Search

Grassland vegetation changes can be assessed based on vegetation growth dynamics and spatial characteristics of vegetation growth or land cover patches. However, previous studies have focused mainly on assessing changes in grasslands based only on vegetation growth condition, with limited consideration of spatial characteristics, which may lead to inaccurate identification of grassland dynamics. Here, a decision tree approach was developed to classify changes in grassland vegetation based on remote sensing data considering not only temporal variation in vegetation cover at the grid cell scale but also temporal variation in both spatial heterogeneity of vegetation cover and spatial configuration of bare land patches at the local landscape scale. The decision tree approach was verified using field and historical data and used to classify vegetation changes in the Xilingol League, Inner Mongolia, China, during the 2000–2019 period. According to the results, the study area was occupied mainly by grasslands with an insignificant change in vegetation growth, followed by those with significant improvement, and it was least occupied by those with significant decrease (but still covering 344 km2). Compared to vegetation cover, a combination of spatial characteristics was more effective in identifying changes in vegetation, including improvement, fragmentation, and degradation, and subtle changes within each process. A slight change in spatial heterogeneity of vegetation cover (such as the spatial coefficient of variation of the normalized difference vegetation index) may have overall impacts on the grassland. Each state during grassland retrogression, from moderate degradation through severe degradation to desertification, can be inferred by changes in spatial configuration (such as connectivity) of bare land patches, as well as the current bare land area. Overall, spatial characteristics can provide important theoretical information to facilitate assessment of grassland ecosystem dynamics and stability and serve as a critical early warning indicator for potential desertification. • An approach for grassland vegetation change classification is developed. • The approach was reliable in classifying vegetation changes in Inner Mongolia. • Considering spatial characteristics is critical to identify vegetation changes. • A slight change in spatial heterogeneity of vegetation may have overall effects. • Changes in connectivity of bare lands can indicate state shifts in grassland. [ABSTRACT FROM AUTHOR]

We developed a cellular automaton model for the spatiotemporal dynamics of grasshopper ( Oedaleus decorus asiaticus ) populations with a density of 3–12 ind m −2 in a typical semiarid steppe. The model simulates grasshopper dynamics within each cell and dispersal between adjacent cells at each time step from the completion of hatching to ovipositing. After the model parameters were obtained or calibrated, the natural growth of grasses (the preferred food of O. d . asiaticus ), and their variation after being fed on by cattle and grasshoppers, were modeled to explore grasshopper–plant interactions. Dispersal rules were formulated by considering the abundance of grasshoppers and grasses and their interactions within both potential source and sink cells, habitat conditions of sink cells, and spatial isolation (or connectedness) between source and sink cells. A case study was conducted at a sample landscape in Xianghuangqi County, Inner Mongolia, China. The temporal variations of the modeled grass biomass and grasshopper density were consistent with actual variations. The modeled densities were within the measured ranges for most of the cells. The estimation accuracy was higher for the 4th and 5th instars than for adults, and was higher for cells with higher initial densities (not lower than 3 ind m −2 ). The modeled results showed that the main factors influencing grasshopper density differed for different instars. During the 2nd and 3rd instar, grasshopper density depended on the number of hatched nymphs and their mortality. During the 4th instar, habitat suitability determined the density threshold that caused grasshoppers to disperse and the potential sink or source cells. This resulted in different density variations between the cells with initial densities >6.0 ind m −2 and those with densities <6.0 ind m −2 . During and after the 5th instar, grasshopper density was very stable due to the slightly decreasing habitat suitability. The modeled results are expected to provide a scientific basis for predicting grasshopper dynamics and controlling grasshopper plagues in heterogeneous spaces. [ABSTRACT FROM AUTHOR]

Grasshopper plagues have posed a severe threat to grasslands in Inner Mongolia, China. How to predict and control grasshopper infestations has become an urgent issue. We sampled 293 plots in Xianghuangqi County, measured the density of the most dominant grasshopper species, Oedaleus decorus asiaticus , and the latitude, longitude, and certain habitat factors at each plot. We estimated the grasshopper habitat suitability using a fuzzy evaluation model combined with 3S technology, and evaluated the grasshopper climatic suitability during the period 2001–2010. The integrated suitability ranks can indicate the corresponding ranks of potential occurrence of grasshoppers (POG) well. The spatial pattern of POG was closely associated with that of habitat suitability. O . decorus asiaticus was most likely to be found at flat sites or southern/eastern slopes with elevations of 1300–1400 m, typical chestnut soil with sand content of 60–80% in the topsoil, and medium vegetation coverage of 30–50% on temperate bunchgrass steppe. The annual variations in spatial pattern of POG were mainly due to variations in climatic suitability. Spring drought and spring high temperature might cause grasshopper plagues. This study provides an effective means for predicting the locations where grasshoppers might occur using relatively stable habitat factors and a few climatic factors. [ABSTRACT FROM AUTHOR]

Scale variance is highly sensitive to multi-scale patterns of variables, which is advantageous in identifying spatial hierarchy and characteristic scale(s). However, the significance of peak(s) in scale variance cannot be statistically tested, and different spatial patterns may be obtained when different zoning systems are used to calculate scale variance. To address these two problems, this study compared the scale levels with peaks in scale variance and the scale levels at which there were breaks in the nature of spatial autocorrelation as identified by shifts in Moran's I scalogram. The estimates for three simulated landscapes showed that accordance between scale levels identified employing the two methods can be used to evaluate the significance of peaks in scale variance and choose a more reasonable zoning system. The approach of scale variance analysis coupled with Moran's I scalogram was also applied to the Xilin River Basin of Inner Mongolia, China. The most vital characteristic scale (64 × 32 km) identified for the growing-season net ecosystem productivity (NEP) of the basin was validated by other spatial pattern analysis methods such as semi-variogram, Moran's I correlogram, and wavelet variance analyses, and the directionality of the chosen zoning systems was found to be similar to the orientation of actual dominant vegetation type patches. The results demonstrate that Moran's I scalogram can be used to improve the interpretation of the results of scale variance analysis and increase the reliability of scale variance analysis for landscapes having a repetitive patch pattern or gradient variation and that the proposed approach is suitable for identifying the hierarchy and the characteristic scales of patterns or processes. In summary, this study used a simple approach to solve two problems in scale variance analysis, thereby improving the methodology and enhancing the theoretical basis of multi-scale analysis. [ABSTRACT FROM AUTHOR]