Delve into the Impact of Object’s Location Intelligence on the Species Distribution Modeling via Deep Learning

The purpose of this study is twofold, one is to establish species distribution model (SDM) of Brainea insignis (cycad fern, CF) based on the data measured by different GNSS receivers (survey-grade and recreational utility), so as to explore the impact of positioning quality on the model. Another one is to overcome the problem with insufficient samples for deep learning algorithm used to establish SDM. The results showed that even a few meters of error can cause the model to degrade, with the kappa value dropping by a maximum of 20.9%. Therefore, researchers cannot ignore the precision of species location intelligence. The way of extracting training samples has proved to be feasible to deep learning algorithm, in which one first establishes a high-potential habitat map by machine learning methods and then clips training areas input to this algorithm. Through this process, the model accuracy of U-net can reach above 90%. This opens up new possibilities for species distribution modeling.