Transferability of species distribution models for the detection of an invasive alien bryophyte using imaging spectroscopy data

Type Article
Date 2018-06
Language English
Author(s) Skowronek Sandra1, Van De Kerchove Ruben2, Rombouts Bjorn3, Aerts Raf3, 4, Ewald Michael5, Warrie Jens4, Schiefer Felix1, Garzon-Lopez Carol6, Hattab Tarek8, Honnay Olivier4, Lenoir Jonathan6, Rocchini Duccio7, Schmidtlein Sebastian5, Ben Somers 3, Feilhauer Hannes1
Affiliation(s) 1 : Friedrich Alexander Univ & Erlangen Nurnberg, Inst Geog, Erlangen, Germany.
2 : Flemish Inst Technol Res VITO, Mol, Belgium.
3 : Katholieke Univ Leuven, Dept Earth & Environm Sci, Div Forest Nat & Landscape, Leuven, Belgium.
4 : Katholieke Univ Leuven, Dept Biol, Ecol Evolut & Biodivers Conservat Sect, Leuven, Belgium.
5 : Karlsruhe Inst Technol, Inst Geog & Geoecol, Karlsruhe, Germany.
6 : Univ Picardie Jules Verne, EDYSAN, FRE 3498, Unite Rech Ecol & Dynam Systemes Anthropisees,CNR, Amiens, France.
7 : Fdn Edmund Mach, Dept Biodivers & Mol Ecol, San Michele All Adige, Italy.
8 : Inst Francais Rech Exploitat Mer, UMR MARBEC, Ave Jean Monnet CS, Sete, France.
Source International Journal Of Applied Earth Observation And Geoinformation (0303-2434) (Elsevier Science Bv), 2018-06 , Vol. 68 , P. 61-72
DOI 10.1016/j.jag.2018.02.001
WOS© Times Cited 11
Keyword(s) Campylopus introflexus, Heath star moss, Hyperspectral, Maxent, Dune ecosystem, Model transfer
Abstract

Remote sensing is a promising tool for detecting invasive alien plant species. Mapping and monitoring those species requires accurate detection. So far, most studies relied on models that are locally calibrated and validated against available field data. Consequently, detecting invasive alien species at new study areas requires the acquisition of additional field data which can be expensive and time-consuming. Model transfer might thus provide a viable alternative. Here, we mapped the distribution of the invasive alien bryophyte Campylopus introflexus to i) assess the feasibility of spatially transferring locally calibrated models for species detection between four different heathland areas in Germany and Belgium and ii) test the potential of combining calibration data from different sites in one species distribution model (SDM). In a first step, four different SDMs were locally calibrated and validated by combining field data and airborne imaging spectroscopy data with a spatial resolution ranging from 1.8 m to 4 m and a spectral resolution of about 10 nm (244 bands). A one-class classifier, Maxent, which is based on the comparison of probability densities, was used to generate all SDMs. In a second step, each model was transferred to the three other study areas and the performance of the models for predicting C. introflexus occurrences was assessed. Finally, models combining calibration data from three study areas were built and tested on the remaining fourth site. In this step, different combinations of Maxent modelling parameters were tested. For the local models, the area under the curve for a test dataset (test AUC) was between 0.57–0.78, while the test AUC for the single transfer models ranged between 0.45–0.89. For the combined models the test AUC was between 0.54–0.9. The success of transferring models calibrated in one site to another site highly depended on the respective study site; the combined models provided higher test AUC values than the locally calibrated models for three out of four study sites. Furthermore, we also demonstrated the importance of optimizing the Maxent modelling parameters. Overall, our results indicate the potential of a combined model to map C. introflexus without the need for new calibration data.

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Skowronek Sandra, Van De Kerchove Ruben, Rombouts Bjorn, Aerts Raf, Ewald Michael, Warrie Jens, Schiefer Felix, Garzon-Lopez Carol, Hattab Tarek, Honnay Olivier, Lenoir Jonathan, Rocchini Duccio, Schmidtlein Sebastian, Ben Somers, Feilhauer Hannes (2018). Transferability of species distribution models for the detection of an invasive alien bryophyte using imaging spectroscopy data. International Journal Of Applied Earth Observation And Geoinformation, 68, 61-72. Publisher's official version : https://doi.org/10.1016/j.jag.2018.02.001 , Open Access version : https://archimer.ifremer.fr/doc/00433/54439/