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.

Determining the driving climatic factors at critical periods and potential legacy effects is crucial for grassland productivity predictions on the Qinghai–Tibet Plateau (QTP). However, studies with limited and ex situ ground samples from highly heterogeneous alpine meadows brought great uncertainties. This study determined the key climatic factors at critical plant developmental stages and the impact of previous plant growth status for interannual aboveground net primary productivity (ANPP) variations in different QTP grassland types. We hypothesize that the impact of climatic factors on grassland productivity varies in different periods and different vegetation types, while its legacy effects are not great. Pixel-based partial least squares regression was used to associate interannual ANPP with precipitation and air temperature at different developmental stages and prior-year ANPP from 2000 to 2019 using remote sensing techniques. Results indicated different findings from previous studies. Precipitation at the reproductive stage (July–August) was the most prominent controlling factor for ANPP which was also significantly affected by precipitation and temperature at the withering (September–October) and dormant stage (November–February), respectively. The influence of precipitation was more significant in alpine meadows than in alpine steppes, while the differentiated responses to climatic factors were attributed to differences in water consumption at different developmental stages induced by leaf area changes, bud sprouting, growth, and protection from frost damage. The prior-year ANPP showed a non-significant impact on ANPP of current year, except for alpine steppes, and this impact was much less than that of current-year climatic factors, which may be attributed to the reduced annual ANPP variations related to the inter-annual carbon circulation of alpine perennial herbaceous plants and diverse root/shoot ratios in different vegetation types. These findings can assist in improving the interannual ANPP predictions on the QTP under global climate change.

Landmarks play an essential role in wayfinding and are closely related to cognitive processes. Eye-tracking data contain massive amounts of information that can be applied to discover the cognitive behaviors during wayfinding; however, little attention has been paid to applying such data to calculating landmark salience models. This study proposes a method for constructing an indoor landmark salience model based on eye-tracking data. First, eye-tracking data are taken to calculate landmark salience for self-location and spatial orientation tasks through partial least squares regression (PLSR). Then, indoor landmark salience attractiveness (visual, semantic and structural) is selected and trained by landmark salience based on the eye-tracking data. Lastly, the indoor landmark salience model is generated by landmark salience attractiveness. Recruiting 32 participants, we designed a laboratory eye-tracking experiment to construct and test the model. Finding 1 proves that our eye-tracking data-based modelling method is more accurate than current weighting methods. Finding 2 shows that significant differences in landmark salience occur between two tasks; thus, it is necessary to generate a landmark salience model for different tasks. Our results can contribute to providing indoor maps for different tasks.

Eye movement research is a burgeoning frontier area in cartography that has attracted much attention from cartographers. However, the substantial amount of relevant literature poses a challenge for researchers aiming to obtain a rapid understanding of the intellectual structure of this research field. The purpose of this paper is to introduce the use of bibliometric analysis methods and multiple visual metaphors to visualize the intellectual structure of eye movement research in cartography, including the classic literature, research theme clusters, and research hotspots, etc. We also explain the use of geovisualization method, which can efficiently represent the spatial distribution of scientific power. Although the analysis results may not fully describe the whole research field, this method is generally applicable. We hope that it will not only help researchers to quickly grasp the evolution and trends of this research field, but will also become a novel method of merging geovisualization with knowledge visualization.

Accurate daily snow cover extent is a significant input for hydrological applications in the Qinghai–Tibetan Plateau (QTP). Although several Moderate Resolution Imaging Spectroradiometer (MODIS) daily cloud-free snow cover products over the QTP are openly accessible, the cloud-gap-filled snow cover from these products has not yet been validated. This study assessed the accuracy of cloud-gap-filled snow cover from three open accessible MODIS daily products based on snow maps retrieved from Landsat TM images. The F1-score (FS) from daily cloud-free MODIS snow cover for the combined MOD10A1F and MYD10A1F (SC1) was 64.4%, which was 7.4% points and 5.3% points higher than the other two commonly used products (SC2 and SC3), respectively. The superior accuracies from SC1 were more evident in regions with altitudes lower than 5000 m, with a weighted average FS by the area percentage of the altitude regions of 58.3%, which was 6.9% points and 9.1% points higher than SC2 and SC3. The improved SC1 accuracies also indicated regional clustering characteristics with higher FS values compared to SC2 and SC3. The lower accuracies of cloud-gap-filled snow cover from SC2 and SC3 were mainly due to the limitation in determining snow cover based on the method of the inferred snow line and the overestimation of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) snow water equivalent (SWE). These results indicate that the temporal filter approach used in SC1 is a good solution to produce daily cloud-gap-filled snow cover data for the QTP because of its higher accuracy and simple computation. The findings can be helpful for the selection of cloud-removal algorithms for determining snow cover dynamics and phenological parameters on the QTP.