高光谱激光雷达：三维生物物理化学生态测量学

生态测量学为生态学、地理学、林学、野生生物学等提供基础的技术支撑,开发适应上述学科发展的新型生态测量技术日渐重要,尤其随着目前生物地球化学的快速推进,开发三维生物物理化学生态测量技术成为当前的研究热点,然而当前的主流遥感测量技术仍无法做到高效准确。高光谱激光雷达技术的发展为解决该问题提供了可能,这也通过基于连续测量的3种单木高光谱激光雷达数据实现反演光合有效辐射、树冠叶绿素含量与氮含量的三维分布及其昼夜变化趋势得到验证。实验结果预示随着高光谱激光雷达的成熟,三维生物物理化学生态测量学将得以建立,相应的三维生物物理化学生态测量技术将进一步推动地理学、生态学、生物地球化学乃至地球系统科学的发展。

A practical method utilizing multi-spectral LiDAR to aid points cloud matching in SLAM

Light Detection and Ranging(LiDAR)sensors are popular in Simultaneous Localization and Mapping(SLAM)owing to their capability of obtaining ranging information actively.Researchers have attempted to use the intensity information that accompanies each range measurement to enhance LiDAR SLAM positioning accuracy.However,before employing LiDAR intensities in SLAM,a calibration operation is usually carried out so that the intensity is independent of the incident angle and range.The range is determined from the laser beam transmitting time.Therefore,the key to using LiDAR intensities in SLAM is to obtain the incident angle between the laser beam and target surface.In a complex environment,it is difficult to obtain the incident angle robustly.This procedure also complicates the data processing in SLAM and as a result,further application of the LiDAR intensity in SLAM is hampered.Motivated by this problem,in the present study,we propose a Hyperspectral LiDAR(HSL)-based-intensity calibration-free method to aid point cloud matching in SLAM.HSL employed in this study can obtain an eight-channel range accompanied by corresponding intensity measurements.Owing to the design of the laser,the eight-channel range and intensity were collected with the same incident angle and range.According to the laser beam radiation model,the ratio values between two randomly selected channels’intensities at an identical target are independent of the range information and incident angle.To test the proposed method,the HSL was employed to scan a wall with different coloured papers pasted on it(white,red,yellow,pink,and green)at four distinct positions along a corridor(with an interval of 60 cm in between two consecutive positions).Then,a ratio value vector was constructed for each scan.The ratio value vectors between consecutive laser scans were employed to match the point cloud.A classic Iterative Closest Point(ICP)algorithm was employed to estimate the HSL motion using the range information from the matched point clouds.According to the test results,we found that pink and green papers were distinctive at 650,690,and 720 nm.A ratio value vector was constructed using 650-nm spectral information against the reference channel.Furthermore,compared with the classic ICP using range information only,the proposed method that matched ratio value vectors presented an improved performance in heading angle estimation.For the best case in the field test,the proposed method enhanced the heading angle estimation by 72%,and showed an average 25.5%improvement in a featureless spatial testing environment.The results of the primary test indicated that the proposed method has the potential to aid point cloud matching in typical SLAM of real scenarios.

Canopy Roughness:A New Phenotypic Trait to Estimate Aboveground Biomass from Unmanned Aerial System

Cost-effective phenotyping methods are urgently needed to advance crop genetics in order to meet the food,fuel,and fiber demands of the coming decades.Concretely,characterizing plot level traits in fields is of particular interest.Recent developments in highresolution imaging sensors for UAS(unmanned aerial systems)focused on collecting detailed phenotypic measurements are a potential solution.We introduce canopy roughness as a new plant plot-level trait.We tested its usability with soybean by optical data collected from UAS to estimate biomass.We validate canopy roughness on a panel of 108 soybean[Glycine max(L.)Merr.]recombinant inbred lines in a multienvironment trial during the R^(2) growth stage.A senseFly eBee UAS platform obtained aerial images with a senseFly S.O.D.A.compact digital camera.Using a structure from motion(SfM)technique,we reconstructed 3D point clouds of the soybean experiment.A novel pipeline for feature extraction was developed to compute canopy roughness from point clouds.We used regression analysis to correlate canopy roughness with field-measured aboveground biomass(AGB)with a leave-one-out cross-validation.Overall,our models achieved a coefficient of determination(R^(2))greater than 0.5 in all trials.Moreover,we found that canopy roughness has the ability to discern AGB variations among different genotypes.Our test trials demonstrate the potential of canopy roughness as a reliable trait for high-throughput phenotyping to estimate AGB.As such,canopy roughness provides practical information to breeders in order to select phenotypes on the basis of UAS data.