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Predicting Vascular Plant Diversity in Anthropogenic Peatlands : Comparison of Modeling Methods with Free Satellite Data

Castillo-Riffart, Ivan; Galleguillos, Mauricio; Lopatin, Javier 1; Perez-Quezada, Jorge F.
1 Institut für Geographie und Geoökologie (IFGG), Karlsruher Institut für Technologie (KIT)


Peatlands are ecosystems of great relevance, because they have an important number of ecological functions that provide many services to mankind. However, studies focusing on plant diversity, addressed from the remote sensing perspective, are still scarce in these environments. In the present study, predictions of vascular plant richness and diversity were performed in three anthropogenic peatlands on Chiloé Island, Chile, using free satellite data from the sensors OLI, ASTER, and MSI. Also, we compared the suitability of these sensors using two modeling methods: random forest (RF) and the generalized linear model (GLM). As predictors for the empirical models, we used the spectral bands, vegetation indices and textural metrics. Variable importance was estimated using recursive feature elimination (RFE). Fourteen out of the 17 predictors chosen by RFE were textural metrics, demonstrating the importance of the spatial context to predict species richness and diversity. Non-significant differences were found between the algorithms; however, the GLM models often showed slightly better results than the RF. Predictions obtained by the different satellite sensors did not show significant differences; nevertheless, the best models were obtained with ASTER (richness: R² = 0.62 and %RMSE = 17.2, diversity: R² = 0.71 and %RMSE = 20.2, obtained with RF and GLM respectively), followed by OLI and MSI. ... mehr

Volltext §
DOI: 10.5445/IR/1000072207
DOI: 10.3390/rs9070681
Zitationen: 18
Web of Science
Zitationen: 18
Zitationen: 18
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Geographie und Geoökologie (IFGG)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2017
Sprache Englisch
Identifikator ISSN: 2072-4292
KITopen-ID: 1000072207
Erschienen in Remote sensing
Verlag MDPI
Band 9
Heft 7
Seiten Art. Nr. 681
Schlagwörter fen, wetland, richness, Shannon index, OLI, ASTER, MSI, random forest, generalized linear models, Sphagnum
Nachgewiesen in Web of Science
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