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| DOI | 10.1016/j.rse.2023.113968 |
| Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR | |
| 发表日期 | 2024 |
| ISSN | 0034-4257 |
| EISSN | 1879-0704 |
| 起始页码 | 302 |
| 卷号 | 302 |
| 英文摘要 | Quantifying forest biomass stocks and their dynamics is important for implementing effective climate change mitigation measures by aiding local forest management, studying processes driving af-, re-, and deforestation, and improving the accuracy of carbon accounting. Owing to the 3 -dimensional nature of forest structure, remote sensing using airborne LiDAR can be used to perform these measurements of vegetation structure at large scale. Harnessing the full dimensionality of the data, we present deep learning systems predicting wood volume and above ground biomass (AGB) directly from the full LiDAR point cloud and compare results to state-of-the-art approaches operating on basic statistics of the point clouds. For this purpose, we devise different neural network architectures for point cloud regression and evaluate them on remote sensing data of areas for which AGB estimates have been obtained from field measurements in the Danish national forest inventory. Our adaptation of Minkowski convolutional neural networks for regression give the best results. The deep neural networks produce significantly more accurate wood volume, AGB, and carbon stock estimates compared to stateof-the-art approaches. In contrast to other methods, the proposed deep learning approach does not require a digital terrain model and is robust to artifacts along the boundaries of the evaluated areas, which we demonstrate for the case where trees protrude into the area from the outside. We expect this finding to have a strong impact on LiDAR-based analyses of biomass dynamics. |
| 英文关键词 | Climate change; Datasets; Neural networks; Forest biomass; LiDAR |
| 语种 | 英语 |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS类目 | Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001155264700001 |
| 来源期刊 | REMOTE SENSING OF ENVIRONMENT
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/288617 |
| 作者单位 | University of Copenhagen; University of Copenhagen; University of Munster |
| 推荐引用方式 GB/T 7714 | . Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR[J],2024,302. |
| APA | (2024).Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR.REMOTE SENSING OF ENVIRONMENT,302. |
| MLA | "Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR".REMOTE SENSING OF ENVIRONMENT 302(2024). |
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