Estimation and verification of green tide biomass based on UAV remote sensing

Since 2007,the Yellow Sea green tide has broken out every summer,causing great harm to the environment and society.Although satellite remote sensing(RS)has been used in biomass research,there are several shortcomings,such as mixed pixels,atmospheric interference,and difficult field validation.The biomass of green tide has been lacking a high-precision estimation method.In this study,high-resolution unmanned aerial vehicle(UAV)RS was used to quantitatively map the biomass of green tides.By utilizing experimental data from previous studies,a robust relationship was established to link biomass to the red-green-blue floating algae index(RGB-FAI).Then,the lab-based model for green tide biomass from visible images taken by the UAV camera was developed and validated by field measurements.Re sults show that the accurate and cost-effective method is able to estimate the green tide biomass and its changes in given local waters of the near and far seas.The study provided an effective complement to the traditional satellite RS,as well as high-precision quantitative techniques for decision-making in disaster management.

Remote sensing image encryption algorithm based on novel hyperchaos and an elliptic curve cryptosystem

Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential.In this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption algorithms.An encryption algorithm tailored for handling the multi-band attributes of remote sensing images is proposed.The algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple images.Moreover,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing security.Experimental results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.

Seamless integration of above-and undercanopy unmanned aerial vehicle laser scanning for forest investigation

Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle(UAV)that flies above and under canopies in a single operation.The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight,thus grants the access to simultaneous high completeness,high efficiency,and low cost.Results:In the experiment,an approximately 0.5 ha forest was covered in ca.10 min from takeoff to landing.The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems,which leads to a 2–4 cm RMSE of the diameter at the breast height estimates,and a 4–7 cm RMSE of the stem curve estimates.Conclusions:Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective.Thus,it is a solution to combine the advantages of the terrestrial static,the mobile,and the above-canopy UAV observations,which is a promising step forward to achieve a fully autonomous in situ forest inventory.Future studies should be aimed to further improve the platform positioning,and to automatize the UAV operation.

Relationship between Vegetation Index and Forest Surface Fuel Load in UAV Multispectral Remote Sensing

[Objectives]To explore the relationship between vegetation index and forest surface fuel load.[Methods]UAV multispectral remote sensing was used to obtain large-scale forest images and obtain structural data of forest surface fuel load.This experimental area was located in Gaoming District,Foshan City,Guangdong Province.The average surface fuel load of the experimental area was as high as 39.33 t/ha,and the forest surface fuel load of Pinus elliottii was the highest.[Results]The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI)had a moderately strong correlation with the forest surface fuel load.The regression model of NDVI(X)and forest surface fuel load(Y)was established:Y=-5.9354X+8.4663,and the regression model of EVI(X)and forest surface fuel load(Y)was established:Y=-5.8485X+6.7271.The study also found that the linear relationship between NDVI and surface fuel load was more significant.[Conclusions]Both NDVI and EVI have moderately strong correlations with forest surface fuel load.NDVI is moderately or strongly correlated with the surface fuel load of Pinus massoniana forest,shrub grassland,broad-leaf forest and bamboo forest,while EVI is only strongly correlated with surface fuel load of broad-leaf forest and bamboo forest.It is expected that the relationship between other vegetation indices and forest surface fuel load can be obtained by the method in this study,so as to find a more universal vegetation index for calculating surface fuel load.

Monitoring coal fires in Datong coalfield using multi-source remote sensing data

Numerous coal fires burn underneath the Datong coalfield because of indiscriminate mining.Landsat TM/ETM,unmanned aerial vehicle（UAV）,and infrared thermal imager were employed to monitor underground coal fires in the Majiliang mining area.The thermal field distributions of this area in 2000,2002,2006,2007,and 2009 were obtained using Landsat TM/ETM.The changes in the distribution were then analyzed to approximate the locations of the coal fires.Through UAV imagery employed at a very high resolution（0.2 m）,the texture information,linear features,and brightness of the ground fissures in the coal fire area were determined.All these data were combined to build a knowledge model of determining fissures and were used to support underground coal fire detection.An infrared thermal imager was used to map the thermal field distribution of areas where coal fire is serious.Results were analyzed to identify the hot spot trend and the depth of the burning point.

Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data

At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.

基于UAV-RS虚拟仿真系统的教学模式探究

针对无人机和遥感测绘(unmanned air vehicle for remote sensing,UAV-RS)相关专业在实际教学中存在的问题,如设备资源和实验时间无法满足学生需求,野外作业中无人机的安全难以管控,天气和环境条件要求苛刻等,以无人机遥感测绘外业为仿真对象,设计开发了虚拟仿真系统,改革实训教学模式。仿真系统确立了无人机飞行操控、地面场景模拟和遥感图像获取3个模块,引导学生自主完成无人机遥感测绘外业过程。评估实验中经过仿真系统学习的学生(实验组)的项目完成率为:初级90%、中级80%、高级75%,均明显高于对照组。表明基于仿真系统的教学模式可提升UAV-RS的外业教学效果,提高复杂项目完成率,并能够激发学生自主学习的积极性。

Intelligent Deep Data Analytics Based Remote Sensing Scene Classification Model

Latest advancements in the integration of camera sensors paves a way for newUnmannedAerialVehicles(UAVs)applications such as analyzing geographical(spatial)variations of earth science in mitigating harmful environmental impacts and climate change.UAVs have achieved significant attention as a remote sensing environment,which captures high-resolution images from different scenes such as land,forest fire,flooding threats,road collision,landslides,and so on to enhance data analysis and decision making.Dynamic scene classification has attracted much attention in the examination of earth data captured by UAVs.This paper proposes a new multi-modal fusion based earth data classification(MMF-EDC)model.The MMF-EDC technique aims to identify the patterns that exist in the earth data and classifies them into appropriate class labels.The MMF-EDC technique involves a fusion of histogram of gradients(HOG),local binary patterns(LBP),and residual network(ResNet)models.This fusion process integrates many feature vectors and an entropy based fusion process is carried out to enhance the classification performance.In addition,the quantum artificial flora optimization(QAFO)algorithm is applied as a hyperparameter optimization technique.The AFO algorithm is inspired by the reproduction and the migration of flora helps to decide the optimal parameters of the ResNet model namely learning rate,number of hidden layers,and their number of neurons.Besides,Variational Autoencoder(VAE)based classification model is applied to assign appropriate class labels for a useful set of feature vectors.The proposedMMF-EDCmodel has been tested using UCM and WHU-RS datasets.The proposed MMFEDC model attains exhibits promising classification results on the applied remote sensing images with the accuracy of 0.989 and 0.994 on the test UCM and WHU-RS dataset respectively.

Application of MR Technology in Teaching:A Case Study of UAV Agriculture&Forestry Plant Protection Curriculum

In order to solve the problems of insufficient training equipment,relatively lack of curriculum resources and single teaching means in the teaching of UAV(unmanned aerial vehicle)applied technology major,this paper studies the application of MR(Mixed Reality)in UAV applied technology major teaching,with the teaching of UAV agriculture&forestry plant protection curriculum as the carrier.The study will solve the pain points in teaching,improve the teaching ability and teaching information level,and increase the talent training quality of UAV,agriculture&forestry plant protection and related majors.Furthermore,it will create a protective,interactive,remote and scalable teaching experience for stu-dents,which can improve the teaching effect and reduce the teaching cost.

基于通感融合的无人机预编码及飞行轨迹设计

无人机(UAVs)具有机动性强,低成本及易部署等特性,通过搭载通信及感知设备,支持通信与感知技术的高效资源共享,无人机可作为融合通信与传感技术的高性能空中平台。该文针对多输入多输出(MIMO)无人机使能的联合通信、感知场景,综合考虑无人机飞行能量、多天线传输及用户业务需求等限制条件,建模无人机通信、感知预编码及飞行轨迹联合优化问题为多目标优化问题,以实现通信用户最低速率最大化及目标最小发现概率最大化。由于通信用户最低速率最大化问题为非凸优化问题,难以直接求解,将原优化问题分解为通信预编码设计子问题及无人机轨迹设计子问题,并采用交替迭代法依次求解两个子问题直至算法收敛,其中,对于通信预编码设计子问题,提出一种基于迫零(ZF)算法的求解策略;对于无人机轨迹设计子问题,提出一种基于连续凸逼近(SCA)算法的求解策略。基于所得到的无人机最优轨迹,将无人机感知位置选择问题建模为加权距离和最小化问题,进而应用泛搜索算法优化确定目标感知位置,并设计基于ZF算法的通信感知预编码联合优化策略,以实现通信感知性能的联合优化。最后通过仿真验证了该文所提算法的有效性。

基于时间序列植被指数的小麦条锈病抗性等级鉴定方法

条锈病严重影响小麦产量,培育抗条锈病的小麦品种至关重要。针对传统育种中抗性鉴定手段单一、效率低的问题,该研究提出了一种通过小麦冠层植被指数的时间序列实现对条锈病不同抗性等级的高效鉴定方法。该方法利用无人机采集自然发病的育种群体小麦(共600个样本,516个基因型)冠层多时相的光谱图像,使用随机蛙跳算法和ReliefF算法筛选出6个条锈病病害严重度的敏感特征:归一化色素叶绿素指数(normalized pigment chlorophyll index,NPCI)、沃尔贝克指数(woebbecke index,WI)、叶绿素红边指数(chlorophyll index rededge,CIrededge)、绿大气抵抗植被指数(green atmospherically resistant index,GARI)、归一化差分植被指数(normalized difference vi,NDVI)、叶绿素绿指数(chlorophyll index green,CIgreen),这些敏感特征在试验群体中的时间序列符合条锈病的发病规律,验证了其作为条锈病发病严重度敏感特征的有效性;基于支持向量机(support vector machine,SVM)算法使用上述敏感特征建立条锈病病害严重度等级分类模型,在测试集的表现中,与使用未经过筛选的原始特征所建立的模型相比在精度、平均准确率、平均召回率和F1分数上分别仅下降6.2%、3.3%、2.7%、4.0%,证明了所筛选敏感特征的有效性;针对一般机器学习算法难以捕捉不同抗性等级样本之间较小的特征变化差异的问题,提出了一种从植被指数时间序列转化生成的二维图像中提取特征实现条锈病抗性等级分类的方法。将敏感特征中能够较好区分不同抗病等级的4个时间序列植被指数(NPCI、GARI、NDVI、WI),通过格拉姆角场方法生成格拉姆角和场图像,并制作成数据集,使用DenseNet121网络进行训练,以实现不同条锈病抗病等级的分类。建立的条锈病抗性等级分类模型中,由NPCI时间序列图像建立的分类模型测试效果最佳,其准确率为0.837,召回率为0.834,F1分数可达0.833,能够较好地实现对群体小麦不同品种(系)的条锈病抗性等级差异的区分,表明基于光谱植被指数时间序列的小麦条锈病抗性等级识别方法可以用于小麦抗病育种中抗性等级的鉴定,并可为其他作物的病害抗性等级鉴定提供一定的参考。

露天矿无人机遥感边坡地表形变提取方法研究

针对当前露天矿边坡监测过程中存在的设备留有监测死角、点位布控缺乏依据、地质隐患解译困难、无人机(Unmanned Aerial Vehicle,UAV)影像点云重构复杂度高等问题,提出了一种基于无人机遥感的边坡地表形变提取方法。首先,通过分析UAV激光点云与影像特点构建点云序列;其次,利用融合尺度不变特征变换(Scale Invariant Feature Transform,SIFT)与圆柱形邻域搜索的改进迭代最近点(Iterative Closest Point,ICP)算法,实现点云序列的精准高效配准,提高边坡形变提取精度;最终,借助数字高程模型(Digital Elevation Model,DEM)叠加分析与可视化,精准定位边坡重点形变区域,直观提取边坡形变位置和大小,并结合正射影像图像特征进行形变区域分析与解译。以南芬露天矿为工程应用实例,研究表明:边坡形变模型标准偏差为0.032 m,对比全球定位系统-实时动态差分(Global Positioning System-Real Time Kinematic,GPS-RTK)实测形变值,形变中误差为0.012 m,能够快速实现大尺度复杂边坡地表扫描与形变提取,从而为地质灾害隐患分析、盲区边坡形变监测与地面监测设备科学布控提供技术支撑。

铁路应急场景下无人机通信感知一体化无线网络资源智能分配算法

面向恶劣自然环境下地面基础设施受损的铁路场景,该文提出一种无人机(UAV)通信感知一体化无线接入网络架构,实现对列车运行环境的实时感知及应急信息回传。考虑到无人机的续航能力有限,通过建立列车制动距离模型与无人机能耗模型,在满足信息回传通信性能与列车环境感知需求的情况下,联合调整无人机飞行速度和通信发射功率以优化无人机整体能耗。通过分析发现,该优化问题符合马尔可夫决策过程(MDP),基于此,提出一种基于深度双Q网络(DDQN)的无人机通信感知一体化无线资源智能分配算法解决上述问题。最后,该文对所提算法的收敛性能、无人机环境感知距离和无人机能耗进行了仿真实验。仿真结果显示,所提算法具有良好的收敛性能,在满足铁路应急场景环境感知及信息回传需求的同时,能够最大化无人机通信作业时长。

农田环境下无人机图像并行拼接识别算法

为改善在农田环境下无人机图像计算速度和效率,该研究提出了一种农田环境下无人机图像并行拼接识别算法。利用倒二叉树并行拼接识别算法,通过提取图像拼接中的变换矩阵,实现拼接识别同时进行。根据边缘设备的CPU核心数和图像数量自动将图像拼接识别任务划分为多个子进程,并分配到不同核心上执行,以提高在农田环境下的计算效率。试验结果表明:相同试验环境和数据集条件下,倒二叉树并行拼接算法的拼接耗时相较于其他算法平均减少了60%~90%左右;在农田环境下,倒二叉树并行拼接识别相较于串行拼接识别的耗时减少了70%,图像识别的平均像素交并比提升了10.17个百分点,说明在农田环境下采用多线程倒二叉树并行算法可以更好地利用农田环境下边缘设备的计算资源,大幅提升无人机图像的拼接和识别的速度,为无人机的快速实时监测提供技术支撑。

基于无人机遥感与深度学习的芨芨草识别方法

芨芨草是干旱、半干旱草原广泛分布的一种多年生杂草,具有极强的环境适应力和种群竞争力,芨芨草的分布状况和变化趋势对于维持区域生态系统平衡和稳定具有重要意义。利用无人机高光谱遥感技术采集内蒙古格根塔拉荒漠草原遥感影像,获得厘米级空间分辨率和纳米级光谱分辨率的图像。采用子区间波段选择法选择代表研究区地物的特征波段,实现数据去除冗余和降维。在ENVI 5.6.1中利用Deep learning模块构建基于U-Net网络的深度学习模型对研究区的芨芨草进行识别,总体分类精度为95.67%,Kappa系数为0.83,均高于其他四种机器学习算法。研究结果表明,基于特征波段选择的深度学习算法能更有效地提取地物的光谱、纹理和形状信息,无人机高光谱低空遥感和深度学习算法的结合为荒漠草原芨芨草的准确、快速识别提供了新途径。

Launching an unmanned aerial vehicle remote sensing data carrier:concept,key components and prospects

Unmanned aerial vehicles(UAV)based remote sensing is an emerging and important data source.Recently,the use of UAVs for remote sensing applications has been rapidly growing owing to their greater availability and the miniaturization of sensors.UAVs are surpassing satellites and aircraft in remote sensing data supply for many local requirements.In comparison with satellite remote sensing data,most UAV remote sensing data is characterized by high resolution,small coverage area,and heterogeneous multi-sources.However,UAVs lack a unified space–time framework and standardized data process.This paper describes a UAV remote sensing data carrier that can be used as an e-commerce platform for data sharing among registered members and a mission planner for new data acquisition.To the best of our knowledge,the data carriers described herein,are the first of their kind.Through seamless docking with UAVs,the data carrier will form a national UAV network,capable of dynamically obtaining very-high-resolution UAV remote sensing images.In practice,a pilot retrieval system of UAV meta data has been developed to provide a catalogue of data product services.

基于无人机多光谱遥感的苹果树冠层SPAD反演

为探讨利用无人机多光谱遥感影像监测苹果树冠层叶绿素含量的可行性,以南疆矮砧密植苹果树为研究对象,利用无人机获取试验区多光谱影像,选取10个植被指数,分析所选植被指数与实测SPAD值的相关性,将与SPAD相关性较好的7个植被指数作为模型的输入变量,利用机器学习构建一元线性回归、偏最小二乘回归、支持向量机回归、随机森林回归和岭回归的苹果树冠层SPAD反演模型,通过精度检验确定最优模型.结果表明,7个植被指数NDVI,EVI,SAVI,OSAVI,GNDVI,RVI,GRVI与SPAD具有较好的相关性,相关系数为0.4~0.7,均在P小于0.01水平上极显著相关.采用随机森林回归建立的模型表现最优,其建模集R 2为0.728,RMSE为2.292,RPD为1.920;验证集R 2为0.702,RMSE为2.527,RPD为1.832.因此,基于无人机多光谱遥感的RF模型可以实现苹果树冠层SPAD的快速准确估算.

基于高分辨率卫星和无人机的广西滨海盐沼面积变化监测

滨海盐沼作为重要的海岸带生态系统,在海岸保护、生物多样性维持、固碳减污等方面发挥了重要的生态服务功能。及时准确地监测滨海盐沼分布情况和动态变化,对于科学地管理和保护本地滨海盐沼生态系统意义重大。本研究基于2019年和2021年多源国产高空间分辨率卫星数据,结合无人机自主性强、灵活机动、不受云遮挡影响的优势,对广西壮族自治区滨海盐沼开展遥感跟踪监测。研究结果表明,广西2021年滨海盐沼总面积为1 341.40 hm2,其中,北海市、防城港市和钦州市3个海滨城市的滨海盐沼面积分别为1 247.82 hm2、49.73 hm2和43.85 hm2。与2019年相比,广西2021年滨海盐沼总面积减少108.96 hm2,其中,北海市互花米草(Spartina alterniflora)面积减少107.05 hm2,钦州市短叶茳芏(Cyperus malaccensis)和芦苇(Phragmites australis)面积减少1.91 hm2,防城港市滨海盐沼面积不变。广西当地对入侵种互花米草的治理卓有成效,互花米草大范围减少,但局部区域的互花米草分布仍呈不断增长的趋势,仍需重视对互花米草的监测与防控工作。

面向6G的多维扩展通感一体化研究综述

面对第6代移动通信(6G)网络立体覆盖的互联感知需求和无线设备广泛接入造成的频谱稀缺问题,基于无人机(UAV)的机动性和智能反射面(IRS)重构无线传播环境特性的多维扩展通感一体化可实现立体网络空间中通信和感知功能的相互协同,有效提升频谱效率和硬件资源的利用率,满足6G万物智联的无线网络愿景。该文针对6G多维扩展通感一体化网络架构展开综述。首先,概述了6G网络愿景和通感一体化的理论基础,并讨论基于UAV和IRS多维扩展通感一体化的应用场景、发展趋势和性能指标。然后,探讨了超大规模多输入多输出天线、太赫兹、无线携能通信、人工智能、隐蔽通信和有源反射面等6G关键前沿技术在基于无人机和智能反射面多维扩展通感一体化网络中的潜在应用。最后,展望了未来6G多维扩展通感一体化的发展方向及关键技术挑战。

基于仿生技术的尾座式VTOL无人机起降稳定控制及轨迹优化

尾座式垂直起降(Vertical takeoff/landing,VTOL)无人机(Unmanned aerial vehicle,UAV)稳定性差,与传统起落架的适配性低。针对上述问题,提出了一种采用自由尾翼技术的新型起落架。该起落架采用串联多级结构,在降低机身高度的同时,保证了巡航状态下尾翼力臂的长度,并通过虚拟中心点力分布技术对动态着陆过程进行调控,简化了控制过程,有利于模式转换过程中的无缝轨迹优化。随后基于猫中心点单点数据集,利用神经网络对起落架控制进行训练,有效提高了起落架自适应起落速度和精度。最后,结合无人机不同模式的参数要求,进行多目标优化和相似性转换,有效提高了尾座式垂直起降无人机的着陆适应性和稳定性。