Nonlinear Characteristics of NPP Based on Ensemble Empirical Mode Decomposition from 1982 to 2015—A Case Study of Six Coastal Provinces in Southeast China
<p>Study area: (<b>a</b>) elevation, (<b>b</b>) average temperature, (<b>c</b>) precipitation, and (<b>d</b>) location of the study area in China.</p> "> Figure 2
<p>Flowchart of the CASA model.</p> "> Figure 3
<p>Unchanged (evergreen broad-leaved forest, deciduous broad-leaved forest, evergreen coniferous forest, deciduous coniferous forest, mixed forest, farmland, wetland, grassland, shrub, urban and rural land) and changed vegetation types in the study area during 1992–2015. The white areas in the study area representing the non-vegetation type.</p> "> Figure 4
<p>Comparison of the NPP calculation results with the MODIS-17A3HGF data during 2000–2015, and with the GLO_PEM data during 2000–2010,respectively: (<b>a</b>) temporal changes in the estimated NPP obtained using the CASA model and the NPP from MODIS and GLO-PEM, (<b>b</b>) the <span class="html-italic">R</span><sup>2</sup> of the CASA-NPP fitted by the MODIS-NPP and GLO_PEM-NPP, (<b>c</b>) the RMSE t of the CASA-NPP fitted by the MODIS-NPP and GLO_PEM-NPP.</p> "> Figure 5
<p>Spatial distribution of the average annual NPP in the six provinces along the southeast coast of China in the unchanged vegetation types from 1982 to 2015.</p> "> Figure 6
<p>EEMD analysis of the average NPP changes during 1982–2015. IMF1-IMF 4 and Residue representing variations on different time scales and long-term trend, respectively.</p> "> Figure 7
<p>Spatial distribution of the variance contribution rates of different time scales to NPP changes, classified using 20% quantiles: (<b>a</b>) 3-year time scale, (<b>b</b>) 6-year time scale, (<b>c</b>) 15-year time scale, and (<b>d</b>) long-term trend.</p> "> Figure 8
<p>Spatial distribution of the non-linear trend of the vegetation NPP changes: (<b>a</b>) unchanged vegetation types, and (<b>b</b>) changed vegetation types. The white areas in (<b>a</b>,<b>b</b>) representing the changed and unchangedvegetation types, respectively.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. CASA Model
2.4. Verification Method of NPP Calculation
2.5. Ensemble Empirical Mode Decomposition (EEMD) Method
- Add a Gaussian white noise series to the original data . The amplitude of the Gaussian white noise series was set to 0.2 times standard deviation of the original data.
- Form the upper and lower envelope curves of the time series data by connecting local maxima and minima with cubic splines, respectively; then the time series data minus the mean value of the upper and lower envelope curves.
- Determine whether satisfies the given criterion (close enough to zero at anywhere). If yes, stop sifting. Otherwise, take as a new time series data and repeat step 2. In this way, obtain the first IMF: .
- Obtain the remainder by subtracting from . If still contains oscillatory component; repeat 2 and 3 but with being the new time series data.
- Repeat steps 1–4 for times ( was set to 100 in this study) with different Gaussian white noise series added each time. Obtain the ensemble means of the corresponding IMFs and trends of the decompositions as the final results.
- Generate 5000 white noise sequences with the same time length as the NPP sequences and conduct EEMD to extract their long-term trend.
- Divide the EEMD trend of the spatial location by the standard deviation of the corresponding NPP data.
- It was found that the propagation value of 1.64 times the standard deviation of the white noise sequence trend is the 90% confidence interval.
- Judge whether the trend value is outside the confidence interval. If it is outside the confidence interval, it is considered significant, otherwise it is considered insignificant.
2.6. Classification of Changed and Unchanged Vegetation Types by Considering Vegetation Type Dynamics
- Changed vegetation type caused by ecological engineering: conversion of non-vegetated land to vegetated land, farmland and urban land to forestland or grassland, and grassland to forestland.
- Changed vegetation type caused by urbanization: conversion of forestland, grassland, and farmland to urban and rural construction land.
- Changed vegetation type caused by agricultural reclamation: conversion of woodland, grassland and unused land into farmland.
- Changed vegetation type caused by vegetation destruction: conversion of vegetated land such as forestland, grassland, and farmland into non-vegetated land.
3. Results
3.1. Verification of NPP Calculation
3.2. Spatial Distribution of the Average Annual NPP
3.3. Changes in the Vegetation NPP during 1982–2015
3.3.1. NPP Changes of the Unchanged Vegetation Types
3.3.2. NPP Changes of the Changed Vegetation Types
3.4. Multi-Time Scale Variations of the Vegetation NPP
3.5. Spatial Distribution of the Nonlinear Trend of the Vegetation NPP
3.5.1. Nonlinear Trend of the Vegetation NPP in Unchanged Vegetation Types
3.5.2. Nonlinear Trend of the Vegetation NPP in Changed Vegetation Types
4. Discussion
4.1. Vegetation NPP Changes in Changed and Unchanged Vegetation Types during 1982–2015
4.2. Variations in the Vegetation NPP on Multiple Time Scales
4.3. Effects of Vegetation Types on Nonlinear Trends in the Vegetation NPP
4.4. Effects of Vegetation Type Dynamics on the Nonlinear Trend of the Vegetation NPP
4.5. Limitation and Future Direction
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Vegetation Types | Maximum Light Utilization (gC/MJ) |
---|---|
Deciduous Broad-Leaved Forest | 1.044 |
Evergreen Broad-Leaved Forest | 1.259 |
Deciduous Coniferous Forest | 1.103 |
Evergreen Coniferous Forest | 1.008 |
Mixed Forest | 1.116 |
Shrub | 0.888 |
Grassland | 0.768 |
Wetland | 0.608 |
Farmland | 0.608 |
Urban and Rural Land | 0.542 |
Other | 0 |
Type | Description |
---|---|
No significant change | the trend is not significant in any year |
Monotonic increase/decrease | the trend exhibits a monotonic increase/decrease with statistical significance in at least one year |
Initial increase then decrease/Initial decrease then increase | the trend initially increases and then decreases/decreases and then increases, including a local maximum/minimum, with statistical significance in at least one year |
Vegetation Types | ECF | EBF | DCF | DBF | MF | SH | GL | WL | FL | UGL |
---|---|---|---|---|---|---|---|---|---|---|
Area (km2) | 84,078.11 | 70,141.72 | 13,649.04 | 13,045.61 | 12,269.77 | 14,884.64 | 10,488.21 | 15,028.32 | 214,879.06 | 8649.18 |
Area Percentage (%) | 18.39 | 15.34 | 2.99 | 2.85 | 2.68 | 3.26 | 2.29 | 3.29 | 47.01 | 1.89 |
NPP (gC/m2) | 1065.58 | 1396.92 | 1183.89 | 1113.12 | 1190.52 | 915.57 | 791.75 | 610.88 | 537.77 | 390.19 |
Total NPP (1010gC) | 8959.18 | 9798.26 | 1615.90 | 1452.14 | 1460.74 | 1362.80 | 830.41 | 918.05 | 11,555.45 | 337.48 |
Percentage of Total NPP (%) | 23.40 | 25.59 | 4.22 | 3.79 | 3.81 | 3.56 | 2.17 | 2.40 | 30.18 | 0.88 |
Vegetation Types | ECF | EBF | DCF | DBF | MF | SH | GL | WL | FL | UGL |
---|---|---|---|---|---|---|---|---|---|---|
NPP Changes Per Unit Area (gC/m2/year) | 152.10 | 194.69 | 167.45 | 164.98 | 161.94 | 144.39 | 106.06 | 79.91 | 96.10 | −10.88 |
Total NPP Changes (1012gC) | 39,195.20 | 34,984.29 | 1154.78 | 1026.63 | 897.90 | 1169.08 | 431.69 | 668.29 | 162,674.19 | −29.82 |
Contribution to Total NPP Change (%) | 16.18 | 14.44 | 0.48 | 0.42 | 0.37 | 0.48 | 0.18 | 0.28 | 67.16 | 0.01 |
Vegetation Change Types | Ecological Engineering | Urbanization | Agricultural Reclamation | Vegetation Destruction | Summary |
---|---|---|---|---|---|
Area (km2) | 3546.92 | 22,393.72 | 30,870.27 | 961.88 | 57,772.79 |
Area Percentage (%) | 6.14 | 38.76 | 53.43 | 1.66 | 100 |
NPP Variation (gC/m2/year) | 49,889.18 | 26,809.73 | −121,900.9 | −13,350.09 | −58,552.08 |
Sum of Absolute Value of NPP Variation (gC/m2/year) | 50,326.49 | 65,590.67 | 186,869.09 | 14,359.83 | 317,146.09 |
Proportion of Sum of Absolute Value of NPP Change (%) | 15.87 | 20.68 | 58.92 | 4.53 | 100 |
Variable Types | Statistical Indicators | IMF1 | IMF2 | IMF3 | IMF4 | Residue |
---|---|---|---|---|---|---|
Period | Mean | 3 | 6 | 15 | 32 | - |
Std | 0.32 | 1.17 | 3.99 | 5.99 | - | |
Variance Contribution | Mean | 41.01 | 17.68 | 11.96 | 2.72 | 26.62 |
Std | 15.84 | 10.32 | 9.2 | 4.59 | 20.73 |
Vegetation Types | Not Significant | Monotonic Increase | Monotonic Decrease | Initial Increase then Decrease | Initial Decrease then Increase |
---|---|---|---|---|---|
Evergreen Coniferous Forest | 40.29 | 32.09 | 2.26 | 10.39 | 14.97 |
Evergreen Broad-Leaved Forests | 38.67 | 29.95 | 3.2 | 8.23 | 19.95 |
Deciduous Coniferous Forest | 37.47 | 29.47 | 3.58 | 8.63 | 20.84 |
Deciduous Broad-Leaved Forest | 34.36 | 33.48 | 2.86 | 9.25 | 20.04 |
Mixed Forest | 38.88 | 29.51 | 3.75 | 6.09 | 21.78 |
Shrub | 35.33 | 31.85 | 2.32 | 9.07 | 21.43 |
Grassland | 35.07 | 29.86 | 4.11 | 7.95 | 23.01 |
Wetland | 39.01 | 27.72 | 3.63 | 9.37 | 20.27 |
Farmland | 35.36 | 32.28 | 4.44 | 12.33 | 15.59 |
Urban and Rural Land | 35.88 | 12.62 | 18.94 | 9.97 | 22.59 |
Nonlinear Trend Classification | Ecological Engineering | Urbanization | Agricultural Reclamation | Vegetation Destruction |
---|---|---|---|---|
Not significant | 9.32 | 40.13 | 21.91 | 0 |
Monotonic increase | 41.53 | 16.38 | 8.76 | 0 |
Monotonic decrease | 0.85 | 9.4 | 35.44 | 37.5 |
Initial increase then decrease | 3.39 | 11.41 | 16.85 | 25 |
Initial decrease then increase | 44.92 | 22.68 | 17.04 | 37.5 |
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Xue, P.; Liu, H.; Zhang, M.; Gong, H.; Cao, L. Nonlinear Characteristics of NPP Based on Ensemble Empirical Mode Decomposition from 1982 to 2015—A Case Study of Six Coastal Provinces in Southeast China. Remote Sens. 2022, 14, 15. https://doi.org/10.3390/rs14010015
Xue P, Liu H, Zhang M, Gong H, Cao L. Nonlinear Characteristics of NPP Based on Ensemble Empirical Mode Decomposition from 1982 to 2015—A Case Study of Six Coastal Provinces in Southeast China. Remote Sensing. 2022; 14(1):15. https://doi.org/10.3390/rs14010015
Chicago/Turabian StyleXue, Peng, Huiyu Liu, Mingyang Zhang, Haibo Gong, and Li Cao. 2022. "Nonlinear Characteristics of NPP Based on Ensemble Empirical Mode Decomposition from 1982 to 2015—A Case Study of Six Coastal Provinces in Southeast China" Remote Sensing 14, no. 1: 15. https://doi.org/10.3390/rs14010015