Using Knowledge Graphs to Analyze the Characteristics and Trends of Forest Carbon Storage Research at the Global Scale
Abstract
:1. Introduction
2. Materials and Methods
2.1. Literature Collection Strategy
2.2. Methods
2.2.1. Bibliometric Analysis
2.2.2. Mann–Kendall Mutation Test
2.2.3. Activity Index (AI) and Attractive Index (AAI)
3. Results
3.1. Macroscopic Characteristics of Published FCS Papers
3.1.1. Interannual Trends in the Number of Published FCS Papers
3.1.2. The Spatial Distribution in the Number of FCS Papers
3.2. The Research Status at Different Levels of the FCS Field
3.2.1. National and Institutional Level
3.2.2. Author Level
3.2.3. Journal Level
3.3. Focal Research Topics and Its Trends
3.3.1. The Most Frequently Used Keywords in FCS Research at Different Stages
3.3.2. The High-Frequency Keywords That Have Emerged and Disappeared in the FCS Research in Recent Years
4. Discussion
4.1. Spatiotemporal Characteristic Analysis of FCS Field
4.2. Research Status Analysis at Different Levels of the FCS Field
4.3. Research Topics and Its Trends Analysis of FCS Field
4.4. Limitations and Future Work
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Name | NoP | %/2057 | TC | AC | Name | NoP | %/2057 | TC | AC |
---|---|---|---|---|---|---|---|---|---|
USA | 331 | 16.09% | 18,653 | 56.35 | Micronesia | 1 | 0.05% | 9 | 10.00 |
China | 237 | 11.52% | 5403 | 22.80 | Papua New Guinea | 1 | 0.05% | 3 | 3.00 |
Canada | 104 | 5.06% | 3781 | 36.36 | Rwanda | 1 | 0.05% | 3 | 3.00 |
Germany | 98 | 4.76% | 5081 | 51.85 | Serbia | 1 | 0.05% | 2 | 2.00 |
India | 96 | 4.67% | 1492 | 15.54 | Solomon Islands | 1 | 0.05% | 2 | 2.00 |
UK | 82 | 3.99% | 5500 | 67.07 | Tajikistan | 1 | 0.05% | 1 | 1.00 |
Brazil | 80 | 3.89% | 3967 | 49.59 | Tunisia | 1 | 0.05% | 0 | 0.00 |
Australia | 64 | 3.11% | 3959 | 61.86 | The United Arab Emirates | 1 | 0.05% | 0 | 0.00 |
Italy | 51 | 2.48% | 1143 | 22.41 | Uganda | 1 | 0.05% | 0 | 0.00 |
Spain | 48 | 2.33% | 1715 | 35.73 | Zambia | 1 | 0.05% | 0 | 0.00 |
Name | NoP | %/3522 | TC | AC |
---|---|---|---|---|
Chinese Academy of Sciences | 89 | 2.53% | 2357 | 26.48 |
United States Forest Service | 82 | 2.33% | 4115 | 50.18 |
University of Chinese Academy of Sciences | 33 | 0.94% | 664 | 20.12 |
Oregon State University | 26 | 0.74% | 1505 | 57.88 |
University of Copenhagen | 25 | 0.71% | 1209 | 48.36 |
Peking University | 21 | 0.60% | 991 | 47.19 |
Chinese Academy of Forestry | 20 | 0.57% | 404 | 20.20 |
Beijing Forestry University | 17 | 0.48% | 186 | 10.94 |
Natural Resources Canada | 16 | 0.45% | 463 | 28.94 |
Northeast Forestry University | 16 | 0.45% | 148 | 9.25 |
No. | Stable Period (1993–2001) | Slow Growth Period (2002–2009) | Rapid Growth Period (2010–2022) | Whole Study Period (1993–2022) | ||||
---|---|---|---|---|---|---|---|---|
Keywords | F | Keywords | F | Keywords | F | Keywords | F | |
1 | CO2 | 6 | Biomass | 34 | Biomass | 358 | Biomass | 397 |
2 | Biomass | 5 | Sequestration | 26 | Dynamics | 238 | Sequestration | 262 |
3 | Budget | 4 | Dynamics | 20 | Sequestration | 234 | Dynamics | 258 |
4 | Vegetation | 4 | Storage | 19 | Storage | 206 | Climate-Change | 249 |
5 | Carbon | 3 | Nitrogen | 17 | Aboveground biomass | 162 | Storage | 226 |
6 | Management | 3 | Carbon storage | 16 | Climate-change | 149 | Carbon stock | 169 |
7 | Carbon storage | 2 | Carbon sequestration | 15 | Management | 122 | Aboveground biomass | 166 |
8 | Cycle | 2 | Net primary production | 14 | Carbon storage | 119 | Ecosystems | 148 |
9 | Deposition | 2 | Management | 13 | Productivity | 106 | Management | 138 |
10 | Dioxide | 2 | Soil | 13 | Carbon sequestration | 104 | Carbon storage | 137 |
11 | Ecosystems | 2 | Carbon stock | 12 | Biodiversity | 92 | Carbon sequestration | 119 |
12 | Landscape | 2 | Ecosystems | 12 | Nitrogen | 92 | Productivity | 113 |
13 | Patterns | 2 | Vegetation | 12 | Organic-carbon | 92 | Nitrogen | 109 |
14 | Russia | 2 | Climate change | 11 | Vegetation | 92 | Vegetation | 107 |
15 | Sequestration | 2 | Climate-change | 11 | Soil carbon | 90 | Soil carbon | 101 |
16 | Succession | 2 | Coarse woody debris | 11 | Climate | 88 | Organic-carbon | 99 |
17 | Tropical forests | 2 | Land-use change | 11 | Growth | 88 | Growth | 98 |
18 | Agriculture | 1 | Budget | 10 | Stocks | 85 | Climate | 97 |
19 | American boreal forests | 1 | Forest management | 10 | Deforestation | 82 | Biodiversity | 94 |
20 | Atmosphere | 1 | Model | 10 | Land-use | 81 | Deforestation | 89 |
No | The Evolution of Keywords in the Past Five Years | The Evolution of Keywords in the Past Year | ||||||
---|---|---|---|---|---|---|---|---|
Emerging Keywords | F | Disappearing Keywords | F | Emerging Keywords | F | Disappearing Keywords | F | |
1 | global patterns | 9 | nutrient dynamics | 10 | coal | 2 | budget | 29 |
2 | leaf-litter | 7 | clear-cut | 7 | environmental covariates | 2 | sink | 29 |
3 | community structure | 6 | kyoto protocol | 7 | environmental-conditions | 2 | allometry | 26 |
4 | redd plus | 6 | leaf-area | 7 | growth model | 2 | forest | 26 |
5 | altitude | 5 | forest inventory and analysis | 6 | important driver | 2 | forest inventory | 23 |
6 | invest model | 5 | land-use history | 6 | plus model | 2 | pine | 21 |
7 | litter quality | 5 | belgium | 5 | soc stock | 2 | root biomass | 21 |
8 | moisture | 5 | budget model | 5 | tropical dry deciduous forest | 2 | sinks | 20 |
9 | random forest | 5 | expansion factors | 5 | 3-pg | 1 | amazon | 17 |
10 | subtropical forests | 5 | organic layer | 5 | 3d modeling | 1 | flux | 16 |
11 | agb | 4 | ponderosa pine forests | 5 | active forest management | 1 | mineral soil | 16 |
12 | google earth engine | 4 | savanna | 5 | active restoration | 1 | spatial-patterns | 16 |
13 | bulk density | 4 | amazonian forest | 4 | agriculture intensification | 1 | tree allometry | 16 |
14 | carbon sequestration potential | 4 | bias | 4 | amazonian deforestation | 1 | dioxide | 15 |
15 | continuous cover forestry | 4 | etm+ | 4 | anthropogenic pressure | 1 | forest floor | 15 |
16 | extrapolation | 4 | field | 4 | climate models | 1 | reforestation | 15 |
17 | forest types | 4 | greenhouse-gas emissions | 4 | climatic factor | 1 | stabilization | 15 |
18 | fuel | 4 | landsat tm data | 4 | combination of als-uav and tls data | 1 | volume | 15 |
19 | mangrove forests | 4 | mixed forest | 4 | potential evaluation | 1 | primary productivity | 14 |
20 | spatial heterogeneity | 4 | modelling | 4 | predictive modeling | 1 | european forests | 14 |
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Li, J.; Wang, J.; He, S.; Liu, C.; Liu, L. Using Knowledge Graphs to Analyze the Characteristics and Trends of Forest Carbon Storage Research at the Global Scale. ISPRS Int. J. Geo-Inf. 2024, 13, 234. https://doi.org/10.3390/ijgi13070234
Li J, Wang J, He S, Liu C, Liu L. Using Knowledge Graphs to Analyze the Characteristics and Trends of Forest Carbon Storage Research at the Global Scale. ISPRS International Journal of Geo-Information. 2024; 13(7):234. https://doi.org/10.3390/ijgi13070234
Chicago/Turabian StyleLi, Jie, Jinliang Wang, Suling He, Chenli Liu, and Lanfang Liu. 2024. "Using Knowledge Graphs to Analyze the Characteristics and Trends of Forest Carbon Storage Research at the Global Scale" ISPRS International Journal of Geo-Information 13, no. 7: 234. https://doi.org/10.3390/ijgi13070234
APA StyleLi, J., Wang, J., He, S., Liu, C., & Liu, L. (2024). Using Knowledge Graphs to Analyze the Characteristics and Trends of Forest Carbon Storage Research at the Global Scale. ISPRS International Journal of Geo-Information, 13(7), 234. https://doi.org/10.3390/ijgi13070234