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DOI	10.1016/j.jag.2018.10.006
	Evaluation of 3D point cloud-based models for the prediction of grassland biomass
	Wijesingha J.; Moeckel T.; Hensgen F.; Wachendorf M.
发表日期	2019
ISSN	15698432
起始页码	352
结束页码	359
卷号	78
英文摘要	Farmers, as well as agronomists, are intrigued by efficient quantification of grassland biomass at field-scale. Canopy surface height (CSH) based non-destructive grassland biomass estimation over a larger area has important advantages compared to destructive methods. 3D point clouds generated from remote sensing (RS) data offer a systematic methodology to derive CSH and estimate grassland biomass. This study evaluated 3D point clouds derived from a terrestrial laser scanner (TLS) and an unmanned aerial vehicle (UAV)-borne structure from motion (SFM) approach for grassland biomass estimation over three grasslands with different composition and management practice in northern Hesse, Germany. TLS data, UAV-borne images and reference biomass data were collected two days before each harvest from the selected grasslands in 2017. Three levels of linear empirical models (i.e. sampling date-specific, grassland-specific and global) were developed to estimate grasslands fresh and dry biomass using CSH derived from TLS and SFM 3D point clouds. The aforementioned three level models resulted in an average nRMSE of 17.2%, 25.3%, and 28.7% respectively for the grassland dry biomass estimation based on CSH from TLS data. Similarly, models based on CSH from SFM data estimated dry biomass with somewhat higher average nRMSE of 19.5%, 27.1%, and, 36.2% respectively. In general, models with 3D point clouds from UAV-borne SFM was slightly outperformed by models with TLS point cloud data. This study also identified that the utilisation of UAV-borne SFM developed digital terrain model as a reference layer to derive CSH could enhance the performance of the models with SFM data. Furthermore, the performance of the biomass estimation models was affected by both species richness and sward heterogeneity of the grasslands. Overall, these results disclosed the potential of 3D point cloud derived from RS for estimating field-scale grassland biomass. © 2018 Elsevier B.V.
英文关键词	3D point cloud; Canopy surface height; Grassland biomass; Structure from motion; Terrestrial laser scanner; Unmanned aerial vehicle
语种	英语
scopus关键词	aerial survey; biomass; canopy; estimation method; grassland; height; prediction; scanner; three-dimensional modeling; unmanned vehicle
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156495
作者单位	Grassland Science and Renewable Plant Resources, Universität Kassel, Steinstraße 19, Witzenhausen, D-37213, Germany
推荐引用方式 GB/T 7714	Wijesingha J.,Moeckel T.,Hensgen F.,et al. Evaluation of 3D point cloud-based models for the prediction of grassland biomass[J],2019,78.
APA	Wijesingha J.,Moeckel T.,Hensgen F.,&Wachendorf M..(2019).Evaluation of 3D point cloud-based models for the prediction of grassland biomass.International Journal of Applied Earth Observation and Geoinformation,78.
MLA	Wijesingha J.,et al."Evaluation of 3D point cloud-based models for the prediction of grassland biomass".International Journal of Applied Earth Observation and Geoinformation 78(2019).