@article{LuftNeumannItzerottetal.2016,
  author    = {Luft, Laura and Neumann, C. and Itzerott, S. and Lausch, A. and Doktor, D. and Freude, M. and Blaum, Niels and Jeltsch, Florian},
  title     = {Digital and real-habitat modeling of Hipparchia statilinus based on hyper spectral remote sensing data},
  series = {International journal of environmental science and technology},
  volume    = {13},
  journal   = {International journal of environmental science and technology},
  publisher = {Springer},
  address   = {New York},
  issn      = {1735-1472},
  doi       = {10.1007/s13762-015-0859-1},
  pages     = {187 -- 200},
  year      = {2016},
  abstract  = {The abandonment of military areas leads to succession processes affecting valuable open-land habitats and is considered to be a major threat for European butterflies. We assessed the ability of hyper spectral remote sensing data to spatially predict the occurrence of one of the most endangered butterfly species (Hipparchia statilinus) in Brandenburg (Germany) on the basis of habitat characteristics at a former military training area. Presence-absence data were sampled on a total area of 36 km(2), and N = 65 adult individuals of Hipparchia statilinus could be detected. The floristic composition within the study area was modeled in a three-dimensional ordination space. Occurrence probabilities for the target species were predicted as niches between ordinated floristic gradients by using Regression Kriging of Indicators. Habitat variance could be explained by up to 81 \% with spectral variables at a spatial resolution of 2 x 2 m by transferring PLSR models to imagery. Ordinated ecological niche of Hipparchia statilinus was tested against environmental predictor variables. N = 6 variables could be detected to be significantly correlated with habitat preferences of Hipparchia statilinus. They show that Hipparchia statilinus can serve as a valuable indicator for the evaluation of the conservation status of Natura 2000 habitat type 2330 (inland dunes with open Corynephorus and Agrostis grasslands) protected by the Habitat Directive (Council Directive 92/43/EEC). The authors of this approach, conducted in August 2013 at Doberitzer Heide Germany, aim to increase the value of remote sensing as an important tool for questions of biodiversity research and conservation.},
  language  = {en}
}
@article{LuftNeumannFreudeetal.2014,
  author    = {Luft, Laura and Neumann, Carsten and Freude, Matthias and Blaum, Niels and Jeltsch, Florian},
  title     = {Hyperspectral modeling of ecological indicators - A new approach for monitoring former military training areas},
  series = {Ecological indicators : integrating monitoring, assessment and management},
  volume    = {46},
  journal   = {Ecological indicators : integrating monitoring, assessment and management},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1470-160X},
  doi       = {10.1016/j.ecolind.2014.06.025},
  pages     = {264 -- 285},
  year      = {2014},
  abstract  = {Military areas are valuable habitats and refuges for rare and endangered plants and animals. We developed a new approach applying innovative methods of hyperspectral remote sensing to bridge the existing gap between remote sensing technology and the demands of the nature conservation community. Remote sensing has already proven to be a valuable monitoring instrument. However, the approaches lack the consideration of the demands of applied nature conservation which includes the legal demands of the EU Habitat Directive. Following the idea of the Vital Signs Monitoring in the USA, we identified a subset of the highest priority monitoring indicators for our study area. We analyzed continuous spectral response curves and tested the measurability of N=19 indicators on the basis of complexity levels aggregated from extensive vegetation assemblages. The spectral differentiability for the floristic as well as faunistic indicators revealed values up to 100\% accuracy. We point out difficulties when it comes to distinguishing faunistic habitat requirements of several species adapted to dry open landscapes, which in this case results in OVERALL ACCURACY of 67, 87-95, and 35\% in the error matrix. In summary, we provide an applicable and feasible method to facilitating monitoring military areas by hyperspectral remote sensing in the following. (C) 2014 Elsevier Ltd. All rights reserved.},
  language  = {en}
}