@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{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}
}