Sacred natural sites, as probably the oldest form of habitat reserve for religious or cultural causes worldwide, are suggested to have an important role in conserving vegetation; however, there are insufficient data supporting the detailed implications of such sites for vegetation conservation. Thus, we evaluated the effectiveness of vegetation conservation on a Tibetan sacred mountain in Yajiang County, Sichuan, China, by investigating species richness and the structural attributes of higher vascular plant communities on and around the sacred mountain from April to June 2009. The results showed that the number of tree species on the sacred mountain was significantly higher than that in the surrounding area, but there were no notable differences in the numbers of shrub and grass species between the two sites. The sacred mountain harbored a greater number of small, short trees compared with the surrounding area, wherein the low-shrub and grass understory was relatively dense. We conclude that the sacred mountain has a positive impact on indigenous vegetation protection, but disparities in the management of the allowed uses of such sites could reduce their conservation effectiveness.
Infrared small target detection systems are an important part of space infrared imaging satellites. However, small infrared target detection is often affected by cirrus false alarm sources with similar grayscales. In this paper, an infrared cirrus detection method based on the tensor robust principal component analysis model (TRPCA) is proposed. The method treats multiple bands of remote sensing data as tensors, but classical tensor nuclear norms cannot represent the tensor rank well; therefore, we use tensor multi-mode expansion sum nuclear norm (TMESNN) to approximate the tensor rank better. First, a set of Landsat-8 data is transformed into a tensor model, and a TRPCA model is constructed by TMESNN and the 1 L norm. Then, this model is solved by Ket augments and the alternating direction method of multipliers (ADMM). Finally, Mallat wavelet transform is used to supplement information and remove clutter, and the final detection result is obtained by adaptive threshold segmentation. Compared with other optimizationbased methods, this method has better detection performance and accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.