Assessing the contemporary status of Nebraska's eastern saline wetlands by using a machine learning algorithm on the Google Earth Engine cloud computing platform

摘要

Nebraska's eastern saline wetlands are globally unique and highly vulnerable inland salt marsh ecosystems. This research aims to evaluate the status of the saline wetlands in eastern Nebraska to discover the conditions of saline wetland hydrology, hydrophytes, and hydraulic soil. The research adopts machine learning and Google Earth Engine to classify Sentinel-2 imagery for water and vegetation classification and the National Agriculture Imagery Program imagery for salinity conditions. Six machine learning models are applied in water, soil, and vegetation detection in the study area. The optimal model (linear kernel SVM) generates an overall accuracy of 99.95% for water classification. For saline vegetation classification, the optimal model is the gradient tree boost with an overall accuracy of 94.07%. The overall accuracy values of saline soil classification using the optimal model (linear kernel SVM) varied among different years. The results of this study show the possibility of an observation approach for continuously monitoring Nebraska's eastern saline wetlands. The water classification results show that the saline wetlands in this area all have a similar temporal water cover pattern within each year. For saline vegetation, the peak season in this area is between June and July. The years 2019 (19.00%) and 2018 (17.69%) had higher saline vegetation cover rates than 2017 (10.54%). The saline soil classification shows that the saline soil area is highly variable in response to changes in the water and vegetation conditions. The research findings provide solid scientific evidence for conservation decision-making in these saline wetland areas.