Grain for Green Project dominates greening in afforested areas rather than that in grass revegetation areas of the Loess Plateau, China-using Deep Crossing LSTM Age network
文献类型：期刊论文
| 作者 | Wang, Xueting1,7; He, Honglin5,6,7; Zhang, Mengyu6,7; Deng, Jianming1; Ren, Xiaoli5,6,7; Lv, Yan3; Liu, Weihua2; Lin, Zining4,6,7; Dong, Shiyu4,6,7 |
| 刊名 | ENVIRONMENTAL RESEARCH LETTERS
 |
| 出版日期 | 2025-08-01 |
| 卷号 | 20期号:8页码:84068 |
| 关键词 | leaf area index loess plateau deep learning grain for green project land cover change global changes |
| ISSN号 | 1748-9326 |
| DOI | 10.1088/1748-9326/adec02 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Vegetation restoration in the Loess Plateau (LP) of China is driven by atmospheric environmentalchanges (climate change, rising CO2, and nitrogen deposition), land cover change (LCC) fromecological restoration projects (ERPs), and change in forest age. However, the dominant factorsinfluencing vegetation restoration remain controversial. This study improved the Deep Crossingnetwork by integrating bidirectional long short-term memory (Bi-LSTM) with embedding,creating the Deep Crossing LSTM Age (DC-LSTM-Age) network. It incorporates land cover type,forest age, and atmospheric environmental factors to reconstruct the leaf area index (LAI). Weinvestigated the LAI increase (greening) driven by various factors and their dynamics in the Grainfor Green Project (GGP) regions of the LP from 2001 to 2021. Results showed that DC-LSTM-Agenetwork effectively simulated LAI values and its temporal dynamics in LCC regions, with superiorvalidation performance (R2=0.87) compared to the Deep Crossing LSTM network (R2=0.84)that excluded forest age and the Bi-LSTM network (R2=0.79) that excluded forest age and landcover type. The greening trend in afforested regions (GGP-Forest, 0.013 m2m(-2)yr(-1)) was muchlarger than in grass revegetation regions (GGP-Grass, 0.005 m(2)m(-2)yr(-1)). Dominant driversvaried by restoration strategy: in GGP-Forest, LCC was the primary driver (0.25 m(2)m(-2), 52.9%),with an increasing impact over time. In GGP-Grass, atmospheric environmental changesdominated (0.127 m(2)m(-2), 78.5%), led by climate change (0.064 m(2)m(-2), 39.4%), CO2rising(0.056 m(2)m(-2), 35%), and nitrogen deposition change (0.007 m(2)m(-2), 4.1%). The CO2fertilization effect showed signs of saturation. This research highlights the crucial role of ERPs inLAI increase. |
| URL标识 | 查看原文 |
| WOS关键词 | CLIMATE-CHANGE ; FOREST AGE ; VEGETATION ; RESTORATION ; TEMPERATURE ; DYNAMICS ; DRIVERS ; DATASET ; TRENDS ; CO2 |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001537103100001 |
| 出版者 | IOP Publishing Ltd |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215639]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | He, Honglin; Zhang, Mengyu |
| 作者单位 | 1.Lanzhou Univ, Coll Ecol, Lanzhou 730000, Gansu, Peoples R China; 2.Fujian Agr & Forestry Univ, Coll Juncao Sci & Ecol, Fuzhou 350002, Peoples R China 3.Shandong Univ Technol, Sch Civil Engn & Geomat, Zibo 255000, Peoples R China; 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 5.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China; 6.Natl Ecosyst Sci Data Ctr, Beijing 100101, Peoples R China; 7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Wang, Xueting,He, Honglin,Zhang, Mengyu,et al. Grain for Green Project dominates greening in afforested areas rather than that in grass revegetation areas of the Loess Plateau, China-using Deep Crossing LSTM Age network[J]. ENVIRONMENTAL RESEARCH LETTERS,2025,20(8):84068. |
| APA | Wang, Xueting.,He, Honglin.,Zhang, Mengyu.,Deng, Jianming.,Ren, Xiaoli.,...&Dong, Shiyu.(2025).Grain for Green Project dominates greening in afforested areas rather than that in grass revegetation areas of the Loess Plateau, China-using Deep Crossing LSTM Age network.ENVIRONMENTAL RESEARCH LETTERS,20(8),84068. |
| MLA | Wang, Xueting,et al."Grain for Green Project dominates greening in afforested areas rather than that in grass revegetation areas of the Loess Plateau, China-using Deep Crossing LSTM Age network".ENVIRONMENTAL RESEARCH LETTERS 20.8(2025):84068. |
入库方式： OAI收割
来源：地理科学与资源研究所
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