Inversion of corn leaf area index using terrestrial laser scanning data and Landsat8 image

被引：0

作者：

Zhang, Mingzheng ^{[1
,2
]}

Su, Wei ^{[1
]}

Wang, Ruiyan ^{[2
]}

机构：

[1] College of Information and Electrical Engineering, China Agricultural University, Beijing

[2] College of Resources and Environment, Shandong Agricultural University, Taian, 271018, Shandong

来源：

Zhongguo Jiguang/Chinese Journal of Lasers | 2015年 / 42卷 / 11期

关键词：

Landsat8; Leaf area index; Remote sensing; Terrestrial laser scanning; Voxel; Voxel-based canopy profiling;

D O I：

10.3788/CJL201542.1114002

中图分类号：

学科分类号：

摘要：

Optical spectral remote sensing images can be used to extract corn canopy structure information rapidly in a large area. However, it cannot provide vertical canopy structure information, which leads to underestimated leaf area index (LAI) result. Terrestrial laser scanning can provide high precision 3D structure information of corn canopy, but only in the limited sampling area. Therefore, these two technologies are combined to extract high precision canopy structure through canopy analysis by using terrestrial laser scanning data voxelization method. Reflectance of large area of corn canopy using Landsat8 optical images is obtained, and accurate corn canopy LAI results are got through regression analysis of canopy structure information of voxel-based canopy profiling. The results show that LAI has the strongest correlation with the normalized difference vegetation index (NDVI), the correlation coefficient R2=0.8086, the root mean square error (RMSE) is 0.1230, and the correlation between LAI and ratio vegetation index (RVI) is the worst, R2=0.7079, RMSE is 0.1520. Based on the validation analysis of the measured values, the average relative error of the three models is lower than 10%, and the credibility of the three models is relatively high. © 2015, Chinese Laser Press. All right reserved.

引用

页数：7

共 20 条

[1]

Luo S., Wang C., Zhang G., Et al., Forest leaf area index (LAI) inversion using airborne LiDAR data, Chinese Jounral of Geophysics, 56, 5, pp. 1467-1475, (2013)

[2]

Chen J.M., Black T.A., Adams R.S., Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand, Agricultural & Forest Meteorology, 56, 1, pp. 129-143, (1991)

[3]

Ahmed O.S., Franklin S.E., Wulder M.A., Et al., Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the random forest algorithm, ISPRS Journal of Photogrammetry & Remote Sensing, 101, pp. 89-101, (2015)

[4]

Tan K., Cheng X., TLS laser intensity correction based on polynomial model, Chinese J Lasers, 42, 3, (2015)

[5]

Hosoi F., Omasa K., Voxel-based 3-D modeling of individual trees for estimating leaf area density using high-resolution portable scanning lidar, IEEE Transactions on Geoscience and Remote Sensing, 44, 12, pp. 3610-3618, (2006)

[6]

Hosoi F., Omasa K., Factors contributing to accuracy in the estimation of the woody canopy leaf area density profile using 3D portable lidar imaging, Journal of Experimental Botany, 58, 12, pp. 3463-3473, (2007)

[7]

Hosoi F., Omasa K., Estimating vertical plant area density profile and growth parameters of a wheat canopy at different growth stages using three-dimensional portable lidar imaging, ISRPS Journal of Photogrammetry and Remote Sensing, 64, 2, pp. 151-158, (2009)

[8]

Hosoi F., Nakai Y., Omasa K., Estimation and error analysis of woody canopy leaf area density profiles using 3-D airborne and groundbased scanning lidar remote-sensing techniques, IEEE Transactions on Geoscience and Remote Sensing, 48, 5, pp. 2215-2223, (2010)

[9]

Zheng G., Moskal L.M., Spatial variability of terrestrial laser scanning based leaf area index, International Journal of Applied Earth Observation and Geoinformation, 19, 10, pp. 226-237, (2012)

[10]

Xu G.C., Pang Y., Li Z.Y., Et al., Classifying land cover based on calibrated full-waveform airborne light detection and ranging data, Chinese Optics Letters, 11, 8, (2013)

← 1 2 →