Generating pseudo large footprint waveforms from small footprint full-waveform airborne LiDAR data for the layered retrieval of LAI in orchards
文献类型：期刊论文
| 作者 | Li, Wang1; Niu, Zheng1; Li, Jing1; Chen, Hanyue1; Gao, Shuai1; Wu, Mingquan1; Li, Dong1 |
| 刊名 | Optics Express
 |
| 出版日期 | 2016 |
| 卷号 | 24期号:9页码:10142-10156 |
| 关键词 | LEAF-AREA INDEX ESTIMATING ABOVEGROUND BIOMASS LASER SCANNER DATA WAVE-FORM LIDAR AIRBORNE LIDAR FOREST BIOMASS PULSE DENSITY TEMPERATE FOREST CONIFER FORESTS LIGHT DETECTION |
| 英文摘要 | Leaf area index (LAI) is a key parameter for the study of biogeochemical cycles in ecosystems. Remote sensing techniques have been widely used to estimate LAIs in a wide range of vegetation types. However, limited by the sensor detection capability, considerable fewer studies investigated the layered estimation of LAIs in the vertical direction, which can significantly affect the precision evaluation of vegetation biophysical and biochemical processes. This study tried to generate a kind of pseudo large footprint waveform from the small footprint full-waveform airborne LiDAR data by an aggregation approach. The layered distribution of canopy heights and LAIs were successfully retrieved based on the large footprint waveform data in an agricultural landscape of orchards with typical multilayer vegetation covers. The Gaussian fitting was conducted on the normalized large footprint waveforms to identify the vertical positions for different vegetation layers. Then, the gap theory was applied to retrieve the layered LAIs. Statistically significant simple linear regression models were fitted between the LiDAR-retrieved and field-observed values for the canopy heights and LAIs in different layers. Satisfactory results were obtained with a root mean square error of 0.36 m for the overstorey canopy height (R2= 0.82), 0.29 m for the understory canopy height (R2= 0.76), 0.28 for overstorey LAI (R2= 0.75), 0.40 for understory LAI (R2= 0.64), and 0.38 for total LAI (R2= 0.69), respectively. To conclude, estimating the layered LAIs in the multi-layer agriculture orchards from the pseudo large footprint waveforms is feasible and the estimation errors are acceptable, which will provide some new ideas and methods for the quantitative remote sensing with vegetation. ©2016 Optical Society of America. |
| 学科主题 | Optics |
| 类目[WOS] | Optics |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20162002374099 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39201]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 2.100101, China 3. Satellite Environment Center, Ministry of Environmental Protection, Beijing 4.100094, China 5. College of Resource and Environmental Science, Fujian Agriculture and Forestry University, Fuzhou 6.350002, China 7. Airborne Remote Sensing Center, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 8.100101, China |
| 推荐引用方式 GB/T 7714 | Li, Wang,Niu, Zheng,Li, Jing,et al. Generating pseudo large footprint waveforms from small footprint full-waveform airborne LiDAR data for the layered retrieval of LAI in orchards[J]. Optics Express,2016,24(9):10142-10156. |
| APA | Li, Wang.,Niu, Zheng.,Li, Jing.,Chen, Hanyue.,Gao, Shuai.,...&Li, Dong.(2016).Generating pseudo large footprint waveforms from small footprint full-waveform airborne LiDAR data for the layered retrieval of LAI in orchards.Optics Express,24(9),10142-10156. |
| MLA | Li, Wang,et al."Generating pseudo large footprint waveforms from small footprint full-waveform airborne LiDAR data for the layered retrieval of LAI in orchards".Optics Express 24.9(2016):10142-10156. |
入库方式： OAI收割
来源：遥感与数字地球研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。