Education for Sustainable Development (ESD) contributes to the achievement of the Sustainable Development Goal (SDGs) when facing major global development issues. It includes formal, informal, and nonformal education, with an emphasis on a shift from “teaching” to “learning.” Using a data tracking approach and a questionnaire, this study aims to provide theoretical and practical suggestions for implementing ESD in senior high schools. Taking a Chinese senior high school as an example, the students in the school surveyed are compared with the global public in different language regions with respect to attention priorities for SDGs; students’ performance between the attention priorities and learning situation related to SDGs and subjects integrated into SDGs between learned and nominated are also analyzed. Gender equality and good health and well-being are the SDGs that students and the public both care most about. And the majority of SDGs receive less concern of the students and the public. By comparing the average amount of news about the SDGs in seven languages, the amount of news in English is far greater than other languages. Besides, different linguistic news has similarities and differences in their attention priorities to the SDGs. The students in the school surveyed only show greater learning performance in one SDGs, climate action. Only politics and economics, geography, and biology are the subjects with outstanding learning performance and high nominations by the students. The languages used to promote SDGs represent different populations and geographical distribution. The high school students’ attention to SDGs is affected by different linguistic media with the global public views, but it also has its particularities. Regarding the deficiency in SDGs awareness and learning performance, the implementation of ESD displays a nonideal status. It is also revealed that ESD should be strengthened. Additionally, during the process of ESD implementation, it is crucial to take full advantage of the combination of formal, informal, and nonformal education, advancing media campaigns for all 17 SDGs as well as fully mobilizing all course subjects to integrate SDGs. To achieve the curriculum reorientation of ESD, all subjects are recommended to connect to SDGs through unique characteristics. Considering the priorities of the students and the public, this research appeals to strengthening ESD through formal, informal, and nonformal channels under the influence from the global development and different linguistic regions’ media promotion. The recommendations can be used as an initiative and a reference for ESD implementation.

Forest plays an important role in global carbon, hydrological and atmospheric cycles and provides a wide range of valuable ecosystem services. Timely and accurate forest-type mapping is an essential topic for forest resource inventory supporting forest management, conservation biology and ecological restoration. Despite efforts and progress having been made in forest cover mapping using multi-source remotely sensed data, fine spatial, temporal and spectral resolution modeling for forest type distinction is still limited. In this paper, we proposed a novel spatial-temporal-spectral fusion framework through spatial-spectral fusion and spatial-temporal fusion. Addressing the shortcomings of the commonly-used spatial-spectral fusion model, we proposed a novel spatial-spectral fusion model called the Segmented Difference Value method (SEGDV) to generate fine spatial-spectra-resolution images by blending the China environment 1A series satellite (HJ-1A) multispectral image (Charge Coupled Device (CCD)) and Hyperspectral Imager (HSI). A Hierarchical Spatiotemporal Adaptive Fusion Model (HSTAFM) was used to conduct spatial-temporal fusion to generate the fine spatial-temporal-resolution image by blending the HJ-1A CCD and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The spatial-spectral-temporal information was utilized simultaneously to distinguish various forest types. Experimental results of the classification comparison conducted in the Gan River source nature reserves showed that the proposed method could enhance spatial, temporal and spectral information effectively, and the fused dataset yielded the highest classification accuracy of 83.6% compared with the classification results derived from single Landsat-8 (69.95%), single spatial-spectral fusion (70.95%) and single spatial-temporal fusion (78.94%) images, thereby indicating that the proposed method could be valid and applicable in forest type classification.

The spatial spread of H5N1 avian influenza, significant ongoing mutations, and long-term persistence of the virus in some geographic regions has had an enormous impact on the poultry industry and presents a serious threat to human health. We applied phylogenetic analysis, geospatial techniques, and time series models to investigate the spatiotemporal pattern of H5N1 outbreaks in China and the effect of vaccination on virus evolution. Results showed obvious spatial and temporal clusters of H5N1 outbreaks on different scales, which may have been associated with poultry and wild-bird transmission modes of H5N1 viruses. Lead–lag relationships were found among poultry and wild-bird outbreaks and human cases. Human cases were preceded by poultry outbreaks, and wild-bird outbreaks were led by human cases. Each clade has gained its own unique spatiotemporal and genetic dominance. Genetic diversity of the H5N1 virus decreased significantly between 1996 and 2011; presumably under strong selective pressure of vaccination. Mean evolutionary rates of H5N1 virus increased after vaccination was adopted in China. A clear signature of positively selected sites in the clade 2.3.2 virus was discovered and this may have resulted in the emergence of clade 2.3.2.1. Our study revealed two different transmission modes of H5N1 viruses in China, and indicated a significant role of poultry in virus dissemination. Furthermore, selective pressure posed by vaccination was found in virus evolution in the country.