Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City,Northwest China |
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| Institution: | 1. School of Geodesy and Geomatics, Jiangsu Normal University, Xuzhou, Jiangsu 221116, China;2. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu 210008, China;3. Center for Spatial Information Science and Systems, George Mason University, VA 22030, USA;4. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;1. Geospatial Laboratory for Environmental Dynamics, University of Idaho, Moscow, ID 83844-1135, USA;2. McCall Outdoor Science School, College of Natural Resources, University of Idaho, McCall, ID 83638, USA;3. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA;4. International Institute for Earth System Science, Nanjing University, 210023, China |
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| Abstract: | Crop biomass is an important ecological indicator of growth, light use efficiency, and carbon stocks in agro-ecosystems. Light detection and ranging (LiDAR) or laser scanning has been widely used to estimate forest structural parameters and biomass. However, LiDAR is rarely used to estimate crop parameters because the short, dense canopies of crops limit the accuracy of the results. The objective of this study is to explore the potential of airborne LiDAR data in estimating biomass components of maize, namely aboveground biomass (AGB) and belowground biomass (BGB). Five biomass-related factors were measured during the entire growing season of maize. The field-measured canopy height and leaf area index (LAI) were identified as the factors that most directly affect biomass components through Pearson's correlation analysis and structural equation modeling (SEM). Field-based estimation models were proposed to estimate maize biomass components during the tasseling stage. Subsequently, the maize height and LAI over the entire study area were derived from LiDAR data and were used as input for the estimation models to map the spatial pattern of the biomass components. The results showed that the LiDAR-estimated biomass was comparable to the field-measured biomass, with root mean squared errors (RMSE) of 288.51 g/m2 (AGB), and 75.81 g/m2 (BGB). In conclusion, airborne LiDAR has great potential for estimating canopy height, LAI, and biomass components of maize during the peak growing season. |
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| Keywords: | Airborne LiDAR Maize biomass Leaf area index Canopy height |
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