Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China

摘要

Quantitative understanding of vegetation vulnerability under drought stress is essential to initiating drought preparedness and mitigation. In this study, a bivariate probabilistic framework is developed for assessing vegetation vulnerability and mapping drought-prone ecosystems more informatively, which is different from previous studies conducted in a deterministic way. The Normalized Difference Vegetation Index (NDVI) is initially correlated to the Standardized Precipitation Index (SPI) at contrasting timescales to evaluate the degree of vegetation dependence on water availability and screen out the vegetation response time. Afterward, the monthly NDVI series is connected with the most correlated SPI to derive joint distributions using a copula method. On such basis, conditional probabilities of vegetation losses are estimated under multiple drought scenarios and used for revealing tempo-spatial patterns of vegetation vulnerability. Particular focus is directed to the Loess Plateau (LP), China, which is a world-famous environmentally fragile area. Results indicate that the proposed framework is valid for vegetation vulnerability assessment as the pair-wise SPI-NDVI observations fall within high-density areas of the estimated NDVI distributions. From a probabilistic perspective, roughly 95% of the LP exhibits greater probability of vegetation losses when suffering from water deficits rather than water surplus. Vegetation loss probabilities reaching their peak (39.7%) in summer indicate the highest vegetation vulnerability to drought stress in summer months sequentially followed by autumn (32.9%) and spring (31.0%), which is linked to marked variations in water requirement at different stages of vegetation growth. Spatially, drought-vulnerable regions are identified in the western edge with vegetation loss probability 20.6% higher than the LP mean value, suggesting higher vulnerability in more arid areas. Irrigation practices and large-scale vegetation restoration,