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Area of Science:

  • Ecology
  • Remote Sensing
  • Computer Science

Background:

  • Wetland mapping is crucial but distinguishing similar freshwater plant communities like graminoid/sedge using remote sensing is challenging.
  • Previous studies primarily utilized medium to low-resolution imagery, with limited research on high-spatial-resolution data and advanced machine learning for heterogeneous wetland classification.

Purpose of the Study:

  • To evaluate the effectiveness of machine learning classifiers, specifically decision trees (DT) and artificial neural networks (ANN), for accurately classifying graminoid/sedge communities.
  • To assess the utility of high-resolution aerial imagery combined with spectral bands, normalized difference vegetation index (NDVI), and texture features for wetland mapping.

Main Methods:

  • Employed high-resolution aerial imagery from Everglades National Park, Florida.
  • Utilized machine learning algorithms: decision trees (DT) and artificial neural networks (ANN), including a multilayer perceptron (MLP) with backpropagation.
  • Incorporated spectral bands, NDVI, and first- and second-order texture features derived from the near-infrared band, analyzing multiple window sizes.

Main Results:

  • The artificial neural network (ANN) achieved a statistically significantly higher accuracy (82.04%) compared to the decision tree (DT) (80.48%) and maximum likelihood (80.56%) classifiers.
  • Using multiple window sizes for texture feature extraction yielded optimal classification results.
  • First-order texture features offered computational advantages with accuracy comparable to second-order features.

Conclusions:

  • Artificial neural networks (ANN) demonstrate superior performance for classifying graminoid/sedge wetland communities using high-resolution imagery and texture data.
  • High-resolution imagery, combined with spectral and texture analysis, is effective for detailed wetland vegetation mapping.
  • Texture features, particularly first-order, provide valuable information for wetland classification and offer computational benefits.