[A new assessment method for the quality of ecological monitoring data: taking CERN's tree growth dataset as a case]

This paper presented a new and simple assessment method for the quality of ecological monitoring data. This method theorized the associations between the data reliability as an ordinal variable with different number of classes and the data sources such as natural main ecological processes, secondary ecological processes, and extraneous and exotic processes, and offered a new data quality index to estimate the quality of the whole dataset by using the reasonableness ratio of observations. The assessment results provided the reliability class of each dataset, good explanations for outlier (or error data) flagging decisions, and quality value of the whole dataset. The method was applied to assess two tree growth datasets from Chinese Ecosystem Research Network (CERN), and the results demonstrated that the new data quality index could quantitatively evaluate the quality of the tree growth datasets. The new method would facilitate the development of corresponding software.