Carbon Dynamics in the Northeastern Qinghai–Tibetan Plateau from 1990 to 2030 Using Landsat Land Use/Cover Change Data
"> Figure 1
<p>Map of the study area (The elevation data is provided by International Scientific & Technical Data Mirror Site, Computer Network Information Center, Chinese Academy of Sciences (<a href="http://www.gscloud.cn" target="_blank">http://www.gscloud.cn</a>). The vector boundary data for counties are provided by the Data Center for Eco-environment Protection in the Qinghai Lake Basin (<a href="http://deep.qherc.org//" target="_blank">http://deep.qherc.org//</a>)).</p> "> Figure 2
<p>Rule matrix of land-use conversion under three scenario objectives in the Qinghai Lake Basin (outside frame: 0 grassland, 1 cropland, 2 construction land, 3 woodland, 4 wetland, 5 water, 6 unused land; inside frame, “1” indicates that land-use conversion is permitted; “0”means the opposite; Abbreviation: NG, Natural Growth scenario; CP, Cropland Protection scenario; EP, Ecological Protection scenario.).</p> "> Figure 3
<p>General modeling framework for the integrated Artificial Neural Networks–Cellular Automata and InVEST models (LULC, land use and land cover; NG, Natural Growth scenario; CP, Cropland Protection scenario; EP, ecological protection scenario; See <a href="#app1-remotesensing-12-00528" class="html-app">Supplementary Materials</a> for additional information on the model).</p> "> Figure 4
<p>The importance of factors for each type of land use in the Random Forest model (implemented on the Python platform). The numbers represent the contribution degree. The greater the number, the greater the contribution of the driving factor to the land-use change.</p> "> Figure 5
<p>Land-use maps in the Qinghai Lake Basin over 1990–2015. These maps were interpreted from Landsat TM 5 and 7 satellite images (<a href="http://earthexplorer.usgs.gov" target="_blank">http://earthexplorer.usgs.gov</a>).</p> "> Figure 6
<p>Land-use simulation map under NG, CP and EP scenarios in the QLB over 2020–2030. The map for 2020–2030 was simulated using ANN–CA model.</p> "> Figure 7
<p>Total carbon storage over 1990–2015 in the QLB (calculation based on Equations (11) and (12).</p> "> Figure 8
<p>Sequestered carbon content of different land uses over 2015–2030 under different scenarios.</p> "> Figure 9
<p>Spatiotemporal variations of carbon storage in the QLB:1990–2030.</p> ">
Abstract
:1. Introduction
- (1)
- Analysis of the trends of LUCC and carbon storage in the QLB from 1990 to 2015.
- (2)
- Simulation of land-use changes and carbon storage over 2020–2030 under different scenarios using an ANN–CA model.
2. Materials and Methods
2.1. Study Area and Data
2.2. Methods
2.2.1. Land Development Scenarios
2.2.2. CA-Based FLUS Model
2.2.3. Variable Importance Measures
2.2.4. Estimation of Carbon Storage based on the InVEST Model
3. Results
3.1. Importance of Driving Factors for each LUC Type in the Model
3.2. Simulation Model Validation
3.3. Land Use and Land Cover Change over 1990–2030
3.4. Temporal Patterns of Carbon Storage
3.4.1. Total Carbon storage and Potential Regional Carbon Losses
3.4.2. Spatial patters of Future Carbon Storage
4. Discussion
4.1. Analysis of Land Use and Cover Change
4.2. Measurement of Carbon Storage in the QLB
4.3. Generality of Models
4.4. Limitations and Future Research
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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LULC | Above Ground | Below Ground | Soil Organic | Dead Organic | Source |
---|---|---|---|---|---|
Grassland | 35.3 | 26.5 | 80.9 | 2.2 | [48,49,50] |
Cropland | 5.7 | 80.7 | 28.4 | 1 | [48,49,50] |
Construction Land | 12 | 0 | 71 | 1 | [48,49,50] |
Woodland | 42.4 | 120 | 236.9 | 67.5 | [48,49,50] |
Wetland | 35 | 90 | 208.5 | 25 | [48,49,50] |
Water | 10 | 8 | 0 | 0 | [48,49,50] |
Unused Land | 4 | 20 | 74.6 | 0 | [48,49,50] |
Grassland | Cropland | Construction Land | Woodland | Wetland | Water | Unused Land | Total | ||
---|---|---|---|---|---|---|---|---|---|
Grassland | 1,865,864 | 14,659 | 3271 | 42,263 | 48,525 | 2335 | 60,820 | 2,037,736 | |
91.57% | 0.72% | 0.16% | 2.08% | 2.38% | 0.11% | 2.99% | |||
Cropland | 15,028 | 37,324 | 1288 | 0 | 73 | 238 | 439 | 54,390 | |
27.63% | 68.62% | 2.37% | 0 | 0.13% | 0.44% | 0.81% | |||
Construction Land | 113 | 203 | 425 | 0 | 0 | 0 | 0 | 741 | |
15.28% | 27.39% | 57.34% | 0 | 0 | 0 | 0 | |||
Woodland | 30,572 | 0 | 0.50 | 13,710 | 782 | 49 | 365 | 45,479 | |
67.22% | 0 | 0 | 30.15% | 1.72% | 0.11% | 0.80% | |||
Wetland | 58,753 | 130 | 146 | 106 | 58,964 | 1063 | 623 | 119,785 | |
49.05% | 0.11% | 0.12% | 0.09% | 49.22% | 0.89% | 0.52% | |||
Water | 3292 | 0.50 | 0 | 0 | 708 | 433,238 | 512 | 437,750 | |
0.75% | 0 | 0 | 0 | 0.16% | 98.97% | 0.12% | |||
Unused Land | 79,321 | 0 | 0 | 236 | 378 | 4561 | 131,958 | 213,453 | |
37.16% | 0 | 0 | 0 | 0.18% | 2.14% | 60.42% | |||
Total | 2,050,812 | 52,317 | 5131 | 56,316 | 109,428 | 441,483 | 191,717 | 2,912,334 |
Land Type | 2015 | 2020 | 2030 | ||||
---|---|---|---|---|---|---|---|
NG | CP | EP | NG | CP | EP | ||
Grassland | 20,508 | 20,539 | 20,508 | 20,539 | 20,599 | 20,599 | 20,713 |
Cropland | 523 | 495 | 504 | 492 | 502 | 516 | 492 |
Construction land | 51 | 31 | 47 | 30 | 31 | 59 | 30 |
Woodland | 563 | 560 | 591 | 619 | 573 | 619 | 562 |
Wetland | 1094 | 1074 | 1244 | 1279 | 1032 | 1258 | 1343 |
Water | 4415 | 4391 | 4403 | 4415 | 4395 | 4411 | 4394 |
Unused land | 1917 | 1984 | 1775 | 1698 | 1940 | 1610 | 1538 |
Carbon Storage | Grassland | Cropland | Construction Land | Woodland | Wetland | Water | Unused Land | Total | |
---|---|---|---|---|---|---|---|---|---|
2015 | 297.16 | 6.01 | 0.43 | 26.29 | 39.23 | 7.95 | 18.91 | 395.97 | |
2020 | NG | 297.60 | 5.68 | 0.25 | 26.15 | 38.49 | 7.90 | 19.56 | 395.64 |
CP | 297.16 | 5.79 | 0.39 | 27.60 | 44.61 | 7.93 | 17.50 | 400.97 | |
EP | 297.60 | 5.65 | 0.25 | 28.92 | 45.85 | 7.95 | 16.75 | 402.96 | |
2030 | NG | 298.48 | 5.76 | 0.26 | 26.73 | 37.00 | 7.91 | 19.13 | 395.28 |
CP | 298.48 | 5.92 | 0.49 | 28.92 | 45.11 | 7.94 | 15.87 | 402.74 | |
EP | 300.13 | 5.65 | 0.25 | 26.25 | 48.14 | 7.91 | 15.17 | 403.50 |
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Li, J.; Gong, J.; Guldmann, J.-M.; Li, S.; Zhu, J. Carbon Dynamics in the Northeastern Qinghai–Tibetan Plateau from 1990 to 2030 Using Landsat Land Use/Cover Change Data. Remote Sens. 2020, 12, 528. https://doi.org/10.3390/rs12030528
Li J, Gong J, Guldmann J-M, Li S, Zhu J. Carbon Dynamics in the Northeastern Qinghai–Tibetan Plateau from 1990 to 2030 Using Landsat Land Use/Cover Change Data. Remote Sensing. 2020; 12(3):528. https://doi.org/10.3390/rs12030528
Chicago/Turabian StyleLi, Jingye, Jian Gong, Jean-Michel Guldmann, Shicheng Li, and Jie Zhu. 2020. "Carbon Dynamics in the Northeastern Qinghai–Tibetan Plateau from 1990 to 2030 Using Landsat Land Use/Cover Change Data" Remote Sensing 12, no. 3: 528. https://doi.org/10.3390/rs12030528