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| DOI | 10.1080/15481603.2024.2345438 |
| The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China's evergreen coniferous forests | |
| 发表日期 | 2024 |
| ISSN | 1548-1603 |
| EISSN | 1943-7226 |
| 起始页码 | 61 |
| 结束页码 | 1 |
| 卷号 | 61期号:1 |
| 英文摘要 | Accurate assessments of forest biomass carbon are invaluable for managing forest resources, evaluating effects on ecological protection, and achieving goals related to climate change and sustainable development. Currently, the integration of optical and synthetic aperture radar (SAR) data has been extensively utilized in estimating forest aboveground biomass carbon (AGC), while it is limited by using single-phase remote sensing images. Time-series data, which capture the interannual dynamic growth and seasonal variations of photosynthetic phenology in forests, can sufficiently describe forest growth characteristics. However, there remains a gap in research focusing on utilizing satellite-based time-series data for AGC estimation, especially for SAR sensors. This study investigated the potential of satellite-based optical and SAR time-series data for estimating AGC. Here, we undertook nine quantitative experiments of AGC estimation from Landsat 8 and Sentinel-1 and tested several regression algorithms (including multiple linear regression (MLR), random forests (RF), artificial neural network (ANN), and extreme gradient boosting (XGBoost)) to explore the contributions of spatiotemporal features to AGC estimation. The results suggested that the XGBoost algorithm was suitable for AGC estimation with explanatory solid power and stable performance. The temporal features representing forest growth trends and periodic change characteristics (such as coefficients of continuous wavelet transform) were more valuable for AGC estimation than spatial features for both sensor types, accounting for around 40% similar to 50% of the variance compared to 17% similar to 25%. The combination of optical and SAR time-series data produced the best performance (R-2 = 0.814, RMSE = 18.789 Mg C/ha, rRMSE = 26.235%), compared with when utilizing optical or SAR time-series data alone (optical: R-2 of 0.657 and rRMSE of 35.317%; SAR: R-2 of 0.672 and rRMSE of 34.701%). Feature importance analysis also verified that temporal features of optical vegetation indices, SWIR 1/2 bands, and SAR backscatter from VV polarization were the most critical variables for AGC estimation. Furthermore, incorporating temporal features into the modeling is illustrated to be effective in reducing saturation effects within high-biomass forests. This study demonstrated the superiority of time-series data for forest carbon estimation. While the applicability of this methodology has only been investigated in evergreen coniferous forests, it may provide a viable approach needed to make full use of increasingly better and free satellite time-series data to estimate forest AGC with high accuracy, supporting policy making of forest management and sustainable development. |
| 英文关键词 | Aboveground biomass; forest carbon storage; remote sensing; time series; machine learning |
| 语种 | 英语 |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| WOS类目 | Geography, Physical ; Remote Sensing |
| WOS记录号 | WOS:001208743900001 |
| 来源期刊 | GISCIENCE & REMOTE SENSING
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/306436 |
| 作者单位 | University of Electronic Science & Technology of China |
| 推荐引用方式 GB/T 7714 | . The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China's evergreen coniferous forests[J],2024,61(1). |
| APA | (2024).The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China's evergreen coniferous forests.GISCIENCE & REMOTE SENSING,61(1). |
| MLA | "The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China's evergreen coniferous forests".GISCIENCE & REMOTE SENSING 61.1(2024). |
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