利用ICESAT/GLAS激光测高数据评估SRTM数据精度

提出利用ICESAT卫星搭载的GLAS激光雷达数据进行SRTM数据的高程精度评价,选择青藏高原东北缘作为研究区,综合利用GIS空间分析方法,计算在不同高程分带和坡度分带上SRTM数据高程精度,并分析其变化规律。结果表明,ICESAT/GLAS与SRTM高程数据间存在明显的相关关系。该地区SRTM数据高程总体精度为5.3m,随着海拔升高,坡度的增大,高程精度呈降低的趋势。在祁连山高山区（4600m-4700m）误差达到12.3m,在40°-50°的坡度带上误差为18.9m,略高于SRTM的标称精度。     

基于高斯过程回归的SRTM数据空洞填补方法

针对现有插值方法对复杂地表模拟能力不足的问题,发展了一种基于非平稳(神经网络)核的高斯过程回归(GPR)方法,并将其应用于SRTM DEM空缺数据填补。以山区SRTM DEM的模拟数据空洞为研究对象,将GPR模拟结果与传统插值方法(TIN、SPLINE和IDW)比较表明:GPR填补精度高于传统插值方法,且空缺区域模拟曲面较好的保持了地形特征。     

念青唐古拉山冰川高程变化的ICESat监测

针对青藏高原冰川高程变化研究较少的问题,该文提出一种大范围区域的冰川高程变化监测方法。基于ICESat激光高度计数据,联合利用SRTM DEM数据,计算念青唐古拉山脉冰川的高程变化,进而反演冰川的冰量变化。结果显示,念青唐古拉山冰川高程在2003—2009年间平均减薄速率为(0.53±0.47)(m·a~(-1)),估算得到冰量年均减少(0.32±0.28)km~3,总体呈逐年减少趋势,证明冰川一直处于消融状态。拉萨和当雄气象站的资料表明,冰川消融主要是由于当地气温升高。     

利用ICESat数据确定北冰洋海冰出水高度——以2005—2006年为例

提出了在结合高程特征变化的基础上利用ICESat海冰观测数据波形参数确定北冰洋海冰出水高度的方法,并与目前常用的最低点法进行比较。结果表明,最低点法计算的海冰出水高度不仅在空间分布上存在系统性误差,并且容易受到粗差影响,而本文的方法则具有较高的可靠性。本文在最低点法中加入高程标准差限制,并与本文提出的方法结合,计算并分析了2005年至2006年6个ICESat任务期的北冰洋海冰出水高度的季节变化。结果表明,2005年夏天的异常气候对于北冰洋海冰具有深远的影响,不仅使当年秋季的海冰出水高度大大减小,而且海冰的组成比例也发生了巨大变化。     

基于地形因子信息量的数字高程模型分辨率的选择——以黄土高原的研究为例

在黄土高原选取代表不同黄土地貌类型的神木、绥德、延川、富县、宜君共5个研究样区,以样区1∶1万地形图所建立的DEM为研究对象,分析不同水平分辨率DEM所提取的坡度和坡向的信息量变化规律,提出了基于互信息的精度指标。在每一样区随机选取20个试验样区,得到上述指标与水平分辨率的函数关系,可以根据其反函数由已知指标求得所需要的水平分辨率数值,并且该函数关系在每一样区的16个随机检验样区通过了检验。实例证明,该函数关系可以在保证精度的条件下节约成本,并可以减小数据量。     

基于Hyperion数据的粘土矿物权重信息提取研究——以江西省赣州地区为例

以Hyperion数据为信息源,以江西省赣州地区为研究区,进行了粘土矿权重信息提取方法研究。通过对光谱角制图(SAM)和匹配滤波(MF)两种方法的对比,提出了将二者相结合确定各类粘土矿物权重信息的综合方法。实验结果表明,该方法能结合二者的优点,弥补相互之不足,从图像背景中准确提取出了各类粘土矿物的权重信息,部分权重大的粘土分布位置及其类型与采样点的位置及样品分析结果相吻合,证明了该方法的可行性。     

基于遥感指数的生态变化评估——以常宁市为例

利用遥感技术进行区域生态变化评估,能够得到周期长、现时性强的结果。以常宁市为例,采用遥感生态指数方法来监测水土流失区的生态变化,选取1990年,2002年及2009年的Landsat TM遥感图像,分别提取绿度、湿度、热度和干度4个生态因子作为评估指标,结合主成分分析方法,定量、客观地评估研究区域20 a间生态变化。结果表明,遥感生态指数方法能够很好地评价水土流失区生态修复的效果,其中,遥感生态指数值上升了22.39%,生态为优良等级所占的面积比例先从1990年的13.086%下降到2002年的4.006%,再上升到了2009年16.699%,说明常宁市经过20 a的水土流失治理,该区域的生态质量先急剧下降再有了较大的改善。通过对常宁市调查分析,以植树造林和施工预防为主的措施对生态质量的改善有较好的效果。     

基于SWBD修复的SRTM DEM的1∶250000水系自动更新——以浙江省瓯江流域为例

以浙江省瓯江流域为例,基于SWBD修复的SRTM DEM数据,采用Arc Hydro Tools水文分析工具自动提取瓯江水系,并分地貌、分河流等级地定量评价水系数据精度,开展1∶250 000水系自动更新的可行性研究。结果表明：①SWBD修复的SRTM DEM的空白区域面积为54.78 km2,有效地弥补了SRTM DEM的数据缺失,进而提高了水系提取的准确度和精度;②与1∶250 000水系数据相比,基于SWBD修复后的SRTM DEM,在小起伏山、中起伏低山、低海拔丘陵上提取的水系数据精度高于其他地貌,而干流、一级支流、二级支流的精度又高于三级支流;③以资源三号卫星ZY-3遥感影像为参照,从水系上采集同名点反复比较点位精度后发现,利用SRTM DEM提取的水系符合制图规范和测绘内业规范（限差1 mm）,可以满足1∶250 000水系自动更新的要求。     

基于国产卫星数据的矿山遥感监测一体化解决方案——以西藏自治区为例

随着高分辨率国产遥感卫星数据的推广应用,矿山遥感监测必将成为该数据的重要应用领域之一。结合西藏自治区矿山遥感监测工作的特点,以资源一号02C(ZY-1 02C)及高分一号(GF-1)卫星影像为数据源,在ArcGIS环境下,提出并实现了从国产卫星遥感数据管理、增强与校正、信息提取、统计分析以及成果图制作等一体化解决方案。该研究成果有助于推进国产卫星遥感数据在矿山遥感监测领域的应用广度和深度,为大规模开展多期次动态矿山遥感监测工作提供技术支持和应用范例。     

MODIS数据支持下的土壤湿度模型构建——以河南商丘地区为例

土壤湿度是农作物在生长过程中主要供水因子,实际测量土壤湿度较为复杂,卫星遥感成为反演和监测土壤湿度的重要手段。本文利用商丘地区2012和2013年MODIS数据,采用滤波方法减小云、气溶胶影响下的MODIS产品噪声,利用农田浅层土壤湿度指数（CSMI）与实测土壤相对湿度值进行了相关性分析,构建了适用于商丘地区的土壤湿度模型,并利用2013年的土壤实测数据对模型反演出来的土壤湿度进行了验证。结果表明,CSMI指数能够有效反演该地区0~50 cm深的土壤湿度值（通过了0.01的显著性检验）。     

大光斑波形数据在土地覆盖分类的适用性分析

针对GLAS地学激光测高系统是冰、云和陆地高程卫星(ICESat)的唯一监测工具,能够记录地表光斑内的地物信息,是否能应用于黄土高原土地覆盖分类的问题进行了研究。利用粒子群和最小二乘法相结合的方法对GLAS波形数据进行高斯分解,获取高斯波个数、波形总能量、波形信号起始和信号结束位置4个波形参数;基于波形自动分类方法对黄土高原水体、森林、城市用地、其他地类(裸地、低矮植被等)进行分类。通过基于覆盖相同研究区域的30 m地表覆盖数据(Globe Land30),验证分类的准确性。结果表明,GLAS大光斑波形数据对黄土高原的4种地类能够很好地进行区分,总分类精度高达87.68%,Kappa系数为65.79%。研究表明,GLAS波形数据可以作为获取土地覆盖信息的有效数据源,为研究黄土高原土地覆盖变化提供更丰富的数据支持。     

Effect of the SRTM global DEM on the determination of a high-resolution geoid model: a case study in Iran

Any errors in digital elevation models (DEMs) will introduce errors directly in gravity anomalies and geoid models when used in interpolating Bouguer gravity anomalies. Errors are also propagated into the geoid model by the topographic and downward continuation (DWC) corrections in the application of Stokes’s formula. The effects of these errors are assessed by the evaluation of the absolute accuracy of nine independent DEMs for the Iran region. It is shown that the improvement in using the high-resolution Shuttle Radar Topography Mission (SRTM) data versus previously available DEMs in gridding of gravity anomalies, terrain corrections and DWC effects for the geoid model are significant. Based on the Iranian GPS/levelling network data, we estimate the absolute vertical accuracy of the SRTM in Iran to be 6.5 m, which is much better than the estimated global accuracy of the SRTM (say 16 m). Hence, this DEM has a comparable accuracy to a current photogrammetric high-resolution DEM of Iran under development. We also found very large differences between the GLOBE and SRTM models on the range of −750 to 550 m. This difference causes an error in the range of −160 to 140 mGal in interpolating surface gravity anomalies and −60 to 60 mGal in simple Bouguer anomaly correction terms. In the view of geoid heights, we found large differences between the use of GLOBE and SRTM DEMs, in the range of −1.1 to 1 m for the study area. The terrain correction of the geoid model at selected GPS/levelling points only differs by 3 cm for these two DEMs.     

A novel approach to estimate canopy height using ICESat/GLAS data: A case study in the New Forest National Park,UK

The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) is a spaceborne LiDAR sensor. It is the first LiDAR instrument which can digitize the backscattered waveform and offer near global coverage. Among others, scientific objectives of the mission include precise measurement of vegetation canopy heights. Existing approaches of waveform processing for canopy height estimation suggest Gaussian decomposition of the waveform which has the limitation to properly characterize significant peaks and results in discrepant information. Moreover, in most cases, Digital Terrain Models (DTMs) are required for canopy height estimation. This paper presents a new automated method of GLAS waveform processing for extracting vegetation canopy height in the absence of a DTM. Canopy heights retrieved from GLAS waveforms were validated with field measured heights. The newly proposed method was able to explain 79% of variation in canopy heights with an RMSE of 3.18 m, in the study area. The unexplained variation in canopy heights retrieved from GLAS data can be due to errors introduced by footprint eccentricity, decay of energy between emitted and received signals, uncertainty in the field measurements and limited number of sampled footprints.Results achieved with the newly proposed method were encouraging and demonstrated its potential of processing full-waveform LiDAR data for estimating forest canopy height. The study also had implications on future full-waveform spaceborne missions and their utility in vegetation studies.     

基于RS和PRA的横山县耕地质量综合评价研究

为了解该县目前的耕地质量现状及其发展趋势,为当地的耕地资源保护提供参考撰写此文。采用PRA技术进行农户调查,得到农户普遍关注的耕地质量因素有坡度、灌溉条件、土壤质地、产量、肥料投入、作物长势、沙化和水蚀程度,将这些因素通过基于遥感数据的SVMI、MSAVI、SARP等指标反映出来,构建耕地质量评价指标体系;基于压力——状态——响应模型(PSR),借助地理信息系统(GIS)构建生产压力指数、耕地状态指数以及农户行为指数,从不同角度分别描述耕地质量,并进行耕地质量综合评价。结果显示,当地将近70%的耕地质量现状较差,并且其中大部分耕地面临巨大的退化危险;60%以上的耕地其社会经济条件和管理行为是不可持续的。最后,针对不同等级耕地提出了政策建议。     

POI与NPP/VIIRS夜光数据空间耦合关系下的城市空间结构分析——以武汉市主城区为例

城市兴趣点（POI）和夜光遥感影像能够直观反映城市社会经济等实体要素的空间分布特征,在城市空间结构研究中发挥着重要作用。本文首先选取长江中游城市群的典型城市代表——武汉市作为研究区,选用研究区2016年POI和NPP/VIIRS夜光遥感数据作为基础研究数据,采用GIS分析工具对POI数据进行了空间核密度分析;然后分别对POI核密度分析结果和NPP/VIIRS夜光遥感数据进行了空间网格化处理;最后采用双因素制图和栅格叠加分析方法对两类数据的空间耦合关系进行了探讨,并在此基础上进一步分析了城市空间结构特征。研究表明,武汉市POI数据和夜光遥感数据的空间耦合性整体较好,空间耦合相一致区域占比为82.15%;但POI数据和夜光遥感数据的空间耦合性在长江沿岸地区也存在部分差异,如硚口区、汉阳区夜光遥感数据和POI数据多以低—中的空间耦合模式为主,而青山区、武昌区和汉口区则多以中—低的空间耦合模式为主。武汉市作为中原城市群的核心城市之一,其城市内部空间结构与长江经济带发展关联密切,通过对POI和夜光遥感数据的空间耦合关系探讨,能够对武汉市空间实体要素的空间结构特征有更加深入的了解。本文结果可为沿江城市内部空间结构的研究提供一种崭新的视角。     

基于CGPS的CRInSAR大气改正——以香港地区为例

基于角反射器的合成孔径雷达干涉测量（Corner Reflector InSAR）在低相干地区的地表形变监测中具有很大应用潜力。在CRInSAR所有误差源中,大气影响是最主要的影响因素之一。针对CRInSAR中的大气影响改正问题,详述了利用连续GPS站的数据和地面气象观测资料来对角反射器的大气影响进行改正。并利用香港地区12个GPS连续站的数据对6个角反射器上的大气影响做出改正,得出了较为可靠的结果。     

Developing a semi-analytical algorithm to estimate particulate organic carbon (POC) levels in inland eutrophic turbid water based on MERIS images: A case study of Lake Taihu

Particulate organic carbon (POC) plays an important role in the carbon cycle in water due to its biological pump process. In the open ocean, algorithms can accurately estimate the surface POC concentration. However, no suitable POC-estimation algorithm based on MERIS bands is available for inland turbid eutrophic water. A total of 228 field samples were collected from Lake Taihu in different seasons between 2013 and 2015. At each site, the optical parameters and water quality were analyzed. Using in situ data, it was found that POC-estimation algorithms developed for the open ocean and coastal waters using remote sensing reflectance were not suitable for inland turbid eutrophic water. The organic suspended matter (OSM) concentration was found to be the best indicator of the POC concentration, and POC has an exponential relationship with the OSM concentration. Through an analysis of the POC concentration and optical parameters, it was found that the absorption peak of total suspended matter (TSM) at 665 nm was the optimum parameter to estimate POC. As a result, MERIS band 7, MERIS band 10 and MERIS band 12 were used to derive the absorption coefficient of TSM at 665 nm, and then, a semi-analytical algorithm was used to estimate the POC concentration for inland turbid eutrophic water. An accuracy assessment showed that the developed semi-analytical algorithm could be successfully applied with a MAPE of 31.82% and RMSE of 2.68 mg/L. The developed algorithm was successfully applied to a MERIS image, and two full-resolution MERIS images, acquired on August 13, 2010, and December 7, 2010, were used to map the POC spatial distribution in Lake Taihu in summer and winter.     

Monitoring early stage invasion of exotic Spartina alterniflora using deep-learning super-resolution techniques based on multisource high-resolution satellite imagery: A case study in the Yellow River Delta,China

Over the past decades, Spartina alterniflora, one of the top exotic invasive plants in China, has expanded throughout coastal China. In the Yellow River Delta (YRD), the rapid expansion of S. alterniflora has caused serious negative ecological effects. Current studies have concentrated primarily on mapping the distribution of S. alterniflora with medium-resolution satellite imagery at the regional or landscape scale, which have a limited capability in early detection and monitoring of the invasive process at the patch scale. In this study, we proposed a framework for monitoring the early stage invasion of S. alterniflora patches in the YRD using multiyear multisource high-spatial-resolution satellite imagery with various ground sampling distances (WorldView-2, SPOT-6, GaoFen-1, GaoFen-2, and GaoFen-6 from 2012 to 2019). First, we proposed to use deep-learning-based image super-resolution models to enhance all images to submeter (0.5 m) resolution. Then, we adopted stepwise evolution analysis-based image segmentation and object-based classification rules to detect and delineate S. alterniflora patches from the super-resolved imagery. By investigating Super-Resolution Convolutional Neural Networks (SRCNN) and Fast Super-Resolution Convolutional Neural Networks (FSRCNN) and comparing these methods with the conventional bicubic interpolation method for image resolution enhancement, we concluded that FSRCNN was superior in constructing spectral and structural details from the 1 m/1.5 m/2 m resolution images to 0.5 m resolution. FSRCNN, in particular, was more effective and efficient in discerning and estimating the size of small S. alterniflora patches (<50 m²). Using our method, 76 of 83 field-measured small patches were accurately detected and the delineated S. alterniflora patch perimeters agreed well with the field-measured patch perimeters (root mean square error [RMSE] = 8.29 m, mean absolute percentage error [MAPE] = 23.46 %). The invasion process showed fast expansion from 2012 to 2015 and slow growth from 2016 to 2019. We observed that the landward limits of S. alterniflora patches were influenced by elevation and vicinity to tidal creeks.