Estimating biomass in temperate grassland with high resolution canopy surface models from UAV-based RGB images and vegetation indices

编号 030020801

推送时间 20191014

研究领域 森林经理 

年份 2019 

类型 期刊 

语种 英语

标题 Estimating biomass in temperate grassland with high resolution canopy surface models from UAV-based RGB images and vegetation indices

来源期刊 JOURNAL OF APPLIED REMOTE SENSING

期 第208期

发表时间 20190901

关键词 unmanned aerial system;  remotely piloted aircraft systems;  forage;  plant height;  biomass;  grassland;  canopy surface?model;  spectral;  vegetation index; 

摘要 Monitoring grassland?biomass?throughout the growing season is of key importance in sustainable, site-specific management decisions. Precision agriculture applications can support these decisions. However, precision agriculture relies on timely and accurate information on plant parameters with a high spatial and temporal resolution. The use of structural and spectral features derived from unmanned aerial vehicle (UAV)-based image data from low-cost sensors is a promising nondestructive approach to assess plant traits such as above-ground?biomass?or plant height. Therefore, the main objectives were (1) to evaluate the potential of low-cost UAV-based canopy surface models to monitor sward height as an indicator of grassland?biomass, (2) to evaluate the potential of vegetation indices from low-cost UAV-based red-greenblue (RGB) digital image data, and (3) to compare the mentioned methods with established methods for?biomass?monitoring such as rising plate meters and spectroradiometer-based narrowband vegetation indices over the growing season in 2017, including three cuts. We compared the accuracy of each single UAV-based height feature and vegetation index using a combined multivariate approach to estimate fresh and dry?biomass. The heterogeneous sward structure with high spatiotemporal variability led to varying performance in?biomass?estimation depending on the growths (time between two cuts) and choice of predictor variable. The results showed that?biomass?prediction by height features provided moderate-to-good results (cross-validation R-2 = 0.57 to 0.73 for dry?biomass?and 0.43 to 0.79 for fresh?biomass), but reference measurements based on rising plate meters were more robust when estimating?biomass. The spectral features (RGB-based vegetation indices and spectroradiometer-based vegetation indices) yielded varying accuracy and suitability for?biomass?prediction. Despite the variability, our findings indicate a promising approach for grassland?biomass?monitoring. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

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