Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China

Abstract

Grasslands are crucial components of ecosystems. In recent years, owing to certain natural and socio-economic factors, alpine grassland ecosystems have experienced significant degradation. This study integrated the frequency ratio model (FR) and Bayesian belief networks (BBN) for grassland degradation risk assessment to mitigate several issues found in previous studies. Firstly, the identification of non-encroached degraded grasslands and shrub-encroached grasslands could help stakeholders more accurately understand the status of different types of alpine grassland degradation. In addition, the index discretization method based on the FR model can more accurately ascertain the relationship between grassland degradation and driving factors to improve the accuracy of results. On this basis, the application of BBN not only effectively expresses the complex causal relationships among various variables in the process of grassland degradation, but also solves the problem of identifying key factors and assessing grassland degradation risks under uncertain conditions caused by a lack of information. The obtained result showed that the accuracies based on the confusion matrix of the slope of NDVI change (NDVIs), shrub-encroached grasslands, and grassland degradation indicators in the BBN model were 85.27, 88.99, and 74.37%, respectively. The areas under the curve based on the ROC curve of NDVIs, shrub-encroached grasslands, and grassland degradation were 75.39% (P < 0.05), 66.57% (P < 0.05), and 66.11% (P < 0.05), respectively. Therefore, this model could be used to infer the probability of grassland degradation risk. The results obtained using the model showed that the area with a higher probability of degradation (P > 30%) was 2.22 million ha (15.94%), with 1.742 million ha (78.46%) based on NDVIs and 0.478 million ha (21.54%) based on shrub-encroached grasslands. Moreover, the higher probability of grassland degradation risk was mainly distributed in regions with lower vegetation coverage, lower temperatures, less potential evapotranspiration, and higher soil sand content. Our research can provide guidance for decision-makers when formulating scientific measures for alpine grassland restoration.

Mapping Forest Restoration Probability and Driving Archetypes Using a Bayesian Belief Network and SOM: Towards Karst Ecological Restoration in Guizhou, China

“…The trend of forest change was characterized by the slope of NDVI (NDVIs), and NDVIs > 0 was regarded as forest restoration. The red pixels represent a positive effect and the blue pixels represent a negative effect between two factors; the flatter the ellipse, the stronger the correlation [20]. The results imply that the selected drivers are directly or indirectly related to change in forest coverage partially.…”

“…The frequency ratio (FR) can be used to rank the driving factors based on the susceptibility of each attribute interval of the factor to the event (Equation ( 2)). Then, the intervals with similar frequency ratios can be merged to realize the scientific division of indicator factor status, which provides a more reliable prior probability for the nodes in the BBN model [20]. Therefore, the 18 potential factors were divided into four levels using FR models (Table 2), and a sample file with a training set (n = 75,762; 80%) and a testing set (n = 18,940; 20%) was generated.…”

“…In addition, a comprehensive effect assessment is needed not only to reveal the uncertainty of forest restoration, but also to identify the distribution of forest restoration at different levels based on the causality between the trends of forest coverage changes and driving factors. Recently, Bayesian belief networks (BBN) have been widely used to measure the risk and benefits to resolve uncertainty based on probability theory [20]. For example, Li et al (2019) [21] applied the BBN model to analyze the risk of forest landscape degradation to resolve the uncertainty of risk assessment due to natural and human disturbances.…”

“…For example, Li et al (2019) [21] applied the BBN model to analyze the risk of forest landscape degradation to resolve the uncertainty of risk assessment due to natural and human disturbances. Further, the method not only utilizes various data sources such as expert knowledge, observation, and empirical data to determine the causality effectively [20], but also interprets the benefits of probability based on quantitative inference patterns [22]. Here, the probability of benefit is expressed as a measure of the confidence level; that is, the reliability of the results [23].…”

Integrating a mixed‐cell cellular automata model and Bayesian belief network for ecosystem services optimization to guide ecological restoration and conservation

“…Based on the BBN model of ecosystem services in 2015, we performed a sensitivity analysis to calculate the variance reduction in the five ecosystem service nodes and to identify the key factors contributing to ecosystem services changes. Variance of belief (VB) based on variance reduction is often used as a sensitivity analysis indicator to quantitatively evaluate whether network nodes will sensitively perceive changes in other nodes (Zhou & Peng, 2021). The formula is as follows: Where: is the target node, is some other nodes, and represents the state of .…”