# Identifying the Driving Forces of Alpine Wetland Dynamic Changes in the Yellow River Source National Park from 2000 to 2020

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}, accounting for approximately 6.29% of the total area of the Three-River-Source region. The YRSNP belongs to tundra climate in the Köppen climate classification, which has the characteristics of low temperature, low precipitation, high evaporation, and strong solar radiation [21]. Alpine wetlands were extremely scattered across the YRSNP, making up at least 30% of the total area. These wetlands included the internationally significant Gyaring Lake Wetland and Ngöring Lake Wetland, as well as the nationally significant Gonagma Wetland and Maduo Lake Wetland [30]. According to the second wetland resource survey data, YRSNP wetland types in the YRSNP include river wetland, lake wetland, and marsh wetland, while human activities are dominated by grazing.

#### 2.2. Data and Preprocessing

#### 2.2.1. Field Survey Data

#### 2.2.2. Remote Sensing Data

#### 2.2.3. Meteorological Data

#### 2.2.4. Other Data

#### 2.3. Methods

#### 2.3.1. Wetland Classification System

#### 2.3.2. Sample Transfer Method

#### 2.3.3. Importance of Features

#### 2.3.4. Random Forest Classification and Accuracy Assessment

#### 2.3.5. Classification Features

#### 2.3.6. Mann–Kendall Analysis

^{1−α/2}, the driving factors data of the study are at the α level. Significant changes are generally taken as α = 0.05. When |Zs| > 1.96, the time series has a significance α < 0.05, and |Zs| < 1.96 means significance α > 0.05.

#### 2.3.7. Trend Analysis

## 3. Results

#### 3.1. Accuracy Evaluation

#### 3.2. Importance of Classification Features

#### 3.3. Dynamic Changes Pattern

^{2}in 2000 to 4561 km

^{2}in 2020, the wetland area had grown. Between 2000 and 2020, the proportion of river wetlands in the study area decreased from 3.32% to 2.00%, whereas the proportion of lake wetlands increased from 7.23% to 7.81%. While the proportion of marsh wetlands increased from 1.47% to 1.53%, the proportion of marsh meadows increased from 9.19% to 12.62%. While the proportion of grassland decreased from 71.42% to 65.66%, the proportion of others increased from 7.31% to 10.35%. Only river wetlands had drastically decreased, with marsh meadows increasing by 3.43% being the most significant change in the wetland category.

^{2}. The spatial transfer of river wetland was significantly influenced by the change in the X-axis direction. The center of gravity of the lake wetland in the study area moved to the northwest by 13,615.22 m from 2000 to 2020, and the standard deviation ellipse area increased by 905.81 km

^{2}. The spatial transfer of the lake wetland was influenced by changes in the X-axis and Y-axis directions. The center of gravity of the marsh wetland in the study area moved to the northwest by 8486.27 m between 2000 and 2020, and the standard deviation ellipse area increased by 352.64 km

^{2}. The spatial transfer of marsh wetland was significantly influenced by the change in the X-axis direction. The center of gravity of marsh meadow in the study area moved to the northeast by 7246.17 m between 2000 and 2020, and the standard deviation ellipse area increased by 426.87 km

^{2}. The spatial transfer of marsh meadows was significantly influenced by the change in the X-axis direction. The results show that except for river wetlands, other wetland types moved toward the concentrated distribution area of lakes in the northeast of the YRSNP.

#### 3.4. Dynamic Changes Characteristics of Driving Factors

#### 3.4.1. Dynamic Changes of Meteorological Factors

#### 3.4.2. Dynamic Changes Characteristics of Soil Moisture

^{3}/m

^{3}(p < 0.001) and that the average soil moisture in the YRSNP was 0.1828 m

^{3}/m

^{3}in 21 years, indicating a low spatial trend in the north.

^{3}/m

^{3}level does not vary considerably and remains constant at about 8% throughout the year. In certain ways, the YRSNP soil is developing toward humidification since the area of 0.1–0.2 m

^{3}/m

^{3}grade soil wetland is decreasing at a rate of 1.82% per year, and correspondingly, the area of 0.2–0.3 m

^{3}/m

^{3}grade soil humidity is increasing at a rate of 1.80% per year.

#### 3.4.3. Dynamic Changes Characteristics of Population Density

^{2}per year (p < 0.001), but the population density base of YRSNP was small, so the change was not significant. This is shown by the changing trend of population density of YRSNP from 2000 to 2020 shows (Figure 14a). The policy of ex-situ poverty alleviation and relocation that has been in place for many years may have something to do with the change in population density. Most of the farmers and herdsmen in villages and towns have relocated to Maduo County City or other counties because of ex-situ poverty alleviation and relocation (http://tjj.qinghai.gov.cn/tjData/qhtjnj/, accessed on 7 April 2023), so the population distribution will be more dispersed. Further analysis combined with Figure 14b reveals that the average YRSNP population density from 2000–2020 was 0.18 people/km

^{2}, and the population of YRSNP is concentrated along G214 road in the middle.

#### 3.4.4. Dominant Factor Identification

## 4. Discussion

#### 4.1. Identification and Classification of the YRSNP Alpine Wetland from 2000 to 2020 Using Remote Sensing

#### 4.2. Spatial-Temporal Change of Alpine Wetland and Meteorological Factors in the YRSNP from 2000 to 2020

^{2}in 2000 to 4561 km

^{2}in 2020, while the proportion of river wetland decreased from 3.32% to 2.00%. The proportion of lake wetland areas increased from 7.23% to 7.81%, marsh wetland increased from 1.47% to 1.53%, and marsh meadow areas increased from 9.19% to 12.62%, which is consistent with the related research results [54]. As shown in Figure 5, there is an apparent change in the area of marsh grassland between 2005 and 2016, which may be due to the low classification accuracy. In addition, the results of the transfer of land use showed that the wetland types were transformed into each other, with some river wetland transformed into lake wetland due to the construction of the Yellow River source hydropower plant and river oscillation, while the increase in precipitation was the main reason for the transformation of marsh wetland into river wetland, and the increase in temperature leads to the decrease of marsh wetland and marsh meadow [21,55,56,57]. The warm season in June, July, and August is the predominant time for precipitation in the YRSNP, according to the mean annual precipitation of 460.61 mm for the region from 2000 to 2020 and the warm season mean precipitation of 244.39 mm. The trend analysis shows that while warm season precipitation does not increase significantly at a rate of 2.28 mm from 2000 to 2020, the annual precipitation of the YRSNP does increase significantly at a rate of 3.17 mm [58,59]. Compared with the Qinghai–Tibet Plateau and the Sanjiangyuan region, it is wetter, and its trend is consistent with the previous research results. The temperature difference between the YRSNP seasons is significant, as seen by the –5.21 °C mean annual temperature of the YRSNP from 2000 to 2020 and the 5.35 °C mean temperature of the warm season. Additionally, the mean annual temperature and warm season mean temperature has increased at a rate of about 0.01 °C, which is consistent with the related research results [60]. The mean annual potential evapotranspiration of the YRSNP from 2000 to 2020 is 573.04 mm, and the mean evapotranspiration during the warm season is 299.02 mm, which is consistent with the findings of the related studies [61]. It is clear that the YRSNP has an obvious deficit effect on precipitation evaporation. However, the specific deficit needs to be calculated in conjunction with the actual evapotranspiration. From 2000 to 2020, the YRSNP will generally be warm and humid, which is consistent with the global and Qinghai–Tibet Plateau trends of climate change. It should be noted that global climate change, which intensifies ocean evaporation and land evapotranspiration and leads to a strong regional hydrological cycle, is the cause of the increase in YRSNP precipitation. However, there is a high probability of extreme precipitation due to the influence of the El Niño Southern Oscillation and North Atlantic Oscillation, which will affect the stability of alpine wetland ecosystems [62,63,64].

#### 4.3. Analysis of Driving Factors of the YRSNP Alpine Wetland Dynamic Changes from 2000 to 2020

#### 4.4. Limitations and Uncertainties

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Yu, H.; Liu, B.; Wang, G.; Zhang, T.; Yang, Y.; Lu, Y.; Xu, Y.; Huang, M.; Yang, Y.; Zhang, L. Grass-livestock balance based grassland ecological carrying capability and sustainable strategy in the Yellow River Source National Park, Tibet Plateau, China. J. Mt. Sci.
**2021**, 18, 2201–2211. [Google Scholar] [CrossRef] - Li, W.; Xue, P.; Liu, C.; Yan, H.; Zhu, G.; Cao, Y. Monitoring and Landscape Dynamic Analysis of Alpine Wetland Area Based on Multiple Algorithms: A Case Study of Zoige Plateau. Sensors
**2020**, 20, 7315. [Google Scholar] [CrossRef] - Yao, T.; Bolch, T.; Chen, D.; Gao, J.; Immerzeel, W.; Piao, S.; Su, F.; Thompson, L.; Wada, Y.; Wang, L.; et al. The imbalance of the Asian water tower. Nat. Rev. Earth Environ.
**2022**, 3, 618–632. [Google Scholar] [CrossRef] - Zhang, Q.; Shen, Z.; Pokhrel, Y.; Farinotti, D.; Singh, V.P.; Xu, C.; Wu, W.; Wang, G. Oceanic climate changes threaten the sustainability of Asia’s water tower. Nature
**2023**, 615, 87–93. [Google Scholar] [CrossRef] [PubMed] - Wang, Y.; Lv, W.; Xue, K.; Wang, S.; Zhang, L.; Hu, R.; Zeng, H.; Xu, X.; Li, Y.; Jiang, L.; et al. Grassland changes and adaptive management on the Qinghai–Tibetan Plateau. Nat. Rev. Earth Environ.
**2022**, 3, 668–683. [Google Scholar] [CrossRef] - HJ 1169-2021; Technical Specification for Investigation and Assessment of National Ecological Status—Field Observation of Wetland Ecosystem. Ministry of Ecology and Environment of the People’s Republic of China: Beijing, China, 2021.
- DeLancey, E.R.; Simms, J.F.; Mahdianpari, M.; Brisco, B.; Mahoney, C.; Kariyeva, J. Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada. Remote Sens.
**2020**, 12, 2. [Google Scholar] [CrossRef] [Green Version] - Liu, T.; Abd-Elrahman, A.; Jon, M.; Wilhelm, V.L. Comparing Fully Convolutional Networks, Random Forest, Support Vector Machine, and Patch-based Deep Convolutional Neural Networks for Objectbased Wetland Mapping using Images from small Unmanned Aircraft System. GISci. Remote Sens.
**2018**, 55, 243–264. [Google Scholar] [CrossRef] - Judah, A.; Hu, B. The Integration of Multi-source Remotely-Sensed Data in Support of the Classification of Wetlands. Remote Sens.
**2019**, 11, 1537. [Google Scholar] [CrossRef] [Green Version] - Banks, S.; White, L.; Behnamian, A.; Chen, Z.; Montpetit, B.; Brisco, B.; Pasher, J.; Duffe, J. Wetland Classification with Multi-Angle/Temporal SAR Using Random Forests. Remote Sens.
**2019**, 11, 670. [Google Scholar] [CrossRef] [Green Version] - Simioni, J.P.D.; Guasselli, L.A.; de Oliveira, G.G.; Ruiz, L.F.C.; de Oliveira, G. A comparison of data mining techniques and multi-sensor analysis for inland marshes delineation. Wetl. Ecol. Manag.
**2020**, 28, 577–594. [Google Scholar] [CrossRef] - Meng, X.; Zhang, S.; Zang, S. Lake Wetland Classification Based on an SVM-CNN Composite Classifier and High-resolution Images Using Wudalianchi as an Example. J. Coast. Res.
**2019**, 93(sp1), 153. [Google Scholar] [CrossRef] - Corcoran, J.; Knight, J.; Gallant, A. Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota. Remote Sens.
**2013**, 5, 3212–3238. [Google Scholar] [CrossRef] [Green Version] - Baker, C.; Lawrence, R.; Montagne, C.; Patten, D. Mapping Wetlands and Riparian Areas Using Landsat ETM+ Imagery and Decision-Tree-Based Models. Wetlands
**2006**, 26, 465–474. [Google Scholar] [CrossRef] - Gosselin, G.; Touzi, R.; Cavayas, F. Polarimetric Radarsat-2 wetland classification using the Touzi decomposition: Case of the Lac Saint-Pierre Ramsar wetland. Can. J. Remote Sens.
**2014**, 39, 491–506. [Google Scholar] [CrossRef] - Soltani, K.; Amiri, A.; Zeynoddin, M.; Ebtehaj, I.; Gharabaghi, B.; Bonakdari, H. Forecasting monthly fluctuations of lake surface areas using remote sensing techniques and novel machine learning methods. Theor. Appl. Climatol.
**2021**, 143, 713–735. [Google Scholar] [CrossRef] - Cai, Y.; Li, X.; Zhang, M.; Lin, H. Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data. Int. J. Appl. Earth Obs.
**2020**, 92, 102164. [Google Scholar] [CrossRef] - Fu, B.; Zuo, P.; Liu, M.; Lan, G.; He, H.; Lao, Z.; Zhang, Y.; Fan, D.; Gao, E. Classifying vegetation communities karst wetland synergistic use of image fusion and object-based machine learning algorithm with Jilin-1 and UAV multispectral images. Ecol. Indic.
**2022**, 140, 108989. [Google Scholar] [CrossRef] - Mahdavi, S.; Salehi, B.; Granger, J.; Amani, M.; Brisco, B.; Huang, W. Remote sensing for wetland classification: A comprehensive review. GISci. Remote Sens.
**2018**, 55, 623–658. [Google Scholar] [CrossRef] - Zhou, H.; Xiao, F.; Zhou, B.; Mao, X.; Ji, H.; Ma, J.; Wang, W.; Song, W.; Ma, C.; Yandi, S.; et al. Present situation, problems and protection strategies of wetland resources in Qinghai Province. Qinghai Sci. Technol.
**2021**, 28, 21–26. [Google Scholar] - Ma, T.; She, Y.; Zhao, L.; Hu, B.; Feng, X.; Zhao, J.; Zhao, Z. Alpine Wetland Evolution and Their Response to Climate Change in the Yellow-River-Source National Park from 2000 to 2020. Water
**2022**, 14, 2351. [Google Scholar] [CrossRef] - Lu, M.; Zou, Y.; Xun, Q.; Yu, Z.; Jiang, M.; Sheng, L.; Lu, X.; Wang, D. Anthropogenic disturbances caused declines in the wetland area and carbon pool in China during the last four decades. Glob. Chang. Biol.
**2021**, 27, 3837–3845. [Google Scholar] [CrossRef] [PubMed] - Lu, D.; Chang, J. Examining human disturbances and inundation dynamics in China’s marsh wetlands by using time series remote sensing data. Sci. Total Environ.
**2023**, 863, 160961. [Google Scholar] [CrossRef] [PubMed] - Yang, J. Studies on eco-environmental change in source regions of the Yangtze and Yellow Rivers of China: Present and future. Sci. Cold Arid. Reg.
**2019**, 11, 173–183. [Google Scholar] - Yan, W.; Wang, Y.; Chaudhary, P.; Ju, P.; Zhu, Q.; Kang, X.; Chen, H.; He, Y. Effects of climate change and human activities on net primary production of wetlands on the Zoige Plateau from 1990 to 2015. Glob. Ecol. Conserv.
**2022**, 35, e2052. [Google Scholar] [CrossRef] - Bian, H.L.; Li, W.; Li, Y.Z.; Ren, B.; Niu, Y.D.; Zeng, Z.Q. Driving forces of changes in China’s wetland area from the first (1999–2001) to second (2009–2011) National Inventory of Wetland Resources. Glob. Ecol. Conserv.
**2020**, 21, e00867. [Google Scholar] [CrossRef] - Zhang, X.; Wang, G.; Xue, B.; Zhang, M.; Tan, Z. Dynamic landscapes and the driving forces in the Yellow River Delta wetland region in the past four decades. Sci. Total Environ.
**2021**, 787, 147644. [Google Scholar] [CrossRef] - Li, M.; Ma, Z. Sensible and Latent Heat Flux Variability and Response to Dry–Wet Soil Moisture Zones Across China. Bound.-Layer Meteorol.
**2015**, 154, 157–170. [Google Scholar] [CrossRef] - Fan, S.; Qin, J.; Sun, H.; Jia, Z.; Chen, Y. Alpine soil microbial community structure and diversity are largely influenced by moisture content in the Zoige wetland. Int. J. Environ. Sci.
**2022**, 19, 4369–4378. [Google Scholar] [CrossRef] - Zheng, J.; Dong, D.; Dong, X.; Liu, J. China Wetland Resources Qinghai Volume; China Forestry Publishing House: Beijing, China, 2015. [Google Scholar]
- Lang, Q.; Niu, Z.; Hong, X.; Yang, X. Remote Sensing Monitoring and Change Analysis of Wetlands in the Tibetan Plateau. Geomat. Inf. Sci. Wuhan Univ.
**2021**, 46, 230–237. [Google Scholar] - Peng, S.; Ding, Y.; Liu, W.; Li, Z. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth Syst. Sci. Data
**2019**, 11, 1931–1946. [Google Scholar] [CrossRef] [Green Version] - Peng, S.; Ding, Y.; Wen, Z.; Chen, Y.; Cao, Y.; Ren, J. Spatiotemporal change and trend analysis of potential evapotranspiration over the Loess Plateau of China during 2011–2100. Agric. For. Meteorol.
**2017**, 233, 183–194. [Google Scholar] [CrossRef] [Green Version] - Zheng, C.; Jia, L.; Zhao, T. A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution. Sci. Data
**2023**, 10, 139. [Google Scholar] [CrossRef] - Huang, H.; Wang, J.; Liu, C.; Liang, L.; Li, C.; Gong, P. The migration of training samples towards dynamic global land cover mapping. ISPRS J. Photogramm.
**2020**, 161, 27–36. [Google Scholar] [CrossRef] - Yan, X.; Niu, Z. Reliability Evaluation and Migration of Wetland Samples. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2021**, 14, 8089–8099. [Google Scholar] [CrossRef] - Breiman, L. Random Forests. Mach. Learn.
**2001**, 45, 5–32. [Google Scholar] [CrossRef] [Green Version] - Sheykhmousa, M.; Mahdianpari, M.; Ghanbari, H.; Mohammadimanesh, F.; Ghamisi, P.; Homayouni, S. Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2020**, 13, 6308–6325. [Google Scholar] [CrossRef] - Kotaridis, I.; Lazaridou, M. Remote sensing image segmentation advances: A meta-analysis. ISPRS J. Photogramm.
**2021**, 173, 309–322. [Google Scholar] [CrossRef] - Olofsson, P.; Foody, G.M.; Herold, M.; Stehman, S.V.; Woodcock, C.E.; Wulder, M.A. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ.
**2014**, 148, 42–57. [Google Scholar] [CrossRef] - Foody, G.M. Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sens. Environ.
**2020**, 239, 111630. [Google Scholar] [CrossRef] - Kaplan, G.; Avdan, U. Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data. ISPRS Int. J. Geo-Inf.
**2018**, 7, 411. [Google Scholar] [CrossRef] [Green Version] - Huo, X.; Niu, Z.; Zhang, B.; Liu, L.; Li, X. Research on Remote Sensing Feature Selection for Alpine Wetland Classification. Natl. Remote Sens. Bull.
**2022**, 27, 1045–1060. [Google Scholar] [CrossRef] - Chen, T.; Bao, A.; Jiapaer, G.; Guo, H.; Zheng, G.; Jiang, L.; Chang, C.; Tuerhanjiang, L. Disentangling the relative impacts of climate change and human activities on arid and semiarid grasslands in Central Asia during 1982–2015. Sci. Total Environ.
**2019**, 653, 1311–1325. [Google Scholar] [CrossRef] [PubMed] - Liu, Y.; Wang, Q.; Zhang, Z.; Tong, L.; Wang, Z.; Li, J. Grassland dynamics in responses to climate variation and human activities in China from 2000 to 2013. Sci. Total Environ.
**2019**, 690, 27–39. [Google Scholar] [CrossRef] [PubMed] - Yu, H.L.; Ding, Q.N.; Meng, B.P.; Lv, Y.Y.; Liu, C.; Zhang, X.Y.; Sun, Y.; Li, M.; Yi, S.H. The Relative Contributions of Climate and Grazing on the Dynamics of Grassland NPP and PUE on the Qinghai-Tibet Plateau. Remote Sens.
**2021**, 13, 3424. [Google Scholar] [CrossRef] - Sen, P.K. Estimates of the Regression Coefficient Based on Kendall’s Tau. J. Am. Stat. Assoc.
**1968**, 324, 1379–1389. [Google Scholar] [CrossRef] - Zhang, B.; Niu, Z.; Zhang, D.; Huo, X. Dynamic Changes and Driving Forces of Alpine Wetlands on the Qinghai–Tibetan Plateau Based on Long-Term Time Series Satellite Data: A Case Study in the Gansu Maqu Wetlands. Remote Sens.
**2022**, 14, 4147. [Google Scholar] [CrossRef] - Zhang, S.; Zhou, B.; Shi, F.; Chen, Q.; Su, S. Study on Information Extraction Method of Alpine Wetland in Qinghai-Xizang Plateau based on Remote Sensing Data of GF-1 Satellite——Taking Maduo County for Example. Plateau Meteorol.
**2020**, 39, 1309–1317. [Google Scholar] - Gitelson, A.A.; Kaufman, Y.J.; Stark, R.; Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ.
**2002**, 80, 76–87. [Google Scholar] [CrossRef] [Green Version] - Feher, L.C.; Osland, M.J.; McKee, K.L.; Whelan, K.R.T.; Coronado-Molina, C.; Sklar, F.H.; Krauss, K.W.; Howard, R.J.; Cahoon, D.R.; Lynch, J.C.; et al. Soil Elevation Change in Mangrove Forests and Marshes of the Greater Everglades: A Regional Synthesis of Surface Elevation Table-Marker Horizon (SET-MH) Data. Estuaries Coasts
**2022**. [Google Scholar] [CrossRef] - Gao, W.; Shen, F.; Tan, K.; Zhang, W.; Liu, Q.; Lam, N.S.N.; Ge, J. Monitoring terrain elevation of intertidal wetlands by utilising the spatial-temporal fusion of multi-source satellite data: A case study in the Yangtze (Changjiang) Estuary. Geomorphology
**2021**, 383, 107683. [Google Scholar] [CrossRef] - Hu, Z.; Zhang, X.; Zhang, X.; Wang, J.; Wang, X. Response of spatio-temporal variation of land surface phenology to alpine wetland landscape evolution from 1990 to 2020. Acta Ecol. Sin.
**2023**, 21. [Google Scholar] - Li, X.L.; Gao, J.; Brierley, G.; Qiao, Y.M.; Zhang, J.; Yang, Y.W. Rangeland Degradation on the Qinghai-Tibet Plateau: Implications for Rehabilitation. Land Degrad. Dev.
**2013**, 24, 72–80. [Google Scholar] [CrossRef] - Zhang, Y.; Yan, J.; Cheng, X. Advances in impact of climate change and human activities on wetlands on the Tibetan Plateau. Acta Ecol. Sin.
**2023**, 43, 2180–2193. [Google Scholar] - Shen, X.; Zhang, J.; Lu, X. Spatio-temporal change of marshes NDVI and its response to climate change in the Qinghai-Tibet Plateau. Acta Ecol. Sin.
**2020**, 40, 6259–6268. [Google Scholar] - Li, Y.; Hou, Z.; Zhang, L.; Song, C.; Piao, S.; Lin, J.; Peng, S.; Fang, K.; Yang, J.; Qu, Y.; et al. Rapid expansion of wetlands on the Central Tibetan Plateau by global warming and El Nino. Sci. Bull.
**2023**, 5, 485–488. [Google Scholar] [CrossRef] [PubMed] - Chen, J.; Yan, F.; Lu, Q. Spatiotemporal Variation of Vegetation on the Qinghai–Tibet Plateau and the Influence of Climatic Factors and Human Activities on Vegetation Trend (2000–2019). Remote Sens.
**2020**, 12, 3150. [Google Scholar] [CrossRef] - Jin, Z.; You, Q.; Wu, F.; Sun, B.; Cai, Z. Changes of climate and climate extremes in the Three-Rivers Headwaters’ Region over the Tibetan Plateau during the past 60 years. Trans. Atmos. Sci.
**2020**, 43, 1042–1055. [Google Scholar] - Sun, Q.; Liu, W.; Gao, Y.; Li, J.; Yang, C. Spatiotemporal Variation and Climate Influence Factors of Vegetation Ecological Quality in the Sanjiangyuan National Park. Sustainability
**2020**, 12, 6634. [Google Scholar] [CrossRef] - Xu, S.; Yu, Z.; Yang, C.; Ji, X.; Zhang, K. Trends in evapotranspiration and their responses to climate change and vegetation greening over the upper reaches of the Yellow River Basin. Agric. For. Meteorol.
**2018**, 263, 118–129. [Google Scholar] [CrossRef] - Wei, J.; Wang, W.; Shao, Q.; Rong, Y.; Xing, W.; Liu, C. Influence of mature El Niño-Southern Oscillation phase on seasonal precipitation and streamflow in the Yangtze River Basin, China. Int. J. Climatol.
**2020**, 40, 3885–3905. [Google Scholar] [CrossRef] - Dong, Y.; Zhai, J.; Zhao, Y.; Li, H.; Qingming, W.; Shan, J.; Huanyu, C.; Ding, Z. Teleconnection patterns of precipitation in the Three-River Headwaters region, China. Environ. Res. Lett.
**2020**, 15, 104050. [Google Scholar] [CrossRef] - Xi, Y.; Miao, C.; Wu, J.; Duan, Q.; Lei, X.; Li, H. Spatiotemporal Changes in Extreme Temperature and Precipitation Events in the Three-Rivers Headwater Region, China. J. Geophys. Res. Atmos.
**2018**, 123, 5827–5844. [Google Scholar] [CrossRef] - Qu, Y.; Zhu, Z.; Montzka, C.; Chai, L.; Liu, S.; Ge, Y.; Liu, J.; Lu, Z.; He, X.; Zheng, J. Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China. J. Hydrol.
**2021**, 592, 125616. [Google Scholar] [CrossRef] - Wang, J.; Xu, D. Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai-Tibet Plateau, China. Remote Sens.
**2022**, 13, 5156. [Google Scholar] [CrossRef] - Ganjurjav, H.; Gao, Q.; Gornish, E.S.; Schwartz, M.W.; Liang, Y.; Cao, X.; Zhang, W.; Zhang, Y.; Li, W.; Wan, Y.; et al. Differential response of alpine steppe and alpine meadow to climate warming in the central Qinghai–Tibetan Plateau. Agric. For. Meteorol.
**2016**, 223, 233–240. [Google Scholar] [CrossRef] [Green Version] - Peng, F.; You, Q.; Xu, M.; Guo, J.; Wang, Y.; Xue, X. Effects of Warming and Clipping on Ecosystem Carbon Fluxes across Two Hydrologically Contrasting Years in an Alpine Meadow of the Qinghai-Tibet Plateau. PLoS ONE
**2014**, 9, e109319. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Wei, J.; Li, X.; Liu, L.; Christensen, T.R.; Jiang, Z.; Ma, Y.; Wu, X.; Yao, H.; López-Blanco, E. Radiation, soil water content, and temperature effects on carbon cycling in an alpine swamp meadow of the northeastern Qinghai–Tibetan Plateau. Biogeosciences
**2022**, 19, 861–875. [Google Scholar] [CrossRef]

**Figure 2.**The distribution of the field survey samples in the YRSNP from 2019 to 2020 (the background is the Landsat 8 image of the study area in 2020).

**Figure 7.**The wetland center of gravity transfer and standard deviation ellipse in the YRSNP from 2000 to 2020. (

**a**) River wetland center of gravity transfer and standard deviation ellipse, (

**b**) lake wetland center of gravity transfer and standard deviation ellipse, (

**c**) marsh wetland center of gravity transfer and standard deviation ellipse, (

**d**) marsh meadow center of gravity transfer and standard deviation ellipse.

**Figure 8.**The Mann–Kendall analysis and change trend of meteorological factors in the YRSNP from 2000 to 2020. (

**a**) The Mann–Kendall analysis of annual precipitation; (

**b**) the change trend of annual precipitation; (

**c**) the Mann–Kendall analysis of warm season precipitation; (

**d**) the change trend of warm season precipitation; (

**e**) the Mann–Kendall analysis of mean annual temperature; (

**f**) the change trend of mean annual temperature; (

**g**) the Mann–Kendall analysis of warm season mean temperature; (

**h**) the change trend of warm season mean temperature; (

**i**) the Mann–Kendall analysis of annual potential evapotranspiration; (

**j**) the change trend of annual potential evapotranspiration; (

**k**) the Mann–Kendall analysis of warm season potential evapotranspiration; (

**l**) the change trend of warm season potential evapotranspiration.

**Figure 9.**The change trend of annual precipitation and warm season precipitation in the YRSNP from 2000 to 2020. (

**a**) The mean annual precipitation; (

**b**) the trend of annual precipitation; (

**c**) the significance of annual precipitation change trend; (

**d**) the mean warm season precipitation; (

**e**) the trend of warm season precipitation; (

**f**) the significance of warm season precipitation change trend.

**Figure 10.**The change trend of mean annual temperature and warm season mean temperature in the YRSNP from 2000 to 2020. (

**a**) The mean annual temperature; (

**b**) the change trend of mean annual temperature; (

**c**) the significance of mean annual temperature change trend; (

**d**) the warm season mean temperature; (

**e**) the trend of warm season mean temperature; (

**f**) the significance of warm season mean temperature change trend.

**Figure 11.**The change trend of annual potential evapotranspiration and warm season potential evapotranspiration in the YRSNP from 2000 to 2020. (

**a**) The mean annual potential evapotranspiration; (

**b**) the trend of annual potential evapotranspiration; (

**c**) the significance of annual potential evapotranspiration change trend; (

**d**) the mean warm season potential evapotranspiration; (

**e**) the trend of warm season potential evapotranspiration; (

**f**) the significance of warm season potential evapotranspiration change trend.

**Figure 12.**Spatial distribution and change trends of soil moisture in the YRSNP from 2000 to 2020. (

**a**) Change trends; (

**b**) spatial distribution using equal interval.

**Figure 14.**Spatial distribution and change trends of population density in the YRSNP from 2000–2020. (

**a**) Change trends; (

**b**) spatial distribution.

**Figure 15.**Random forest regression analysis results graph (SM: soil moisture; PR: population density; WPET: warm season potential evapotranspiration; APET: annual potential evapotranspiration; WMT: warm season mean temperature; MAT: mean annual temperature; WAP: warm season precipitation; AP: annual precipitation).

**Table 1.**Information of various remote sensing image parameters used in the YRSNP from 2000 to 2020.

Remote Sensing Image | Dataset | Main Band Information |
---|---|---|

Landsat 5 TM | USGS Landsat 5 Level 2, Collection 2, Tier 1 | B1 Blue 0.45–0.52 μm 30 m B2 Green 0.52–0.60 μm 30 m B3 Red 0.63–0.69 μm 30 m B4 NIR 0.76–0.90 μm 30 m B5 SWIR1 1.55–1.75 μm 30 m B6 LWIR 10.40–12.50 μm 120 m/60 m B7 SWIR2 2.08–2.35 μm 30 m |

Landsat 7 ETM+ | USGS Landsat 7 Level 2, Collection 2, Tier 1 | |

Landsat 8 OLI | USGS Landsat 8 Level 2, Collection 2, Tier 1 | B2 Blue 0.45–0.52 μm 30 m B3 Green 0.53–0.60 μm 30 m B4 Red 0.63–0.68 μm 30 m B5 NIR 0.85–0.89 μm 30 m B6 SWIR1 1.56–1.67 μm 30 m B7 SWIR2 2.10–2.30 μm 30 m |

Wetland Category | Landsat Remote Sensing Image | Description |
---|---|---|

River wetland | Natural linear waterbody with flowing water in the wetland area | |

Lake wetland | Natural polygon waterbody with standing water in the wetland area | |

Marsh wetland | Naturally formed, the center is mostly patchy, and low vegetation covers the surrounding area | |

Marsh meadow | Natural wetland and surrounded by large areas of tall grass |

Primary Classification Feature | Secondary Classification Feature | Tertiary Classification Feature | Formula | |
---|---|---|---|---|

Landsat 5 7 | Landsat 8 | |||

Spectral feature | Band | Blue, Green, Red, NIR, SWIR1, SWIR2 | Blue (B1), Green (B2), Red (B3), NIR (B4), SWIR1 (B5), SWIR2 (B7) | Blue (B2), Green (B3), Red (B4), NIR (B5), SWIR1 (B6), SWIR2 (B7) |

Spectral index | Water index | MNDWI | $\frac{\mathrm{B}2-\mathrm{B}5}{\mathrm{B}2+\mathrm{B}5}$ | $\frac{\mathrm{B}3-\mathrm{B}6}{\mathrm{B}3+\mathrm{B}6}$ |

NDWI | $\frac{\mathrm{B}2-\mathrm{B}4}{\mathrm{B}2+\mathrm{B}4}$ | $\frac{\mathrm{B}3-\mathrm{B}5}{\mathrm{B}3+\mathrm{B}5}$ | ||

NDWI_B | $\frac{\mathrm{B}1-\mathrm{B}3}{\mathrm{B}1+\mathrm{B}3}$ | $\frac{\mathrm{B}2-\mathrm{B}4}{\mathrm{B}2+\mathrm{B}4}$ | ||

RNDWI | $\frac{\mathrm{B}5-\mathrm{B}3}{\mathrm{B}5+\mathrm{B}3}$ | $\frac{\mathrm{B}6-\mathrm{B}4}{\mathrm{B}6+\mathrm{B}4}$ | ||

EWI | $\frac{(\mathrm{B}2-\mathrm{B}4-\mathrm{B}7)}{(\mathrm{B}2+\mathrm{B}4+\mathrm{B}7)}$ | $\frac{(\mathrm{B}3-\mathrm{B}5-\mathrm{B}7)}{(\mathrm{B}3+\mathrm{B}5+\mathrm{B}7)}$ | ||

SWI | $\mathrm{B}1+\mathrm{B}3-\mathrm{B}4$ | $\mathrm{B}2+\mathrm{B}4-\mathrm{B}5$ | ||

AWEI | $4\times (\mathrm{B}2-\mathrm{B}5)-(0.25\times \mathrm{B}4+2.75\times \mathrm{B}7)$ | $4\times (\mathrm{B}3-\mathrm{B}6)-(0.25\times \mathrm{B}5+2.75\times \mathrm{B}7)$ | ||

UGWI | $\frac{{\mathrm{B}2}^{3}-(\mathrm{B}1+\mathrm{B}3+\mathrm{B}4)}{{\mathrm{B}2}^{3}+(\mathrm{B}1+\mathrm{B}3+\mathrm{B}4)}$ | $\frac{{\mathrm{B}3}^{3}-(\mathrm{B}2+\mathrm{B}4+\mathrm{B}5)}{{\mathrm{B}3}^{3}+(\mathrm{B}2+\mathrm{B}4+\mathrm{B}5)}$ | ||

Vegetation index | NDVI | $\frac{\mathrm{B}4-\mathrm{B}3}{\mathrm{B}4+\mathrm{B}3}$ | $\frac{\mathrm{B}5-\mathrm{B}4}{\mathrm{B}5+\mathrm{B}4}$ | |

VIgreen | $\frac{\mathrm{B}2-\mathrm{B}3}{\mathrm{B}2+\mathrm{B}3}$ | $\frac{\mathrm{B}3-\mathrm{B}4}{\mathrm{B}3+\mathrm{B}4}$ | ||

RVI | $\frac{\mathrm{B}4}{\mathrm{B}3}$ | $\frac{\mathrm{B}5}{\mathrm{B}4}$ | ||

RDVI | $\frac{\mathrm{B}4-\mathrm{B}3}{\sqrt{\mathrm{B}4+\mathrm{B}3}}$ | $\frac{\mathrm{B}5-\mathrm{B}4}{\sqrt{\mathrm{B}5+\mathrm{B}4}}$ | ||

MSR | $\frac{(\mathrm{B}4-\mathrm{B}3)-1}{\sqrt{\mathrm{B}4+\mathrm{B}3}+1}$ | $\frac{(\mathrm{B}5-\mathrm{B}4)-1}{\sqrt{\mathrm{B}5+\mathrm{B}4}+1}$ | ||

MCARI | $[\left(\mathrm{B}4-\mathrm{B}3\right)-0.2\times (\mathrm{B}4-\mathrm{B}2\left)\right]\times \frac{\mathrm{B}4}{\mathrm{B}3}$ | $[\left(\mathrm{B}5-\mathrm{B}4\right)-0.2\times (\mathrm{B}5-\mathrm{B}3\left)\right]\times \frac{\mathrm{B}5}{\mathrm{B}4}$ | ||

Red edge index | CIre | $(\mathrm{B}4/\mathrm{B}3)-1$ | $(\mathrm{B}5/\mathrm{B}4)-1$ | |

Build-up index | NDBI | $\frac{\mathrm{B}5-\mathrm{B}4}{\mathrm{B}5+\mathrm{B}4}$ | $\frac{\mathrm{B}6-\mathrm{B}5}{\mathrm{B}6+\mathrm{B}5}$ | |

Bare land index | BSI | $\frac{(\mathrm{B}3+\mathrm{B}5)-(\mathrm{B}4+\mathrm{B}8)}{(\mathrm{B}3+\mathrm{B}5)+(\mathrm{B}4+\mathrm{B}8)}$ | $\frac{(\mathrm{B}4+\mathrm{B}6)-(\mathrm{B}5+\mathrm{B}2)}{(\mathrm{B}4+\mathrm{B}6)+(\mathrm{B}5+\mathrm{B}2)}$ | |

Snow index | NDSI | $\frac{\mathrm{B}2-\mathrm{B}11}{\mathrm{B}2+\mathrm{B}11}$ | $\frac{\mathrm{B}3-\mathrm{B}6}{\mathrm{B}3+\mathrm{B}6}$ | |

Topographic feature | Elevation | |||

Slope | ||||

Aspect |

**Table 4.**Evaluation table of wetland remote sensing classification accuracy in study area based on a random forest model.

Year | User Accuracy | Overall Accuracy | Kappa | |||||
---|---|---|---|---|---|---|---|---|

River Wetland | Lake Wetland | Marsh Wetland | Marsh Meadow | Grassland | Others | |||

2000 | 0.8024 | 0.9687 | 0.6923 | 0.7567 | 0.8132 | 0.9230 | 0.8427 | 0.7951 |

2001 | 0.8513 | 0.9821 | 0.5714 | 0.8055 | 0.7398 | 0.9459 | 0.8309 | 0.7799 |

2002 | 0.7938 | 0.8936 | 0.7647 | 0.7000 | 0.8000 | 0.9673 | 0.8311 | 0.7818 |

2003 | 0.8314 | 0.9791 | 0.6500 | 0.8717 | 0.7572 | 0.9540 | 0.8377 | 0.7915 |

2004 | 0.8666 | 0.9814 | 0.7272 | 0.7878 | 0.7777 | 0.8965 | 0.8409 | 0.7907 |

2005 | 0.8674 | 0.9375 | 0.9000 | 0.7428 | 0.8000 | 0.9500 | 0.8594 | 0.8192 |

2006 | 0.8805 | 0.9076 | 0.8333 | 0.7560 | 0.7924 | 0.9462 | 0.8535 | 0.8110 |

2007 | 0.8461 | 0.9800 | 0.6306 | 0.7560 | 0.7732 | 0.9566 | 0.8426 | 0.7954 |

2008 | 0.8536 | 0.9999 | 0.8333 | 0.7878 | 0.8553 | 0.9565 | 0.8878 | 0.8538 |

2009 | 0.8409 | 0.9999 | 0.8181 | 0.7407 | 0.7793 | 0.9277 | 0.8523 | 0.8106 |

2010 | 0.8764 | 0.9999 | 0.7500 | 0.7272 | 0.8079 | 0.9101 | 0.8659 | 0.8279 |

2011 | 0.9066 | 0.9999 | 0.7692 | 0.6410 | 0.8344 | 0.9340 | 0.8689 | 0.8311 |

2012 | 0.9062 | 0.9655 | 0.6666 | 0.6086 | 0.7160 | 0.9750 | 0.8295 | 0.7777 |

2013 | 0.9012 | 0.9649 | 0.7333 | 0.8461 | 0.7973 | 0.9635 | 0.8738 | 0.8393 |

2014 | 0.8875 | 0.9830 | 0.7500 | 0.7931 | 0.7583 | 0.9518 | 0.8578 | 0.8175 |

2015 | 0.8593 | 0.9999 | 0.6428 | 0.8400 | 0.7417 | 0.9489 | 0.8412 | 0.7926 |

2016 | 0.8536 | 0.9800 | 0.8571 | 0.6304 | 0.7758 | 0.9999 | 0.8379 | 0.7882 |

2017 | 0.8333 | 0.9642 | 0.8571 | 0.7407 | 0.7939 | 0.9615 | 0.8543 | 0.8082 |

2018 | 0.9090 | 0.9365 | 0.9166 | 0.6410 | 0.7435 | 0.9382 | 0.8341 | 0.7881 |

2019 | 0.9047 | 0.9791 | 0.6400 | 0.6808 | 0.8040 | 0.9021 | 0.8400 | 0.7964 |

2020 | 0.8645 | 0.9682 | 0.7391 | 0.7209 | 0.8074 | 0.9459 | 0.8521 | 0.8118 |

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## Share and Cite

**MDPI and ACS Style**

Ma, T.; Zhao, L.; She, Y.; Hu, B.; Feng, X.; Gongbao, J.; Zhang, W.; Zhao, Z.
Identifying the Driving Forces of Alpine Wetland Dynamic Changes in the Yellow River Source National Park from 2000 to 2020. *Water* **2023**, *15*, 2557.
https://doi.org/10.3390/w15142557

**AMA Style**

Ma T, Zhao L, She Y, Hu B, Feng X, Gongbao J, Zhang W, Zhao Z.
Identifying the Driving Forces of Alpine Wetland Dynamic Changes in the Yellow River Source National Park from 2000 to 2020. *Water*. 2023; 15(14):2557.
https://doi.org/10.3390/w15142557

**Chicago/Turabian Style**

Ma, Tao, Li Zhao, Yandi She, Bixia Hu, Xueke Feng, Jiancuo Gongbao, Wei Zhang, and Zhizhong Zhao.
2023. "Identifying the Driving Forces of Alpine Wetland Dynamic Changes in the Yellow River Source National Park from 2000 to 2020" *Water* 15, no. 14: 2557.
https://doi.org/10.3390/w15142557