Estimating Leaf Area Index of Maize Using Airborne Discrete-Return LiDAR Data
文献类型：期刊论文
| 作者 | Nie, Sheng1; Wang, Cheng1; Dong, Pinliang1; Xi, Xiaohuan1; Luo, Shezhou1; Zhou, Hangyu1 |
| 刊名 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
 |
| 出版日期 | 2016 |
| 卷号 | 9期号:7页码:3259-3266 |
| 关键词 | SURFACE-ENERGY-BALANCE CLEAR-SKY DAYS CARBON-DIOXIDE EXCHANGE SOUTH-CENTRAL NEBRASKA REMOTELY-SENSED DATA SENSITIVITY-ANALYSIS MAPPING EVAPOTRANSPIRATION REGIONAL EVAPOTRANSPIRATION WATER-USE FLUX PARAMETERIZATIONS |
| 通讯作者 | Wang, Cheng (wangcheng@radi.ac.cn) |
| 英文摘要 | The leaf area index (LAI) is an important vegetation biophysical parameter, which plays a critical role in gas-vegetation exchange processes. Several studies have recently been conducted to estimate vegetation LAI using airborne discrete-return Light Detection and Ranging (LiDAR) data. However, few studies have been carried out to estimate the LAI of low-statue vegetation, such as the maize. The objective of this research is to explore the potential of estimating LAI for maize using airborne discrete-return LiDAR data. The LAIs of maize were estimated by a method based on the Beer-Lambert law and a method based on the allometric relationship, respectively. In addition, a new height threshold method for separating ground returns from canopy returns was proposed to better estimate the LAI of maize. Moreover, the two LAI estimation methods were also evaluated using the leave-one-out cross-validation method. Results indicate that the new height threshold method performs better than the traditional height threshold method in separating grounds returns from LiDAR returns. The coefficient of variation of detrended return heights within a field was a good parameter to estimate the LAI of maize. In addition, results also indicate that the method based on the Beer-Lambert law (R2= 0.849, RMSE = 0.256) was more accurate than the method based on the allometric relationship (R2= 0.779, RMSE = 0.315) in low-LAI regions, while only the method based on the allometric relationship is suitable for estimating the LAI of maize in high-LAI regions. © 2008-2012 IEEE. |
| 学科主题 | Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology |
| 类目[WOS] | Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20162002389415 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39453]  |
| 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. University of Chinese Academy of Sciences, Beijing 2.100049, China 3. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 4.100094, China 5. Department of Geography, University of North Texas, Denton 6.TX 7.76203, United States 8. Hohai University, Nanjing 9.210098, China |
| 推荐引用方式 GB/T 7714 | Nie, Sheng,Wang, Cheng,Dong, Pinliang,et al. Estimating Leaf Area Index of Maize Using Airborne Discrete-Return LiDAR Data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(7):3259-3266. |
| APA | Nie, Sheng,Wang, Cheng,Dong, Pinliang,Xi, Xiaohuan,Luo, Shezhou,&Zhou, Hangyu.(2016).Estimating Leaf Area Index of Maize Using Airborne Discrete-Return LiDAR Data.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,9(7),3259-3266. |
| MLA | Nie, Sheng,et al."Estimating Leaf Area Index of Maize Using Airborne Discrete-Return LiDAR Data".IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9.7(2016):3259-3266. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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