Applying Bayesian Belief Network to explore key determinants for nature-based solutions’ acceptance of local stakeholders

2022

Land Degrad Dev

An ecosystem is a complex system with a large number of dynamic variables, which poses challenges to the optimization of ecosystem services. However, traditional ecosystem services optimization methods do not take into account the complexity and uncertainty of variables. To address this complexity and uncertainty, we propose an innovative approach using a mixed‐cell cellular automata (MCCA) model and a Bayesian belief network (BBN) model for ecosystem service optimization. This approach was applied to the southern region of Sichuan Province, China, using an existing dataset to simulate land use patterns and predict ecosystem services in 2035 under different development scenarios. To achieve ecological restoration and conservation, we also determined the key factor combinations and key ecological regions at various ecosystem service levels. Results showed that ecological protection scenario design has important significance as a reference for maintaining and ameliorating regional ecosystem services and functions. We also identified that the highest level of ecosystem services was mainly located in the areas with the highest net primary productivity (NPP), the highest slope, the highest forestland area, and low ET. According to these findings, some suggestions for ecological restoration and conservation in key regions were put forward. This approach fully considers the uncertainty of factors; therefore, it can be used as an effective tool for designing ecosystem management strategies.

Section: Discussionmentioning

confidence: 99%

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

2022

Land Degrad Dev

“…The FR method obtains a prior probability, and then the BBN model is used to infer the posterior probability of the event. Compared with the node classification method based on the characteristics of the node itself in the previous studies ( Dai et al, 2021 ; Sakib et al, 2021 ), the FR method is graded the driving factors based on the importance of each attribute interval of the factor to the susceptibility of the event, which is more scientific. Therefore, as the premise of the BBN model interference, the FR model can provide a relatively reliable prior probability.…”

Section: Discussionmentioning

confidence: 99%

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

2021

Front. Plant Sci.

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.

“…Therefore, this model is very valuable. Further, it can be used to re-evaluate forest restoration probabilities using updated or replaced data [42], to enable formulation of reasonable and effective management measures by the stakeholder. As a result, the BBN model exhibits relatively strong reliability and practicability.…”

Section: Assessing the Potential Forest Restoration Probabilitymentioning

confidence: 99%

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

Chen

2022

Remote Sensing

To address ecological threats such as land degradation in the karst regions, several ecological restoration projects have been implemented for improved vegetation coverage. Forests are the most important types of vegetation. However, the evaluation of forest restoration is often uncertain, primarily owing to the complexity of the underlying factors and lack of information related to changes in forest coverage in the future. To address this issue, a systematic case study based on the Guizhou Province, China, was carried out. First, three archetypes of driving factors were recognized through the self-organizing maps (SOM) algorithm: the high-strength ecological archetype, marginal archetype, and high-strength archetype dominated by human influence. Then, the probability of forest restoration in the context of ecological restoration was predicted using Bayesian belief networks in an effort to decrease the uncertainty of evaluation. Results show that the overall probability of forest restoration in the study area ranged from 22.27 to 99.29%, which is quite high. The findings from regions with different landforms suggest that the forest restoration probabilities of karst regions in the grid and the regional scales were lower than in non-karst regions. However, this difference was insignificant mainly because the ecological restoration in the karst regions accelerated local forest restoration and decreased the ecological impact. The proposed method of driving-factor clustering based on restoration as well as the method of predicting restoration probability have a certain reference value for forest management and the layout of ecological restoration projects in the mid-latitude ecotone.