Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau
<p>The MP’s topographic map (<b>a</b>) and the distribution of surface cover type (<b>b</b>). Note: MSP (meadow steppe), TSP (typical steppe), DSP (desert steppe), ASP (alpine steppe), SLD (sandy land), DST (desert), BLF (broad-leaved forest), CRF (coniferous forest), SHR (shrub), CRP (cropland), and WAT (water body).</p> "> Figure 2
<p>Conceptual structural equation mode. Note: Arrows between variables to NPP identified the cause and effect relations. Note: precipitation (PRE), temperature (TEMP), soil moisture (SM), and solar radiation (SR).</p> "> Figure 3
<p>Comparison between the simulated NPP using the CASA model and MODIS NPP.</p> "> Figure 4
<p>Spatial distribution (<b>a</b>) and the average value of different vegetation types (<b>b</b>) of NPP during the growing seasons from 2000 to 2019 in the MP.</p> "> Figure 5
<p>The temporal variations of the MP’s NPP (<b>a</b>) for different vegetation (<b>b</b>) from 2000 to 2019 during the growing seasons (The * mark on the graph represents <span class="html-italic">p</span> < 0.05).</p> "> Figure 6
<p>The spatial distribution of NPP’s changing trend during the growing seasons from 2000 to 2019 in the MP ((<b>a</b>) NPP’s spatial change rate; (<b>b</b>) the classification of the significance of the changing trend).</p> "> Figure 7
<p>The q-statistics of driving factors influencing NPP’s spatial pattern in the MP.</p> "> Figure 8
<p>Explanatory power of the interaction of various factors influencing NPP’s spatial pattern in the MP (The * mark on the graph represents non-linear enhancement; unmarked indicates bivariate enhancements).</p> "> Figure 9
<p>Spatial variations of NDVI (<b>a</b>); SR (<b>b</b>); TEMP (<b>c</b>); PRE (<b>d</b>); and SM (<b>e</b>) during the growing seasons from 2000 to 2019 in the MP.</p> "> Figure 10
<p>The established SEM for NPP in the MP (<b>a</b>) and different vegetation types ((<b>b</b>): MSP; (<b>c</b>): TSP; (<b>d</b>): DSP; (<b>e</b>): ASP; (<b>f</b>): CRF; (<b>g</b>): BLF; (<b>h</b>): SLD; (<b>i</b>): SHR; (<b>j</b>): CRP). Note: The thickness of the arrows was proportional to the standardized path coefficients shown on each arrow. Blue lines represent positive effects; red lines represent negative effects; solid lines represent significant paths (<span class="html-italic">p</span> < 0.05); dotted lines represent insignificant paths.</p> ">
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Study Area
2.2. Data Sources and Preprocessing
2.2.1. Topographic Dataset
2.2.2. MODIS Datasets
2.2.3. Reanalysis Dataset
2.2.4. Breathing Earth System Simulator Solar Radiation
2.2.5. Vegetation Type
2.2.6. Soil Type
3. Methods
3.1. Data Preprocessing
3.2. Modified CASA Model
3.3. Theil–Sen Trend and Mann–Kendall Test
3.4. GDM
3.5. SEM
4. Results
4.1. Spatial Distribution of Vegetation NPP in the MP
4.2. Spatiotemporal Variations of NPP in the MP
4.3. Main Factors Affecting NPP’s Spatial Pattern
4.4. Spatio-Temporal Variations of Different Driving Factors
4.5. Pathway Analysis of the Impact of Driving Factors on NPP Changes
5. Discussion
5.1. Interpretation of the Spatial Distribution Characteristics of the MP’s Vegetation NPP
5.2. Effect Pathways of Different Driving Factors on NPP Changes
6. Conclusions
- (1)
- NPP’s spatial distribution in the MP during the growing season showed a decreasing trend from the northeast to the southwest. For different vegetation types covered by this study, NPP ranked as follows: broad-leaved forest > meadow steppe > coniferous forest > cropland > shrub > typical steppe > sandy land > alpine steppe > desert steppe.
- (2)
- The NPP during the growing season showed an increasing trend in different vegetation types, with significant variations in NPP for different vegetation types except for desert steppe and broad-leaved forest. In addition to providing larger vegetation carbon stocks, forest ecosystems also maintain more stable productivity levels.
- (3)
- Vegetation cover, moisture condition, and solar radiation were the dominant factors in NPP’s spatial distribution, followed by temperature and topographic elements. These factors contributed to the spatial distribution of NPP in descending order of explanation: the NDVI (0.86), solar radiation (0.71), precipitation (0.67), vegetation type (0.67), soil moisture (0.57), soil type (0.57), temperature (0.26), elevation (0.19), slope (0.11), and aspect (0.006).
- (4)
- The SEM constructed for this study explained 17% to 65% of the NPP variations, and the NPP change was dominated by the direct effects of the NDVI and moisture condition (precipitation and soil moisture). The total effects of NPP variations in the MP in absolute value were as follows: NDVI (0.47) > precipitation (0.33) > soil moisture (0.16) > temperature (0.14) > solar radiation (0.02). The effects of the NDVI and climate change on NPP varied by different vegetation types, with soil moisture being the dominant moisture factor for steppes and forests in determining NPP variations, while precipitation was the dominant moisture factor in sandy land, shrub, and cropland. Additionally, NPP variations were less influenced by the temperature variations for different vegetation types.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data | Temporal Resolution | Spatial Resolution | Period | Dataset Name | Source |
---|---|---|---|---|---|
Elevation, slope, and aspect | - | 30 m × 30 m | - | ASTER GDEM | http://Ipdaac.usgs.gov/products/ (accessed on 8 August 2022) |
NDVI | 16-day | 500 m × 500 m | 2000–2019 | MOD13A1 | https://lpdaacsvc.cr.usgs.gov/appeears/ (accessed on 8 August 2022) |
NPP | yearly | 500 m × 500 m | 2000–2019 | MOD17A2 | https://lpdaacsvc.cr.usgs.gov/appeears/ (accessed on 8 August 2022) |
Temperature, precipitation, and soil moisture | daily | 0.1° × 0.1° | 2000–2019 | ERA5-land | https://cds.climate.copernicus.eu/cdsapp#!/search?Type=dataset (accessed on 8 August 2022) |
Solar radiation | daily | 0.05° × 0.05° | 2000–2019 | BESS | https://www.environment.snu.ac.kr/bess-rad (accessed on 8 August 2022) |
Vegetation type | - | 500 m × 500 m | 2009 | Vegetation type | National Atlas of Mongolia and 1:1,000,000 Inner Mongolia vegetation map [17] (accessed on 8 August 2022) |
Soil type | - | 1 km | - | FAO-HWSD | http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (accessed on 8 August 2022) |
Pathway | MP | MSP | TSP | DSP | ASP | CRF | BLF | SLD | SHR | CRP | |
---|---|---|---|---|---|---|---|---|---|---|---|
PRE | PRE → NPP | 0.11 ** | −0.20 ** | 0.37 ** | 0.33 ** | −0.12 ** | −0.01 | −0.32 ** | 0.19 ** | −0.04 | 0.41 ** |
PRE → NDVI → NPP | 0.09 ** | 0.01 | 0.06 ** | −0.01 | 0.24 ** | 0.02 ** | −0.03 ** | 0.15 ** | 0.28 ** | −0.31 ** | |
PRE → SM → NPP | 0.08 ** | 0.22 ** | 0.02 ** | −0.11 ** | 0.23 ** | 0.13 ** | 0.22 ** | 0.16 ** | 0.17 ** | 0.00 | |
PRE → SM → NDVI → NPP | 0.05 ** | 0.05 ** | 0.05 ** | −0.08 ** | 0.14 ** | 0.19 ** | 0.08 ** | 0.07 ** | −0.08 * | 0.33 ** | |
Total | 0.33 ** | 0.08 ** | 0.50 ** | 0.13 ** | 0.49 ** | 0.33 ** | −0.05 | 0.57 ** | 0.33 ** | 0.43 ** | |
SR | SR → NPP | −0.03 ** | −0.04 ** | −0.03 ** | −0.05 ** | −0.08 ** | 0.07 ** | 0.15 ** | 0.05 ** | 0.11 ** | 0.10 ** |
SR → NDVI → NPP | 0.04 ** | −0.02 ** | 0.02 ** | −0.11 ** | 0.09 ** | −0.02 ** | −0.03 ** | 0.04 ** | 0.30 ** | 0.19 ** | |
SR → SM → NPP | 0.01 ** | 0.02 ** | 0.00 | 0.00 | 0.06 ** | 0.03 ** | 0.07 ** | 0.00 | −0.01 ** | 0.00 | |
SR → SM → NDVI → NPP | 0.00 ** | 0.00 | 0.00 | 0.00 | 0.02 ** | 0.03 ** | 0.02 ** | 0.00 | 0.01 * | 0.01 ** | |
Total | 0.02 ** | −0.04 ** | −0.01 | −0.16 ** | 0.09 ** | 0.11 ** | 0.21 ** | 0.09 ** | 0.41 ** | 0.30 ** | |
TEMP | TEMP → NPP | −0.19 ** | −0.30 ** | −0.14 ** | −0.02 ** | 0.17 ** | 0.01 | −0.09 ** | 0.15 ** | 0.10 ** | 0.16 ** |
TEMP → NDVI → NPP | 0.08 ** | 0.04 ** | 0.08 ** | 0.04 ** | −0.01 | 0.08 ** | 0.05 ** | 0.00 | 0.10 ** | −0.15 ** | |
TEMP → SM → NPP | −0.02 ** | −0.02 ** | −0.01 ** | 0.05 ** | −0.04 ** | −0.03 ** | −0.05 ** | −0.10 ** | −0.06 ** | 0.00 | |
TEMP → SM → NDVI → NPP | −0.01 ** | 0.00 | −0.01 ** | 0.02 ** | −0.02 ** | −0.03 ** | −0.01 ** | −0.03 ** | 0.02 * | −0.10 ** | |
Total | −0.14 ** | −0.28 ** | −0.08 ** | 0.09 ** | 0.10 ** | 0.03 * | −0.10 ** | 0.02 | 0.16 ** | −0.09 ** | |
NDVI | NDVI → NPP | 0.47 ** | 0.21 ** | 0.38 ** | 0.54 ** | 0.61 ** | 0.50 ** | 0.25 ** | 0.64 ** | 0.74 ** | 0.69 ** |
SM | SM → NPP | 0.11 ** | 0.28 ** | 0.04 ** | −0.22 ** | 0.36 ** | 0.18 ** | 0.32 ** | 0.27 ** | 0.24 ** | 0.00 |
SM → NDVI → NPP | 0.05 ** | 0.05 ** | 0.05 ** | −0.08 ** | 0.14 ** | 0.19 ** | 0.08 ** | 0.07 ** | −0.08 * | 0.33 ** | |
Total | 0.16 ** | 0.33 ** | 0.09 ** | −0.30 ** | 0.50 ** | 0.37 ** | 0.40 ** | 0.34 ** | 0.16 ** | 0.33 ** |
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Yin, C.; Chen, X.; Luo, M.; Meng, F.; Sa, C.; Bao, S.; Yuan, Z.; Zhang, X.; Bao, Y. Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau. Remote Sens. 2023, 15, 1986. https://doi.org/10.3390/rs15081986
Yin C, Chen X, Luo M, Meng F, Sa C, Bao S, Yuan Z, Zhang X, Bao Y. Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau. Remote Sensing. 2023; 15(8):1986. https://doi.org/10.3390/rs15081986
Chicago/Turabian StyleYin, Chaohua, Xiaoqi Chen, Min Luo, Fanhao Meng, Chula Sa, Shanhu Bao, Zhihui Yuan, Xiang Zhang, and Yuhai Bao. 2023. "Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau" Remote Sensing 15, no. 8: 1986. https://doi.org/10.3390/rs15081986