Abstract
Discriminating marsh vegetation is critical for the rapid assessment and management of wetlands. The study area, Honghe National Nature Reserve (HNNR), a typical freshwater wetland, is located in Northeast China. This study optimized the parameters (mtry and ntrees) of an object-based random forest (RF) algorithm to improve the applicability of marsh vegetation classification. Multidimensional datasets were used as the input variables for model training, then variable selection was performed on the variables to eliminate redundancy, which improved classification efficiency and overall accuracy. Finally, the performance of a new generation of Chinese high-spatial-resolution Gaofen-1 (GF-1) and Ziyuan-3 (ZY-3) satellite images for marsh vegetation classification was evaluated using the improved object-based RF algorithm with accuracy assessment. The specific conclusions of this study are as follows: (1) Optimized object-based RF classifications consistently produced more than 70.26% overall accuracy for all scenarios of GF-1 and ZY-3 at the 95% confidence interval. The performance of ZY-3 imagery applied to marsh vegetation mapping is lower than that of GF-1 imagery due to the coarse spatial resolution. (2) Parameter optimization of the object-based RF algorithm effectively improved the stability and classification accuracy of the algorithm. After parameter adjustment, scenario 3 for GF-1 data had the highest classification accuracy of 84% (ZY-3 is 74.72%) at the 95% confidence interval. (3) The introduction of multidimensional datasets improved the overall accuracy of marsh vegetation mapping, but with many redundant variables. Using three variable selection algorithms to remove redundant variables from the multidimensional datasets effectively improved the classification efficiency and overall accuracy. The recursive feature elimination (RFE)-based variable selection algorithm had the best performance. (4) Optical spectral bands, spectral indices, mean value of green and NIR bands in textural information, DEM, TWI, compactness, max difference, and shape index are valuable variables for marsh vegetation mapping. (5) GF-1 and ZY-3 images had higher classification accuracy for forest, cropland, shrubs, and open water.
Highlights
Scenario 3 for GF-1 data had the highest classification accuracy of 84% (ZY-3 is 74.72%) at the 95% confidence interval
(3) The introduction of multidimensional datasets improved the overall accuracy of marsh vegetation mapping, but with many redundant variables
Freshwater wetlands are defined as transitional zones between terrestrial and aquatic systems that provide multiple service functions such as water storage, flood control, carbon sink, and wildlife habitats [1,2]
Summary
Freshwater wetlands are defined as transitional zones between terrestrial and aquatic systems that provide multiple service functions such as water storage, flood control, carbon sink, and wildlife habitats [1,2]. Marsh vegetation mapping had been mainly conducted by visual interpretation or classification of optical images based on pixel-based or object-based machine learning algorithms [8,9,10,11,12]. Machine learning algorithms, such as K-nearest neighbor (KNN), support vector machine (SVM), classification and regression tree (CART), and random forest (RF), have been utilized to classify wetland vegetation in recent years because of their flexibility in interpreting complex nonlinear relationships without considering any statistical assumptions [13,14,15,16]. Due to the complex spatial distribution pattern and spatial heterogeneity of marsh vegetation associations, it is essential to customize an object-based RF classification model with tuning parameters for marsh vegetation mapping
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