Yanlong Shen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yanlong Shen has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Guoqiang Ma
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
1 shared publication - Hang Gao
School of Architecture & Urban Planning, Shenzhen University, Shenzhen 518060, China
1 shared publication - Jiaojiao Shen
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China
1 shared publication - Jing Yuan
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
1 shared publication - Tao Che
Key Laboratory of Cryospheric Science and Frozen Soil Engineering Heihe Remote Sensing Experimental Research Station Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou China
1 shared publication - Xiaoyan Wang
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China
1 shared publication
Latest Publications
- 20 November 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 62 (01962892) , 1-8
A Novel Snow Cover Occurrence Index (SCOI) for the Dynamics of Snow Duration and Glacier Extent in Mountainous Regions
The Snow Cover Occurrence Index (SCOI), defined as the ratio of the number of times that a pixel is classified as snow to the number of times that the pixel is observed in optical remote sensing data over a given year, can effectively mitigate the influence of clouds and holds great potential for extracting the annual snow duration and glacier extent in mountainous regions. The SCOI of the Qinghai-Tibet Plateau is calculated and analyzed on the basis of Landsat images from 1985 to 2021. The results indicate the following: 1) the evaluation based on station snow depth reveals that the SCOI is stable when the number of combined years reaches 5. 2) The SCOI has a strong correlation with snow cover days (SCD) determined from Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products. 3) The SCOI has good potential for glacier extraction and exhibits a high level of consistency with glacier boundary survey data. Overall, owing to the higher spatial resolution and longer duration of the Landsat-based SCOI, it can accurately describe the distribution characteristics and changes in snow cover and glaciers in complex mountainous areas.
- 20 November 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 62 (01962892) , 1-8
A Novel Snow Cover Occurrence Index (SCOI) for the Dynamics of Snow Duration and Glacier Extent in Mountainous Regions
The Snow Cover Occurrence Index (SCOI), defined as the ratio of the number of times that a pixel is classified as snow to the number of times that the pixel is observed in optical remote sensing data over a given year, can effectively mitigate the influence of clouds and holds great potential for extracting the annual snow duration and glacier extent in mountainous regions. The SCOI of the Qinghai-Tibet Plateau is calculated and analyzed on the basis of Landsat images from 1985 to 2021. The results indicate the following: 1) the evaluation based on station snow depth reveals that the SCOI is stable when the number of combined years reaches 5. 2) The SCOI has a strong correlation with snow cover days (SCD) determined from Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products. 3) The SCOI has good potential for glacier extraction and exhibits a high level of consistency with glacier boundary survey data. Overall, owing to the higher spatial resolution and longer duration of the Landsat-based SCOI, it can accurately describe the distribution characteristics and changes in snow cover and glaciers in complex mountainous areas.
- Open Access
Spatial Differentiation of Ecotourist Perceptions Based on the Random Forest Model: The Case of the Gansu Section of the Yellow River Basin
Ecotourism is vital for coordinating regional ecological protection with socio-economic development. The Gansu section of the Yellow River Basin is a typical ecologically fragile area in China, and it holds a distinctive position in ecological protection and high-quality development. This study explores spatial differentiation in ecotourist perceptions and their distinct effects on ecotourist satisfaction, revisitation, and recommendation. It uses four cities (Gannan, Linxia, Lanzhou, and Baiyin) in the Gansu section of the Yellow River (mainstream) as examples, employing a questionnaire survey to collect ecotourists’ perception data and applying a random forest model and one-way ANOVA for analysis. It was found that: (1) rich ecotourism potential exists in the Gansu section of the Yellow River Basin as an ecologically fragile area; (2) there is spatial differentiation in ecotourist perceptions, and among the four regions, Baiyin stands out for its nature and atmosphere perception, and Lanzhou excels in accessibility and service perception; (3) spatial disparities exist in the influencing factors of ecotourist satisfaction, revisitation, and recommendation. Ecotourists in districts with unique natural resources, such as Gannan and Baiyin, prioritize nature perception, whereas districts with abundant natural resources and an established foundation for ecotourism development, such as Linxia and Lanzhou, emphasize service and atmosphere perception. This study constructs a new research framework to explore spatial variations in ecotourists’ perceptions, assisting ecotourism destinations to meet the needs of ecotourists from the supply side, and presents distinctive strategies and recommendations for the development of ecotourism in similar ecologically fragile areas.
Open AccessSpatial Differentiation of Ecotourist Perceptions Based on the Random Forest Model: The Case of the Gansu Section of the Yellow River Basin
Ecotourism is vital for coordinating regional ecological protection with socio-economic development. The Gansu section of the Yellow River Basin is a typical ecologically fragile area in China, and it holds a distinctive position in ecological protection and high-quality development. This study explores spatial differentiation in ecotourist perceptions and their distinct effects on ecotourist satisfaction, revisitation, and recommendation. It uses four cities (Gannan, Linxia, Lanzhou, and Baiyin) in the Gansu section of the Yellow River (mainstream) as examples, employing a questionnaire survey to collect ecotourists’ perception data and applying a random forest model and one-way ANOVA for analysis. It was found that: (1) rich ecotourism potential exists in the Gansu section of the Yellow River Basin as an ecologically fragile area; (2) there is spatial differentiation in ecotourist perceptions, and among the four regions, Baiyin stands out for its nature and atmosphere perception, and Lanzhou excels in accessibility and service perception; (3) spatial disparities exist in the influencing factors of ecotourist satisfaction, revisitation, and recommendation. Ecotourists in districts with unique natural resources, such as Gannan and Baiyin, prioritize nature perception, whereas districts with abundant natural resources and an established foundation for ecotourism development, such as Linxia and Lanzhou, emphasize service and atmosphere perception. This study constructs a new research framework to explore spatial variations in ecotourists’ perceptions, assisting ecotourism destinations to meet the needs of ecotourists from the supply side, and presents distinctive strategies and recommendations for the development of ecotourism in similar ecologically fragile areas.