Analysis of Spatial and Temporal Evolution of Ecosystem Services and Driving Factors in the Yellow River Basin of Henan Province, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of the Study Area
2.2. Data Sources
2.3. Functional Analysis of Ecosystem Services under the Invest Model
2.3.1. Carbon Stock Modules
2.3.2. Water Yield Module
2.3.3. Soil Conservation Module
2.4. Carbon Stock Estimation Based on Multi-Source Data
2.4.1. Feature Variable Extraction
2.4.2. Forest Biomass Estimation Model
2.5. Geodetector-Based Analysis of Ecosystem Service Function Drivers
3. Results and Analysis
3.1. InVEST Model Assessment Results and Analysis
3.1.1. Carbon Stock Assessment Analysis, 2000–2020
- (1)
- Spatial and temporal variations in carbon stock
- (2)
- Carbon stock in different land types
3.1.2. Assessment and Analysis of Water Yield, 2000–2020
- (1)
- Spatial and temporal variations in water yield
- (2)
- Water yield in different land use types
3.1.3. Soil Conservation Assessment Analysis from 2000 to 2020
- (1)
- Spatiotemporal changes in soil conservation
- (2)
- Soil conservation in diverse land types
3.2. Analysis of Drivers of Ecosystem Service Function
3.2.1. Analysis of Carbon Stock Drivers
3.2.2. Analysis of Water Yield Drivers
3.2.3. Analysis of Soil Conservation Drivers
4. Discussion
4.1. Methodological Limitations
4.2. Results Discussion
4.3. Socio-Ecological Implications
5. Conclusions
- (1)
- The total carbon stock shows an increasing trend, going from 6.33 × 108 t in 2000 to 6.45 × 108 t in 2020. Total water yield showed a “falling-rising” pattern, with a low of 5.23 × 1010 m3 in 2015 and a peak of 7.37 × 1010 m3 in 2000. The total soil conservation exhibits a “decline-rise-decline-rise” pattern, with an overall decreasing trend.
- (2)
- The spatial distribution of the three ecosystem services has a high degree of consistency. The southwestern, central-eastern, and northern regions of the study area demonstrate better ecosystem service functions, with higher levels of carbon stock, water yield, and soil conservation. The land use distribution in these regions is predominantly forested.
- (3)
- Broad-leaved forests had the highest carbon stock per unit area, followed by coniferous forests. Coniferous and broad-leaved mixed forests ranked highest for water yield density. Soil conservation was highest in coniferous and broad-leaved mixed forests.
- (4)
- Population density primarily influenced changes in carbon stock. Water yield was mainly driven by precipitation, while soil conservation was most affected by slope. Population density ∩ DEM and precipitation provide the strongest explanatory power for carbon stock. The interaction between precipitation and other factors exhibits the highest explanatory power for water yield. Similarly, slope ∩ precipitation shows the greatest explanatory power for soil conservation.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data Name | Data Sources | |
---|---|---|
Remote sensing data | Landsat image data | the Google Earth Engine (GEE) |
GEDI L4A data | LP DACC (https://doi.org/10.3334/ORNLDAAC/1986, accessed on 23 August 2022) | |
MOD17A3 data | NASA Earth Data Platform (https://earthdata.nasa.gov/, accessed on 23 August 2022) | |
Land cover data | the Chinese Academy of Sciences Big Data Geoscience Project (https://data.casearth.cn, accessed on 30 July 2022) | |
Meteorological data | Average annual temperature | the National Earth System Science Data Center (https://www.geodata.cn/, accessed on 5 September 2022) |
Average annual precipitation | the Resource Environment Science and Data Center at the Institute of Geographic Sciences and Resources, the Chinese Academy of Sciences (https://www.resdc.cn, accessed on 5 September 2022) | |
Average annual potential evapotranspiration | the Global Aridity and PET Database (https://cgiarcsi.community/data/global-aridity-and-pet-database/, accessed on 4 September 2022) | |
The maximum root depth in the soil | Version 1.2 of the Harmonized World Soil Database | |
DEM | the Geospatial Data Cloud (http://www.gscloud.cn/, accessed on 30 July 2022) |
Code | LULC | Cabove | Cbelow | Csoil | Cdead |
---|---|---|---|---|---|
1 | farmlands | 5.97 | 1.13 | 58.2 | 0 |
2 | broad-leaved forests | 13.95 | 5.58 | 89.9 | 5.55 |
3 | coniferous forests | 25.73 | 11.84 | 90.2 | 5.85 |
4 | shrubbery | 4.5 | 0.8 | 25 | 0 |
5 | coniferous and broad-leaved mixed | 6.5 | 1.4 | 67.3 | 0 |
6 | grasslands | 0.63 | 2.82 | 60.2 | 0.19 |
7 | construction land | 0 | 0 | 60 | 0 |
8 | bare land | 0 | 0 | 53.3 | 0 |
9 | wetlands | 3.7 | 6.55 | 0 | 1.23 |
10 | waters | 0 | 0 | 0 | 0 |
Parameter | Source | Method | Formula |
---|---|---|---|
Slope | DEM | ArcGIS Extraction | — |
DEM | DEM | — | — |
NDVI | Landsat 8 band4, band5 | ENVI band operation | |
EVI | Landsat 8 band2, band4, band5 | ENVI band operation |
Training Data (R2) | Training Data (RMSE) | Testing Data (R2) | Testing Data (RMSE) | |
---|---|---|---|---|
Random forest regression | 0.66 | 24.82 | 0.56 | 28.68 |
Geographically weighted regression | 0.72 | 22.47 | 0.67 | 25.12 |
LULC | Soil Carbon Density (t/ha) |
---|---|
farmlands | 58.2 |
broad-leaved forests | 89.9 |
coniferous forests | 90.2 |
shrubbery | 25 |
coniferous and broad-leaved mixed | 67.3 |
grasslands | 60.2 |
construction land | 60 |
bare land | 53 |
wetlands | 26.2 |
waters | 0 |
Driving Factor | Unit (Unit of Measure) | Variable |
---|---|---|
Elevation | Meter | X1 |
Slope | Degree | X2 |
Mean annual temperature | Celsius degrees | X3 |
Mean annual evapotranspiration | millimeter | X4 |
Mean annual precipitation | millimeter | X5 |
Population density | People/km2 | X6 |
GDP | Ten thousand yuan | X7 |
LULC | 2000 | 2005 | 2010 | 2015 | 2020 | |||||
---|---|---|---|---|---|---|---|---|---|---|
MEAN | SUM | MEAN | SUM | MEAN | SUM | MEAN | SUM | MEAN | SUM | |
(t/ha) | (t) | (t/ha) | (t) | (t/ha) | (t) | (t/ha) | (t) | (t/ha) | (t) | |
farmlands | 62.18 | 444,447,434 | 62.53 | 435,156,415 | 64.20 | 439,566,773 | 66.00 | 442,548,113 | 66.40 | 437,529,999 |
broad-leaved forests | 124.66 | 131,357,701 | 124.44 | 133,199,681 | 128.24 | 134,955,449 | 130.73 | 137,125,932 | 133.06 | 139,182,232 |
coniferous forests | 123.68 | 9,427,511 | 124.63 | 9,649,304 | 128.88 | 9,830,614 | 130.20 | 9,937,850 | 131.57 | 10,108,686 |
shrubbery | 30.71 | 30,926,532 | 32.84 | 32,979,687 | 34.18 | 34,408,397 | 31.60 | 31,674,407 | 37.61 | 37,437,367 |
coniferous and broad-leaved mixed | 104.79 | 5,431,361 | 103.22 | 5,665,145 | 109.03 | 5,651,559 | 110.45 | 5,716,760 | 113.30 | 5,847,339 |
grasslands | 63.97 | 11,476,263 | 63.55 | 11,117,307 | 65.18 | 11,349,664 | 67.00 | 11,644,138 | 67.66 | 11,944,238 |
construction land | 0.00 | 0 | 0.50 | 417,698 | 2.31 | 2,226,676 | 1.50 | 1,659,302 | 2.00 | 2,441,504 |
bare land | 53.30. | 115,300 | 52.64 | 122,964 | 54.73 | 136,062 | 54.50 | 154,704 | 55.00 | 181,571 |
wetlands | 2.62 | 33,714 | 4.51 | 66,048 | 3.64 | 60,137 | 4.38 | 89,127 | 4.34 | 110,167 |
waters | 0.00 | 0 | 3.13 | 178,772 | 1.76 | 105,761 | 1.50 | 94,499 | 2.00 | 130,657 |
LULC | 2000 | 2005 | 2010 | 2015 | 2020 | |||||
---|---|---|---|---|---|---|---|---|---|---|
MEAN | SUM | MEAN | SUM | MEAN | SUM | MEAN | SUM | MEAN | SUM | |
(mm) | (105 m3) | (mm) | (105 m3) | (mm) | (105 m3) | (mm) | (105 m3) | (mm) | (105 m3) | |
farmlands | 713.77 | 510,209.69 | 594.32 | 413,597.54 | 522.91 | 358,033.37 | 475.84 | 319,049.33 | 544.69 | 358,923.83 |
broad-leaved forests | 722.74 | 76,159.51 | 678.63 | 72,640.21 | 743.11 | 78,200.93 | 574.01 | 60,209.19 | 643.17 | 67,275.35 |
coniferous forests | 697.13 | 5313.98 | 670.82 | 5193.86 | 717.85 | 5475.56 | 561.39 | 4284.96 | 650.03 | 4994.40 |
shrubbery | 685.71 | 69,044.43 | 624.03 | 62,670.26 | 670.03 | 67,454.21 | 544.42 | 54,572.63 | 569.18 | 56,649.54 |
coniferous and broad-leaved mixed | 774.96 | 4016.66 | 738.79 | 4054.69 | 895.87 | 4643.53 | 614 | 3177.92 | 723.31 | 3733.11 |
grasslands | 610.54 | 10,953.25 | 577.8 | 10,107.72 | 590.41 | 10,281.13 | 547.3 | 9511.06 | 549.24 | 9696.13 |
construction land | 856.27 | 57,449.61 | 728.95 | 60,896.20 | 681.08 | 65,606.18 | 618.44 | 68,411.92 | 679.06 | 82,896.37 |
bare land | 768.88 | 167.27 | 619.52 | 144.73 | 576.09 | 143.23 | 542.64 | 154.03 | 576.12 | 190.19 |
wetlands | 595.11 | 764.66 | 525.69 | 769.36 | 443.73 | 733.02 | 419.07 | 852.25 | 463.19 | 1175.82 |
waters | 673.83 | 3258.69 | 571.6 | 3262.41 | 515.24 | 3091.24 | 491.71 | 3097.75 | 526 | 3436.28 |
total | — | 737,337.76 | — | 633,336.97 | — | 593,662.40 | — | 523,321.041 | — | 588,971.04 |
LULC | 2000 | 2005 | 2010 | 2015 | 2020 | |||||
---|---|---|---|---|---|---|---|---|---|---|
MEAN | SUM | MEAN | SUM | MEAN | SUM | MEAN | SUM | MEAN | SUM | |
(t/ha) | (t) | (t/ha) | (t) | (t/ha) | (t) | (t/ha) | (t) | (t/ha) | (t) | |
farmlands | 124.94 | 892,894,647 | 105.2 | 731,992,472 | 103.65 | 709,523,148 | 96.65 | 647,782,320 | 94.14 | 618,244,237 |
broad-leaved forests | 2168.86 | 2,284,766,697 | 1941.71 | 2,077,785,374 | 2430.82 | 2,557,282,693 | 1713.45 | 1,795,713,278 | 1855.4 | 1,926,287,063 |
coniferous forests | 2715.86 | 206,904,023 | 2519.13 | 194,929,844 | 2968.51 | 226,300,551 | 2154.01 | 164,150,571 | 2416.8 | 183,063,619 |
shrubbery | 1645.64 | 1,656,878,548 | 1421.69 | 1,427,664,880 | 1745.19 | 1,756,810,566 | 1314.88 | 1,317,780,576 | 1274.36 | 1,264,849,202 |
coniferous and broad-leaved mixed | 2886.14 | 149,523,934 | 2600.01 | 142,635,418 | 3601.08 | 186,570,303 | 2174.02 | 112,423,976 | 2520.69 | 128,984,358 |
grasslands | 858.5 | 153,989,292 | 821.87 | 143,749,763 | 940.12 | 163,681,336 | 886.62 | 153,982,268 | 724.27 | 127,479,804 |
construction land | 86.93 | 58,321,795 | 67.87 | 56,696,949 | 65.44 | 63,033,507 | 59.17 | 65,448,329 | 62.22 | 75,892,169 |
bare land | 315.69 | 686,692 | 230.92 | 539,324 | 223.65 | 555,891 | 191.38 | 543,039 | 176.91 | 582,019 |
wetlands | 141.02 | 1,803,907 | 115.99 | 1,690,840 | 104.17 | 1,714,350 | 91.11 | 1,843,971 | 84.97 | 2,055,458 |
waters | 146.41 | 7,073,007 | 119.22 | 6,798,493 | 116.85 | 7,004,450 | 112.39 | 7,064,835 | 105.88 | 6,741,030 |
Type of Forest | 2000 | 2005 | 2010 | 2015 | 2020 |
---|---|---|---|---|---|
evergreen forests | 58.93 | 58.36 | 60.37 | 62.11 | 63.69 |
shrubbery | 11.43 | 12.21 | 12.36 | 13.20 | 13.23 |
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Fan, L.; Wang, X.; Chen, Z.; Chen, R.; Liu, X.; He, Y.; Wang, S. Analysis of Spatial and Temporal Evolution of Ecosystem Services and Driving Factors in the Yellow River Basin of Henan Province, China. Forests 2024, 15, 1547. https://doi.org/10.3390/f15091547
Fan L, Wang X, Chen Z, Chen R, Liu X, He Y, Wang S. Analysis of Spatial and Temporal Evolution of Ecosystem Services and Driving Factors in the Yellow River Basin of Henan Province, China. Forests. 2024; 15(9):1547. https://doi.org/10.3390/f15091547
Chicago/Turabian StyleFan, Liting, Xinchuang Wang, Zhichao Chen, Runbo Chen, Xuejie Liu, Yateng He, and Shunzhong Wang. 2024. "Analysis of Spatial and Temporal Evolution of Ecosystem Services and Driving Factors in the Yellow River Basin of Henan Province, China" Forests 15, no. 9: 1547. https://doi.org/10.3390/f15091547