Spatio-Temporal Evolution and Prediction of Carbon Storage in Guilin Based on FLUS and InVEST Models
文献类型：期刊论文
| 作者 | He, Yunlin1,4; Ma, Jiangming1,4; Zhang, Changshun3; Yang, Hao1,4 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-03-01 |
| 卷号 | 15期号:5页码:1445 |
| 关键词 | carbon storage SDGs land use change FLUS-InVEST model spatial autocorrelation analysis Guilin |
| DOI | 10.3390/rs15051445 |
| 文献子类 | Article |
| 英文摘要 | In the context of sustainable development and dual-carbon construction, to quantify the carbon storage and its spatial-temporal distribution characteristics of Guilin City and predict the carbon storage of Guilin City in 2035 under different future scenarios, this study set four future scenarios based on SDGs and the sustainable development plan of Guilin City: natural development, economic priority, ecological priority, and sustainable development. At the same time, FLUS and InVEST models and GeoDa 1.20and ArcGIS software were used to establish a coupling model of land use change and ecosystem carbon storage to simulate and predict the distribution and change of ecosystem carbon storage based on land use change in the future. The results showed that: (1) From 2005 to 2020, forest land was the main type of land use in Guilin, and cropland and impervious continued to expand. In 2035, the forest land under four different future scenarios will be an important transformation type; (2) From 2005 to 2020, the carbon storage in the northwest of Guilin was relatively high, and the carbon loss area was larger than the carbon increase area. The carbon storage in the ecological priority scenario in 2035 is the highest, reaching 874.76 x 10(6) t. The aboveground carbon storage (ACG) is the main carbon pool in Guilin. Most of the regions with high carbon storage are located in the northwest and northeast of Guilin. No matter what scenario, the carbon storage in the main urban area is maintained at a low level; (3) In 2035, the distribution of carbon storage in Guilin has a strong spatial positive correlation, with more hot spots than cold spots. The high-value areas of carbon storage are concentrated in the northwest and east, whereas the low-value areas are concentrated in the urban area of Guilin. |
| WOS关键词 | LAND-USE SIMULATION ; DEVELOPMENT GOALS ; CHINA ; AREA ; LANDSCAPE ; SCENARIO ; CITY |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:000948064200001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200730]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 作者单位 | 1.Guangxi Normal Univ, Key Lab Ecol Rare & Endangered Species & Environm, Minist Educ, Guilin 541006, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Guangxi Normal Univ, Inst Sustainable Dev & Innovat, Guilin 541006, Peoples R China 4.Guangxi Key Lab Landscape Resources Conservat & Su, Guilin 541006, Peoples R China |
| 推荐引用方式 GB/T 7714 | He, Yunlin,Ma, Jiangming,Zhang, Changshun,et al. Spatio-Temporal Evolution and Prediction of Carbon Storage in Guilin Based on FLUS and InVEST Models[J]. REMOTE SENSING,2023,15(5):1445. |
| APA | He, Yunlin,Ma, Jiangming,Zhang, Changshun,&Yang, Hao.(2023).Spatio-Temporal Evolution and Prediction of Carbon Storage in Guilin Based on FLUS and InVEST Models.REMOTE SENSING,15(5),1445. |
| MLA | He, Yunlin,et al."Spatio-Temporal Evolution and Prediction of Carbon Storage in Guilin Based on FLUS and InVEST Models".REMOTE SENSING 15.5(2023):1445. |
入库方式： OAI收割
来源：地理科学与资源研究所
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