Retrieval and validation of vertical LAI profile derived from airborne and spaceborne LiDAR data at a deciduous needleleaf forest site
文献类型：期刊论文
| 作者 | Wang, Yao; Fang, Hongliang; Zhang, Yinghui; Li, Sijia; Pang, Yong; Ma, Tian; Li, Yu |
| 刊名 | GISCIENCE & REMOTE SENSING
 |
| 出版日期 | 2023-12-31 |
| 卷号 | 60期号:1页码:2214987 |
| 关键词 | Leaf area index (LAI) vertical profile digital hemispherical photography (DHP) airborne laser scanning (ALS) Global Ecosystem Dynamics Investigation (GEDI) terrestrial laser scanning (TLS) |
| ISSN号 | 1943-7226 |
| DOI | 10.1080/15481603.2023.2214987 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Leaf area index (LAI) is defined as one half of the total green leaf area per unit ground surface area. Its vertical profile is critical for understanding the remote sensing radiative transfer processes. LAI profile has been derived from airborne and spaceborne LiDAR data, such as the Global Ecosystem Dynamics Investigation (GEDI) installed on the International Space Station. However, the capability of various algorithms for the LAI profile estimation with airborne LiDAR is not clearly evaluated, and the estimated LAI profiles, including the GEDI LAI products, are not been fully validated. This study conducted a quantitative retrieval and validation of the LAI profiles using terrestrial and airborne laser scanning (TLS and ALS) and spaceborne GEDI data over a deciduous needleleaf forest site in northern China. The vertical LAI profile was estimated in the field using an upward digital hemispherical photography (DHP) attached to a portable measurement system in 2020 and 2021. A suite of new LiDAR indices combining both LiDAR return number and return intensity was explored for the LAI profile estimation. All LAI profiles obtained from the DHP, TLS, ALS, and GEDI during the leaf-on season and leaf-off season were compared. The DHP shows a good agreement with the TLS LAI profiles (R-2 = 0.97). The LAI profile derived from the ALS data using the combined light penetration index (LPIRI) agrees well (R-2 >= 0.86) with the DHP, TLS, and GEDI estimates. In general, the LPIRI is advantageous for regional LAI profile mapping from ALS. The GEDI cumulative LAI corresponds well with the DHP during the leaf-on season (R-2 = 0.90, RMSE = 0.23), but underestimates during the leaf-off season (R-2 = 0.70, RMSE = 0.14, bias=-0.13). The underestimation is attributed to the higher canopy and ground reflectance ratio (rho(v)/rho(g)) assigned in the algorithm and the height discrepancy between the GEDI and field measurements. For the GEDI LAI profile product, further validation and improvement are necessary for other biome types and landscape conditions, especially during the leaf-off season. |
| 学科主题 | Physical Geography ; Remote Sensing |
| WOS关键词 | LEAF-AREA INDEX ; WAVE-FORM LIDAR ; PREDICTIVE MODELS ; FOLIAGE PROFILE ; INTENSITY DATA ; TERRESTRIAL ; HEIGHT ; SENSITIVITY ; VARIABLES ; METRICS |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/193751]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Chinese Academy of Sciences 2.Institute of Geographic Sciences & Natural Resources Research, CAS 3.University of Chinese Academy of Sciences, CAS 4.Chongqing Normal University 5.Chinese Academy of Forestry 6.Research Institute of Forest Resources Information Technique, CAF |
| 推荐引用方式 GB/T 7714 | Wang, Yao,Fang, Hongliang,Zhang, Yinghui,et al. Retrieval and validation of vertical LAI profile derived from airborne and spaceborne LiDAR data at a deciduous needleleaf forest site[J]. GISCIENCE & REMOTE SENSING,2023,60(1):2214987. |
| APA | Wang, Yao.,Fang, Hongliang.,Zhang, Yinghui.,Li, Sijia.,Pang, Yong.,...&Li, Yu.(2023).Retrieval and validation of vertical LAI profile derived from airborne and spaceborne LiDAR data at a deciduous needleleaf forest site.GISCIENCE & REMOTE SENSING,60(1),2214987. |
| MLA | Wang, Yao,et al."Retrieval and validation of vertical LAI profile derived from airborne and spaceborne LiDAR data at a deciduous needleleaf forest site".GISCIENCE & REMOTE SENSING 60.1(2023):2214987. |
入库方式： OAI收割
来源：地理科学与资源研究所
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