TROS-Ⅰ型流动人卫激光测距仪光学系统设计

介绍了车载流动型人卫激光测距仪光学系统(包括激光发射系统、接收系统及微光电视监视系统)的设计思想和方法,着重叙述了光学系统的结构设计、关键部件几何尺寸的确定及重要元器件的选取。设计基于流动型仪器应满足的防振、体积小、调校方便、抗环境干扰能力强等特点。多年的实测证明,这种光学系统不仅能满足流动型人卫激光测距仪总体设计要求,而且经济实用。     

GPS测量误差与数据处理的质量控制

针对GPS测量结果的误差来源,从GPS卫星信号传播过程、观测条件和数据处理等方面讨论了GPS控制网数据处理的质量控制问题,介绍怎样进行基线解算和网平差的质量控制,并阐述了提高GPS网精度的若干方法。     

IKONOS影像生产1∶10 000地理信息产品的研究

简要介绍了IKONOS卫星遥感影像适用于地理信息产品生产的特点,然后通过试验生产,并与传统的航片相比较,详细分析了利用IKONOS影像作为数据源生产1∶10000基础信息产品在精度、生产流程、工作效率、生产周期、外业工作量和价格等方面的情况。     

基于D-InSAR技术的金沙江地区滑坡形变监测与分析

滑坡是发生在我国山区的主要地质灾害类型,金沙江地区由于地势较高、地形复杂、多云多雨的特点,给传统的滑坡监测增加了难度。合成孔径雷达差分干涉测量技术(Differential interferometry synthetic aperture radar,D-InSAR)已在滑坡地面沉降监测中得到了广泛应用。本文选取金沙江上游沿岸作为研究区域,基于2018年8月11日与9月28日的Sentinel-1A影像及SRTM1数据,利用GAMMA软件及D-InSAR技术监测到金沙江地区的地表形变,成功识别出金沙江右岸的一处滑坡灾害。研究结果显示,在此滑坡的坡顶部分出现了约2.5 cm的沉降,而在坡底部分由于崩塌物的累积,地面出现了约3 cm的抬升。从实验结果可以得出,InSAR技术是一种有效的滑坡变形监测手段,利用Sentinel-1A卫星的SAR数据对滑坡区域进行形变监测,可以得到较好的干涉结果。     

基于与Jason-2数据比对的Jason-3卫星高度计全球数据质量评估

Jason-3卫星高度计于2016年1月17日成功发射,2016年2月12日进入预定轨道,与Jason-2高度计同轨进入编队飞行阶段,并落后Jason-2高度计约1分20秒,两者相距约560 km。2016年9月1日,Jason-2高度计变换轨道,编队飞行阶段结束,两高度计进入平行轨道,以增加卫星高度计对地观测的空间覆盖。本研究主要开展了Jason-3高度计的数据质量的评估与检验,包括Jason-3高度计数据可用性和有效性的验证,以及Jason-3高度计和校正辐射计各参数的数据质量监测。重点开展了Jason-2与Jason-3高度计各项参数的综合比较,利用Jason-2与Jason-3高度计编队飞行阶段的数据精确评估了两高度计参数的一致性,并从全球数据角度分析了Jason-3高度计获取各参数的能力以及稳定性;通过与Jason-2互交叉点比较分析评估Jason-3高度计海面高度数据质量情况,验证Jason-3高度计数据精度。结果表明,Jason-3高度计的数据质量满足高度计测高的要求,具有与Jason-1、Jason-2、T/P等高度计相同或更高的测高精度以监测全球海平面变化,此外,Jason-3有效波高参数数据质量明显优于Jason-2高度计。     

两种测高重力异常反演海底地形方法比较

针对基于测高重力异常反演海底地形理论众多、选取标准无法确定的情况,利用中国南海海域内的测高重力异常和船测水深数据研究比较了重力地质法(GGM)和SmithSandwell (SAS)法两种精度高、计算速度相对较快的海底地形反演理论。其中,GGM方法的密度差异常数Δρ由向下延拓技术确定为2.15 g·cm-3,SAS方法采用移去-恢复技术得到反演波段内重力异常和水深数据。结果表明:测线分布条件一定时,水深多在-1 000 m左右或反演区域岛礁、海山等复杂海底地形较多时选取SAS方法,水深主要在-3 000 m以深的区域或海底地形复杂程度不高时选取GGM方法则能获取更好的效果,其效果最优处与船测水深在检核点处的差值最优平均值能达-0.61 m,标准差可达14.67 m。     

基于核函数NPSSB模型的HY-2A卫星雷达高度计海况偏差研究

利用机器学习的方法,对14个周期HY-2A卫星高度计数据:风速、有效波高和海面高度差值进行训练,探究海况偏差和风速、有效波高之间的关系,创建海况偏差核函数非参数模型(NPSSB),并与参数模型中具有代表性的BM3、BM4模型进行对比。研究表明:(1)核函数NPSSB模型能够很好的反映SSB与U、SWH之间的关系,SSB与U呈二次函数关系,SSB与SWH呈反比例函数关系;(2)核函数NPSSB模型对SSB的模拟能力与训练数据集相关,数据量越多,模拟能力越好;(3)核函数NPSSB模型与BM3、BM4模型都存在0^-0.03 m的差值,随着风速和有效波高的增加,差值的绝对值越大。     

遗传算法优化的BP神经网络卫星钟差预报

针对BP (Back Propagation)神经网络模型预测卫星钟差中权值和阈值的最优化问题, 提出了基于遗传算法优化的BP神经网络卫星钟差短期预报模型, 给出了遗传算法优化BP神经网络的基本思想、具体方法和实施步骤. 为验证该优化模型的有效性和可行性, 利用北斗卫星导航系统(BeiDou navigation satellite system, BDS)卫星钟差数据进行钟差预报精度分析, 并将其与灰色模型(GM(1,1))和BP神经网络模型预报的结果比较分析. 结果表明: 该模型在短期钟差预报中具有较好的精度, 优于GM(1,1)模型和BP神经网络模型.     

The potential of Pléiades images with high angle of incidence for reconstructing the coastal cliff face in Normandy (France)

To monitor chalk cliff face along the Normandy coast (NW France) which is prone to erosion, we tested the potential of cliff face 3D reconstruction using pairs of images with high angle of incidence at different dates from the agile Pléiades satellites. The verticality aspect of the cliff face brings difficulties in the 3D reconstruction process. Furthermore, the studied area is challenging mainly because the cliff face is north-oriented (shadow). Pléiades images were acquired over several days (multi-date stereoscopic method) with requested incidence angles until 40°. 3D reconstructions of the cliff face were compared using two software: ASP® and ERDAS IMAGINE®. Our results are twofold. Firstly, despite ASP® provides denser point clouds than ERDAS IMAGINE® (an average of 1.60 points/m² from 40° incidence angle stereoscopic pairs on the whole cliff face of Varengeville-sur-Mer against 0.77 points/m² respectively), ERDAS IMAGINE® provides more reliable point clouds than ASP® (precision assessment on the Varengeville-sur-Mer cliff face of 0.31 m ± 2.53 and 0.39 m ± 4.24 respectively), with a better spatial distribution over the cliff face and a better representation of the cliff face shape. Secondly, the quality of 3D reconstructions depends mostly on the amount of noise from raw images and on the shadow intensity on the cliff face (radiometric quality of images).     

Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R² = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R² = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km² due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R² = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R² = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km²–100 km²). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R² = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R² = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.