Construction of Karst Landscape Ecological Security Pattern Based on Conflict between Human and Nature in Puzhehei
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Date Sources
- (1)
- Google Earth provided the Digital Elevation Model (DEM, ASTER GDEM 30 M)(https://earth.google.com.hk/, accessed on 19 December 2022);
- (2)
- The dataset of surface water and normalized differential vegetation index(NDVI, 30 m spatial resolution) were obtained from the Geospatial Data Cloud website (http://www.gscloud.cn/, accessed on 28 December 2020);
- (3)
- The distribution data and sensitivity data of rocky desertification were derived from the research results of Xu Hongfeng [24] et al.; the data were processed via Landsat through the Google Earth Engine cloud computing platform (accessed on 25 December 2020);
- (4)
- Soil erodible data were created using the China Soil Database at a scale of 1:1 million (https://vdb3.soil.csdb.cn/, accessed on 13 December 2022);
- (5)
- The roadway vector data were obtained through the OpenStreetMap website (https://www.openstreetmap.org/, accessed on 2 December 2022);
- (6)
- The land use data (10 m spatial resolution) were obtained from the European Space Agency (https://viewer.esa-worldcover.org/, accessed on 27 November 2022). Six different types of classifications were made for the current study region: forest land, water areas, grassland, cultivated land, unutilized land, and construction land. After accuracy verification, the Kappa coefficient was verified to be 0.9342, with an overall classification accuracy of 94.9013%.
2.3. Analysis Methods
2.3.1. Ecological Source Areas Identification
Identification of Landscape Elements Based on MSPA Analysis
Selection of Ecological Source Areas Based on Landscape Connectivity
2.3.2. Ecological Resistance Surface Construction and Correction
Ecological Resistance Surface Construction
Ecological Resistance Surface Correction
2.3.3. Ecological Corridor Identification and Importance Grading
Ecological Corridor Identification Based on the MCR Model
Ecological Corridor Identification Based on the Circuit Theory
Importance Classification of Ecological Corridor Based on the Gravity Model
2.3.4. Ecological Node Identification and Importance Grading
Ecological Node Identification
Importance Classification of Ecological Nodes
3. Results
3.1. Determination of Ecological Protection Source Areas
3.2. Construction of Ecological Resistance Surfaces
3.3. Ecological Corridor Identification and Importance Classification
3.4. Ecological Node Identification and Importance Classification
3.5. Construction of Karst Landscape Ecological Security Pattern of Puzhehei
4. Discussion
5. Conclusions
- (1)
- The findings of this study indicate that the Puzhehei karst area encompasses a total of 166.6572 km2 of ecological land, representing 46.06% of the present study area. Additionally, the ecological source areas within this region amount to 77.275 km2, accounting for 21.36% of the total study area.
- (2)
- The MCR model and circuit theory were utilized to identify a total of 78 ecological corridors, spanning a combined length of 545.186 km. These corridors were categorized into 12 crucial corridors and 66 general corridors.
- (3)
- In this study, a total of 51 ecological nodes were identified, encompassing 11 “source-type ecological nodes”, 30 “ecological pinch points”, and 10 “ecological barriers”. Among these nodes, 16 were identified as crucial ecological nodes.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Resistance Types | Resistance Factors | Resistance Level | Weight | ||||
---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |||
Natural environmental factors | Topographic location index | 0.7899–1.0740 | 0.6632–0.7899 | 0.5489–0.6632 | 0.4409–0.5489 | 0.2864–0.4409 | 0.3115 |
Slope (°) | ≥46° | 36–45° | 26–35° | 16–25° | 0–15° | 0.2130 | |
Slope direction | Plane (-1) north (0–22.5) (337.5–360) | Northwest (22.5–67.5), Northwestern (292.5–337.5) | East (67.5–112.5), west (247.5–292.5) | Southeast (112.5–157.5), Southwestern (202.5–247.5) | South (157.5–202.5) | 0.0628 | |
Hydrological analysis | 1–77 | 77–130 | 130–183 | 183–264 | 264–348 | 0.0893 | |
NDVI (%) | 80–100 | 60–80 | 40–60 | 20–40 | 0–20 | 0.1193 | |
Soil erodibility | >0.0204 | 0.0149–0.0204 | 0.0143–0.0149 | 0.0114–0.0143 | <0.0114 | 0.0612 | |
Socio-economic factors | Land use | Forest land, water area | Grassland | Cultivated land | Unused land | Construction land | 0.0347 |
Road buffer zones | >400 | 300–400 | 200–300 | 100–200 | <100 | 0.0490 | |
Construction land buffer zones | >400 | 300–400 | 200–300 | 100–200 | <100 | 0.0245 | |
Cultivated land buffer zones | >400 | 300–400 | 200–300 | 100–200 | <100 | 0.0347 |
Landscape Types | Area/km2 | The Proportion of MSPA Landscape Types Area/% | The Proportion of Total Area/% 364.8644 |
---|---|---|---|
Core | 132.5291 | 79.52% | 36.32% |
Islet | 1.2911 | 0.78% | 0.35% |
Perforation | 5.0212 | 3.01% | 1.38% |
Edge | 23.0371 | 13.82% | 6.31% |
Loop | 0.8575 | 0.51% | 0.24% |
Bridge | 0.7787 | 0.47% | 0.21% |
Branch | 3.1425 | 1.89% | 0.86% |
Total | 166.6572 | 100% | 45.67% |
Number | Patch Number | Patch Importance Index | Area/km2 |
---|---|---|---|
1 | 20 | 52.3485 | 10.3311 |
2 | 26 | 44.8115 | 20.1055 |
3 | 17 | 34.8515 | 20.9108 |
4 | 24 | 34.2455 | 2.5871 |
5 | 23 | 30.5922 | 1.5565 |
6 | 14 | 18.1885 | 6.6360 |
7 | 27 | 14.1676 | 1.8099 |
8 | 12 | 14.0698 | 2.0097 |
9 | 15 | 11.2695 | 2.4178 |
10 | 10 | 10.9068 | 3.7875 |
11 | 6 | 10.5786 | 5.1231 |
Ecological Source Areas Interaction Matrix Constructed Based on the Gravity Model | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
1 | 0 | 2.3850 | 0.7308 | 12.9011 | 3.0525 | 2.0896 | 23.1563 | 4.2395 | 3.8941 | 1.2655 | 1.3457 |
2 | 0 | 0.4274 | 1.8713 | 0.9189 | 0.8921 | 2.2689 | 1.5424 | 1.0086 | 13.2688 | 0.6370 | |
3 | 0 | 1.2350 | 1.3526 | 3.1784 | 0.4718 | 0.9068 | 1.7658 | 0.3181 | 0.9251 | ||
4 | 0 | 10.7706 | 3.9073 | 5.0729 | 6.6753 | 9.5275 | 1.1548 | 2.1088 | |||
5 | 0 | 4.2818 | 1.5982 | 2.5632 | 7.8276 | 0.6465 | 1.1789 | ||||
6 | 0 | 1.2093 | 5.4604 | 9.6484 | 0.7534 | 5.1436 | |||||
7 | 0 | 2.0888 | 1.9575 | 1.0697 | 0.7815 | ||||||
8 | 0 | 5.2561 | 1.4962 | 4.7159 | |||||||
9 | 0 | 0.7798 | 3.1282 | ||||||||
10 | 0 | 0.5946 | |||||||||
11 | 0 |
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Share and Cite
Li, C.; Wang, J.; Liu, X.; Xu, K. Construction of Karst Landscape Ecological Security Pattern Based on Conflict between Human and Nature in Puzhehei. Sustainability 2024, 16, 908. https://doi.org/10.3390/su16020908
Li C, Wang J, Liu X, Xu K. Construction of Karst Landscape Ecological Security Pattern Based on Conflict between Human and Nature in Puzhehei. Sustainability. 2024; 16(2):908. https://doi.org/10.3390/su16020908
Chicago/Turabian StyleLi, Chunhua, Jin Wang, Xincen Liu, and Kejian Xu. 2024. "Construction of Karst Landscape Ecological Security Pattern Based on Conflict between Human and Nature in Puzhehei" Sustainability 16, no. 2: 908. https://doi.org/10.3390/su16020908