Wetland Vegetation Classification through Multi-Dimensional Feature Time Series Remote Sensing Images Using Mahalanobis Distance-Based Dynamic Time Warping

Efficient methodologies for vegetation-type mapping are significant for wetland's management practices and monitoring. Nowadays, dynamic time warping (DTW) based on remote sensing time series has been successfully applied to vegetation classification. However, most of the previous related studies only focused on Normalized Difference Vegetation Index (NDVI) time series while ignoring multiple features in each period image. In order to further improve the accuracy of wetland vegetation classification, Mahalanobis Distance-based Dynamic Time Warping (MDDTW) using multi-dimensional feature time series was employed in this research. This method extends the traditional DTW algorithm based on single-dimensional features to multi-dimensional features and solves the problem of calculating similarity distance between multi-dimensional feature time series. Vegetation classification experiments were carried out in the Yellow River Delta (YRD). Compared with different classification methods, the results show that the K-Nearest Neighbors (KNN) algorithm based on MDDTW (KNN-MDDTW) has achieved better classification accuracy; the overall accuracy is more than 90%, and kappa is more than 0.9.

Automated summary

Efficient methodologies for wetland vegetation mapping are crucial for wetland management and monitoring. This research employed Mahalanobis Distance-based Dynamic Time Warping (MDDTW) using multi-dimensional feature time series to improve the accuracy of wetland vegetation classification. Experimental results in the Yellow River Delta (YRD) showed that the K-Nearest Neighbors (KNN) algorithm based on MDDTW (KNN-MDDTW) achieved high classification accuracy, with an overall accuracy of over 90% and a kappa value exceeding 0.9.