The spatial local accuracy of land cover datasets over the Qiangtang Plateau,High Asia

We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.

     

Experimental Study of a Command Governor Adaptive Depth Controller for an Unmanned Underwater Vehicle

Unmanned Underwater Vehicles (UUVs) are increasingly being used in advanced applications that require them to operate in tandem with human divers and around underwater infrastructure and other vehicles. These applications require precise control of the UUVs which is challenging due to the non-linear and time varying nature of the hydrodynamic forces, presence of external disturbances, uncertainties and unexpected changes that can occur within the UUV’s operating environment. Adaptive control has been identified as a promising solution to achieve desired control within such dynamic environments. Nevertheless, adaptive control in its basic form, such as Model Reference Adaptive Control (MRAC) has a trade-off between the adaptation rate and transient performance. Even though, higher adaptation rates produce better performance they can lead to instabilities and actuator fatigue due to high frequency oscillations in the control signal. Command Governor Adaptive Control (CGAC) is a possible solution to achieve better transient performance at low adaptation rates. In this study CGAC has been experimentally validated for depth control of a UUV, which is a unique challenge due to the unavailability of full state measurement and a greater thrust requirement. These in turn leads to additional noise from state estimation, time-delays from input noise filters, higher energy expenditure and susceptibility to saturation. Experimental results show that CGAC is more robust against noise and time-delays and has lower energy expenditure and thruster saturation. In addition, CGAC offers better tracking, disturbance rejection and tolerance to partial thruster failure compared to the MRAC.     

高分六号遥感卫星新增波段下的树种分类精度分析

高分六号卫星具有覆盖广、多种分辨率、波段多的优势,能为遥感解译提供更丰富的信息。为探究高分六号卫星新增波段在森林树种识别上的应用,本文以覆盖根河市阿龙山林业局的一期高分六号宽幅影像为数据源,基于特征优化空间算法（Feature Space Optimization,FSO）和最大似然分类法,分别利用高分六号的前4个波段和所有波段（8波段）的光谱、纹理等特征进行了森林树种分类,并逐一添加新增波段特征确定了各波段的贡献率排名。结果表明：在加入了优选出的均匀性纹理、均值纹理和角二阶矩纹理3种纹理特征后,前4波段和8波段的分类精度比只基于光谱特征时的精度分别高出13.23%和24.63%;利用8波段信息比只利用前4波段在基于光谱特征上的精度高11.88%,在基于光谱+纹理特征上则高23.24%;基于8波段光谱+纹理特征的树种分类精度最高,达到68.74%,新增4波段的贡献率排名为B6>B5>B8>B7,说明新增红边波段对于本次树种分类试验的贡献率最高,能为北方树种识别提供有效帮助。     

基于多源遥感数据的区域生态系统服务价值年际动态监测 ——以中原城市群为例

通过对多源遥感数据在生态系统服务价值（ESV）遥感模型中的尺度效应分析,选择满足最佳空间分辨率和长时间序列的遥感数据,对中原城市群区域2001~2013年的ESV实现了逐年逐像元水平的动态监测。结果表明：该区应用于遥感模型输入数据的最适空间分辨率为30~1 000 m,相对于30 m尺度,其他尺度估算结果的相对偏差均小于0.4%;结合年际动态监测的需求,选择了MODIS数据产品（空间分辨率500 m,时间尺度1 a）作为遥感模型的最佳数据源;研究区ESV总值在研究期内整体上呈显著增长趋势,增速约为8.6亿元/a,但在持续增长过程中经历了3次波动,且表现得越来越剧烈;在空间上,研究区ESV多年均值呈现出明显的不均衡性,表现为从西南向东部递减的趋势。研究表明此方法简单易行,初步实现了区域ESV年际动态监测遥感模型的准业务化运行。     

博尔塔拉蒙古自治州植被覆盖度估算

针对传统植被覆盖度估算方法只能估算高密度覆盖的绿色光合植被的问题,该文提出了一种可进行绿色光合植被、非绿色光合植被和裸地覆盖度估算的方法,选取博尔塔拉蒙古自治州2010年和2016年的LandsatTM/OLI影像,通过构建NDVI-DFI特征空间提取端元特征值,运用像元三分模型估算,并与像元二分模型估算结果和实地采样估算结果对比。结果表明:像元三分模型估算与实际情况相符,且精度较好。2016年博州地区植被覆盖度较2010年增长显著,生态环境得到明显改善。像元三分模型能够较好地估算光合/非光合植被覆盖度,提高遥感获取植被信息的能力,为科学评估生态环境质量提供参考。     

遥感监测下的渝东南小城镇镇区拓展特征探究

DUAN Songjiang     

苏北废黄河三角洲海岸时空演变遥感分析

基于遥感与地理信息系统技术,使用苏北废黄河三角洲海岸地区1978,1987和2000年的Landsat卫星遥感数据,选取岸线指标与波段,提取岸线信息,生成1978-1987年和1987-2000年海岸土地增减时空变化分布图,建立岸线变化距离及增速衡量指标,定量分析了废黄河三角洲海岸面积变化特征,以及灌河口-中山河口、中山河口-扁担港口和扁担港口-双洋河口以南岸段的时空变化格局与分布特征.研究结果表明,废黄河三角洲海岸的自然侵蚀速率呈减小趋势,但侵蚀作用仍在继续.侵蚀强度以废黄河口地区为中心,向南北两侧逐渐减弱.人工保滩护岸措施在一定程度上影响着自然侵蚀格局.其内部各岸段分别呈现缓蚀、侵蚀和基本侵淤平衡的变化格局.     

GPS车载导航的交互式多模型算法

GPS动态定位数据的处理广泛采用卡尔曼滤波技术,而应用卡尔曼滤波要求运动模型准确可靠,但由于载体真实运动的复杂多变,任何单一模型都难以全面描述,致使单一模型的滤波都容易出现模型误差。针对这一问题,将机动目标跟踪领域广泛应用的交互式多模型算法引入到车载导航中。通过分析车辆的运动特点,选取匀速直线模型和当前统计模型进行交互;同时考虑到车载终端计算能力有限,将状态变量在各方向解耦。仿真显示,在机动时改进的算法和单一模型的自适应算法基本相当,但在非机动时改进的算法明显占优。     

几种常见影像融合方法的比较

简述了多源遥感影像融合过程,并利用IKONOS-2的全色影像和多光谱影像,对常用的几种影像融合方法进行了试验与比较,同时对实验结果进行分析和讨论,结果表明小波变换融合算法总体优于其他几种融合方法。     

The application of hyperspectral remote sensing to coast environment investigation

Requirements for monitoring the coastal zone environment are first summarized. Then the application of hyperspectral remote sensing to coast environment investigation is introduced, such as the classification of coast beaches and bottom matter, target recognition, mine detection, oil spill identification and ocean color remote sensing. Finally, what is needed to follow on in application of hyperspectral remote sensing to coast environment is recommended.