Quantitative Analysis of Driving Factors of Grassland Degradation: A Case Study in Xilin River Basin, Inner Mongolia

Abstract

Current literature suggests that grassland degradation occurs in areas with poor soil conditions or noticeable environmental changes and is often a result of overgrazing or human disturbances. However, these views are questioned in our analyses. Based on the analysis of satellite vegetation maps from 1984, 1998, and 2004 for the Xilin River Basin, Inner Mongolia, China, and binary logistic regression (BLR) analysis, we observe the following: (1) grassland degradation is positively correlated with the growth density of climax communities; (2) our findings do not support a common notion that a decrease of biological productivity is a direct indicator of grassland degradation; (3) a causal relationship between grazing intensity and grassland degradation was not found; (4) degradation severity increased steadily towards roads but showed different trends near human settlements. This study found complex relationships between vegetation degradation and various microhabitat conditions, for example, elevation, slope, aspect, and proximity to water.

Comparison of Modeling Grassland Degradation with and without Considering Localized Spatial Associations in Vegetation Changing Patterns

“…While constructing more road networks expanded the domain of human activities, it also might lead to more damage to the grassland vegetation due to the easier access to vegetated areas. The side impact on vegetation due to road networks on grasslands has been documented by a few studies investigated in Inner Mongolia and Mongolia [18,57]. The paper [57] also analyzed a few reasons, including the hardness of the top layer and soil erosion, for the damage imposed by road tracks on grasslands.…”

“…We found that the average neighborhood characteristic for DD developed from RR and NS showed a negligible change to window size when it was beyond seven units. Considering this result, as well as the previous study on vegetation density interpolation [18], d was set to seven (equivalent to a window with 1.75 km in width).…”

“…There were five recorded animal species, that is, sheep, horse, buffalo, camel, and donkey. Since different livestock have varied impacts on grass consumption and vegetation damage, each of the livestock was converted to a standard animal unit, called a sheep unit, through a conversion coefficient to compute the grazing intensity [18]. By converting all of the animal species to sheep unit, it is more convenient to model the overall grazing effect.…”

“…For example, steppe degradation in arid and semi-arid areas could be the result of both climate conditions, particularly due to limited precipitation, and intensified human activities [16,17]. Furthermore, studies on the driving factors of grassland degradation, both natural and human-induced, presented some critical insights for understanding the pathways of vegetation deterioration and their mechanisms [18,19]. Typical examples of natural driving factors included climatic conditions, local environments (e.g., topography such as elevation, slope, or aspect), and soil properties [20][21][22].…”

“…Binary logistic regression (BLR) has been widely applied to studying cause-effect relationships. BLR is capable of predicting the odds of being a case or not (responses from a binary variable) based on the values of the independent variables (predictors), and thus is fit for studying vegetation degradation [18]. We compared two models based on binary logistic regression: the traditional binary logistic regression model (simply referred to as BLR hereafter), and a binary logistic regression model with neighborhood interactions (referred to as BLR-NIs).…”