(1) Selection of evaluation factors

The most important characteristics of fragile ecological zones are the instability of their internal structures and their sensitivity to external interference. The instability of its internal structure is related to ecological environment resource factors in fragile areas. The karst environment is a unique ecological environment system in the geographical environment. It is in a state of extremely strong and rapid variation in the energy cycle of carbon, water, and calcium. It has low environmental capacity, small biomass, high sensitivity to changes in the ecological environment system, and resistance to It has a series of ecological vulnerability characteristics such as weak interference ability and poor stability. The main reasons for the fragility of the karst environment are:

(1) Slow soil formation, thin soil layers, and discontinuous soil cover caused by the exposure of carbonate rocks lead to low productivity;

(2) High population density and low population carrying capacity have resulted in prominent contradictions between people and food and people and land;

(3) Large terrain undulations and low vegetation coverage have led to soil erosion and rocky desertification. Severe;

(4) Severe drought and flood disasters caused by the unique double-layer hydrogeological structure of karst environment.

In order to objectively evaluate vulnerability, a unified evaluation index system must be established and the vulnerability levels of different vulnerable areas must be quantified. Only then can it be possible to assess the karst ecologically fragile areas in the entire region. An overall objective understanding. Based on this, this book starts from the main environmental influencing factors of karst ecological zones and selects eight influencing factors from five aspects: carbonate rock exposure area, landform type, terrain slope, and degree of rocky desertification to establish an evaluation index system (Figure 35), to evaluate the vulnerability of Guangxi’s karst ecological environment.

Figure 35 Eco-environmental vulnerability evaluation index system of karst areas in Guangxi Autonomous Region

(2) The weight of each evaluation index

This project uses the analytic hierarchy process to determine the evaluation The weight of factors, the Analytical Hierarchy Process (AHP) was proposed by an American mathematician (T.L.SE A Y). It is a comprehensive evaluation method that combines qualitative analysis and quantitative analysis. It has been used in various fields such as society, economy, science and technology at home and abroad. Be widely used. This evaluation is based on the scores of several experts who have been engaged in karst research for a long time. It uses the scaling method from 1 to 9 and its reciprocal to construct a judgment matrix. The square root method is used to calculate the eigenroots and eigenvectors of the judgment matrix. This eigenvector It is the order of importance of each evaluation factor, that is, the distribution of weight coefficients. The calculation results are shown in Table 77.

Table 77 Weight of various evaluation indicators

(3) Evaluation indicator data integration supported by GIS

Based on the evaluation set in the evaluation system factors, and produce relevant thematic information maps such as carbonate rock distribution, terrain slope, landform types, rocky desertification, etc. Then the spatial analysis function of GIS is used to integrate the thematic information of the evaluation factors into the corresponding basic evaluation units. This data integration operation adopts a grid structure. In order to ensure good spatial overlap of different thematic data levels, each data layer adopts a unified coordinate system and projection system.

1. Generation of data layer

The terrain slope layer uses terrain contours to construct a TIN model, and then derives the slope data set from the elevation data model.

Digitize vector layers such as carbonate rocks, landform types, rocky desertification, forests, and shrub coverage, and convert the vector data layers into raster data layers.

2. Reclassify the data set

Since carbonate rock distribution, terrain slope, landform type, rocky desertification degree, and groundwater resource distribution are data sets with different dimensions, they do not have Comparability,In order to merge these data sets, quantitative processing,reclassification of the data sets is required. That is to say, set the same hierarchical system for them. This same hierarchical system refers to the vulnerability of each unit.

The carbonate salt distribution, terrain slope, landform type, rocky desertification degree, and groundwater resource distribution data sets will be reclassified below. Among them, fragile attributes are assigned higher values.

Reclassify carbonate rock distribution layer data: pure limestone is assigned a value of 4, pure dolomite is assigned a value of 3, interbedded limestone and dolomite is assigned a value of 3, and carbonate rock interbedded with clastic rock is assigned a value of 2 , the interbedded carbonate rock and clastic rock, and the interbedded carbonate rock between clastic rock are assigned a value of 1, indicating that the greater the content of carbonate rock, the more fragile it is. Assigning a blank value to non-carbonate rock areas means that only carbonate rock areas are considered in this calculation. The output reclassified dataset will become a new layer.

Reclassify slope data: assign a value of 4 to slopes >35°, assign a value of 3 to 25° to 35°, assign a value of 2 to 15° to 25°, assign a value to 5° to 15°, and assign a value of 0 to <5°. . It shows that the steeper the slope, the more fragile it is.

Reclassify the landform type layer data, and divide the vulnerability of landform types into 5 levels according to the population carrying capacity of different landform types: karst depression areas are assigned a value of 4, peak clusters, valleys, and karst ridges are assigned a value of 3 , the peak forest valley area is assigned a value of 2, the solitary peak plain area is assigned a value of 1, and the non-karst area is assigned a value of 0.

Reclassify the soil erosion intensity data and classify it according to the degree of soil erosion. Extreme erosion is assigned a value of 5, strong erosion is assigned a value of 4, moderate erosion is assigned a value of 3, light erosion area is assigned a value of 2, and slightly erosion area is assigned a value of 1.

Reclassify the rocky desertification layer data and classify it according to the degree of rocky desertification. Severe rocky desertification is assigned a value of 4, moderate rocky desertification is assigned a value of 3, mild rocky desertification is assigned a value of 2, and slight rocky desertification is assigned a value of 2. The value of 1 is assigned to the transformed area, and the value of 0 is assigned to the non-rocky desertification area.

Reclassify the forest coverage data. Areas with coverage >40% are assigned a value of 0, areas with a coverage of 30% to 40% are assigned a value of 1, areas with a coverage of 20% to 30% are assigned a value of 2, and areas with a coverage of 10% to 20% are assigned a value of 2. The area is assigned a value of 3, and <10% of the areas are assigned a value of 4.

The reclassification level of shrub coverage is the same as that of forest coverage.

Reclassify the drought and flood patch data layer, assigning a value of 4 to drought and flood patches, 2 to dry patches, and 0 to other areas.

3. Weighted re-merging of data sets

After reclassification, each data set is unified into the same hierarchical system. According to the results obtained by the analytic hierarchy process, different Layer weighting value, and then perform a weighted merge operation of the data set. The calculation result data set shows the vulnerability of each bin, with higher values ??indicating more vulnerability.

4. Vulnerability classification of karst ecological areas

Since there is currently no unified standard for grading the ecological environment vulnerability of karst mountainous areas, there is no universally recognized evaluation basis. According to the rules set in the evaluation model, the evaluation standards are divided into four vulnerability levels: potential vulnerability, mild vulnerability, moderate vulnerability, and severe vulnerability. The calculated result value is α<0.5, which means there is no obvious vulnerability zone, and 0.52.75 is an extremely vulnerable area.

(4) Evaluation results of ecological vulnerability of karst areas in Guangxi

Through model operation, the ecological vulnerability degree zoning information of karst areas in Guangxi can be obtained. The results are shown in graphics (see inside cover, color Figure 2) output in the form. The ecological environment vulnerability of Guangxi's karst areas reflects the combined effects of special natural factors and human activities in the karst areas. From the vulnerability assessment results, the extremely ecological and severely fragile areas of the karst areas in Guangxi account for 3% and 3% of the total area of ??the karst areas respectively. 43%, mainly occurring in pure carbonate rock, peak-cluster depression landforms and areas with frequent combination of drought and flood disasters. The area of ??moderately vulnerable areas accounts for 44% of the total area. As can be seen from Figure 34, the counties with high vulnerability to peak-cluster depression landforms are Duan County, Dahua County, Jingxi County, Nandan County, Xincheng County, Pingguo County, Donglan County, Tiandeng County, Lingyun County, Ma Shan County, Fengshan County, Bama County, Huanjiang County, etc.