Predicting Vascular Plant Diversity in Anthropogenic Peatlands: Comparison of Modeling Methods with Free Satellite Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Ground Data
2.3. Remote Sensing Predictors
2.4. Statistical Models
- Identify the proper model family to deal with the statistical properties of the observed variables. To predict species richness, we compared the normalized quantile-plots of the residuals of several GLMs, using the model families which are generally recommended for count data: Poisson, Quasi-Poisson, and negative binomial. All models tested were set with log-link functions. To predict the diversity, we tested model families (recommended for continuous positive data) as Gamma with inverse, identity, and log-link functions. Likewise, we tested less adequate Gaussian error distribution with log-link function. In all cases we tested the models using the best (only one) predictor, ranked by recursive feature elimination (RFE; see below). By the shape of the normalized quantile-plots of the residuals, we selected the Poisson error distribution with a log-link function for the species richness models, while the Gaussian error distribution with log-link function was selected for the prediction of diversity.
- Select the subset of predictors for each model. As GLMs cannot cope with multi-collinearity among independent predictors, a selection and ranking of the most important predictors was performed by the recursive feature elimination (RFE) algorithm [41]. This algorithm operates based in an iterative procedure, in which one predictor at a time is eliminated and ranked by its importance. We used random forest as the kernel. RFE was implemented using a leave-one-out cross validation procedure to acquire the RMSE and the number and importance of predictors. Models were selected based on a tradeoff between a low RMSE and a low number of predictors. In addition, we followed the recommendations of Hair et al. [42], who state that the number of observations should not be less than 5 per predictor, and ideally should be between 15 and 20.
- With the first subset obtained, the correlation between these predictors was assessed using the Pearson correlation coefficient (r), selecting the pairs of predictors with r ≥ ǀ0.6ǀ and eliminating, one by one, the least important predictors according to the RFE ranking. This process was carried out independently for each sensor model, so the models could be compared.
- Select the final model. The final number of predictors were determined by calculating several models and adding a single predictor in each run (starting with the most important predictor according to RFE). The best number of predictors was assessed by comparing the Akaike information criterion (AIC) and deviance [43]. We selected the model using three predictors.
2.5. Model Validation
2.6. Predictive Species Map
3. Results
3.1. Model Performance
3.2. Prediction Map
4. Discussion
4.1. Selected Predictors and Their Ecological Implications
4.2. Satellite Sensor Comparison
4.3. Model Comparisons
5. Conclusions
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Predictors | Reference or Equation | OLI | ASTER | MSI |
---|---|---|---|---|
Bands | ||||
Blue | B | x | x | |
Green | G | x | x | x |
Red | R | x | x | x |
Near Infrared | NIR | x | x | x |
Short Wave Infrared | SWIR | x | x | |
Index | ||||
Transformed Vegetation Index | x | x | x | |
All Normalized Difference Index | x | x | x 1 | |
Tasseled Cap Transformation | TCT = Greenness, Brightness, and Wetness [40] | x | x | |
Modified Soil Adjusted Vegetation Index | with L = 0.5 | x | x | |
Difference Vegetation Index | x | x | x | |
Simple Ratio | x | x | x | |
Modified Simple Ratio | x | x | x | |
Renormalized Difference Vegetation Index | x | |||
Enhanced Vegetation Index | x | x | ||
Soil Adjusted Vegetation Index | x | x | x | |
Three-Band Index Wang | a, b, and c are spectral bands | x | ||
Three-Band Vegetation Index Tian | a, b, and c are spectral bands | x |
Sensor | Predictors | Textural Metrics | GLM | RF |
---|---|---|---|---|
Richness models | ||||
OLI | NDI | Mean | x | x |
NIR Band | x | x | ||
NDI | Contrast | x | ||
ASTER | Red Band | Mean | x | x |
NDI | Standard deviation | x | x | |
NDI | Correlation | x | ||
MSI | SR | Correlation | x | x |
TCT Brightness using NIR Band | x | x | ||
SR | Homogeneity | x | x | |
Shannon Index models | ||||
OLI | NDI | Mean | x | x |
NDI | x | |||
ASTER | 3BSI_W | Mean | x | x |
SR | Standard deviation | x | ||
SR | Mean | x | x | |
MSI | SR | Correlation | x | x |
SR | Homogeneity | x | ||
NIR Band | Contrast | x | x |
Sensor | R² | %RMSE | Bias | ||||||
---|---|---|---|---|---|---|---|---|---|
GLM | RF | GLM | RF | GLM | RF | ||||
Richness models | |||||||||
OLI | 0.59 | 0.57 | - | 18.3 | 18.5 | - | 0.02 | 0.04 | - |
ASTER | 0.60 | 0.62 | - | 17.7 | 17.2 | - | 0.03 | 0.03 | - |
MSI | 0.60 | 0.54 | - | 18.3 | 19.1 | - | 0.03 | 0.04 | - |
Shannon index model | |||||||||
OLI | 0.63 | 0.62 | - | 22.8 | 21.8 | - | 0.05 | 0.04 | - |
ASTER | 0.71 | 0.68 | - | 20.2 | 20.5 | - | 0.03 | 0.04 | - |
MSI | 0.52 | 0.52 | - | 25.6 | 25.1 | - | 0.05 | 0.05 | - |
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Castillo-Riffart, I.; Galleguillos, M.; Lopatin, J.; Perez-Quezada, A.J.F. Predicting Vascular Plant Diversity in Anthropogenic Peatlands: Comparison of Modeling Methods with Free Satellite Data. Remote Sens. 2017, 9, 681. https://doi.org/10.3390/rs9070681
Castillo-Riffart I, Galleguillos M, Lopatin J, Perez-Quezada AJF. Predicting Vascular Plant Diversity in Anthropogenic Peatlands: Comparison of Modeling Methods with Free Satellite Data. Remote Sensing. 2017; 9(7):681. https://doi.org/10.3390/rs9070681
Chicago/Turabian StyleCastillo-Riffart, Ivan, Mauricio Galleguillos, Javier Lopatin, and And Jorge F. Perez-Quezada. 2017. "Predicting Vascular Plant Diversity in Anthropogenic Peatlands: Comparison of Modeling Methods with Free Satellite Data" Remote Sensing 9, no. 7: 681. https://doi.org/10.3390/rs9070681