Ecosystem Health Assessment of World Natural Heritage Sites Based on Remote Sensing and Field Sampling Verification: Bayanbulak as Case Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Source and Processing
2.2.1. Data Sources
2.2.2. Data Processing
2.3. Method
2.3.1. Ecosystem Health Assessment Framework
2.3.2. Ecosystem Health Assessment
- Ecosystem Health Assessment Model
- Ecosystem Health Assessment Indicators
2.3.3. Spatial Autocorrelation Analysis
2.3.4. Verification Based on Field Data
3. Results
3.1. Changes in Ecosystem Landscape Types
3.2. Ecosystem Health Assessment
3.2.1. Changes in EHA indicators
3.2.2. Changes in Ecosystem Health
3.3. Spatial Autocorrelation Analysis
3.4. Field Verification
4. Discussion
4.1. Ecosystem Health Assessment model
4.2. Ecosystem Health Assessment
4.3. Limitations and Future Prospects
5. Conclusions
- While changes were observed in the area proportions of the various ecosystem landscape types from 2000 to 2018, the overall landscape structure did not change. The fragmentation of the landscape in the study area increased in general during the three periods. However, the degree of fragmentation remained small.
- All the EHA indicators showed a decline, with the decline in ES being the most significant. Thus, the overall ecosystem health in the study area also declined. However, the ecosystem health within the property zone remained high and essentially unchanged. Further, the area proportions of poor health were extremely low and were mostly distributed within the buffer zone. Therefore, in general, the ecosystem of the study area was in a healthy state.
- The spatial distribution of the ecosystem health exhibited obvious agglomeration characteristics, with the degree of agglomeration increasing over time. With respect to the local spatial autocorrelation, the high-high clusters were mainly distributed within the property zone, and the degree of agglomeration enhanced over time. On the other hand, the low-low clusters were mainly distributed in the buffer zone, and the degree weaken over time.
- The results of EHA obtained through RS monitoring were positively correlated with those of the field sampling of the vegetation in the study area. The areas with high levels of ecosystem health also exhibited high sampling vegetation index values. This confirmed the suitability of using RS imaging for monitoring ecosystem health.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Indices | Indicators | Description | |
---|---|---|---|
EV | NDVI | EV refers to the primary productivity of the ecosystem. NDVI is widely used in ecosystem primary productivity evaluation. | |
EO | landscape heterogeneity (LH) | Shannon’s diversity index (SHDI) | The higher the Shannon’s diversity index, the higher the heterogeneity, and the stronger the landscape organization. |
Landscape connectivity (LC) | landscape fragmentation (FN) | Refers to the degree of landscape fragmentation, which reflects the spatial complexity of the landscape as a whole in the study area. | |
connectivity of patches with important ecological functionality (IC) | Swamp grassland fragmentation (FN1) | Refers to the degree of swamp grassland landscape fragmentation, which reflects the complexity of landscape component space. | |
swamp grassland patch cohesion index (COHESION) | The patch concentration index describes the natural connectivity of the corresponding patch type. The higher the patch concentration index, the better the connectivity, and the stronger the landscape organization. | ||
ER | resilience coefficient | Set the resilience coefficient according to the resilience of different ecosystem types, the value is between 0–1. | |
ES | ecosystem service value | Refer to Xie et al. (2015, 2017) to improve the value coefficient of terrestrial ecosystem services in China, and set the ecosystem service value of ecosystem types [47,48]. |
Ecosystem Type | Swamp Grassland | HCG | MCG | LCG | Water | Riverbed | Construction Land | Sand | Bare Land |
---|---|---|---|---|---|---|---|---|---|
RC | 0.9 | 0.8 | 0.7 | 0.6 | 0.8 | 0.5 | 0.2 | 0.1 | 0.2 |
Service Type | Food Production | Raw Material | Water Supply | Gas Regulation | Climate Regulation | Purify Environment | Soil Maintenance | Biodiversity | Landscape Aesthetics |
---|---|---|---|---|---|---|---|---|---|
SWG | 1146.40 | 1123.93 | 5821.93 | 4270.92 | 8092.26 | 8092.26 | 5192.54 | 17690.59 | 10632.33 |
HCG | 674.36 | 899.14 | 1775.80 | 3102.03 | 7395.43 | 3798.87 | 3776.39 | 6676.12 | 3664.00 |
MCG | 539.48 | 719.31 | 1420.64 | 2481.63 | 5916.34 | 3039.09 | 3021.11 | 5340.89 | 2931.20 |
LCG | 404.61 | 539.48 | 1065.48 | 1861.22 | 4437.26 | 2279.32 | 2265.83 | 4005.67 | 2198.40 |
water | 1798.28 | 517.01 | 18634.68 | 1730.85 | 5147.58 | 12475.57 | 2090.50 | 5732.02 | 4248.44 |
riverbed | 606.92 | 202.31 | 6226.55 | 674.36 | 1798.28 | 4473.22 | 809.23 | 2023.07 | 1461.10 |
CL | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
sand | 22.48 | 67.44 | 44.96 | 247.26 | 224.79 | 696.83 | 292.22 | 269.74 | 112.39 |
bare land | 0.00 | 0.00 | 0.00 | 44.96 | 0.00 | 224.79 | 44.96 | 44.96 | 22.48 |
Landscape Type | Water | Swamp Grassland | Sand | HCG | MCG | LCG | Riverbed | Construction Land | Bare Land | Study Area |
---|---|---|---|---|---|---|---|---|---|---|
2000 | 0.0058 | 0.0012 | 0.2439 | 0.0022 | 0.0076 | 0.0487 | 0.0083 | 0.0705 | 0.2448 | 0.0061 |
2011 | 0.0218 | 0.0010 | 0.2615 | 0.0048 | 0.0050 | 0.0868 | 0.0094 | 0.0525 | 0.2163 | 0.0076 |
2018 | 0.0367 | 0.0012 | 0.2754 | 0.0037 | 0.0051 | 0.0651 | 0.0120 | 0.0566 | 0.2369 | 0.0084 |
Year | EV | EO | ER | ES | EH | |
---|---|---|---|---|---|---|
overall study area | 2000 | 0.7758 | 0.5431 | 0.8468 | 0.6507 | 0.6802 |
2011 | 0.6927 | 0.5397 | 0.8068 | 0.5820 | 0.6324 | |
2018 | 0.7000 | 0.5416 | 0.7956 | 0.5611 | 0.6251 | |
property zone | 2000 | 0.7515 | 0.6377 | 0.9024 | 0.7928 | 0.7531 |
2011 | 0.7072 | 0.6345 | 0.8796 | 0.7544 | 0.7276 | |
2018 | 0.7400 | 0.6368 | 0.8794 | 0.7445 | 0.7330 | |
buffer zone | 2000 | 0.8095 | 0.4560 | 0.7802 | 0.4881 | 0.6046 |
2011 | 0.6788 | 0.4529 | 0.7177 | 0.3826 | 0.5284 | |
2018 | 0.6638 | 0.4528 | 0.6952 | 0.3477 | 0.5075 |
Vegetation Index | Good | Relatively Good | Ordinary | Relatively Poor |
---|---|---|---|---|
vegetation coverage | 92.22 ± 1.88 | 80.58 ± 5.28 | 72.90 ± 3.75 | 39.50 ± 5.5 |
Simpson diversity index | 0.71 ± 0.04 | 0.64 ± 0.05 | 0.63 ± 0.02 | 0.53 ± 0.10 |
Shannon-Wiener diversity index | 1.56 ± 0.12 | 1.43 ± 0.12 | 1.31 ± 0.07 | 1.12 ± 0.23 |
Margalef richness index Pielou evenness index | 1.39 ± 0.17 0.68 ± 0.02 | 1.44 ± 0.16 0.63 ± 0.04 | 1.21 ± 0.11 0.65 ± 0.02 | 1.08 ± 0.08 0.60 ± 0.08 |
Vegetation Index | rs | P value |
---|---|---|
vegetation coverage | 0.341 | 0.019 |
Simpson diversity index | 0.323 | 0.035 |
Shannon-Wiener diversity index | 0.318 | 0.038 |
Margalef richness index | 0.170 | 0.277 |
Pielou evenness index | 0.149 | 0.341 |
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Wang, Z.; Yang, Z.; Shi, H.; Han, F.; Liu, Q.; Qi, J.; Lu, Y. Ecosystem Health Assessment of World Natural Heritage Sites Based on Remote Sensing and Field Sampling Verification: Bayanbulak as Case Study. Sustainability 2020, 12, 2610. https://doi.org/10.3390/su12072610
Wang Z, Yang Z, Shi H, Han F, Liu Q, Qi J, Lu Y. Ecosystem Health Assessment of World Natural Heritage Sites Based on Remote Sensing and Field Sampling Verification: Bayanbulak as Case Study. Sustainability. 2020; 12(7):2610. https://doi.org/10.3390/su12072610
Chicago/Turabian StyleWang, Zhi, Zhaoping Yang, Hui Shi, Fang Han, Qin Liu, Jianwei Qi, and Yayan Lu. 2020. "Ecosystem Health Assessment of World Natural Heritage Sites Based on Remote Sensing and Field Sampling Verification: Bayanbulak as Case Study" Sustainability 12, no. 7: 2610. https://doi.org/10.3390/su12072610