Accuracy assessment of automatically derived digital elevation models from aster data in mountainous terrain

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite was designed to generate along‐track stereo images. The data are available at low cost, providing a feasible opportunity for generating digital elevation models (DEMs) in areas where little or no elevation data are yet available. This study evaluates the accuracy of DEMs extracted from ASTER data covering mountainous terrain. For an assessment of the achieved accuracies in the Andean study site, comparisons were made to similar topographical conditions in Switzerland, where reference data were available. All raw DEMs were filtered and interpolated by the post‐processing tools included with PCI Geomatica, the software package used. After carefully checking the DEM quality, further post‐processing was undertaken to eliminate obvious artefacts such as peaks and sinks. Accuracy was tested by comparing the DEMs in the Swiss Alps to three reference models. The achieved results of the generated DEMs are promising, considering the extreme terrain. Given accurate and well‐distributed ground control points (GCPs), it is possible to generate DEMs with a root mean square (RMS) error between 15?m and 20?m in hilly terrain and about 30?m in mountainous terrain. The DEMs are very accurate in nearly flat regions and on smooth slopes with southern expositions: errors are generally within ±10?m in those cases. Larger errors do appear in forested, snow covered or shady areas and at steep cliffs and deep valleys with extreme errors of a few hundred metres. The evaluation showed that the quality of the DEMs is sufficient for enabling atmospheric, topographic and geometric correction to various satellite datasets and for deriving additional products.     

Scene-based method for spatial misregistration detection in hyperspectral imagery

Hyperspectral imaging (HSI) sensors suffer from spatial misregistration, an artifact that prevents the accurate acquisition of the spectra. Physical considerations let us assume that the influence of the spatial misregistration on the acquired data depends both on the wavelength and on the across-track position. A scene-based method, based on edge detection, is therefore proposed. Such a procedure measures the variation on the spatial location of an edge between its various monochromatic projections, giving an estimation for spatial misregistration, and also allowing identification of misalignments. The method has been applied to several hyperspectral sensors, either prism, or grating-based designs. The results confirm the dependence assumptions on lambda and theta, spectral wavelength and across-track pixel, respectively. Suggestions are also given to correct for spatial misregistration.     

Aerosol mapping over land with imaging spectroscopy using spectral autocorrelation

A new method for aerosol retrieval over land is proposed that makes explicit use of the contiguous, high-resolution spectral coverage of imaging spectrometers. The method is labelled Aerosol Retrieval by Interrelated Abundances (ARIA) and is based on unmixing of the short-wave infrared sensor signal by region-specific endmembers, assuming low aerosol radiative influence in this spectral region. Derived endmember abundances are transferred to the visible part of the spectrum in order to approximate surface reflectance where aerosol influence is generally strongest. Spectral autocorrelation of surface spectra is a precondition for ARIA and demonstrated using a reference spectrum database. The re-mixed surface reflectance is used as input quantity for the inversion of aerosol optical depth τ_a at 0.55 µm wavelength on a pixel basis. Except for the choice of endmembers and the atmospheric vertical profile, no a priori assumptions on the image scene are required. The potential of the presented method for aerosol retrieval is demonstrated for an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scene, collected in California in 2000. Comparisons with existing aerosol retrieval methods showed encouraging results in terms of achieved spatial smoothness and degree of uncertainty of aerosol optical depth across the scene.     

The spectral database SPECCHIO for improved long-term usability and data sharing

The organised storage of spectral data described by metadata is important for long-term use and data sharing with other scientists. Metadata describing the sampling environment, geometry and measurement process serves to evaluate the suitability of existing data sets for new applications. There is a need for spectral databases that serve as repositories for spectral field campaign and reference signatures, including appropriate metadata parameters. Such systems must be (a) highly automated in order to encourage users entering their spectral data collections and (b) provide flexible data retrieval mechanisms based on subspace projections in metadata spaces.The recently redesigned SPECCHIO system stores spectral and metadata in a relational database based on a non-redundant data model and offers efficient data import, automated metadata generation, editing and retrieval via a Java application.RSL is disseminating the database and software to the remote sensing community in order to foster the use and further development of spectral databases.     

Spatial PSF Nonuniformity Effects in Airborne Pushbroom Imaging Spectrometry Data

Efficient and accurate imaging spectroscopy data processing asks for perfectly consistent (i.e., ideally uniform) data in both the spectral and spatial dimensions. However, real pushbroom-type imaging spectrometers are affected by various point spread function (PSF) nonuniformity artifacts. First, individual pixels or lines may be missing in the raw data due to bad pixels originating from the detector, readout errors, or even electronic failures. Second, so-called smile and keystone optical aberrations are inherent to imaging spectrometers. Appropriate resampling strategies are required for the preprocessing of such data if emphasis is put on spatial PSF uniformity. So far, nearest neighbor interpolations have been often recommended and used for resampling. This paper shall analyze the radiometric effects if linear interpolation is used to optimize the spatial PSF uniformity. For modeling interpolation effects, an extensive library of measured surface reflectance spectra as well as real imaging spectroscopy data over various land cover types are used. The real measurements are systematically replaced by interpolated values, and the deviation between original and resampled spectra is taken as a quality measure. The effects of nearest neighbor resampling and linear interpolation methods are compared. It is found that linear interpolation methods lead to average radiometric errors below 2% for the correction of spatial PSF nonuniformity in the subpixel domain, whereas the replacement of missing pixels leads to average errors in the range of 10%-20%     

Geometric and radiometric correction of TM data of mountainousforested areas

Methodologies to improve Landsat TM (Thematic Mapper) forest classifications of alpine regions through the removal of some radiometric and geometric distortions are tested. Besides correcting for sensor and system induced errors, geometric errors are corrected using a digital elevation model. Then scene-related effects such as differences in illumination as well as the height dependent atmospheric influence and adjacency effects are corrected. The improvement of the accuracy of a forest classification is demonstrated by comparison with ground truth data     

Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction

We evaluate the potential of deriving fractional cover (fCover) and leaf area index (LAI) from discrete return, small footprint airborne laser scanning (ALS) data. fCover was computed as the fraction of laser vegetation hits over the number of total laser echoes per unit area. Analogous to the concept of contact frequency, an effective LAI proxy was estimated by a fraction of first and last echo types inside the canopy. Validation was carried out using 83 hemispherical photographs georeferenced to centimeter accuracy by differential GPS, for which the respective gap fractions were computed over a range of zenith angles using the Gap Light Analyzer (GLA). LAI was computed by GLA from gap fraction estimations at zenith angles of 0-60°. For ALS data, different data trap sizes were used to compute fCover and LAI proxy, the range of radii was 2-25 m. For fCover, a data trap size of 2 m radius was used, whereas for LAI a radius of 15 m provided best results. fCover was estimated both from first and last echo data, with first echo data overestimating field fCover and last echo data underestimating field fCover. A multiple regression of fCover derived from both echo types with field fCover showed no increase of R² compared to the regression of first echo data, and thus, we only used first echo data for fCover estimation. R² for the fCover regression was 0.73, with an RMSE of 0.18. For the ALS LAI proxy, R² was lower, at 0.69, while the RMSE was 0.01. For LAI larger radii (∼ 15 m ) provided best results for our canopy types, which is due to the importance of a larger range of zenith angles (0-60°) in LAI estimation from hemispherical photographs. Based on the regression results, maps of fCover and LAI were computed for our study area and compared qualitatively to equivalent maps based on imaging spectrometry, revealing similar spatial patterns and ranges of values.     

LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management

Vegetation structure is an important parameter in fire risk assessment and fire behavior modeling. We present a new approach deriving the structure of the upper canopy by segmenting single trees from small footprint LIDAR data and deducing their geometric properties. The accuracy of the LIDAR data is evaluated using six geometric reference targets, with the standard deviation of the LIDAR returns on the targets being as low as 0.06 m. The segmentation is carried out by using cluster analysis on the LIDAR raw data in all three coordinate dimensions. From the segmented clusters, tree position, tree height, and crown diameter are derived and compared with field measurements. A robust linear regression of 917 tree height measurements yields a slope of 0.96 with an offset of 1 m and the adjusted R² resulting at 0.92. However, crown diameter is not well matched by the field measurements, with R² being as low as 0.2, which is most certainly due to random errors in the field measurements. Finally, a geometric reconstruction of the forest scene using a paraboloid model is carried out using values of tree position, tree height, crown diameter, and crown base height.     

Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats

Dry grassland sites are amongst the most species-rich habitats of central Europe and it is necessary to design effective management schemes for monitoring of their biomass production. This study explored the potential of hyperspectral remote sensing for mapping aboveground biomass in grassland habitats along a dry-mesic gradient, independent of a specific type or phenological period. Statistical models were developed between biomass samples and spectral reflectance collected with a field spectroradiometer, and it was further investigated to what degree the calibrated biomass models could be scaled to Hyperion data. Furthermore, biomass prediction was used as a surrogate for productivity for grassland habitats and the relationship between biomass and plant species richness was explored. Grassland samples were collected at four time steps during the growing season to capture normally occurring variation due to canopy growth stage and management factors. The relationships were investigated between biomass and (1) existing broad- and narrowband vegetation indices, (2) narrowband normalized difference vegetation index (NDVI) type indices, and (3) multiple linear regression (MLR) with individual spectral bands. Best models were obtained from the MLR and narrowband NDVI-type indices. Spectral regions related to plant water content were identified as the best estimators of biomass. Models calibrated with narrowband NDVI indices were best for up-scaling the field-developed models to the Hyperion scene. Furthermore, promising results were obtained from linking biomass estimations from the Hyperion scene with plant species richness of grassland habitats. Overall, it is concluded that ratio-based NDVI-type indices are less prone to scaling errors and thus offer higher potential for mapping grassland biomass using hyperspectral data from space-borne sensors.     

A physically-based model to correct atmospheric and illuminationeffects in optical satellite data of rugged terrain

A physically-based model to correct atmospheric and topographically induced illumination effects in optical satellite data is developed and tested. Special emphasis is put on the impact of rugged terrain. Ground reference data for various land use classes enables the assessment of the corrections' influence on land use classifications. The estimation of surface reflectance is achieved in a two-step procedure. First irradiance components and atmospheric parameters are calculated for horizontal surfaces using the atmo-code 6S, E. Vermonte et al. (1994), then the influence of the topography on the parameters is integrated using DEM data