The environmental factors influencing NDVI spatial pattern and its change in the Qinling-Daba Mountains remain unclear

The gap

Evidence profile

Sourced from the stated research gap and limitations section and future work of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 2 journals. Those papers have been cited 9 times in total.

Supporting evidence — 3 representative gaps

• Pattern and change of NDVI and their environmental influencing factors for 1986–2019 in the Qinling-Daba Mountains of central China (2024) · Frontiers in Forests and Global Change · cited 9× · doi

  The environmental factors influencing NDVI spatial pattern and its change in the Qinling-Daba Mountains remain unclear. Previous studies have focused on the relationship between climate change and vegetation dynamics, but have neglected other environmental factors. There is a need to investigate the spatial pattern of NDVI and its dynamics in the Qinling-Daba Mountains.

  generalstated research gap
  Keywords: environmental factors influencing ndvi spatial pattern change qinling-daba
• Disentangling Topographic Constraints and the Climatic and Anthropogenic Pathways Shaping Vegetation Greenness in the Southeastern Qinghai–Xizang Plateau (2026) · Remote Sensing · doi

  Resampling continuous raster inputs to a nominal 1 km grid obscures narrow valleys, forest edges, aspect contrasts, and local elevational variability, - The OPGD and PLS-SEM assessed spatial variation in the annual mean NDVI rather than the long-term NDVI trends, - The annual mean NDVI has limited sensitivity to intra-annual phenology and may saturate in high-cover forests

  generallimitations sectionevidence 5/5
  Keywords: resampling continuous raster inputs nominal grid obscures narrow
• Topographic heterogeneity regulates nonlinear vegetation responses to climate and human activities in China’s ecological transition zone (2026) · Frontiers in Forests and Global Change · doi

  This study examined vegetation dynamics and their climatic, topo- graphic, and anthropogenic drivers in the Qinling–Daba Mountains utilizing long-term NDVI data and a machine-learning framework. Several uncertainties warrant acknowledgment. First, NDVI primarily indicates vegetation greenness and may saturate in densely vegetated areas. This limitation restricts its ability to represent biomass and ecosys- tem functioning (Kang et al., 2021). Additionally, annual maximum NDVI fails to capture seasonal dynamics or time-lagged vegetation responses. Second, aggregating multi-source datasets to a 1-km analysis scale may obscure fine-scale heterogeneity, particularly in 30-m NDVI and topographic variables, thereby introducing scale-dependent uncer- tainty. Third, multicollinearity among predictors, especially DEM, TMP, PET, SM, and VPD, may influence SHAP-based importance rankings. Consequently, the identified relationships should be viewed as model- based associations rather than independent causal effects. Fourth, although a 10-km spatial-block validation was implemented to mitigate spatial dependence and information leakage, the original random sam- pling design may still restrict sample representativeness across environ- mental gradients. Furthermore, factors such as ecological restoration and land management were not explicitly considered (Shi et al., 2020). Future studies should integrate multi-seasonal vegetation indices and lagged environmental variables to enhance the characterization of delayed vegetation responses. Additionally, higher-resolution observa- tions and field measurements may help mitigate scale-related uncertain- ties. Employing spatially stratified sampling along with more rigorous spatial cross-validation could enhance sample representativeness and model robustness. Furthermore, alternative attribution methods or grouped predictors could be utilized to evaluate the stability of SHAP- based importance rankings. Including detailed information on ecologi- cal restoration and human activities would also deepen the Frontiers in Forests and Global Change 16 frontiersin.org Su et al. 10.3389/ffgc.2026.1948232 understanding of vegetation dynamics in the context of environmen- tal change. associations should be interpreted cautiously, rather than as direct causal relationships.

  generalfuture workevidence 5/5
  Keywords: vegetation ndvi scale dynamics based spatial additionally seasonal lagged responses multi variables predictors shap importance

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