Estimation of Grassland Aboveground Biomass From UAV-Mounted Hyperspectral Image by Optimized Spectral Reconstruction
KANG Xiao-yan1,2, ZHANG Ai-wu1,2*, PANG Hai-yang1,2
1. Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
2. Engineering Research Center of Spatial Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China
摘要： 准确及时地预测牧草的地上生物量AGB(above ground biomass)是实现牧草生长监测和合理放牧的前提。无人机高光谱遥感可快速获取高空间、光谱和辐射分辨率的遥感影像，已被广泛应用于精准农业和高通量植物表型等领域。为探究无人机高光谱影像(unmanned aerial vehicle hyper-spectral image, UAV-HSI)对草原牧草AGB预测的适用性，获取了青海省典型草场样区的UAV-HSI、样方牧草AGB和相关辅助数据。然而，UAV-HSI具有较大的数据量级，不便于被广泛采集、存储和传输，也会显著影响数据处理的效率，严重制约其被有效利用。着眼于此，提出一种兼顾数据简化和光谱保真的牧草冠层光谱重建优化方法，以期在有效降低数据量的同时，保证牧草AGB的预测精度。首先，利用残差量化方法对UAV-HSI进行特征参量化处理，获得低数据量级的多阶二值立方体(Hi)及系数矩阵(βi)，并以此替代原始数据进行存储和传输；其次，利用Hi和βi进行光谱重建，得到初构光谱PRS(preliminarily reconstructed spectra)；最后，运用Savitzky-Golay滤波对PRS进行优化，即为OPRS(optimized PRS)。以样区牧草光谱为例，对OPRS的光谱保真性，即OPRS与原始光谱之间的相关系数、光谱角和光谱矢量距离进行分析，结果表明，在3种保真性指标上，OPRS均明显优于同阶的PRS。进而，分析了牧草AGB与光谱变量之间的相关性，结果表明，与原始光谱和PRS相比，OPRS各波段对牧草AGB的预测能力相对较高且最为稳定。而后，利用偏最小二乘法回归构建了牧草AGB的反演模型，结果表明，在原始光谱、1～4阶PRS和1～4阶OPRS等9种光谱中，4阶和3阶OPRS的AGB预测精度分别达到了最优和次优水平，二者的AGB预测相对分析误差RPD(ratio of performance to deviation)分别为2.31和2.23，比原始光谱模型的RPD分别高0.26和0.18。在降低1个数量级的情况下，OPRS取得了优于原始光谱的牧草AGB预测性能，说明OPRS兼具数据简化和牧草AGB准确预测性能，为UAV-HSI估算牧草AGB提供了一种新的有效解决方案。
关键词：无人机; 高光谱遥感; 光谱重建; 草地植被; 地上生物量
Abstract：The accurate and timely estimation of above-ground biomass (AGB) is crucial for grassland monitoring and rational grazing. The unmanned aerial vehicle (UAV)-based hyperspectral remote sensing can obtain images with high spatial, spectral and radiometric resolutions in a short time and has been widely used in many fields such as precision agricultural and high-throughput plant phenotype. To explore the applicability of UAV-based hyperspectral image (UAV-HSI) in grassland AGB prediction, we collected UAV-HSI, grassland AGB, and relevant auxiliary data in a grassland sample area of Qinghai Province. However, it is not only inconvenient to widely collect, store and transmit, but also inefficient in data processing for UAV-HSI because of large data volumes, which may restrict practices of UAV-HIS widely. For resolving the above problems, a spectral reconstruction and optimization method considering both data simplification and spectral fidelity was proposed, attempting to ensure the performance of grassland AGB prediction and effective reduction of data volumes. First, using the residual quantization, we obtained several binary cubes (Hi) and corresponding coefficient matrices (βi) with low volumes. Hi and βi can replace the original data for storage and transmission. Second, preliminarily reconstructed spectra (PRS) can be produced by Hi and βi. Third, through the Savitzky-Golay (SG) filter, optimized PRS (OPRS) can be achieved by enhancing the spectral fidelity. To demonstrate the effectiveness of OPRS, we carried out the spectral fidelity experiment. Taking a grassland canopy spectrum as an example, three fidelity indices, i. e., the spectral vector distance (SVD), spectral correlation coefficient (SCC), and spectral angle mapping (SAM), were analyzed. Results showed that, on the three fidelity indices, OPRS was superior to PRS. And then, the correlations between AGB and OPRS bands were discussed. Compared with raw spectra and PRS, OPRS achieved the relatively high and most stable potential in forage AGB prediction. Furthermore, the partial least squares regression (PLSR) was used to calibrate models of grassland AGB prediction. Results demonstrated that, among raw spectra, order -1 to -4 PRS, and order -1 to -4 OPRS, The prediction performances of order -4 and -3 OPRS reached the optimal and sub-optimal levels with RPD (the ratio of performance to deviation)=2.31 and 2.23, respectively. Their RPD values were 0.26 and 0.18 higher than that of the original spectra, respectively. Therefore, with a reduction of one order of magnitude, OPRS achieved a better performance than the original spectrum in the grassland AGB prediction. In other words, OPRS had advantages with both data simplification and accurate grassland AGB prediction. This study provides a new solution to estimate grassland AGB for UAV-HIS effectively.
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