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Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

Gradient-Based Assessment of Habitat Quality for Spectral Ecosystem Monitoring

Helmholtz Center Potsdam, German Research Center for Geosciences, Telegrafenberg, Potsdam 14473, Germany
Ecostrat GmbH Berlin, Marschnerstraße 10, Berlin 12203, Germany
Karlsruhe Institute of Technology (KIT), Institute of Geography and Geoecology, Karlsruhe 76131, Germany
Helmholtz Center for Environmental Research-UFZ, Permoserstr 15, Leipzig 04318, Germany
Author to whom correspondence should be addressed.
Academic Editors: Norbert Pfeifer, András Zlinszky, Hermann Heilmeier, Heiko Balzter, Bernhard Höfle, Bálint Czúcz and Prasad S. Thenkabail
Remote Sens. 2015, 7(3), 2871-2898;
Received: 30 November 2014 / Revised: 2 March 2015 / Accepted: 4 March 2015 / Published: 10 March 2015
(This article belongs to the Special Issue Remote Sensing and GIS for Habitat Quality Monitoring)
The monitoring of ecosystems alterations has become a crucial task in order to develop valuable habitats for rare and threatened species. The information extracted from hyperspectral remote sensing data enables the generation of highly spatially resolved analyses of such species’ habitats. In our study we combine information from a species ordination with hyperspectral reflectance signatures to predict occurrence probabilities for Natura 2000 habitat types and their conservation status. We examine how accurate habitat types and habitat threat, expressed by pressure indicators, can be described in an ordination space using spatial correlation functions from the geostatistic approach. We modeled habitat quality assessment parameters using floristic gradients derived by non-metric multidimensional scaling on the basis of 58 field plots. In the resulting ordination space, the variance structure of habitat types and pressure indicators could be explained by 69% up to 95% with fitted variogram models with a correlation to terrestrial mapping of >0.8. Models could be used to predict habitat type probability, habitat transition, and pressure indicators continuously over the whole ordination space. Finally, partial least squares regression (PLSR) was used to relate spectral information from AISA DUAL imagery to floristic pattern and related habitat quality. In general, spectral transferability is supported by strong correlation to ordination axes scores (R2 = 0.79–0.85), whereas second axis of dry heaths (R2 = 0.13) and first axis for pioneer grasslands (R2 = 0.49) are more difficult to describe. View Full-Text
Keywords: Natura 2000; conservation status; ordination; ecological gradients; imaging spectroscopy; PLSR Natura 2000; conservation status; ordination; ecological gradients; imaging spectroscopy; PLSR
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MDPI and ACS Style

Neumann, C.; Weiss, G.; Schmidtlein, S.; Itzerott, S.; Lausch, A.; Doktor, D.; Brell, M. Gradient-Based Assessment of Habitat Quality for Spectral Ecosystem Monitoring. Remote Sens. 2015, 7, 2871-2898.

AMA Style

Neumann C, Weiss G, Schmidtlein S, Itzerott S, Lausch A, Doktor D, Brell M. Gradient-Based Assessment of Habitat Quality for Spectral Ecosystem Monitoring. Remote Sensing. 2015; 7(3):2871-2898.

Chicago/Turabian Style

Neumann, Carsten, Gabriele Weiss, Sebastian Schmidtlein, Sibylle Itzerott, Angela Lausch, Daniel Doktor, and Maximilian Brell. 2015. "Gradient-Based Assessment of Habitat Quality for Spectral Ecosystem Monitoring" Remote Sensing 7, no. 3: 2871-2898.

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