Quantification of Loess Landforms from Three-Dimensional Landscape Pattern Perspective by Using DEMs

Quantitative analysis of the differences and the exploration of the evolution models of different loess landform types are greatly important to the in-depth understanding of the evolution process and mechanism of the loess landforms. In this research, several typical loess landform areas in the Chinese Loess Plateau were selected, and the object-oriented image analysis (OBIA) method was employed to identify the basic loess landform types. Three-dimensional (3D) landscape pattern indices were introduced on this foundation to measure the morphological and structural features of individual loess landform objects in more detail. Compared with the traditional two-dimensional (2D) landscape pattern indices, the indices consider the topographic features, thereby providing more vertical topographic information. Furthermore, the evolution modes between different loess landform types were discussed. Results show that the OBIA method achieved satisfying classification results with an overall accuracy of 88.12%. There are evident differences in quantitative morphological indicators among loess landform types, especially in indicators such as total length of edge, mean patch size, landscape shape index, and edge dimension index. Meanwhile, significant differences are also found in the combination of loess landform types corresponding to different landform development stages. The degree of surface erosion became increasingly significant as loess landforms developed, loess tableland area rapidly reduced or even vanished, and the dominant loess landform types changed to loess ridge and loess hill. Hence, in the reconstruction and management of the Loess Plateau, the loess tableland should be the key protected loess landform type. These preliminary results are helpful to further understand the development process of loess landforms and provide a certain reference for regional soil and water conservation.

The Potential of Integrating Landscape, Geochemical and Economical Indices to Analyze Watershed Ecological Environment

A river watershed is a complicated ecosystem, and its spatial structure and temporal dynamics are driven by various natural factors such as soil properties and topographic features, human activities, and their interactions. Thus, characterizing the river watershed ecosystem and monitoring its dynamics is very challenging. In this study, we explored the characteristics of the ecosystem and environment of Yalong River watershed in Ganzi Tibetan Autonomous Prefecture, Sichuan Province of China by analyzing and modeling the relationships among economic indices, heavy metal elements and landscape metrics. Landsat 8 data were used to generate a land cover classification map and to derive landscape pattern indices. Governmental finance statistics yearbook data were referred to provide economic indices. Moreover, a total of 9 water samples were collected from the upstream to the downstream to obtain the values of heavy metal concentrations in the water body. Then, both correlation and regression analyses were applied to analyze and model the relationships among these indices. The results of this study showed that 1) The ecological status and process of this river watershed could be explained by analyzing the relationships among the economic indices, heavy metal elements and landscape pattern indices selected based on correlation analysis; 2) Compared with the economic indices, the accumulated economic indices were more significantly correlated with most of the heavy metal elements and should be applied for the integrated assessment of the watershed ecological environment; 3) Landscape pattern indices SHDI and IJI had strong correlations with the important economic indices Population and Population Density and could be used for the integrated assessment of the watershed characteristics; 4) Compared with land cover area, land cover area ratios were more sensitive to the variation of the economic indices. The dominated land cover types Forest and Grassland had strong relationships with the economic indices; and 5) Cu and Zn had significant correlations with the landscape pattern indices. This study implied that analyzing and modeling the relationships among the economic indices, heavy metal elements and landscape pattern indices can provide a powerful tool for characterizing the ecosystem of the river watershed and useful guidelines for the watershed management and sustainable development.

Quantifying bias in pattern indices extracted from spatially offset landscape samples

Modern ecological models often account for the influence of the surrounding environment by using landscape pattern indices (LPIs) as measures of landscape structure. Ideally, the landscape samples from which these LPIs are extracted should be centered on the locations where the response variable was measured. However, in situations where this is not possible due to a lack of adequate full-coverage landcover data, the question arises as to what degree this circumstance creates a bias in the value of the LPIs, thereby obscuring their relation with the response variable. To address this question, we extracted four representative LPIs from 30 rectangular (3 × 6 km) landscape samples evenly distributed across a 10 000 km2 boreal forest study area. These rectangles were subjected to systematic displacements across a range of distances (0.5 to 2.5 km) and directions, after which we recomputed the LPIs. We found that a 1 km spatial offset led to an average of 15% deviation of original LPI values. Unfortunately, as the offset increased, the range of resulting deviations also widened, making it difficult to predict this effect. Our findings fill a gap in the literature on landscape pattern analysis and suggest that researchers should avoid LPIs extracted from spatially offset landscape samples.