利用GLAS激光测高数据评估DSM产品质量及精度优化   总被引：2，自引：0，他引：2  

胡柳茹  唐新明  李国元  付冬暇  窦显辉  赵世湖 《测绘通报》2019,(11):39-43

提出了一种利用卫星激光测高数据直接优化提升数字表面模型（DSM）产品精度的方法。选取境外中亚地区的资源三号DSM开展试验，通过采用多准则约束方法提取激光高程控制点，分别利用偏度、中值、线性、二次多项式等进行DSM误差修正，发现4种模型均能有效消除DSM系统误差，其中基于二次多项式的方法更适用于平地和丘陵地貌，线性模型更适用于高山地貌。试验验证了采用卫星激光测高数据优化境外DSM技术流程的可行性，最终可提高DSM的绝对高程精度。  相似文献   

山东南部近海秋季长蛇鲻相对资源量的分布及其影响因素     

刘逸文  张崇良  昝肖肖  孙铭  徐宾铎  薛莹  任一平 《中国海洋大学学报(自然科学版)》2020,50(4):45-53

为探究长蛇鲻(Saurida elongate)的生态习性和分布规律,并为长蛇鲻资源的合理利用与养护提供科学依据,本文根据2016年秋季在山东南部近海进行的渔业资源与环境调查数据,分析了该海域长蛇鲻的分布特征,研究长蛇鲻成体、幼体的分布差异,并利用广义可加模型(GAM)研究其分布与生物因子和环境因子的关系。结果表明,长蛇鲻成体与幼体的分布存在差异,成体分布范围广,幼体主要分布在30 m等深线及以浅水域。GAM模型的结果表明,饵料生物、底层水温、水深和底层盐度是影响长蛇鲻相对资源量分布的主要因子。成体、幼体的分布与影响因子的关系差异极显著(P<0.01)。长蛇鲻成体的相对资源量随饵料生物和底层水温的增加表现为先上升后下降的趋势,而幼体呈现一致上升趋势;成体和幼体的相对资源量随水深增加均呈下降趋势;幼体相对资源量随底层盐度增加有明显上升趋势,而盐度对成体的影响不显著。本研究认为山东南部近海是长蛇鲻的重要栖息地,水温和盐度是成体和幼体分布差异的可能原因。  相似文献   

Impact of Grazing Exclusion on the Surface Heat Balance in North Tibet     

FENG Yunfei  DI Yingwei  ZHANG Jing  ZHANG Xianzhou  SHI Peili  Niu Ben 《资源与生态学报(英文版)》2020,11(3):283-289

The grazing exclusion program used by the Tibetan government to protect the ecological environment has changed the vegetation and impacted the surface heat balance in North Tibet. However, little information is available to describe the in?uences of the current grazing exclusion program on local surface heat balance. This study uses the records of fenced grassland patch locations to identify the impact of grazing exclusion on surface heat balance in North Tibet. The records of fenced grassland patch locations, including the longitude, latitude, and elevation of the vertices of each fenced patch (polygon shapes), were provided by the agriculture and animal husbandry bureaus of the counties where the patches were located. ArcGIS 10.2 was used to create polygon shapes based on patch location records. Based on satellite data and the surface heat balance system determined by the model, values for changes in land surface temperature (LST), albedo and evapotranspiration (ET) induced by grazing exclusion were obtained. All of these can influence surface heat balance and alter the fluctuation of LST in the northern Tibetan Plateau. The LST trends for day and night showed an asymmetric diurnal variation, with a larger magnitude of warming in the day than cooling at night. The maximum decrease in absorbed shortwave of LST (-0.5 - -0.4 ℃ per decade) occurred in the central region, while the minimum decrease (-0.2 - -0.1 ℃ per decade) occurred in the eastern region. The decreased latent heat lead to the LST increased maximum (>1 ℃ per decade) occurred in the central region, The eastern region increased at a rate of 0.2-0.5 ℃ per decade, while the minimum increase (0-0.1 ℃ per decade) occurred in the northwestern region.  相似文献   

机载WIDAS数据的Landsat卫星反照率初步验证   总被引：1，自引：1，他引：0  

何丹丹  焦子锑  董亚冬  张小宁  丁安心  尹思阳  崔磊  常雅轩 《遥感学报》2019,23(1):53-61

随着精细化监测的需求,中高空间分辨率的地表反照率产品逐渐成为气候模型的主要输入。目前,中高空间分辨率反照率产品的验证主要基于地表站点的通量塔观测数据,区域机载飞行数据的验证依然相对较少。因此,本文基于区域机载数据验证Landsat反照率产品。针对内蒙古自治区根河森林试验区所获取的机载红外广角双模式成像仪(WIDAS)多角度反射率数据,应用BRDF原型反演算法估算其反照率,分析了应用机载数据验证中高空间分辨率反照率产品的潜力。2016年内蒙古根河森林试验区机载WIDAS飞行多角度观测的可用多角度范围为25°,以前的研究表明BRDF原型反照率反演算法表现出对小观测角度的反照率反演结果的鲁棒性。因此,机载WIDAS反照率在一定程度可用于星载反照率的验证。首先,基于核驱动模型和各向异性平整指数(AFX)提取了试验区4种MODIS二向性反射分布函数(BRDF)原型;然后,将其作为先验知识应用到根河森林WIDAS机载数据的反照率反演中;最后,用WIDAS反照率和单个地面通量塔观测的反照率对Landsat卫星数据的反照率进行初步验证。验证结果表明Landsat反照率与WIDAS反照率结果较为一致,但略有低估,总体均方根误差(RMSE)约为0.02,偏差为0.0057。在多角度观测范围较小时,BRDF原型的反照率反演算法可为星载地表反照率的验证提供了一种有效的验证手段。  相似文献   

海表面温度时空变异特征及对验证误差影响     

蒋锦刚  徐曜  聂晨晖  潘骁骏  周斌 《遥感学报》2019,23(2):336-348

海洋要素的变化存在明显的区域性和季节性的变化特性,本文选择海洋要素中最为突出的海表面温度(SST)要素作为主要分析参数,设计时空变异参数的计算指标,分析时空变异对验证误差影响的关系,通过研究及试验的数据精度验证,证明了时空变异是造成误差的直接原因之一。强烈的时空属性变异,在验证过程中会引入很大的验证误差,处于不同变异等级区划的数据,其验证结果相对误差可达13.08%,变异越剧烈的区域,精度验证效果越差,验证误差就越大,这些误差并非完全是遥感产品的误差,验证结果不具有代表性,不能真实的反映遥感产品的误差特征。对于SST等海洋遥感产品验证时,需要考虑时空变异对验证误差的影响和贡献,合理选择验证试验区域、代表性的评价数据集和科学的评价方法。  相似文献   

Spatial and temporal variations of albedo on nine glaciers in western China from 2000 to 2011          下载免费PDF全文

Jie Wang  Baisheng Ye  Yuhuan Cui  Xiaobo He  Guojing Yang 《水文研究》2014,28(9):3454-3465

This research demonstrates the spatiotemporal variations of albedo on nine glaciers in western China during 2000–2011, by the albedo derived from two types of datasets: Landsat TM/ETM + images and MOD10A1 product. Then, the influence factors of glacier albedo and its relationship with glacier mass balance are also analyzed by the correlation approach, which is frequently used in geostatistics. The paper finds that there are different spatiotemporal variations over the glaciers in western China: (1) For a single glacier, the albedo varies gently with altitude on its tongue and increases fast in the middle part, while in the accumulation zones, the albedo value appears in the form of fluctuation. This could provide a quantitative method to retrieve the snowline by determining the threshold albedo value of snowpack and bare ice. (2) For the glaciers in western China, the albedo decreases with distance to the center of Tibetan Plateau (TP). This may relate to the elevation of glacier, for the speed of glacier retreat highly depends on air temperature. (3) In the summer period, albedo on most glaciers declines over the last 12 years, and it decreases much faster in southeastern TP than other regions, for which air temperature overwhelms the black carbon concentration. In addition, the trend of glacier albedo in summer is greatly correlated with that of measured glacier mass balance, which implies that the long‐term albedo datasets by remote sensing technology could be used to monitor and predict the change of glacier mass balance in the future. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures          下载免费PDF全文

J. L. McCreight  A. G. Slater  H. P. Marshall  B. Rajagopalan 《水文研究》2014,28(3):933-957

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

Two-stage permutation tests for determining homogeneity within a spatial cluster     

Qiliang Liu  Wenkai Liu  Min Deng  Yaolin Liu 《International journal of geographical information science》2019,33(9):1718-1738

The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications.  相似文献   

Global Statistical Assessment and Cross-Calibration with Jason-2 for Reprocessed HY-2 A Altimeter Data     

Maofei Jiang  Ke Xu  Yalong Liu 《Marine Geodesy》2018,41(3):289-312

HY-2 A (Haiyang-2 A) satellite was launched on August 16, 2011 and radar altimeter is one of its main payloads. We reprocessed two years of HY-2 A altimeter sensor geophysical dataset records (SGDR) data. This paper presents the main results in terms of reprocessed HY-2 A altimeter data quality: verification of data availability and validity, monitoring several relevant altimeter parameters, and assessment of the HY-2 A altimeter system performances. A cross-calibration analysis of reprocessed HY-2 A altimeter data with Jason-2 was conducted. The reprocessed HY-2 A altimeter data show good quality and have a low level of noise with respect to Jason-2. The same geophysical correction methods were used to calculate the sea surface height (SSH) for the two missions. The mean standard deviations of the crossover differences for HY-2 A and Jason-2 are 5.24 cm and 5.34 cm, respectively. The mean standard deviation of the crossover differences between HY-2 A and Jason-2 is 5.37 cm. These show that HY-2 A can provide SSH measurements at almost the same level of accuracy as Jason-2. The relative SSH bias between HY-2 A and Jason-2 due to the Ultra Stable Oscillator (USO) drift is obviously observed, and it can affect the calculation of mean sea level and should be further studied and corrected.  相似文献   

A New Retracking Technique for Brown Peaky Altimetric Waveforms     

Fukai Peng 《Marine Geodesy》2018,41(2):99-125

A new Brown-Peaky (BP) retracker has been developed for peaky waveforms that usually appear within ～10 km to the coastline. The main feature of the BP is that it fits peaky waveforms using the Brown model without introducing a peak function. The retracking strategy first detects the peak location and width of a waveform using an adaptive peak detection method, and then estimates retracking parameters using a weighted least squares (WLS) estimator. The WLS assigns a downsized weight to corrupted waveform gates, but an equal weight to other normal waveform gates. The BP retracker has been applied to 4-year Jason-1 waveform (2002–2006) in two Australian coastal zones. The results retracked by BP, MLE4 and ALES retrackers have been validated against tide-gauge observations located at Burnie, Lorne and Broome. The comparison results show that three retrackers have similar performance over open oceans with the correlation coefficient (～0.7) and RMSE (～13 cm) between altimetric and tide-gauge sea levels for distance >7 km offshore. The main improvement of BP retracker occurs for distance ≤7 km to the coastline, where validation results indicate that data retracked by BP are more accurate (15–21 cm) than those by ALES (16–24 cm) and MLE4 (19–37 cm).  相似文献