Ecological Security Pattern Construction in Karst Area Based on Ant Algorithm

Abstract

Constructing the ecological security pattern is imperative to stabilize ecosystem services and sustainable development coordination of the social economy and ecology. This paper focuses on the Karst region in southeastern Yunnan, which is ecologically fragile. This paper selects the main types of ecosystem services and identifies the ecological source using hot spot analysis for Guangnan County. An inclusive consideration of the regional ecologic conditions and the rocky desertification formation mechanism was made. The resistance factor index system was developed to generate the basic resistance surface modified by the ecological sensitivity index. The Ant algorithm and Kernel density analysis were used to determine ecological corridor range and ecological restoration points that constructed the ecological security pattern of Guangnan County. The results demonstrated that, firstly, there were twenty-three sources in Guangnan County, with a total area of 1292.77 km2, accounting for 16.74% of the total. The forests were the chief ecological sources distributed in the non-Karst area, where Bamei Town, Yangliujing Township and Nasa Town had the highest distribution. Secondly, the revised resistance value is similar to “Zhe (Zhetu Township)-Lian (Liancheng Town)-Yang (Yangliujing Township)-Ban (Bambang Township)”. The values were lower in the north and higher in the south, which is consistent with the regional distribution of Karst. Thirdly, the constructed ecological security pattern of the “Source-Corridor-Ecological restoration point” paradigm had twenty-three ecological corridors. The chief ecological and potential corridor areas were 804.95 km2 and 621.2 km2, respectively. There are thirty-eight ecological restoration points mainly distributed in the principal ecological corridors and play a vital role in maintaining the corridor connectivity between sources. The results provide guidance and theoretical basis for the ecological security patterns construction in Karst areas, regional ecologic security protection and sustainable development promotion.

Regulation simulation of land‐use ecological security, based on a CA model and GIS: A case‐study in Xingguo County, China

“…For example, Li, Xiao, et al (2020); Li, Li, & Xia (2020) used the CA‐Markov model to predict the changes of temporal and spatial in the land use of the Pearl River Delta urban agglomeration in 2025, and simulated the land‐use pattern under three different scenarios. Therefore, it has become a new trend to add algorithms for dynamic simulation in the construction of LUESP (Zhao et al, 2021). In Figure 5, this paper constructs the LUESP under three security scenarios to provide decision makers with the choice of multi‐scenario LUESP, consequently proposing a reasonable land‐use ecological security regulation strategy.…”

“…Therefore, in recent decades researchers have concentrated on ecological security (Cumming & Allen, 2017; Li et al, 2015; Steffen et al, 2015). At present, the research on of ecological land security has paid limited attention to the definition of the concept (Feng, 2017; Xu et al, 2014), construction of an evaluation indicator system (Liu et al, 2019; Zhao et al, 2021), selection and application of evaluation methods (Feng et al, 2018; Guo & Wang, 2019), or ecological land ecological risk assessment (Li, Li, & Xia, 2020; Li, Xiao, et al, 2020; Yang & Cai, 2020), and establishing ecological security patterns (Wang et al, 2020; Wang & Pan, 2019). Although there is no agreement on a definition of ecological land security, the existing concepts contain two meanings: one is to emphasize the health and sustainability of the land ecosystem itself, and another one is to emphasize the stable ecological services and security capacity for humans provided by a land ecosystem.…”

Designing Ecological Security Patterns Based on the Framework of Ecological Quality and Ecological Sensitivity: A Case Study of Jianghan Plain, China

“…For example, in the Karst area, to construct the basic resistance surface, researchers need to consider the mechanism for the rock desertification formation. When choosing indicators for constructing resistance surface, researcher could combine the regular indicators such as elevation, slope, land cover type, vegetation coverage, and particular indicators that could illustrate the Karst geography, such as lithology and soil thickness [102]. The logical framework of this research is in line with national needs, while the selection of indicators is not universal.…”

Harmonic detection technology based on ant colony optimization BP neural network

“…Ant colony algorithm (ACO) is a simulated optimization algorithm to simulate ant foraging behavior. It was first proposed by Italian scholar Dorigo m et al In 1991 and first used to solve TSP (traveling salesman problem) [9][10] . Then, the basic principle and mathematical model of ant colony algorithm are systematically studied.…”