@article{BrellSeglGuanteretal.2019, author = {Brell, Maximilian and Segl, Karl and Guanter, Luis and Bookhagen, Bodo}, title = {3D hyperspectral point cloud generation}, series = {ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing}, volume = {149}, journal = {ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0924-2716}, doi = {10.1016/j.isprsjprs.2019.01.022}, pages = {200 -- 214}, year = {2019}, abstract = {Remote Sensing technologies allow to map biophysical, biochemical, and earth surface parameters of the land surface. Of especial interest for various applications in environmental and urban sciences is the combination of spectral and 3D elevation information. However, those two data streams are provided separately by different instruments, namely airborne laser scanner (ALS) for elevation and a hyperspectral imager (HSI) for high spectral resolution data. The fusion of ALS and HSI data can thus lead to a single data entity consistently featuring rich structural and spectral information. In this study, we present the application of fusing the first pulse return information from ALS data at a sub-decimeter spatial resolution with the lower-spatial resolution hyperspectral information available from the HSI into a hyperspectral point cloud (HSPC). During the processing, a plausible hyperspectral spectrum is assigned to every first-return ALS point. We show that the complementary implementation of spectral and 3D information at the point-cloud scale improves object-based classification and information extraction schemes. This improvements have great potential for numerous land cover mapping and environmental applications.}, language = {en} } @article{BrellSeglGuanteretal.2017, author = {Brell, Maximilian and Segl, Karl and Guanter, Luis and Bookhagen, Bodo}, title = {Hyperspectral and Lidar Intensity Data Fusion: A Framework for the Rigorous Correction of Illumination, Anisotropic Effects, and Cross Calibration}, series = {IEEE transactions on geoscience and remote sensing}, volume = {55}, journal = {IEEE transactions on geoscience and remote sensing}, publisher = {Inst. of Electr. and Electronics Engineers}, address = {Piscataway}, issn = {0196-2892}, doi = {10.1109/TGRS.2017.2654516}, pages = {2799 -- 2810}, year = {2017}, abstract = {The fusion of hyperspectral imaging (HSI) sensor and airborne lidar scanner (ALS) data provides promising potential for applications in environmental sciences. Standard fusion approaches use reflectance information from the HSI and distance measurements from the ALS to increase data dimen-sionality and geometric accuracy. However, the potential for data fusion based on the respective intensity information of the complementary active and passive sensor systems is high and not yet fully exploited. Here, an approach for the rigorous illumination correction of HSI data, based on the radiometric cross-calibrated return intensity information of ALS data, is presented. The cross calibration utilizes a ray tracing-based fusion of both sensor measurements by intersecting their particular beam shapes. The developed method is capable of compensating for the drawbacks of passive HSI systems, such as cast and cloud shadowing effects, illumination changes over time, across track illumination, and partly anisotropy effects. During processing, spatial and temporal differences in illumination patterns are detected and corrected over the entire HSI wavelength domain. The improvement in the classification accuracy of urban and vegetation surfaces demonstrates the benefit and potential of the proposed HSI illumination correction. The presented approach is the first step toward the rigorous in-flight fusion of passive and active system characteristics, enabling new capabilities for a variety of applications.}, language = {en} } @article{MielkeRogassBoescheetal.2016, author = {Mielke, Christian and Rogass, Christian and B{\"o}sche, Nina Kristine and Segl, Karl and Altenberger, Uwe}, title = {EnGeoMAP 2.0-Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission}, series = {Remote sensing}, volume = {8}, journal = {Remote sensing}, publisher = {MDPI}, address = {Basel}, issn = {2072-4292}, doi = {10.3390/rs8020127}, pages = {392 -- 414}, year = {2016}, abstract = {Algorithms for a rapid analysis of hyperspectral data are becoming more and more important with planned next generation spaceborne hyperspectral missions such as the Environmental Mapping and Analysis Program (EnMAP) and the Japanese Hyperspectral Imager Suite (HISUI), together with an ever growing pool of hyperspectral airborne data. The here presented EnGeoMAP 2.0 algorithm is an automated system for material characterization from imaging spectroscopy data, which builds on the theoretical framework of the Tetracorder and MICA (Material Identification and Characterization Algorithm) of the United States Geological Survey and of EnGeoMAP 1.0 from 2013. EnGeoMAP 2.0 includes automated absorption feature extraction, spatio-spectral gradient calculation and mineral anomaly detection. The usage of EnGeoMAP 2.0 is demonstrated at the mineral deposit sites of Rodalquilar (SE-Spain) and Haib River (S-Namibia) using HyMAP and simulated EnMAP data. Results from Hyperion data are presented as supplementary information.}, language = {en} } @article{BrellRogassSegletal.2016, author = {Brell, Maximilian and Rogass, Christian and Segl, Karl and Bookhagen, Bodo and Guanter, Luis}, title = {Improving Sensor Fusion: A Parametric Method for the Geometric Coalignment of Airborne Hyperspectral and Lidar Data}, series = {IEEE transactions on geoscience and remote sensing}, volume = {54}, journal = {IEEE transactions on geoscience and remote sensing}, publisher = {Inst. of Electr. and Electronics Engineers}, address = {Piscataway}, issn = {0196-2892}, doi = {10.1109/TGRS.2016.2518930}, pages = {3460 -- 3474}, year = {2016}, abstract = {Synergistic applications based on integrated hyperspectral and lidar data are receiving a growing interest from the remote-sensing community. A prerequisite for the optimum sensor fusion of hyperspectral and lidar data is an accurate geometric coalignment. The simple unadjusted integration of lidar elevation and hyperspectral reflectance causes a substantial loss of information and does not exploit the full potential of both sensors. This paper presents a novel approach for the geometric coalignment of hyperspectral and lidar airborne data, based on their respective adopted return intensity information. The complete approach incorporates ray tracing and subpixel procedures in order to overcome grid inherent discretization. It aims at the correction of extrinsic and intrinsic (camera resectioning) parameters of the hyperspectral sensor. In additional to a tie-point-based coregistration, we introduce a ray-tracing-based back projection of the lidar intensities for area-based cost aggregation. The approach consists of three processing steps. First is a coarse automatic tie-point-based boresight alignment. The second step coregisters the hyperspectral data to the lidar intensities. Third is a parametric coalignment refinement with an area-based cost aggregation. This hybrid approach of combining tie-point features and area-based cost aggregation methods for the parametric coregistration of hyperspectral intensity values to their corresponding lidar intensities results in a root-mean-square error of 1/3 pixel. It indicates that a highly integrated and stringent combination of different coalignment methods leads to an improvement of the multisensor coregistration.}, language = {en} } @article{FoersterWilczokBrosinskyetal.2014, author = {F{\"o}rster, Saskia and Wilczok, Charlotte and Brosinsky, Arlena and Segl, Karl}, title = {Assessment of sediment connectivity from vegetation cover and topography using remotely sensed data in a dryland catchment in the Spanish Pyrenees}, series = {Journal of soils and sediments : protection, risk assessment and remediation}, volume = {14}, journal = {Journal of soils and sediments : protection, risk assessment and remediation}, number = {12}, publisher = {Springer}, address = {Heidelberg}, issn = {1439-0108}, doi = {10.1007/s11368-014-0992-3}, pages = {1982 -- 2000}, year = {2014}, abstract = {Many Mediterranean drylands are characterized by strong erosion in headwater catchments, where connectivity processes play an important role in the redistribution of water and sediments. Sediment connectivity describes the ease with which sediment can move through a catchment. The spatial and temporal characterization of connectivity patterns in a catchment enables the estimation of sediment contribution and transfer paths. Apart from topography, vegetation cover is one of the main factors driving sediment connectivity. This is particularly true for the patchy vegetation cover typical of many dryland environments. Several connectivity measures have been developed in the last few years. At the same time, advances in remote sensing have enabled an improved catchment-wide estimation of ground cover at the subpixel level using hyperspectral imagery. The objective of this study was to assess the sediment connectivity for two adjacent subcatchments (similar to 70 km(2)) of the Isabena River in the Spanish Pyrenees in contrasting seasons using a quantitative connectivity index based on fractional vegetation cover and topography data. The fractional cover of green vegetation, non-photosynthetic vegetation, bare soil and rock were derived by applying a multiple endmember spectral mixture analysis approach to the hyperspectral image data. Sediment connectivity was mapped using the index of connectivity, in which the effect of land cover on runoff and sediment fluxes is expressed by a spatially distributed weighting factor. In this study, the cover and management factor (C factor) of the Revised Universal Soil Loss Equation (RUSLE) was used as a weighting factor. Bi-temporal C factor maps were derived by linking the spatially explicit fractional ground cover and vegetation height obtained from the airborne data to the variables of the RUSLE subfactors. The resulting connectivity maps show that areas behave very differently with regard to connectivity, depending on the land cover and on the spatial distribution of vegetation abundances and topographic barriers. Most parts of the catchment show higher connectivity values in August as compared to April. The two subcatchments show a slightly different connectivity behaviour that reflects the different land cover proportions and their spatial configuration. The connectivity estimation can support a better understanding of processes controlling the redistribution of water and sediments from the hillslopes to the channel network at a scale appropriate for land management. It allows hot spot areas of erosion to be identified and the effects of erosion control measures, as well as different land management scenarios, to be studied.}, language = {en} } @article{BrosinskyFoersterSegletal.2014, author = {Brosinsky, Arlena and F{\"o}rster, Saskia and Segl, Karl and Lopez-Tarazon, Jos{\´e} Andr{\´e}s and Pique, Gemma and Bronstert, Axel}, title = {Spectral fingerprinting: characterizing suspended sediment sources by the use of VNIR-SWIR spectral information}, series = {Journal of soils and sediments : protection, risk assessment and remediation}, volume = {14}, journal = {Journal of soils and sediments : protection, risk assessment and remediation}, number = {12}, publisher = {Springer}, address = {Heidelberg}, issn = {1439-0108}, doi = {10.1007/s11368-014-0927-z}, pages = {1965 -- 1981}, year = {2014}, abstract = {Knowledge of sediment sources is a prerequisite for sustainable management practices and may furthermore improve our understanding of water and sediment fluxes. Investigations have shown that a number of characteristic soil properties can be used as "fingerprints" to trace back the sources of river sediments. Spectral properties have recently been successfully used as such characteristics in fingerprinting studies. Despite being less labour-intensive than geochemical analyses, for example, spectroscopy allows measurements of small amounts of sediment material (> 60 mg), thus enabling inexpensive analyses even of intra-event variability. The focus of this study is on the examination of spectral properties of fluvial sediment samples to detect changes in source contributions, both between and within individual flood events. Sediment samples from the following three different origins were collected in the Isabena catchment (445 km(2)) in the central Spanish Pyrenees: (1) soil samples from the main potential source areas, (2) stored fine sediment from the channel bed once each season in 2011 and (3) suspended sediment samples during four flood events in autumn 2011 and spring 2012 at the catchment outlet as well as at several subcatchment outlets. All samples were dried and measured for spectral properties in the laboratory using an ASD spectroradiometer. Colour parameters and physically based features (e.g. organic carbon, iron oxide and clay content) were calculated from the spectra. Principal component analyses (PCA) were applied to all three types of samples to determine natural clustering of samples, and a mixing model was applied to determine source contributions. We found that fine sediment stored in the river bed seems to be mainly influenced by grain size and seasonal variability, while sampling location-and thus the effect of individual tributaries or subcatchments-seem to be of minor importance. Suspended sediment sources were found to vary between, as well as within, flood events; although badlands were always the major source. Forests and grasslands contributed little (< 10 \%), and other sources (not further determinable) contributed up to 40 \%. The analyses further suggested that sediment sources differ among the subcatchments and that subcatchments comprising relatively large proportions of badlands contributed most to the four flood events analyzed. Spectral fingerprints provide a rapid and cost-efficient alternative to conventional fingerprint properties. However, a combination of spectral and conventional fingerprint properties could potentially permit discrimination of a larger number of source types.}, language = {en} } @article{BrosinskyFoersterSegletal.2014, author = {Brosinsky, Arlena and F{\"o}rster, Saskia and Segl, Karl and Kaufmann, Hermann}, title = {Spectral fingerprinting: sediment source discrimination and contribution modelling of artificial mixtures based on VNIR-SWIR spectral properties}, series = {Journal of soils and sediments : protection, risk assessment and remediation}, volume = {14}, journal = {Journal of soils and sediments : protection, risk assessment and remediation}, number = {12}, publisher = {Springer}, address = {Heidelberg}, issn = {1439-0108}, doi = {10.1007/s11368-014-0925-1}, pages = {1949 -- 1964}, year = {2014}, abstract = {Knowledge of the origin of suspended sediment is important for improving our understanding of sediment dynamics and thereupon support of sustainable watershed management. An direct approach to trace the origin of sediments is the fingerprinting technique. It is based on the assumption that potential sediment sources can be discriminated and that the contribution of these sources to the sediment can be determined on the basis of distinctive characteristics (fingerprints). Recent studies indicate that visible-near-infrared (VNIR) and shortwave-infrared (SWIR) reflectance characteristics of soil may be a rapid, inexpensive alternative to traditional fingerprint properties (e.g. geochemistry or mineral magnetism). To further explore the applicability of VNIR-SWIR spectral data for sediment tracing purposes, source samples were collected in the Isabena watershed, a 445 km(2) dryland catchment in the central Spanish Pyrenees. Grab samples of the upper soil layer were collected from the main potential sediment source types along with in situ reflectance spectra. Samples were dried and sieved, and artificial mixtures of known proportions were produced for algorithm validation. Then, spectral readings of potential source and artificial mixture samples were taken in the laboratory. Colour coefficients and physically based parameters were calculated from in situ and laboratory-measured spectra. All parameters passing a number of prerequisite tests were subsequently applied in discriminant function analysis for source discrimination and mixing model analyses for source contribution assessment. The three source types (i.e. badlands, forest/grassland and an aggregation of other sources, including agricultural land, shrubland, unpaved roads and open slopes) could be reliably identified based on spectral parameters. Laboratory-measured spectral fingerprints permitted the quantification of source contribution to artificial mixtures, and introduction of source heterogeneity into the mixing model decreased accuracies for some source types. Aggregation of source types that could not be discriminated did not improve mixing model results. Despite providing similar discrimination accuracies as laboratory source parameters, in situ derived source information was found to be insufficient for contribution modelling. The laboratory mixture experiment provides valuable insights into the capabilities and limitations of spectral fingerprint properties. From this study, we conclude that combinations of spectral properties can be used for mixing model analyses of a restricted number of source groups, whereas more straightforward in situ measured source parameters do not seem suitable. However, modelling results based on laboratory parameters also need to be interpreted with care and should not rely on the estimates of mean values only but should consider uncertainty intervals as well.}, language = {en} } @article{SeglGuanterKaufmannetal.2010, author = {Segl, Karl and Guanter, Luis and Kaufmann, Hermann and Schubert, Josef and Kaiser, Stefan and Sang, Bernhard and Hofer, Stefan}, title = {Simulation of spatial sensor characteristics in the context of the EnMAP Hyperspectral mission}, issn = {0196-2892}, doi = {10.1109/Tgrs.2010.2042455}, year = {2010}, abstract = {The simulation of remote sensing images is a valuable tool for defining future Earth observation systems, optimizing instrument parameters, and developing and validating data-processing algorithms. A scene simulator for optical Earth observation data has been developed within the Environmental Mapping and Analysis Program (EnMAP) hyperspectral mission. It produces EnMAP-like data following a sequential processing approach consisting of five independent modules referred to as reflectance, atmospheric, spatial, spectral, and radiometric modules. From a modeling viewpoint, the spatial module is the most complex. The spatial simulation process considers the satellite-target geometry, which is adapted to the EnMAP orbit and operating characteristics, the instrument spatial response, and the sources of spatial nonuniformity (keystone, telescope distortion and smile, and detector coregistration). The spatial module of the EnMAP scene simulator is presented in this paper. The EnMAP spatial and geometric characteristics will be described, the simulation methodology will be presented in detail, and the capability of the EnMAP simulator will be shown by illustrative examples.}, language = {en} }