scholarly journals Peer Review #1 of "Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data (v0.1)"

2019 ◽  
Author(s):  
D Szpakowski
Keyword(s):  
Remote Sensing ◽  
Peer Review ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Above Ground Biomass ◽  
Sensing Data ◽  
Ground Biomass ◽  
Separation Strategy ◽  
Stem Leaf

scholarly journals Forest above Ground Biomass Inversion by Fusing GLAS with Optical Remote Sensing Data

2016 ◽  
Vol 5 (4) ◽  
pp. 45 ◽  
Author(s):  
Xiaohuan Xi ◽  
Tingting Han ◽  
Cheng Wang ◽  
Shezhou Luo ◽  
Shaobo Xia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Above Ground Biomass ◽  
Sensing Data ◽  
Ground Biomass

Reprint of: Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area

2012 ◽  
Vol 17 ◽  
pp. 102-110 ◽  
Author(s):  
Xin Tian ◽  
Zhongbo Su ◽  
Erxue Chen ◽  
Zengyuan Li ◽  
Christiaan van der Tol ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Arid Area ◽  
Above Ground Biomass ◽  
Sensing Data ◽  
Ground Biomass

scholarly journals Above Ground Biomass Assessment from Combined Optical and SAR Remote Sensing Data in Surat Thani Province, Thailand

2016 ◽  
Vol 08 (04) ◽  
pp. 506-516 ◽  
Author(s):  
Kilaparthi Kiran Kumar ◽  
Masahiko Nagai ◽  
Apichon Witayangkurn ◽  
Kunnaree Kritiyutanant ◽  
Shinichi Nakamura
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Above Ground Biomass ◽  
Sensing Data ◽  
Ground Biomass ◽  
Biomass Assessment ◽  
Sar Remote Sensing

scholarly journals Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7593 ◽  
Author(s):  
Yaohui Zhu ◽  
Chunjiang Zhao ◽  
Hao Yang ◽  
Guijun Yang ◽  
Liang Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Lidar Data ◽  
Optical Remote Sensing ◽  
Mean Square ◽  
Above Ground Biomass ◽  
Multispectral Data ◽  
Separation Strategy ◽  
Stem Leaf

Above-ground biomass (AGB) is an important indicator for effectively assessing crop growth and yield and, in addition, is an important ecological indicator for assessing the efficiency with which crops use light and store carbon in ecosystems. However, most existing methods using optical remote sensing to estimate AGB cannot observe structures below the maize canopy, which may lead to poor estimation accuracy. This paper proposes to use the stem-leaf separation strategy integrated with unmanned aerial vehicle LiDAR and multispectral image data to estimate the AGB in maize. First, the correlation matrix was used to screen optimal the LiDAR structural parameters (LSPs) and the spectral vegetation indices (SVIs). According to the screened indicators, the SVIs and the LSPs were subjected to multivariable linear regression (MLR) with the above-ground leaf biomass (AGLB) and above-ground stem biomass (AGSB), respectively. At the same time, all SVIs derived from multispectral data and all LSPs derived from LiDAR data were subjected to partial least squares regression (PLSR) with the AGLB and AGSB, respectively. Finally, the AGB was computed by adding the AGLB and the AGSB, and each was estimated by using the MLR and the PLSR methods, respectively. The results indicate a strong correlation between the estimated and field-observed AGB using the MLR method (R2 = 0.82, RMSE = 79.80 g/m2, NRMSE = 11.12%) and the PLSR method (R2 = 0.86, RMSE = 72.28 g/m2, NRMSE = 10.07%). The results indicate that PLSR more accurately estimates AGB than MLR, with R2 increasing by 0.04, root mean square error (RMSE) decreasing by 7.52 g/m2, and normalized root mean square error (NRMSE) decreasing by 1.05%. In addition, the AGB is more accurately estimated by combining LiDAR with multispectral data than LiDAR and multispectral data alone, with R2 increasing by 0.13 and 0.30, respectively, RMSE decreasing by 22.89 and 54.92 g/m2, respectively, and NRMSE decreasing by 4.46% and 7.65%, respectively. This study improves the prediction accuracy of AGB and provides a new guideline for monitoring based on the fusion of multispectral and LiDAR data.

Estimation of forest above-ground biomass based on HJ1B and ALOS/PALSAR remote sensing data

2016 ◽  
Vol 36 (13) ◽  
Author(s):  
王新云 WANG Xinyun ◽  
郭艺歌 GUO Yige ◽  
何杰 HE Jie
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Above Ground Biomass ◽  
Alos Palsar ◽  
Sensing Data ◽  
Ground Biomass
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