This study proposed a multi-target hierarchical detection (MTHD) method to simultaneously and automatically detect multiple directional land cover changes. MTHD used a hierarchical strategy to detect both abrupt and trend land cover changes successively. First, Grubbs’ test eliminated short-lived changes by considering them outliers. Then, the Brown-Forsythe test and the combination of Tomé’s method and the Chow test were applied to determine abrupt changes. Finally, Sen’s slope estimation coordinated with the Mann-Kendall test detection method was used to detect trend changes. Results demonstrated that both abrupt and trend land cover changes could be detected accurately and automatically. The overall accuracy of abrupt land cover changes was 87.0% and the kappa index was 0.74. Detected trends of land cover change indicated high consistency between NDVI (Normalized Difference Vegetation Index), change trends from LTS (Landsat Thematic Mapper and Enhanced Thematic Mapper Plus time series dataset), and MODIS (Moderate Resolution Imaging Spectroradiometer) time series datasets with the percentage of samples indicating consistency of 100%. For cropland, trends of millet yield per unit and average NDVI of cropland indicated high consistency with a linear regression determination coefficient of 0.94 (p < 0.01). Compared with other multi-target change detection methods, the changes detected by the MTHD could be related closely with specific ecosystem changes, reducing the risk of false changes in the area with frequent and strong interannual fluctuations.

Reconstructing normalized difference vegetation index (NDVI) time series datasets is essential for monitoring long-term changes in terrestrial vegetation. Here, a temporal–spatial iteration (TSI) method was developed to estimate the NDVIs of contaminated pixels, based on reliable data. The NDVIs of contaminated pixels were first computed through linear interpolation of adjacent high-quality pixels in the time series. Then, the NDVIs of remaining contaminated pixels were determined based on the NDVI of a high-quality pixel located in the same ecological zone, showing the most similar NDVI change trajectories. These two steps were repeated iteratively, using the estimated NDVIs as high-quality pixels to predict undetermined NDVIs of contaminated pixels until the NDVIs of all contaminated pixels were estimated. A case study was conducted in Inner Mongolia, China. The accuracies of estimated NDVIs using TSI were higher than the asymmetric Gaussian, Savitzky–Golay, and window-regression methods. Root mean square error (RMSE) and mean absolute percent error (MAPE) decreased by 16.7%–86.6% and 18.3%–33.0%, respectively. The TSI method performed better over a range of environmental conditions, the variation of performance by the compared methods was 1.4–5 times that of the TSI method. The TSI method will be most applicable when large numbers of contaminated pixels exist.

Based on limited controlled experiments, both advanced and delayed shifts in flowering phenology induced by precipitation and snow cover have been reported on the Qinghai–Tibetan Plateau (QTP). To clarify the impact of precipitation and snow cover on flowering phenology, we conducted a comprehensive statistical analysis of the temporal change in flowering phenology and its responses to precipitation and snow cover changes using regression models built on the largest collection of ground phenological observation data on the QTP. We found that first flowering date (FFD) for the early-flowering time series significantly advanced at the rate of −0.371 ± 0.149 days/year (p < 0.001), whereas FFD mid-to-late-flowering time series showed no trend at the rate of 0.158 ± 0.193 days/year (p = 0.108). Cumulative pre-season precipitation regressed with FFD positively for early-flowering time series, with the explained variation ranging from 11.7 to 49.4% over different pre-season periods. The negative impact of precipitation on flowering phenology is unexpected, because an increase in precipitation should not hamper plant growth in the semi-arid and arid environments on the QTP. However, precipitation was found to be inversely correlated with temperature. Thus, it is likely that temperature, and not precipitation, regulated flowering phenology over the study period. No relationship was found between FFD and snow-cover melt date or duration. This result indicated that snow cover may not affect flowering phenology significantly, which may be because plant flowering time was much later than the snow-cover melt date on the QTP. These findings contrast the results of controlled experiments on the QTP, which showed that precipitation regulated flowering phenology, and with other studies that showed that snow-cover melting time determined flowering dates of early-flowering species in high latitude and Arctic zones in Europe and North America, where the low-temperature environment is similar to the QTP. These findings can improve flowering phenology models, assist in the prediction of phenological responses of herbaceous plants to climate change, and forecast changes in the structure and function of the grassland ecosystem on the QTP.