Jiuzhaigou Valley Scenic and Historic Interest Area

World Heritage Research

Discover scientific research and academic studies that deepen our understanding of Jiuzhaigou Valley Scenic and Historic Interest Area from its history and significance to its conservation, management, and contemporary challenges.

• Object‑oriented landslide identification and susceptibility assessment for the Jiuzhaigou earthquake by integrating NDVI difference

  A groundbreaking study identifies and maps earthquake-triggered landslides (EqTLs) with unprecedented accuracy in the Jiuzhaigou Valley. By integrating object-oriented detection with a Random Forest susceptibility model—enhanced by Normalized Difference Vegetation Index (NDVI) differences—the research pinpoints 841 landslides (covering 11.96 km²), primarily concentrated near the Huya Fault and on steep slopes (30–50°). The model achieved high accuracy (AUC = 0.98), highlighting fault distance, peak ground acceleration (PGA), and slope angle as critical controls. Notably, 95% of landslides were found in very high susceptibility zones, with a density of 2.83/km². This approach offers a robust framework for spatial analysis and risk management in alpine valley regions, advancing our understanding of seismic-induced geological hazards.

  Tang, Q., Qin, Y., Lai, Y., Lu, Z., Li, Y., Wang, R., & Li, F. (2026). Object‑oriented landslide identification and susceptibility assessment for the Jiuzhaigou earthquake by integrating NDVI difference. Geocarto International, 41(1). https://doi.org/10.1080/10106049.2026.2614145

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• A ChiMerge–WOE Ensemble Learning Framework for Landslide Susceptibility Assessment in Jiuzhaigou County, China

  A novel framework combining ChiMerge discretization, Weight of Evidence (WOE) transformation, and tree-based ensemble learning achieves high accuracy in mapping landslide susceptibility in Jiuzhaigou Valley, China. The approach outperforms conventional methods by identifying statistically meaningful thresholds in conditioning factors and quantifying their association with landslides, leading to an AUC of 0.9432 for the best-performing model (WOE-CatBoost). High-risk zones, covering 28.59% of the study area, account for 85.96% of observed landslides, primarily in steep valleys, fractured lithological areas, and regions affected by human activities such as road construction and tourism development. This method enhances both prediction accuracy and interpretability, offering critical spatial support for landslide prevention and sustainable land management.

  Liu, Y., Zhang, L., Zhang, Y., Yao, Y., & Bao, Z. (2026). A ChiMerge–WOE Ensemble Learning Framework for Landslide Susceptibility Assessment in Jiuzhaigou County, China. Sustainability, 18(13), 6488. https://doi.org/10.3390/su18136488

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• Seasonal Coloration and Ecological Adaptations of Adventitious Roots of Four Salicaceous Species in Jiuzhaigou World Natural Heritage Site, Southwestern China

  Researchers have uncovered a novel link between the seasonal coloration of adventitious roots in Salicaceous species and environmental conditions, revealing their potential as bioindicators for wetland ecosystems. The study found that these roots, prominent in Jiuzhaigou Valley's tufa wetlands, transition from red or pink hues in summer to reddish-brown, maroon, or gray shades during other seasons due to the oxidation of proanthocyanidins influenced by temperature and light. This dynamic color change reflects seasonal climate fluctuations, offering a new biomarker for assessing environmental impacts. The roots also exhibit specialized adaptations, such as well-developed aerenchyma and floating habits, which enhance oxygen uptake in aquatic habitats. The findings provide insights into ecosystem management in karst areas.

  Liu, T., Xu, J., Xiao, W., Zhou, L., Chen, Y., Qiao, X., Deng, S., Du, Z., & Tang, Y. (2025). Seasonal Coloration and Ecological Adaptations of Adventitious Roots of Four Salicaceous Species in Jiuzhaigou World Natural Heritage Site, Southwestern China. Ecology and Evolution, 15(4). Portico. https://doi.org/10.1002/ece3.71218

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• Quantitative risk assessment and eco-engineering mitigation strategies for debris flows utilizing dynamic process simulation in Shuzheng gully, Jiuzhaigou valley scenic and historic interest area

  A novel pine pile–gabion composite dam (PPGD) system, integrated with dynamic process simulation and adaptive eco-engineering strategies, effectively mitigates debris flow risks while preserving ecological integrity. Using high-resolution modeling, researchers simulated debris flows under 20- and 50-year rainfall return periods in Shuzheng gully, Jiuzhaigou Valley, revealing peak velocities of 6.49 m/s and discharges of 38.33 m³/s for the latter. The PPGD system reduced total inundation extent by 45.78% and decreased peak flow velocities downstream by up to 45.34%, demonstrating its potential for sustainable risk management in ecologically sensitive UNESCO World Heritage sites. This approach balances risk reduction with environmental conservation, offering a scalable solution for mountain areas vulnerable to debris flows.

  Yang, D., Zong, X., Wu, B., Wang, Y., Liang, B., Wan, F., Liu, J., & Peng, J. (2025). Quantitative risk assessment and eco-engineering mitigation strategies for debris flows utilizing dynamic process simulation in Shuzheng gully, Jiuzhaigou valley scenic and historic interest area. Frontiers in Earth Science, 13. https://doi.org/10.3389/feart.2025.1649068

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• Low-Altitude UAV Trajectory Optimization for Complex 3D Terrains Based on Energy Consumption

  A novel three-dimensional flight energy consumption model for UAVs, combined with an upgraded ant colony algorithm, significantly reduces energy usage by 9.1% to 36.3% compared to existing greedy algorithms while ensuring comprehensive coverage of complex terrains like Jiuzhaigou Valley. The approach incorporates dynamic pheromone updating and elite strategies to optimize climb energy consumption and obstruction penalties, addressing the limitations of traditional two-dimensional trajectory planning. Simulation experiments using real topographic data validate its effectiveness in enhancing UAV efficiency for applications such as environmental monitoring and geographic mapping.

  Ouyang, N., Xie, J., & Lin, F. (2025). Low-Altitude UAV Trajectory Optimization for Complex 3D Terrains Based on Energy Consumption. 2025 6th International Conference on Computer Engineering and Application (ICCEA), 01–08. https://doi.org/10.1109/iccea65460.2025.11103265

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• Influencing eco-conscious travelers: The role of green marketing in promoting sustainable destinations

  Green marketing by eco-tourism providers significantly boosts tourists' intentions to visit sustainable destinations, primarily through fostering pro-environmental attitudes and trust in green destinations. A study of 317 tourists in Jiuzhaigou Valley, China, found that biospheric values—strong environmental concern—amplified the effect of destination trust on visit intentions, especially for high-value tourists, while eco-friendly attitudes consistently influenced intentions regardless of these values. The research applies customer engagement theory to show how meaningful green marketing interactions drive value co-creation and sustainable tourism behaviors. These insights offer actionable strategies for marketers and policymakers aiming to engage eco-conscious travelers effectively.

  Chang, W., Li, H., khamarudin, M., & Khan, W. A. (2025). Influencing eco-conscious travelers: The role of green marketing in promoting sustainable destinations. Acta Psychologica, 260, 105506. https://doi.org/10.1016/j.actpsy.2025.105506

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• Analysis of disaster-affected population mobility through grid-aggregated mobile location data: The 2017 Jiuzhaigou earthquake, China

  A novel analysis of grid-aggregated mobile location data reveals distinct evacuation patterns following the 2017 Jiuzhaigou earthquake, despite China's privacy regulations limiting individual trajectory data. Using empirical orthogonal function (EOF) analysis, researchers identified two primary evacuation routes: one extending northwest from Chuanzhusi Town and another southeast through Shuanghe Town. The study found a continuous outflow of population from the Jiuzhaigou Valley between midnight and 20:00, with significant increases in mobile activity along key roads during early morning hours (midnight to 10:00). Unlike traditional clustering methods, EOF analysis effectively pinpointed impact zones and inferred evacuation directions without relying on individual trajectory data. This approach demonstrates the potential of grid-based mobile data for disaster research, offering critical insights into population mobility during crises.

  Lin, Z., Mao, R., Zhao, H., Tang, Z., Yang, S., & Pan, P. (2025). Analysis of disaster-affected population mobility through grid-aggregated mobile location data: The 2017 Jiuzhaigou earthquake, China. PLOS One, 20(10), e0335415. https://doi.org/10.1371/journal.pone.0335415

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