Submitted:
25 July 2024
Posted:
25 July 2024
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Abstract
Keywords:
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Basis of Classification of Suburban Rural Landscape Functions
2.2.1. Rural Landscape Production Function
2.2.2. Rural Landscape Life Function
2.2.3. Ecological Function of Rural Landscape
2.3. Design of Rural Landscape Function Evaluation Index
2.4. Research Methods
2.4.1. Single Function Evaluation Of Landscape
2.4.2. Quantification of The Landscape Production-Living-Ecological Functions Trade-Offs And Synergistic Relationship
2.4.3. Spatial trade-off and synergistic relationship of the landscape production-living-ecological functions
2.5 Research Data
3. Results
3.1. Rural Landscape Function Evaluation Results
3.2. Temporal Evolution of The Function Trade-Off And Synergistic Relationship Of The Production-Living-Ecological Functions Of The Landscape
3.2.1. Temporal Pattern of Rural Landscape Synergy in Jiashan County
3.2.2. Temporal Pattern of Rural Landscape Trade-Off in Jiashan County
3.2.3. Temporal Pattern of the Transformation Of The Rural Landscape Trade-Off And Synergistic Relationship in Jiashan County
3.2.4. Temporal Pattern of the Rural Landscape Compatibility in Jiashan County
3.3. Analysis of the spatial evolution of the tradeoff and synergistic relationship between the landscape production-living-ecological functions
3.3.1. Spatial Pattern of the Trade-Off And Synergistic Relationship Of The Production-Living-Ecological Functions In The Rural Landscape
3.3.2. Spatiotemporal Evolution of the Trade-Off And Synergy Between The Production, Life, And Ecological Functions
3.3.3. Trade-Offs Between Sub-Functions -Spatiotemporal Evolution of Synergistic Relationships
4. Discussion
5. Conclusions
6. Limitations
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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| Target Layer | Criterion Layer | Index Layer | Calculation Method or Index Significance | Index Relationship | Index Weight |
|---|---|---|---|---|---|
| Production function (PF) | Agricultural production function (APF) |
Agricultural productivity per capita | Gross value of primary industry/total township population (10,000 yuan/person). | Forward direction | 6.02% |
| Agricultural output value per land | Agricultural output value/cultivated land area (Yuan/square meter). | Forward direction | 4.53% | ||
| Economic development function (EDF) |
Industrial contribution rate | (Output value of primary industry + output value of tertiary industry)/township GDP. | Forward direction | 10.94% | |
| Township urbanization level | Township construction land/total area of township. | Forward direction | 5.39% | ||
| Living function (LF) |
Space carrying function (SCF) | Per capita living area | Township construction land area/total population (km2). | Forward direction | 7.25% |
| Population density | Reflects the population carrying capacity of the township. | Forward direction | 12.27% | ||
| Landscape aesthetic function (LAF) | Landscape connectivity | CONTAG (0, 100%] - The degree of agglomeration or spread of different patch types in the landscape. The greater the value is, the better the patch connectivity is. | Forward direction | 6.44% | |
| Landscape diversity | SHDI [0, +∞) - The larger the value is, the more abundant the patch types and distributions in the landscape. | Forward direction | 5.60% | ||
| Ecological function (EF) |
Ecological regulation function (ERF) | NDVI | Reflect the vegetation state of the township. | Forward direction | 23.91% |
| Ecological synergy degree | SHEI [0, 1) - A smaller value indicates that the landscape is more dominated by a few dominant types, and a larger value indicates that the distribution of all of the landscape types is more uniform. | Forward direction | 5.80% | ||
| Environmental maintenance function (EMF) | PM2.5 | Reflects the degree of air pollution in towns and villages. | Reverse direction | 4.32% | |
| Landscape fragmentation | The landscape division Index is one of the indicators used to evaluate landscape fragmentation, and it mainly measures the degree of fragmentation of views. | Reverse direction | 7.54% |
| Data | Data Source | Application Indicators |
|---|---|---|
| Multi-period land use/land cover remote sensing monitoring data for China [35] | The multi-period land use/land cover remote sensing monitoring Chinese National Land Use and Cover Change (CNLUCC) database from the Chinese Academy of Sciences has a resolution of 30 m. | Landscape connectivity, landscape diversity index, and landscape fragmentation |
| Satellite-derived PM2.5 [36] | The global and regional PM2.5 concentrations are estimated using information from satellite, modelling, and monitoring sources. The aerosol optical depth and simulation [Goddard Earth Observing System with Chemistry (GEOS-Chem)] from multiple satellites (MODIS, VIIRS, MISR, and SeaWiFS) and their respective retrievals (Dark Target, Deep Blue, and MAIAC) are combined to determine the relative uncertainties based on observations using ground-based solar photometers [Aerosol Robotic Network (AERONET)] to produce geophysical estimates. This explains most of the differences in ground-level PM2.5 measurements. Additional information from PM2.5 measurements is then tallied at a resolution of 0.01°. | PM2.5 |
| GDP | The China km grid GDP spatial distribution dataset from the Resources and Environmental Sciences Data Registration and Publication System, Chinese Academy of Sciences (http://www.resdc.cn/DOI). | GDP |
| Population density [37] | China’s 1 km population density dataset was downloaded from WorldPop (https://hub.worldpop.org/). | Population density |
| Normalized Difference Vegetation Index (NDVI) | Landsat 7 and Landsat 8 images with a resolution of 30 m were downloaded from NASA, and the NDVI index was calculated in ArcGIS Pro (https://www.jiashan.gov.cn/). | NDVI |
| Administrative boundary data | The base map is from the standard map service system of the Ministry of Natural Resources, and the review number is GS(2023)2767. | \ |
| Jiashan County Yearbook for 2001, 2011, and 2021 | Jiashan County Statistics Bureau for (https://www.jiashan.gov.cn/). | Agricultural earnings, industrial output, and commercial activity |
| Category | Feature | 2000 | 2010 | 2020 |
|---|---|---|---|---|
| Target layer | PF | 0.043 | 0.091 | 0.108 |
| LF | 0.070 | 0.143 | 0.152 | |
| EF | 0.310 | 0.087 | 0.102 | |
| Criterion layer | APF | 0.020 | 0.050 | 0.052 |
| EDF | 0.023 | 0.041 | 0.056 | |
| SCF | 0.039 | 0.064 | 0.075 | |
| LAF | 0.031 | 0.077 | 0.079 | |
| ERF | 0.242 | 0.039 | 0.039 |
| Rural Landscape Function Synergy Type | 2000 | 2010 | 2020 | |||
|---|---|---|---|---|---|---|
| Correlation Coefficient | P-Value | Correlation Coefficient | P-Value | Correlation Coefficient | P-Value | |
| PF-LF | −0.25 | 0.516 | 0.55 | 0.125 | 0.15 | 0.7 |
| PF-EF | 0.433 | 0.244 | 0.217 | 0.576 | 0.05 | 0.898 |
| LF-EF | −0.15 | 0.7 | −0.117 | 0.765 | −0.567 | 0.112 |
| APF-EDF | 0.286 | 0.493 | −0.527 | 0.145 | −0.700* | 0.036 |
| APF-SCF | −0.571 | 0.139 | −0.405 | 0.279 | −0.700* | 0.036 |
| APF-LAF | 0.071* | 0.008 | 0.720* | 0.029 | 0.717* | 0.03 |
| APF-ERF | −0.31* | 0.04 | 0.851** | 0.004 | 0.733* | 0.025 |
| APF-EMF | 0.143 | 0.736 | −0.736* | 0.024 | −0.633 | 0.067 |
| EDF-SCF | 0.452 | 0.26 | 0.613 | 0.079 | 0.800** | 0.01 |
| EDF-LAF | −0.405 | 0.32 | −0.4 | 0.286 | −0.617 | 0.077 |
| EDF-ERF | 0.119 | 0.779 | −0.492 | 0.179 | −0.6 | 0.088 |
| EDF-EMF | −0.048 | 0.911 | 0.583 | 0.099 | 0.733* | 0.025 |
| SCF-LAF | −0.19 | 0.651 | −0.58 | 0.102 | −0.817** | 0.007 |
| SCF-ERF | 0.405 | 0.32 | −0.154 | 0.693 | −0.5 | 0.17 |
| SCF-EMF | −0.286 | 0.493 | 0.336 | 0.376 | 0.6 | 0.088 |
| LAF-ERF | 0.667 | 0.071 | 0.695* | 0.038 | 0.700* | 0.036 |
| LAF-EMF | −0.786* | 0.021 | −0.717* | 0.03 | −0.783* | 0.013 |
| ERF-EMF | −0.952** | 0 | −0.915** | 0.001 | −0.867** | 0.002 |
| Rural Landscape Function Synergy Type | 2000 | 2010 | 2020 | |||
|---|---|---|---|---|---|---|
| Moran’s I | Z-Value | Moran’s I | Z-Value | Moran’s I | Z-Value | |
| PF-LF | −0.0863 | −1.0237 | 0.3171 | 2.2568 | −0.1111 | −0.6131 |
| PF-EF | 0.1299 | 1.1015 | −0.0957 | −0.3133 | 0.111 | 0.6636 |
| LF-EF | −0.2152 | −1.5692 | 0.0288 | −0.0399 | −0.3997 | −2.7636 |
| APF-EDF | −0.1333 | −0.424 | 0.1815 | 0.7392 | 0.0323 | −0.2552 |
| APF-SCF | 0.0059 | −0.3166 | 0.0097 | −0.2245 | 0.0652 | 0.0348 |
| APF-LAF | −0.0398 | −1.6717 | 0.2665 | 1.8745 | 0.2243 | 1.6523 |
| APF-ERF | 0.0104 | −0.3515 | 0.2062 | 1.5868 | 0.1796 | 1.3963 |
| APF-EMF | −0.0047 | 0.3975 | −0.141 | −1.2271 | −0.179 | −1.397 |
| EDF-SCF | −0.0227 | 0.0049 | −0.1681 | −0.5528 | −0.0477 | 0.2823 |
| EDF-LAF | −0.1692 | −1.3248 | 0.1249 | 0.4548 | −0.1605 | −1.4015 |
| EDF-ERF | −0.134 | −1.0232 | 0.159 | 0.6713 | −0.158 | −1.4562 |
| EDF-EMF | 0.156 | 1.1781 | −0.194 | −0.8462 | 0.169 | 1.4948 |
| SCF-LAF | 0.044 | 0.3829 | −0.052 | −0.6616 | −0.094 | −0.9531 |
| SCF-ERF | −0.0092 | 0.261 | −0.1226 | −1.0871 | −0.1343 | −1.206 |
| SCF-EMF | −0.0126 | −0.3724 | 0.1017 | 0.9138 | 0.1243 | 1.1151 |
| LAF-ERF | 0.178 | 1.6897 | 0.4774 | 2.7977 | 0.4597 | 2.7344 |
| LAF-EMF | −0.1937 | −1.6544 | −0.3953 | −2.4541 | −0.463 | −2.7251 |
| ERF-EMF | −0.0638 | −0.984 | −0.241 | −1.781 | −0.3482 | −2.2479 |
| Legend | Significance |
|---|---|
| Non-significant area (Compatible) | P > 0.05 indicates a non-significant region, that is, the function of the region is compatible. |
| Significant H-H region (Synergy) | P < 0.05 indicated a significant region, and there was synergy among the regional functions, as well as synergy in the surrounding areas, so the spatial heterogeneity was small and the relationship was stable. |
| Significant L-L region (Trade-off) | There were trade-offs between the regional functions, and the surrounding areas were also trade-offs, so the spatial heterogeneity was small and the relationship was stable. |
| Significant L-H region (Trade-off - peripheral region is synergistic) | The regional functions were trade-offs, but the surrounding areas were synergistic, so the spatial heterogeneity was large and the relationship was unstable. |
| Significant H-L region (Synergy-peripheral region as trade-off) | There was synergy among the regional functions, but the surrounding areas were trade-offs, so the spatial heterogeneity was large and the relationship was unstable. |
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