Spatiotemporal Evolution Analysis and Future Scenario Prediction of Rocky Desertification in a Subtropical Karst Region

Landscape change is a dynamic feature of landscape structure and function over time which is usually affected by natural and human factors. The evolution of rocky desertification is a typical landscape change that directly affects ecological environment governance and sustainable development. Guizhou is one of the most typical subtropical karst landform areas in the world. Its special karst rocky desertification phenomenon is an important factor affecting the ecological environment and limiting sustainable development. In this paper, remote sensing imagery and machine learning methods are utilized to model and analyze the spatiotemporal variation of rocky desertification in Guizhou. Based on an improved CA-Markov model, rocky desertification scenarios in the next 30 years are predicted, providing data support for exploration of the evolution rule of rocky desertification in subtropical karst areas and for effective management. The specific results are as follows: (1) Based on the dynamic degree, transfer matrix, evolution intensity, and speed, the temporal and spatial evolution of rocky desertification in Guizhou from 2001 to 2020 was analyzed. It was found that the proportion of no rocky desertification (NRD) areas increased from 48.86% to 63.53% over this period. Potential rocky desertification (PRD), light rocky desertification (LRD), middle rocky desertification (MRD), and severe rocky desertification (SRD) continued to improve, with the improvement showing an accelerating trend after 2010. (2) An improved CA-Markov model was used to predict the future rocky desertification scenario; compared to the traditional CA-Markov model, the Lee–Sallee index increased from 0.681 to 0.723, and figure of merit (FOM) increased from 0.459 to 0.530. The conclusions of this paper are as follows: (1) From 2001 to 2020, the evolution speed of PRD was the fastest, while that of SRD was the slowest. Rocky desertification control should not only focus on areas with serious rocky desertification, but also prevent transformation from NRD to PRD. (2) Rocky desertification will continue to improve over the next 30 years. Possible deterioration areas are concentrated in high-altitude areas, such as the south of Bijie and the east of Liupanshui.

Internationalizing Local Tourism: Maros Rammang-Rammang Karst Region and the Standardization and Certification of Community Tourism Businesses in Indonesia

The Unhas KKN-PPM program aims to provide a comprehensive understanding to local communities, who live in the Rammang-Rammang Karst tourist attraction area in Salenrang Village, Maros Regency, about the importance of standardization and certification of tourism businesses so that the Rammang-Rammang Karst tourism object can be managed by the community. locally professionally. In addition, this program also aims to provide assistance to the local community to communicate their needs related to the procurement of more complete tourism facilities and infrastructure to the Regional Government of Maros Regency and the Regional Government of South Sulawesi Province. The mission to be achieved is that the community is expected to be able to actively participate in the development and promotion of tourism in the Rammang-Rammang Karst tourist attraction so that later it can make this destination an international tourism destination that brings in many foreign tourists. With this, the potential and economic contribution of the Rammang-Rammang Karst tourism object can be maximized for the welfare of local communities and increasing state income.

Nonlinear characteristics of the vegetation change and its response to climate change in the karst region of southwest China

The vegetation is known to be sensitive to both climate change and anthropogenic disturbance. However, the relationship between changes in vegetation and climate is unclear in karst regions. The nonlinear characteristics of vegetation change and its possible relationships with driving factors in the karst region of southwest China are revealed, using methods of Ensemble Empirical Mode Decomposition, Mann-Kendall, and Partial Least Squares Regression. The results show that: (1) vegetation changes demonstrate an increasing trend with an abrupt change in 2002. Multiple time scales of 3, 6, 10, and 25-year are observed in vegetation variations, dominated by long-term trend and the short time scale of 3-year with variance contributions of 58.10% and 28.63%. (2) The relationship of climate indexes with vegetation changes shows r2 = 0.78 ( p < 0.01) based on the reconstruction of characteristic scales, indicating significant great relationship. In space, the area percentage with relationship of climate to vegetation is more than 50%, and the impact is much greater after the abrupt change of vegetation in 2002 ( r2 are 0.24–0.91 and 0.42–0.99, respectively). In addition, the correlation between vegetation change and ecological engineering is 0.15 ( p < 0.01). The results indicate that climate change is the main impact factor of vegetation change, ecological engineering has positive influences in improving vegetation condition, and methods of scales decomposition and abrupt detection could reveal some hidden information for better understanding ecosystems in karst regions.

Gut Microbiota Reveals the Environmental Adaption in Gastro-intestinal Tract of Wild Boar in Karst Region of Southwest China

BackgroundGut microbes, has become one of the research hotspots in animal ecology, playing an important role in monitoring dietary adaptation and health status of host. However, there are few studies on the gut microbiota in the stomach, small intestine (ileum) and large intestine (cecum, colon and rectum) of wild boar. ResultsAlpha diversity and Beta diversity showed there were significant differences in the abundance and distribution of microbes in gastrointestinal tract of wild boar. Firmicutes and Bacteroidetes were the most dominant phyla in stomach, cecum, colon and rectum of wild boar, while Proteobacteria and Firmicutes were the most dominant in ileum. At genus level, there were different leading genera in stomach (Prevotella and Lactobacillus), small intestine (Escherichia-Shigella and Lactobacillus) and large intestine (Ruminococcaceae_UCG-005, Christensenellaceae_R-7_group and Escherichia-Shigella). PICRUSt function predictive analysis suggested that there were significant differences in microbial metabolic pathways among five locations of wild boar. ConclusionsThis study comprehensively revealed the differences in composition of microbial community in gastrointestinal trac of wild boar. Future work links microbes with the metabolites to accurately reveal the health of wild boar.

Calculation Method for the Critical Thickness of a Karst Cave Roof at the Bottom of a Socketed Pile

The thickness of a karst cave roof at the bottom of a socketed pile plays an important role in the vertical bearing capacity of the socketed pile in the karst region. In practice, its thickness is simply recommended to be not less than 3 times the diameter of the socketed pile, regardless of the geological conditions and the size of the cave itself. In this study, we present an approach for calculating the critical thickness-to-diameter ratio of a karst cave roof η (η = h/d, the ratio of karst cave roof thickness to pile diameter) based on the generalized Hoek–Brown criterion by virtue of the limit analysis method, which considers the pile tip load, hardness degree of the intact rock, and rock mass quality. The analysis results show that less load at the bottom of the pile, higher quality of rock mass, and more hard rock all lead to a smaller critical thickness-diameter ratio, whereas the critical thickness-to-diameter ratio is greater. The validity of the proposed method is verified through a physical model test.

Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm

Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN model to design the K-DBN model with the highest estimation accuracy is selected for AGB inversion and spatial mapping. The results showed that: (1) The determination coefficients R2 of DBN is 0.602, which are 0.208, 0.101 higher than that of linear regression (LR) and random forest (RF) model. (2) The K-DBN algorithm was designed based on FCD to optimize the DBN model, which can alleviate the common problems of low-value overestimation and high-value underestimation in AGB estimation to a certain extent to improve the estimation accuracy. The maximum R2 of the model reached 0.848, and we mapped the forest AGB using the K-DBN model in the study area in 2015. The conclusion of this study: Based on multi-source optical and radar data, the retrieval accuracy of forest AGB can be improved by considering the FCD, and the deep learning algorithm K-DBN is excellent in forest AGB remote sensing estimation. These research results provide a new method and data support for the spatio-temporal dynamic remote sensing monitoring of forest AGB in karst areas.