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Remote Sens. 2017, 9(7), 681; https://doi.org/10.3390/rs9070681

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

1
Department of Environmental Science and Renewable Natural Resources, University of Chile, Casilla 1004, 8820808 Santiago, Chile
2
Center for Climate Resilience Research (CR)2, University of Chile, 8370449 Santiago, Chile
3
Institute of Geography and Geoecology, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, Germany
4
Institute of Ecology and Biodiversity, Las Palmeras 3425, 7800003 Santiago, Chile
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra and Prasad S. Thenkabail
Received: 15 March 2017 / Revised: 26 June 2017 / Accepted: 27 June 2017 / Published: 2 July 2017
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Abstract

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. Diversity obtained higher accuracies than richness; nonetheless, accurate predictions were achieved for both, demonstrating the potential of free satellite data for the prediction of relevant community characteristics in anthropogenic peatland ecosystems. View Full-Text
Keywords: fen; wetland; richness; Shannon index; OLI; ASTER; MSI; random forest; generalized linear models; Sphagnum fen; wetland; richness; Shannon index; OLI; ASTER; MSI; random forest; generalized linear models; Sphagnum
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Castillo-Riffart, I.; Galleguillos, M.; Lopatin, J.; Perez-Quezada, J.F. Predicting Vascular Plant Diversity in Anthropogenic Peatlands: Comparison of Modeling Methods with Free Satellite Data. Remote Sens. 2017, 9, 681.

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