Vegetation Classification and Evaluation of Yancheng Coastal Wetlands Based on Random Forest Algorithm from Sentinel-2 Images

Abstract

The identification of wetland vegetation is essential for environmental protection and management as well as for monitoring wetlands’ health and assessing ecosystem services. However, some limitations on vegetation classification may be related to remote sensing technology, confusion between plant species, and challenges related to inadequate data accuracy. In this paper, vegetation classification in the Yancheng Coastal Wetlands is studied and evaluated from Sentinel-2 images based on a random forest algorithm. Based on consistent time series from remote sensing observations, the characteristic patterns of the Yancheng Coastal Wetlands were better captured. Firstly, the spectral features, vegetation indices, and phenological characteristics were extracted from remote sensing images, and classification products were obtained by constructing a dense time series using a dataset based on Sentinel-2 images in Google Earth Engine (GEE). Then, remote sensing classification products based on the random forest machine learning algorithm were obtained, with an overall accuracy of 95.64% and kappa coefficient of 0.94. Four indicators (POP, SOS, NDVIre, and B12) were the main contributors to the importance of the weight analysis for all features. Comparative experiments were conducted with different classification features. The results show that the method proposed in this paper has better classification.

Mapping and Classification of the Liaohe Estuary Wetland Based on the Combination of Object-Oriented and Temporal Features

“…In contrast to radar remote sensing data, Sentinel-2 images boast high spatial resolution and quick revisit rates, showcasing promising capabilities in intricate wetland monitoring. Wang et al undertook vegetation classification within the coastal wetlands of Yancheng using the random forest algorithm with Sentinel-2 imagery [8]. Ivanova et al conducted the classification and mapping of the Straldzha Complex Protected Area utilizing Sentinel-2 satellite imagery [9].…”

“…This involved assessing whether the selected features could effectively discriminate among the 12 land covers. Following the formula(8) in Section III, part E, we calculated the JM distance for the 18 selected features, and the results are presented in Fig.5. The JM distances for all the feature pairs exceeded 1.9, with a maximum value of 2 and a minimum value of 1.96.…”