Spatiotemporal evolution and Sustainably comprehensive zoning optimization of production-living-ecological functions in the Mountain-Flatland areas.

Examining the spatiotemporal changes of territorial space is crucial for addressing the conflict between economic-social development and the natural environment and achieving optimal territorial space utilization. However, there is a research gap regarding the spatial characteristics and optimization in the mountain-flatland area. To address this gap, this paper focuses on the urban agglomeration in Central Yunnan (UACY) as a representative mountain-flatland area. A mountain-flatland classification model was established. Based on the evaluation of production- living- ecological functions, the economic models were introduced to measure the balance degree, and further researched the spatiotemporal evolution and coupling coordination characteristics by spatial analysis from 2010 to 2020. The findings indicate the following: (1) The study area exhibited distinct mountain-flatland differentiation, with "western mountainous counties (MCs)/semi-mountainous and semi-flatland counties (SMSFCs), central flatland counties (FCs), and eastern SMSFCs". production function (PF) primarily formed a cluster in the central-northeastern areas of FCs and of SMSFCs, living function (LF) was highly clustered in the central areas of FCs, remained stable, and ecological function (EF) was significantly clustered in the northwestern regions of MCs and of SMSFCs, significantly enhanced in the northeast. (2) The imbalance degree followed the order LF > PF > EF, showing a decreasing trend primarily driven by intra-group imbalances within FCs, SMSFCs, and MCs. The coordinate areas were mainly concentrated in central FCs, and the dysfunctional areas was largely located in MCs and SMSFCs, the degree was improved, especially in northwestern and southeastern MCs and SMSFCs. (3) The study area fell into 18 functional areas, optimized into 13 areas, with recommendations for differentiated development control paths to achieve an optimization of PLEFs. These results provide theoretical references for promoting sustainable utilization of territorial resources and facilitating high-quality regional development in UACY and other parts of the country.

Qualitative-quantitative identification and functional zoning analysis of production-living-ecological space: a case study of Urban Agglomeration in Central Yunnan, China.

Territorial space exhibits multiple functional attributes, which comprise production, living, and ecological functions usually. Optimizing the production-living-ecological space (PLES) has become the key to territorial and spatial planning; the scientific identification of the PLES lays a foundation for space optimization and has important guiding significance in territorial spatial zoning. To achieve the integration of macro-scale and micro-scale PLES, with the Urban Agglomeration in Central Yunnan as the research area in this study, the PLES functional identification systems from the administrative unit scale and the grid scale are constructed. The types of PLES are determined by integrating qualitative and quantitative evaluation results and using an improved primacy index model from a composite spatial perspective. On that basis, the division of comprehensive zoning is achieved for land use functions through kernel density analysis. As indicated by the results, the model is capable of reflecting the macro background of the PLES functions in administrative regions while characterizing the micro differences at the grid level in administrative units. There are significant differences in the production, living, and ecological functional spaces in the Urban Agglomeration. Production functions are concentrated in the central and northeastern, living functions are concentrated in the central, and ecological functions are concentrated in the western and northeastern, with significantly consistent or complementary spatial distributions of each other. The PLES of Urban Agglomeration includes production space (PS), ecological space (ES), production-living space (P-LS), production-ecological space (P-ES), living-ecological space (L-ES), and production-living-ecological space (P-L-ES), placing a focus on ES, P-ES, and P-L-ES, which marks significant differences in spatial distribution among different spatial types. The study area is divided into 24 functional zones, which are classified into 6 categories, and optimization paths are proposed. This study will provide a reference for territorial and spatial planning in spatial functional zoning, spatial pattern optimization, and land management applications.

Landslide Susceptibility Evaluation of Machine Learning Based on Information Volume and Frequency Ratio: A Case Study of Weixin County, China.

A landslide is one of the most destructive natural disasters in the world. The accurate modeling and prediction of landslide hazards have been used as some of the vital tools for landslide disaster prevention and control. The purpose of this study was to explore the application of coupling models in landslide susceptibility evaluation. This paper used Weixin County as the research object. First, according to the landslide catalog database constructed, there were 345 landslides in the study area. Twelve environmental factors were selected, including terrain (elevation, slope, slope direction, plane curvature, and profile curvature), geological structure (stratigraphic lithology and distance from fault zone), meteorological hydrology (average annual rainfall and distance to rivers), and land cover (NDVI, land use, and distance to roads). Then, a single model (logistic regression, support vector machine, and random forest) and a coupled model (IV-LR, IV-SVM, IV-RF, FR-LR, FR-SVM, and FR-RF) based on information volume and frequency ratio were constructed, and the accuracy and reliability of the models were compared and analyzed. Finally, the influence of environmental factors on landslide susceptibility under the optimal model was discussed. The results showed that the prediction accuracy of the nine models ranged from 75.2% (LR model) to 94.9% (FR-RF model), and the coupling accuracy was generally higher than that of the single model. Therefore, the coupling model could improve the prediction accuracy of the model to a certain extent. The FR-RF coupling model had the highest accuracy. Under the optimal model FR-RF, distance from the road, NDVI, and land use were the three most important environmental factors, ac-counting for 20.15%, 13.37%, and 9.69%, respectively. Therefore, it was necessary for Weixin County to strengthen the monitoring of mountains near roads and areas with sparse vegetation to prevent landslides caused by human activities and rainfall.

Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China.

In complex mountainous areas where earthquakes are frequent, landslide hazards pose a significant threat to human life and property due to their high degree of concealment, complex development mechanism, and abrupt nature. In view of the problems of the existing landslide hazard susceptibility evaluation model, such as poor effectiveness and inaccuracy of landslide hazard data and the need for experts to participate in the calculation of a large number of evaluation factor weight classification statistics. In this paper, a combined SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) and PSO-RF (Particle Swarm Optimization-Random Forest) algorithm was proposed to evaluate the susceptibility of landslide hazards in complex mountainous regions characterized by frequent earthquakes, deep river valleys, and large terrain height differences. First, the SBAS-InSAR technique was used to invert the surface deformation rates of the study area and identified potential landslide hazards. Second, the study area was divided into 412,585 grid cells, and the 16 selected environmental factors were analyzed comprehensively to identify the most effective evaluation factors. Last, 2722 landslide (1361 grid cells) and non-landslide (1361 grid cells) grid cells in the study area were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide and non-landslide data, the performances of the PSO-RF algorithm and three other machine learning algorithms, BP (back propagation), SVM (support vector machines), and RF (random forest) algorithms were compared. The results showed that 329 potential landslide hazards were updated using the surface deformation rates and existing landslide cataloguing data. Furthermore, the area under the curve (AUC) value and the accuracy (ACC) of the PSO-RF algorithm were 0.9567 and 0.8874, which were higher than those of the BP (0.8823 and 0.8274), SVM (0.8910 and 0.8311), and RF (0.9293 and 0.8531), respectively. In conclusion, the method put forth in this paper can be effectively updated landslide data sources and implemented a susceptibility prediction assessment of landslide disasters in intricate mountainous areas. The findings can serve as a strong reference for the prevention of landslide hazards and decision-making mitigation by government departments.

Estimation of SO42- ion in saline soil using VIS-NIR spectroscopy under different human activity stress.

SO42- ion is an important indicator of soil salinization degree, but there are few researches on quantitative inversion of SO42- content based on hyperspectral and fractional-order derivative (FOD). This study aimed to improve the prediction accuracy of SO42- content in arid regions using visible and near-infrared (VIS-NIR) spectroscopy. The study area was divided into three regions according to different human activity stress, namely, lightly affected region (Region A), moderately affected region (Region B) and severely affected region (Region C). The combination estimation method of spectral transformations (R, R, 1/R, lgR, 1/lgR), FOD, significance test band (STB), and partial least squares regression (PLSR) were been constructed, and four models (FULL-PLSR, FOD-FULL-PLSR, IOD-STB-PLSR, FOD-STB-PLSR) were also used to compare and analyze the estimation accuracy. Simulation results show that the optimal prediction model of three regions is FOD-STB-PLSR, its spectral transformation is established by R, 1/R and R in Region A, B, and C, respectively. Its RPD is 2.4701, 3.4679 and 1.9781, and its optimal FOD derivative is located at 1.8-, 1.1- and 1.1-order, respectively. It means that FOD can fully extract VIS-NIR spectroscopy details, the higher-order FOD is more capable of extracting characteristic data than low-order FOD, and the predictive ability of the best estimation model is very good, extremely strong and relatively good in Region A, B and C, respectively. Compared with the best IOD-STB-PLSR of each region, the RPD of the optimal FOD-STB-PLSR model has increased more than 38%, 32%, and 19%, respectively. This study shows that the proposed FOD-STB-PLSR model is suitable for estimating the SO42- ion content of saline soil under different human activity stresses, and the study can provide a certain technical reference value for the monitoring of saline soil in arid areas.