, n = 1384), and a significant mediating effect of Catenibacteriumon on the association between altitude and QUI (proportion mediated = 25.2%, P = 0.038) was also noticed. Additionally, the acetic acid, butyric acid, and total amount of seven SCFAs of the low-altitude group were significantly higher than that of the high-altitude group (P < 0.05). ConclusionHigh-altitude exposure may decrease bone mineral density in adults, thus increasing the risk of osteoporosis. The modulation of gut microbiota may be a potential strategy for alleviating the decrease of bone mineral density.

COMPARISON OF ASTER AND SRTM DIGITAL ELEVATION MODELS AT ONE-ARC-SECOND RESOLUTION OVER TURKEY

In February 2000, the “Shuttle Radar Topography Mission (SRTM)” satellite captured elevation data by scanning the Earth landmasses between the 60 o North and South latitudes. After the mission of 11 days, the collected data were processed, and a Digital Elevation Model (DEM) within one arc-second resolution for United States and three arc-second resolutions for the other parts of the globe was created and published on the NASA servers. Recently, a global SRTM DEM with one-arc-second resolution has been released. Additionally, ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) is a sensor boarded on the Terra satellite in 1999. The sensor has been collecting satellite imagery since 2000. The ASTER GDEM at one-second resolution was released to the public, which is the most complete DEM of the earth ever made. In this study, SRTM and ASTER DEMs with one arc-second resolution over Turkish territory was evaluated by means of a local DEM produced from 1:25K national topographic maps. Results show that the accuracy of the SRTM DEM is better than the ASTER GDEM with respect to the local DEM.

An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery

This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM) data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC), which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3), Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results.

Accuracy analysis of the 2014–2015 Global Shuttle Radar Topography Mission (SRTM) 1 arc-sec C-Band height model using International Global Navigation Satellite System Service (IGS) Network

Global Shuttle Radar Topography Mission (SRTM) data products have been widely used in Earth Sciences without an estimation of their accuracy and reliability even though large outliers exist in them. The global 1 arc-sec, 30 m resolution, SRTM C-Band (C-30) data collected in February 2000 has been recently released (2014–2015) outside North America. We present the first global assessment of the vertical accuracy of C-30 data using Ground Control Points (GCPs) from the International GNSS Service (IGS) Network of high-precision static fiducial stations that define the International Terrestrial Reference Frame (ITRF). Large outliers (height error ranging from –1285 to 2306 m) were present in the C-30 dataset and 14% of the data were removed to reduce the root mean square error (RMSE) of the dataset from ∼187 to 10.3 m which is close to the SRTM goal of an absolute vertical accuracy of RMSE ∼10 m. Globally, for outlier-filtered data from 287 GCPs, the error or difference between IGS and SRTM heights exhibited a non-normal distribution with a mean and standard error of 6.5 ± 0.5 m. Continent-wise, only Australia, North and South America complied with the SRTM goal. At stations where all the X- and C-Band SRTM data were present, the RMSE of the outlier-filtered C-30 data was 11.7 m. However, the RMSE of outlier-included dataset where C- and X-Band data were present was ∼233 m. The results suggest that the SRTM data must only be used after regional accuracy analysis and removal of outliers. If used raw, they may produce results that are statistically insignificant with RMSE in 100s of meters.

A comparison of lidar, radar, and field measurements of canopy height in pine and hardwood forests of southeastern North America

Forest canopy height is essential information for many forest management activities and is a critical parameter in models of ecosystem processes. Several methods are available to measure canopy height from single-tree to regional and global scales, but the methods vary widely in their sensitivities, leading to different height estimates even for identical stands. We compare four technologies for estimating canopy height in pine and hardwood forests of the Piedmont region of North Carolina, USA: (1) digital elevation data from the global Shuttle Radar Topography Mission (SRTM) C-band radar interferometry, (2) X- and P-band radar interferometry from the recently developed airborne Geographic Synthetic Aperture Radar (GeoSAR) sensor, (3) small footprint lidar measurements (in pine only), and (4) field measurements acquired by in situ forest mensuration. Differences between measurements were smaller in pine than in hardwood forests, with biases ranging from 5.13 to 12.17 m in pine (1.60–13.77 m for lidar) compared to 6.60–15.28 m in hardwoods and RMSE from 8.40 to 14.21 m in pine (4.73–14.92 m for lidar) compared to 9.54–16.84 in hardwood. GeoSAR measurements of canopy height were among the most comparable measurements overall and showed potential for successful calibration, with R 2 = 0.87 in pine canopies and R 2 = 0.38 in hardwood canopies from simple linear regression. An improved calibration based on differential canopy penetration is presented and applied to SRTM measurements, resulting in canopy height estimates in pine forests with RMSE and standard error <4.00 m. Each of the remotely sensed methods studied produces reasonable and consistent depictions of canopy height that can be compared with data of similar provenance, but due to differences in underlying sensitivities between the methods, comparisons between measurements from various sources require cross-calibration and will be most useful at broad scales.

Identifying global dust source areas using high‐resolution land surface form

Dust originates from specific, landscape‐scale (≈1–10 km) features; however, dust erodibility is commonly parameterized in global climate models using grid‐cell‐scale (≈1–5°) relationships. We examine surface characteristics from the high‐ resolution (90 m at equator) global Shuttle Radar Topography Mission digital elevation model and aerosol optical thickness (AOT) measurements from the Multiangle Imaging Spectroradiometer (MISR) instrument to find landscape‐scale characteristics common to dust producing regions. Regions with climatologically high aerosol optical thickness are associated with extremely low‐slope landscapes at ≈5 km scale that also have high‐elevation variance, likely due to aeolian features. We refer to these landscape characteristics as “levelness” and “residual landscape roughness,” and we extrapolate these relationships globally to identify geomorphologically based dust erodibility functions on the basis of levelness and residual landscape roughness criteria. We test these with the Dust Entrainment and Deposition model embedded in the Model for Atmospheric Transport and Chemistry global atmospheric tracer transport model, driven by reanalysis meteorology. The modeled global dust spatial distributions calculated with these erodibility parameters agree well with observations of MISR AOT and deposition and in the modeling framework used here represent an improvement over existing parameterizations in predicting the spatial patterns of dust sources in the Sahara and the relative amount of emission of sources in the Dust Belt to those in the Southern Hemisphere; however, agreement with observations is less good in high‐relief areas such as the Asian dust source regions, possibly because of the coarse resolution of the meteorological fields used to drive the model.

Automatic digital terrain model generation using Cartosat‐1 stereo images

PurposeCartosat‐1 is the first Indian Remote Sensing satellite, developed for topographic mapping, able to collect in‐track high‐resolution stereo images with a 2.5 m pixel size. In the framework of the Cartosat‐1 Scientific Assessment Programme (C‐SAP), the Politecnico di Milano University (Italy) evaluated the performances of the Cartosat‐1 satellite in the generation of digital terrain models (DTMs) from stereo‐couples. The purpose of this paper is to describe in detail the outcomes for the Salon de Provence (France) test site, with respect to existing standards and products actually used in France and also to provide a comparison with the global Shuttle Radar Topography Mission's DTM freely available from by NASA.Design/methodology/approachThe Cartosat‐1 data processing was done using the commercial off‐the‐shelf software ENVI®, selected for investigating the capabilities and limits of the system using standard image processing tools, so from the point of view of a typical remote sensing user. The data processing involved the following aspects: data pre‐processing; optimization of the DTM's extraction procedure; analysis of the influence of ground control points' (GCPs) in the generated DTMs; analysis of the influence of the DTM's resolution in the elevation accuracy; and post‐processing refinement.FindingsWhen generating relative DTMs an error was observed in elevation of some hundreds of meters. After georeferencing, the root mean square error (RMSE) was between 9.0 and 14.2 m and the LE90 between 16.1 and 19.0 m. When generating absolute DTMs, the optimum number of GCPs was found to be 9, with a regular geometric distribution (4.6 m RMSE and 6.5 m LE90 for 10 m grid cell size). Post‐processing may be applied to enhance results (1.6 m RMSE and 2.0 m LE90 for 10 m grid cell size). In this case, the absolute DTMs fulfilled and also overcame the standards required for the IGNs and Spot Image's Reference 3D®.Originality/valueThis paper describes the outcomes of the C‐SAP led by the International Society for Photogrammetry and Remote Sensing and the Indian Space Research Organisation for evaluating the capabilities of the last Cartosat‐1 satellite. The aim is to provide remote sensing users a comprehensive study about the potentialities and limits of the Cartosat‐1 images for multi‐resolution DTM generation (from 5 to 90 m grid cell size).

Earth observation image and DEM information aggregation for realistic 3-D visualization of natural landscapes

This paper presents a system to integrate digital elevation model (DEM) enhancement and Earth Observation (EO) image analysis for realistic three-dimensional (3-D) rendering applications. There is an increasing interest in interferometric synthetic aperture radar (InSAR) data, principally due to the availability of the nearly global Shuttle Radar Topography Mission coverage. To remove artifacts and noise from an InSAR DEM, a nonstationary Bayesian filtering is applied that preserves structural information. Land-cover or man-made structures are easily recognized in an optical image. The corresponding geometry encapsulated in the DEM differs from our implicit perception and generally leads to unrealistic 3-D rendering. To improve this, DEM regularization is achieved using only the visualization dataset (optical image and DEM). It consists of extracting relevant information from the optical image and integrate them in the filtered DEM. To gather image information, an object-based description of large optical EO images is obtained in two stages 1) an image is segmented to create a partition of regions and 2) a novel dynamical algorithm is proposed to extract the regions and encode them in a tree structure. Regions are modeled by objects primitives stored in a database. Spatial relationships between regions are reflected by the presented tree of regions. Using the object-based description generation, structures to be integrated into the DEM are interactively selected and classified among a set of user-thematic. Each thematic is associated with a corresponding elevation modeling and enables to estimate the region's 3-D structure. The proposed object line processing provides more realistic 3-D visualizations.