Kaidong Feng

The composition and dynamics of wetland plant communities play a critical role in maintaining the functionality of wetland ecosystems and serve as important indicators of wetland degradation and restoration. Accurately identifying wetland plant communities using remote sensing techniques remains challenging due to the complex environment and cloud contamination. Here, we applied a sample migration method based on change vector analysis and a random forest (RF) classifier incorporating SHapley Additive exPlanations (SHAP) to explore the spatiotemporal changes of wetland plant communities in the western Songnen Plain of China between 2016 and 2022, and to better understand the decision logic of the RF model. Our work achieved accurate annual wetland classification at the community scale, with an average overall accuracy of 89.5% and an average kappa coefficient of 0.87. Our analysis revealed different spatiotemporal change characteristics of wetland plant communities in the western Songnen Plain and three national nature reserves. The SHAP model showed that MOS_IRECI is the most important feature determining the prediction results of the RF model, and the importance of the features differs at global and local levels. This study confirms the feasibility of annual dynamic monitoring of wetland plant communities at a regional scale. The results are expected to provide a reference for the fine and sustainable management of wetland resources in the western Songnen Plain, as well as valuable data support for related wetland ecology research.

The propagation of the invasive Spartina alterniflora ( S. alterniflora ) has seriously affected the health of coastal wetland ecosystems in China and thus requires an urgent response. In this article, we construct a feature vector set containing phenological and other time-series features based on the google earth engine platform by combining dense time-series images from the sentinel-1 and sentinel-2 satellites. We obtained the dataset of the annual distribution of S. alterniflora in the Yellow river delta from 2016 to 2021 by developing an object-oriented random forest classification model. The results show that S. alterniflora has different phenological features from other wetland plants that played an important role in its classification based on the images. A combination of multiple phenological and temporal features improved the classification accuracy of S. alterniflora (multi-year average overall accuracy: 95.38%; user accuracy: 95.01%; producer accuracy: and 95.17%). Our results suggest that from 2016 to 2021, the growth rate of the area occupied by S. alterniflora was 2.17 km ² per year, and a new patch of the S. alterniflora appeared in the south of the study area in 2018. The article here provides scientific data to support the monitoring and control of the invasive S. alterniflora at a large scale.

Phragmites australis (P. australis) is one of the most important plant species found in wetland ecosystems, and its aboveground biomass (AGB) is a key indicator for assessing the quality or health of a wetland site. In this study, we combined Sentinel-1/2 images and field observation data collected in 2020, to delineate the distribution of P. australis in the Momoge Ramsar Wetland site by using a random forest method, and further, to estimate AGB by comparing multiple linear regression models. The results showed that the overall classification accuracy of P. australis using the random forest method was 89.13% and the P. australis area in the site was 135.74 km² in 2020. Among various remote sensing variables, the largest correlation coefficient was observed between dry weight of AGB of P. australis and Sentinel-2 red edge B₇, and between fresh weight of P. australis AGB and red edge B₅. The optimal models for estimating dry and fresh weight of P. australis AGB were multiple linear regression models, with an accuracy of 75.4% and 69.2%, respectively. In 2020, it was estimated that the total fresh weight of P. australis AGB in this Ramsar site was 21.2 × 10⁷ kg and the total dry weight was 7.2 × 10⁷ kg. The larger weight of P. australis AGB was identified mainly at central and western sites. The application of Sentinel-2 red-edge band for AGB estimation can significantly improve the model estimation accuracy. The findings of this study will provide a scientific basis for the management and protection of wetland ecosystems and sustainable utilization of P. australis resources.

Understanding the spatial patterns of plant communities is important for sustainable wetland ecosystem management and biodiversity conservation. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral data with high spatial resolution have become ideal for accurate classification of wetland plant communities. In this article, four dominant plant communities (Phragmites australis, Typha orientalis, Suaeda glauca, and Scirpus triqueter) and two unvegetated cover types (water and bare land) in the Momoge Ramsar wetland site were classified. This was achieved using UAV hyperspectral images and three object- and pixel-based machine-learning classification algorithms [random forest (RF), convolutional neural network (CNN), and support vector machine (SVM)]. First, spectral derivative analysis, logarithmic analysis, and continuum removal analysis identified the wavelength at which the greatest difference in reflectance occurs. Second, dimensionality reduction of hyperspectral images was conducted using principal component analysis. Subsequently, an optimal feature combination for community mapping was formed based on data transformation (spectral features, vegetation indices, and principal components). Image objects were obtained by segmenting the optimum object feature subsets. Finally, distribution maps of communities were produced by using three machine-learning classification algorithms. Our results reveal that object-based image analysis outperforms pixel-based methods, with overall accuracies (OAs) of 80.29-87.75%; RF has the highest OA of 87.75% (Kappa = 0.864), followed consecutively by CNN (OA = 83.31%, Kappa = 0.829) and SVM (OA = 80.29%, Kappa = 0.813). Phragmites australis dominates the plant community (55.9%) at the study area, followed by Typha orientalis (16.2%), Suaeda glauca (16.2%), and Scirpus triqueter (4.6%). The results highlight the importance of spectral transformation features in red-edge regions. The mapping results will help establish basic information for subsequent studies involving habitat suitability assessment at this study site.