An Automatic Identification Method of Crested Ibis (Nipponia nippon) Habitat Based on Spatiotemporal Density Detection
2022
To address the current challenges of the heavy workload, time-consuming nature and labor-intensiveness involved in existing crested ibis’s (Nipponia nipponTemminck, 1835) habitat identification approaches, this paper proposes an automatic habitat identification method based on spatiotemporal density detection. With consideration of the characteristics of the crested ibis’s trajectory data, such as aggregation, repeatability, and uncertainty, this method achieves detecting the crested ibis’s stopping points by using the spatial characteristics of the trajectory data. On this basis, an improved spatiotemporal clustering-based DBSCAN method is proposed in this paper, incorporating temporal characteristics of the trajectory data. By combining the spatial and temporal features, the proposed method is able to accurately identify the roosting and foraging sites among the crested ibis’s stopping points. Supported by remote sensing images and field investigations, it was found that the method proposed in this paper has a good clustering effect and can effectively identify the crested ibis’s foraging sites and overnight roosting areas. Specifically, the woodland, farmland, and river areas are the common foraging sites for the crested ibis, while the woodland with large trees is their common overnight site. Therefore, the method proposed in this paper can provide technical support for identifying and protecting the crested ibis’s habitats.
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