ALBERT

Description: Code multipath is an unmanaged error source in precise global navigation satellite system (GNSS) observation processing that limits GNSS positioning accuracy. A new technique for mitigating multipath by installing a wave-absorbing shield is presented in this paper. The wave-absorbing shield was designed according to a GNSS requirement of received signals and collected measurements to achieve good performance. The wave-absorbing shield was installed at the KUN1 and SHA1 sites of the international GNSS Monitoring and Assessment System (iGMAS). Code and carrier phase measurements of three constellations were collected on the dates of the respective installations plus and minus one week. Experiments were performed in which the multipath of the measurements obtained at different elevations was mitigated to different extents after applying the wave-absorbing shield. The results of an analysis and comparison show that the multipath was mitigated by approximately 17%–36% on all available frequencies of BeiDou Navigation Satellite System (BDS), Global Positioning System (GPS), and Global Navigation Satellite System (GLONASS) satellites. The three-dimensional accuracies of BDS, GPS, and GLONASS single-point positioning (SPP) were, respectively, improved by 1.07, 0.63 and 0.49 m for the KUN1 site, and by 0.72, 0.79 and 0.73 m for the SHA1 site. Results indicate that the multipath of the original observations was mitigated by using the wave-absorbing shield.

Description: Forests, Vol. 8, Pages 377: Variations of Climate-Growth Response of Major Conifers at Upper Distributional Limits in Shika Snow Mountain, Northwestern Yunnan Plateau, China Forests doi: 10.3390/f8100377 Authors: Yun Zhang Dingcai Yin Mei Sun Hang Wang Kun Tian Derong Xiao Weiguo Zhang Improved understanding of climate-growth relationships of multiple species is fundamental to understanding and predicting the response of forest growth to future climate change. Forests are mainly composed of conifers in Northwestern Yunnan Plateau, but variations of growth response to climate conditions among the species are not well understood. To detect the growth response of multiple species to climate change, we developed residual chronologies of four major conifers, i.e., George’s fir (Abies georgei Orr), Likiang spruce (Picea likiangensis (Franch.) E.Pritz.), Gaoshan pine (Pinus densata Mast.) and Chinese larch (Larix potaninii Batalin) at the upper distributional limits in Shika Snow Mountain. Using the dendroclimatology method, we analyzed correlations between the residual chronologies and climate variables. The results showed that conifer radial growth was influenced by both temperature and precipitation in Shika Snow Mountain. Previous November temperature, previous July temperature, and current May precipitation were the common climatic factors that had consistent influences on radial growth of the four species. Temperature in the previous post-growing season (September–October) and moisture conditions in the current growing season (June–August) were the common climatic factors that had divergent impacts on the radial growth of the four species. Based on the predictions of climate models and our understanding of the growth response of four species to climate variables, we may understand the growth response to climate change at the species level. It is difficult to predict future forest growth in the study area, since future climate change might cause both increases and decreases for the four species and indirect effects of climate change on forests should be considered.

Description: Sustainability, Vol. 10, Pages 43: A Model to Measure Tourist Preference toward Scenic Spots Based on Social Media Data: A Case of Dapeng in China Sustainability doi: 10.3390/su10010043 Authors: Yao Sun Hang Ma Edwin Chan Research on tourist preference toward different tourism destinations has been a hot topic for decades in the field of tourism development. Tourist preference is mostly measured with small group opinion-based methods through introducing indicator systems in previous studies. In the digital age, e-tourism makes it possible to collect huge volumes of social data produced by tourists from the internet, to establish a new way of measuring tourist preference toward a close group of tourism destinations. This paper introduces a new model using social media data to quantitatively measure the market trend of a group of scenic spots from the angle of tourists’ demand, using three attributes: tourist sentiment orientation, present tourist market shares, and potential tourist awareness. Through data mining, cleaning, and analyzing with the framework of Machine Learning, the relative tourist preference toward 34 scenic spots closely located in the Dapeng Peninsula is calculated. The results not only provide a reliable “A-rating” system to gauge the popularity of different scenic spots, but also contribute an innovative measuring model to support scenic spots planning and policy making in the regional context.