Extraction of bajadas from digital elevation models and satellite imagery

A methodology was designed for the extraction of bajadas from the 15 min US Geological Survey digital elevation models and Landsat Thematic Mapper imagery. The method was demonstrated for the Death Valley-California where progressive eastward tilting has enabled the west-side fans to coalesce and form bajadas. First, the drainage that crossed the uplands and the bajadas was extracted from the DEM. The drainage pixels were successively grown by checking the surrounding pixels on the basis of their gradient. It was concluded that for gradient in the interval [2°,11°] the upslope bajadas border was segmented. In order to eliminate the drainage pixels that belonged to the uplands, the drainage pixels were subtracted. Then, the isolated small 8-connected foreground pixels were identified and subtracted too. Finally, region growing was performed again to the remaining pixels with the same growing criterion. Isolated 8-connected background pixels, representing almost flat regions inside bajadas, were identified and merged to the segmented pixels. At the end, by taking into account the spectral response in the satellite image, the downslope border of bajadas was segmented. The extracted polygon was in agreement with the information depicted on (a) the US Geological Survey topographic map of scale 1:100,000 and (b) the satellite image and (c) the polygon classified manually by a photo-interpreter.     

Digital elevation model generation using ascending and descending ERS-1/ERS-2 tandem data

In this study we show how ascending and descending ERS-1/ERS-2 tandem data can be used to generate a precise digital elevation model (DEM). In particular we present the advantages, for DEM generation, of combining ascending and descending interferometric data pairs with large baselines; unwrapping problems caused by the high fringe rates of these data are minimized by applying a flattening technique based on the use of a data pair with smaller baseline. The presented experiments carried out on the data of an area with steep topography (Etna volcano, Italy) confirm the potentiality of the proposed technique.     

Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery

Soil moisture is important information in semiarid rangelands where vegetation growth is heavily dependent on the water availability. Although many studies have been conducted to estimate moisture in bare soil fields with Synthetic Aperture Radar (SAR) imagery, little success has been achieved in vegetated areas. The purpose of this study is to extract soil moisture in sparsely to moderately vegetated rangeland surfaces with ERS-2/TM synergy. We developed an approach to first reduce the surface roughness effect by using the temporal differential backscatter coefficient (Δσ_wet-dry⁰). Then an optical/microwave synergistic model was built to simulate the relationship among soil moisture, Normalized Difference Vegetation Index (NDVI) and Δσ_wet-dry⁰. With NDVI calculated from TM imagery in wet seasons and Δσ_wet-dry⁰ from ERS-2 imagery in wet and dry seasons, we derived the soil moisture maps over desert grass and shrub areas in wet seasons. The results showed that in the semiarid rangeland, radar backscatter was positively correlated to NDVI when soil was dry (m_v<10%), and negatively correlated to NDVI when soil moisture was higher (m_v>10%). The approach developed in this study is valid for sparse to moderate vegetated areas. When the vegetation density is higher (NDVI>0.45), the SAR backscatter is mainly from vegetation layer and therefore the soil moisture estimation is not possible in this study.     

A test of detecting spring leaf flush within the Alaskan boreal forest using ERS-2 and Radarsat SAR data

This study tests whether ERS-2 and Radarsat Standard Beam SAR data could be used to detect spring green-up of broadleaf forests on floodplains in interior Alaska. SAR data were acquired for pre- and post-budburst dates during the spring of 1997, 1998 and 1999. The images were radiometrically calibrated to dB and then geometrically co-registered to a colour infrared Landsat TM image. Ten broadleaf and ten conifer floodplain forest polygons were then delineated from the Landsat TM image for the extent of each SAR image. The mean dB from each broadleaf and conifer stand was computed from each SAR image to test whether there was a consistent change in mean dB associated with spring leaf-out of broadleaf trees. There was no evidence that the 1997 or 1998 ERS-2 dB SAR data were useful for detecting spring leaf flush. The 1998 Radarsat broadleaf stands all increased in dB after budburst, but this was attributed to an increase in surface moisture from rain, rather than due to spring leaf flush. The 1999 Radarsat stands all decreased in SAR dB after budburst, while control conifer stands increased and decreased in dB during the same time period. Confounding factors such as changing surface moisture, and possibly freezing/ thawing effects may mask any change in ERS-2 or Radarsat SAR dB due to spring leaf flush.     

An automated scheme for glacial lake dynamics mapping using Landsat imagery and digital elevation models: a case study in the Himalayas

Glacial lakes in alpine regions are sensitive to climate change. Mapping and monitoring these lakes would improve our understanding of regional climate change and glacier-related hazards. However, glacial lake mapping over large areas using remote sensing remains a challenge because of various disturbing factors in glacial and periglacial environments. This article presents an automated mapping algorithm based on hierarchical image segmentation and terrain analysis to delineate glacial lake extents. In this algorithm, each glacial lake is delineated with a local segmentation value, and the topographic features derived from digital elevation models (DEMs) are also used to separate mountain shadows from glacial lakes. About 100 scenes of Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images from circa 1990, circa 2000 and 2009 were used to map the glacial lakes and their changes over the entire Himalayas. The results show that the algorithm can map the glacial lakes effectively and efficiently. Mountain shadows or melting glaciers can be differentiated from glacial lakes automatically, and those lakes with mountain shadows can also be identified. Area changes of more than 1000 glacial lakes show that the glacial lakes in the Himalayas have experienced mixed directions of change, while the overall lake areas are expanding at an accelerated rate in the past two decades, indicating great changes to the glacial lakes in the Himalayas.     

Intersection between spacecraft viewing vectors and digital elevation models

A method is presented for determining the latitude, longitude, and altitude of each pixel in a remotely sensed image of a planet for the case where there is significant surface topography. The method works by sequentially stepping along the line-of-sight of each pixel in an image until an intersection with the planet surface is detected. The position and altitude of each pixel on the planet can then be used for further analysis and allows comparison with other data sets. For pixels where no intersection occurs the altitude and location of the tangent point are determined. These pixels are important as they provide views of the planet limb and are useful for studying the vertical structure of the atmosphere. Provision is made for reference to an oblate spheroid when calculating the tangent point, which is required for atmospheric applications on oblate planets such as the Earth. The algorithm requires a digital elevation model, along with the viewing geometry and position of the instrument. Illustrative examples are given using the Martian MOLA topography data set for oblique and limb viewing cases.     

Cardiology education using hypermedia and digital imagery.

A computer-based educational system for the study of cardiovascular imaging is described. This system, based on HyperCard * and a standard Macintosh II, integrates hypertext retrieval, computer graphics, sound, and medical images into a single interactive environment stored on a standard hard disk. This 'hypermedia' approach allows arbitrary complexity coupled with direct, immediate, easy traversal of the images and related text, which provides the opportunity for students to move at their own pace, choose their own direction through the material and repeat as often as desired. Storage on magnetic medium allows for easy updating with new studies and material in order to keep pace with advances in medical imaging technology. The system could be mastered onto CD-ROM for ease of distribution if so desired. The system includes a tutorial on the basics of digital image representation and example studies from cineangiography, nuclear medicine, echocardiography and magnetic resonance imaging of the heart. Quantitative techniques for evaluation of left ventricular function are explained using computer graphics overlays on the original medical images. Color encoded functional images are also included as an aid to visualization of ventricular performance data. The system has proven useful as a primer for digital imaging in cardiology prior to specific case study in a traditional mentor relationship.     

Surface hydrology of low-relief landscapes: Assessing surface water flow impedance using LIDAR-derived digital elevation models

Conventional hydrologic analyses of digital elevation models (DEMs) perform well in areas of high topographic relief, where surface water flow is typically unidirectional, convergent, spatially static, and directed toward a single discharge point at the edge of a catchment. Such analyses do not perform well on landscapes with low topographic relief (e.g., floodplains, river deltas, coastal wetlands, and estuaries) where surface water flow is influenced by subtle topographic depressions and may be bidirectional, divergent, and spatially dynamic in response to hydrologic forcing such as tides or variation in river discharge. We developed a framework for hydrologic analysis of low-relief landscapes using a high-resolution (1 m) DEM derived from light detection and ranging (LIDAR) data collected over a ~ 8.8 km² section of the Umatilla River Floodplain, Oregon, USA. Our approach assessed the pattern and characteristics of “hydrologic facets” (landscape patches that have high internal surface water connectivity and therefore function as a single hydrologic unit), where facet boundaries were defined by subtle topographic divides across the floodplain. We initially identified nearly 6000 small (fine-scale) hydrologic facets using standard GIS processing algorithms. We located the divide between each pair of adjacent facets, and determined “hydrologic impedance” (i.e., the maximum change in river stage necessary to inundate the divide) for each divide (n = ~ 17,000). Using hydrologic impedance values, we analyzed patterns of surface water connectivity among the fine-scale facets and aggregated groups of adjacent facets that had high connectivity. This process yielded a reduced number of larger facets useful for hydrologic analysis at coarser spatial scales. We compared results derived using several alternate rule sets for aggregating facets. With appropriate aggregation rules, the results are useful for generating optimal link-and-node flow networks to support hydrologic modeling of surface water flux across low-relief landscapes.     

Rapid setup of hydrological and hydraulic models using OpenStreetMap and the SRTM derived digital elevation model

A stepwise procedure has been developed in Python to extract information from OpenStreetMap (OSM) for hydrological and hydraulic models using existing and newly developed tools. The procedure focuses on the extraction of paved areas and water bodies. Road density is used to fill in gaps in OSM polygon coverage. Furthermore, it includes automatic downloading of Shuttle Radar Topography Mission (SRTM) elevation data and improving the elevation model with man-made landscape features such as elevated roads that are sampled from OSM. This is useful for hydraulic modelling in data scarce flood plain areas, where sharp elevation differences are dominated by man-made elevated elements. Test cases in Europe, South East Asia and East Africa demonstrate the potential of the procedure, although large differences in completeness of OSM coverage suggest it is best used in combination with other data sources.     

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.     

Mapping mine wastes and analyzing areas affected by selenium-rich water runoff in southeast Idaho using AVIRIS imagery and digital elevation data

Remotely sensed hyperspectral and digital elevation data from southeastern Idaho are combined in a new method to assess mine waste contamination. Waste rock from phosphorite mining in the area contains selenium, cadmium, vanadium, and other metals. Toxic concentrations of selenium have been found in plants and soils near some mine waste dumps. Eighteen mine waste dumps and five vegetation cover types in the southeast Idaho phosphate district were mapped by using Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) imagery and field data. The interaction of surface water runoff with mine waste was assessed by registering the AVIRIS results to digital elevation data, enabling determinations of (1) mine dump morphologies, (2) catchment watershed areas above each mine dump, (3) flow directions from the dumps, (4) stream gradients, and (5) the extent of downstream wetlands available for selenium absorption.Watersheds with the most severe selenium contamination, such as the South Maybe Canyon watershed, are associated with mine dumps that have large catchment watershed areas, high stream gradients, a paucity of downstream wetlands, and dump forms that tend to obstruct stream flow. Watersheds associated with low concentrations of dissolved selenium, such as Angus Creek, have mine dumps with small catchment watershed areas, low stream gradients, abundant wetlands vegetation, and less obstructing dump morphologies.     

Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions

Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though synthetic aperture radar (SAR) systems have the advantages of cloud penetration, all-weather coverage, high spatial resolution, day/night acquisitions, and signal independence of the solar illumination angle. In this study, we obtained coincident optical and SAR images of an agricultural area to investigate the use of SAR imagery for farm management. The optical and SAR data were normalized to indices ranging from 0 to 1 based on the meteorological conditions and sun/sensor geometry for each date to allow temporal analysis. Using optical images to interpret the response of SAR backscatter (σ^o) to soil and plant conditions, we found that SAR σ^o was sensitive to variations in field tillage, surface soil moisture, vegetation density, and plant litter. In an investigation of the relation between SAR σ^o and soil surface roughness, the optical data were used for two purposes: (1) to filter the SAR images to eliminate fields with substantial vegetation cover and/or high surface soil moisture conditions, and (2) to evaluate the results of the investigation. For dry, bare soil fields, there was a significant correlation (r²=.67) between normalized SAR σ^o and near-infrared (NIR) reflectance, due to the sensitivity of both measurements to surface roughness. Recognizing the limitations of optical remote sensing data due to cloud interference and atmospheric attenuation, the findings of this study encourage further studies of SAR imagery for crop and soil assessment.     

Regression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images

Mangrove wetlands have been rapidly diminishing because of human pressures worldwide. The Guangdong Province in South China, which has the largest area of mangrove wetlands in the nation, is under severe threat as a result of rapid urbanization and economic development. In this paper, comparisons were made between optical Landsat TM images and Radarsat fine‐mode images for estimating wetland biomass. Regression and analytical models were used to establish the relationships between remote sensing data and wetland biomass. The optimal parameter values for the analytical model were determined using genetic algorithms. Experiments indicate that the models using Radarsat fine‐mode images have significant accuracy improvement in terms of Root Mean‐Square Error (RMSE) whereas the use of the single Normalized Difference Vegetation Index (NDVI) may produce serious errors in biomass estimation. The Radarsat images can obtain more accurate trunk information about mangrove forests because of higher resolution and side‐looking geometry. The use of genetic algorithms can help to decompose backscatter into vegetation and soil backscattering, which is very useful for ecological modelling.     

An approach using animal distribution models and Landsat imagery

Abstract

Grazing animals have been responsible for changes in vegetation and for widespread erosion in Australia's arid zone. To minimize these adverse changes and to manage the intensity ofland use in relation to land capability, it is necessary to have information on the distribution and intensity of grazing and trampling, both of which are highly uneven. This paper describes a way of modelling the distribution of grazing and of generating the pattern of movement by cattle in a large paddock in central Australia for a particular configuration of watering points, fence lines and vegetation types. The method uses Landsat Multispectral Scanner (MSS) data and a cattle distribution model based on the convection—diffusion equation. The model relates the number of animals grazing to distance from water and preference for particular vegetation types. The solution to the conveetion—diffusion equation is the inverse Gaussian density function which can be used to calculate the total number of animals at a particular distance from water. The number of animals in individual grid.cells at that distance may then be generated from observed probability distributions. The cattle distribution model has to be fitted and is first derived from observed data for wet, dry and average rainfall conditions. A procedure is then described in which background changes in MSS band 5 over time and those due to grazing are separated on the basis of distance from water and vegetation type. The band 5 grazing effects provide a surrogate measure of grazing intensity and are used to estimate preferences for different vegetation types and to calibrate the animal distribution models. The results are very similar to those obtained from observed cattle distributions. The models may be used predictively to determine the effect of different watering point and fence line locations. Patterns of movement are determined by assuming that animals take the shortest paths from their grazing areas to water and cumulating the number of animals following each path. The patterns of movement derived from Landsat band 5 data are very similar to those determined from observed cattle distributions.     

Automatic computation of pebble roundness using digital imagery and discrete geometry

The shape of sedimentary particles is an important property, from which geographical hypotheses related to abrasion, distance of transport, river behavior, etc. can be formulated. In this paper, we use digital image analysis, especially discrete geometry, to automatically compute some shape parameters such as roundness, i.e. a measure of how much the corners and edges of a particle have been worn away.In contrast to previous work in which traditional digital images analysis techniques, such as Fourier transform, are used, we opted for a discrete geometry approach that allowed us to implement Wadell's original index, which is known to be more accurate, but more time consuming to implement in the field.Our implementation of Wadell's original index is highly correlated (92%) with the roundness classes of Krumbein's chart, used as a ground-truth. In addition, we show that other geometrical parameters, which are easier to compute, can be used to provide good approximations of roundness.We also used our shape parameters to study a set of pebbles digital images taken from the Progo basin river network (Indonesia). The results we obtained are in agreement with previous work and open new possibilities for geomorphologists thanks to automatic computation.     

Comparison and fusion of LIDAR and InSAR digital elevation models over urban areas

In this paper we analyse a multiple sensor dataset corresponding to three-dimensional data coming from interferometric radar (InSAR) or laser ranging (LIDAR) measurements. We consider digital elevation models (DEMs) extracted from a single LIDAR scan plus multiple SAR scans of the same area, downtown Denver, CO, USA. Horizontal resolution for both datasets is 2.5?m, a value allowing good characterization of sparse tall buildings. Fusion of DEMs extracted from InSAR data originated during flights orthogonal to each other allows reduction of layover and shadowing. A novel strategy combining advantages of existing fusion techniques is proposed. Results from individual and combined techniques are presented, compared and discussed. The ability of characterizing buildings allows us to raise the fusion strategy scope from the pixel-level up to the feature level, once 3D features are extracted. Related results are presented and discussed.

Finally, fusion of InSAR and LIDAR data is considered. LIDAR can reliably determine building footprints, thus relieving the problem of multiple bouncing radar pulses. Some results show that analysis of combined InSAR and LIDAR data on a small area can provide an improvement in DEMs extracted from a much larger area where only InSAR data are available.     

Extraterrestrial digital elevation models: constraints on planetary evolution,with focus on Mars

Global digital elevation models of Mars, Venus and the Moon have been used to study the evolution of these planetary bodies and the early history of our solar system. A global digital elevation model of Mars has recently been produced from altimetry data collected by the Mars Orbiter Laser Altimeter on the Mars Global Surveyor orbiter. Over half a billion data points were collected over the five years of operation and the end result was the first centre of mass-based geodetic grid of Mars. This product has vertical resolution of ～1 m and spatial resolution ≤500 m and has helped to define and test many hypotheses related to the planet's interior and surface evolution. Higher resolution digital elevations models (DEMs) have also been created from stereo images taken by the High Resolution Stereo Camera on the Mars Express spacecraft. These data have spatial resolution of up to 10 m, but only cover ～25% of the planet. Both sets of DEMs have been applied to a broad range of geological and geophysical problems. Here, I focus on the application of Martian DEMs to assess the history of water on the Red Planet, which allows improved understanding of Mars' astrobiological potential.     

SPOT5-HRS digital elevation models and the monitoring of glacier elevation changes in North-West Canada and South-East Alaska

Monitoring the response of land ice to climate change requires accurate and repeatable topographic surveys. The SPOT5-HRS (High Resolution Stereoscopic) instrument covers up to 120 km by 600 km in a single pass and has the potential to accurately map the poorly known topography of most glaciers and ice caps. The acquisition of a large HRS archive over ice-covered regions is planned by the French Space Agency (CNES) and Spotimage, France during the 2007–2008 International Polar Year (IPY). Here, we report on the accuracy and value of HRS digital elevation model (DEM) over ice and snow surfaces.

A DEM is generated by combining tools available from CNES with the PCI Orthoengine^SE software, using HRS images acquired in May 2004 over South-East Alaska (USA) and northern British Columbia (Canada). The DEM is evaluated through comparison with shuttle radar topographic mission (SRTM) DEM and ICESAT data, on and around the glaciers. A horizontal shift of 50 m is found between the HRS and SRTM DEMs and is attributed to errors in the SRTM DEM. Over ice-free areas, HRS elevations are 7 m higher than those of SRTM, with a standard deviation of ± 25 m for the difference between the two DEMs. The 7-m difference is partly attributed to the differential penetration of the electromagnetic waves (visible for HRS; microwave for SRTM) in snow and vegetation.

We also report on the application of sequential DEMs (SRTM DEM in February 2000 and HRS DEM in May 2004) for the monitoring of glacier elevation changes. We map the topographic changes induced by a surge of one tributary of Ferris Glacier. Maximum surface lowering of 42 (± 10) m and rising of 77 (± 10) m are observed in the 4 years time interval. Thinning rates up to 10 (± 2.5) m/yr are observed at low altitudes and confirm the ongoing wastage of glaciers in South-East Alaska.     

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.     

Analysis of large aeolian (wind-blown) bedforms using the Shuttle Radar Topography Mission (SRTM) digital elevation data

Dry land areas cover large parts of the land masses of Earth. Of these a large portion is mantled by aeolian (wind-blown) deposits and subjected to aeolian process, i.e., the transport of sand or dust by wind. One of the most prominent morphologies created by wind-driven deposits are dunes and in their larger form draas or mega dunes. These mega dunes tend to be widely spaced; very large; often forming a base on which smaller dunes form, producing a compound dune pattern.In February 2000 the Shuttle Radar Topography Mission, onboard the space shuttle Endeavour flew an interferometric radar system whose objective was to create a DEM of nearly all Earth land masses. In this paper DEM data from the C-band 90 m data set and the X-band 30 m data set were used to map and characterize the height and spacing of the large sand seas (Ergs) on Earth. Only the larger forms of dunes could be mapped reliably. The dune spacing and heights extracted from the SRTM DEMs tended to be in overall agreement with those reported in the literature. In a comparison between the X-band and the C-band data it was found that the X-band data are more sensitive to the smaller scale undulations on the compound dunes and better revealed the full height of the dunes.