基于ICESat-2的陕西省SRTM和ASTER GDEM数据对比分析研究

SRTM1 DEM和ASTER GDEM V3作为全球公开使用的最新DEM数据,为地形研究、GIS应用、资源管理、环境监测等领域提供重要的数据支持。本研究旨在分析它们在不同地形和地表覆盖物区域的误差分布情况,探究其在地形表达中的优势、劣势。选取陕西省特有地形,结合地形剖面和曲面误差方法,首次利用ICESat-2分析比较了SRTM1 DEM和ASTER GDEM V3数据的垂直精度在卫星轨道、地形地貌、土地覆盖中的误差分布。通过研究发现：STRM1 DEM和ASTER GDEM V3的总体平均误差分别为0.056 m和5.301 m,受轨道因子的影响,SRTM1 DEM垂直精度高于ASTER GDEM V3。坡度对数据的精度影响占比最大,林地的数据精度显示最低,地貌中平原地区影响最小,但误差随地形起伏不断增加。SRTM1 DEM误差曲面跨度小于ASTER GDEM V3,对于地形表达比ASTER GDEM V3更加精确。分析结果进一步验证了两种DEM数据测绘原理不同导致精度差异。     

Near-global validation of the SRTM DEM using satellite radar altimetry

This paper reports the results of a near-global validation of the SRTM DEM dataset, using a unique database of completely independent height measurements derived from satellite altimeter echoes, primarily gathered by ERS-1. These heights are obtained using a rule-based expert system which identifies each echo as 1 of 11 different characteristic shapes, and selects the optimal retracking algorithm to obtain best range to surface. The results of this comparison, which includes over 54 million altimeter derived heights, show generally very good agreement with the SRTM data, with global statistics for mean difference of 3 m and a standard deviation of 16 m. Quantitative validation results are given for each continent and are summarised here.

	Mean difference (m)	Standard deviation of differences (m)
Africa	1.86	15.62
Australia	1.09	11.49
Eurasia	2.54	16.09
North America	3.15	15.18
South America	12.22	18.51
Global	3.60	16.16

Full-size table

     

Forest biomass mapping from lidar and radar synergies

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed.In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R² of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and was used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R² of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar-SAR synergy was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.     

基于SRTMv4的异龙湖流域地形因子提取

以最新SRTMv4版DEM作为基础数据,选择异龙湖流域为研究区域,利用ArcGIS 10平台中的水文分析扩展模块提取异龙湖流域的流域边界和流域水文特征两方面的流域地形因子。根据提取结果与获取到的实测数据进行有效性检验:①与实际调查数据的水系网和1∶5万水系图对照比较,发现基于SRTM DEM所提取出的流域地形因子等信息是合理有效的。②与1∶5万DEM所提取流域水网进行对比发现基于SRTM DEM所提取的数据精度较为满意,在相同河网密度情况下,基于SRTM的DEM与1∶5万DEM的水网细节表现高度一致,具有可靠性。     

航天飞机雷达测图计划大块区域无效数据填补方法

在总结国内外对SRTM无效数据填补方法的基础上,针对直接用低精度的GTOPO30数据进行镶嵌填充SRTM无效数据会产生边界不光滑,细节表现不好的问题,采用Laplacian迭代修复算法。该算法是经典的基于偏微分方程(PDE)的图像修复算法。实例对比验证表明,该方法在对SRTM大块无效数据填补效果上明显好于用GTOPO30数据直接填补的效果,是获取完整SRTM数据的有效方法之一。     

Quality assessment of SRTM C- and X-band interferometric data: Implications for the retrieval of vegetation canopy height

The Shuttle Radar Topography Mission distinguished itself as the first near-global spaceborne mission to demonstrate direct sensitivity to vertical vegetation structure. Whether this sensitivity is viewed as exploitable signal or unwanted bias, a great deal of interest exists in retrieving vegetation canopy height information from the SRTM data. This study presents a comprehensive application-specific assessment of SRTM data quality, focusing on the characterization and mitigation of two primary sources of relative vertical error: uncompensated Shuttle mast motion and random phase noise. The assessment spans four test sites located in the upper Midwestern United States and examines the dependence of data quality on both frequency, i.e., C-band vs. X-band, and the number of acquired datatakes. The results indicate that the quality of SRTM data may be higher than previously thought. Novel mitigation strategies include a knowledge-based approach to sample averaging, which has the potential to reduce phase noise error by 43 to 80%. The strategies presented here are being implemented as part of an ongoing effort to produce regional- to continental-scale estimates of vegetation canopy height within the conterminous U.S.     

An empirical InSAR-optical fusion approach to mapping vegetation canopy height

Exploiting synergies afforded by a host of recently available national-scale data sets derived from interferometric synthetic aperture radar (InSAR) and passive optical remote sensing, this paper describes the development of a novel empirical approach for the provision of regional- to continental-scale estimates of vegetation canopy height. Supported by data from the 2000 Shuttle Radar Topography Mission (SRTM), the National Elevation Dataset (NED), the LANDFIRE project, and the National Land Cover Database (NLCD) 2001, this paper describes a data fusion and modeling strategy for developing the first-ever high-resolution map of canopy height for the conterminous U.S. The approach was tested as part of a prototype study spanning some 62,000 km² in central Utah (NLCD mapping zone 16). A mapping strategy based on object-oriented image analysis and tree-based regression techniques is employed. Empirical model development is driven by a database of height metrics obtained from an extensive field plot network administered by the USDA Forest Service-Forest Inventory and Analysis (FIA) program. Based on data from 508 FIA field plots, an average absolute height error of 2.1 m (r = 0.88) was achieved for the prototype mapping zone.     

SPOT5-HRS digital elevation models and the monitoring of glacier elevation changes in North-West Canada and South-East Alaska

Monitoring the response of land ice to climate change requires accurate and repeatable topographic surveys. The SPOT5-HRS (High Resolution Stereoscopic) instrument covers up to 120 km by 600 km in a single pass and has the potential to accurately map the poorly known topography of most glaciers and ice caps. The acquisition of a large HRS archive over ice-covered regions is planned by the French Space Agency (CNES) and Spotimage, France during the 2007–2008 International Polar Year (IPY). Here, we report on the accuracy and value of HRS digital elevation model (DEM) over ice and snow surfaces.

A DEM is generated by combining tools available from CNES with the PCI Orthoengine^SE software, using HRS images acquired in May 2004 over South-East Alaska (USA) and northern British Columbia (Canada). The DEM is evaluated through comparison with shuttle radar topographic mission (SRTM) DEM and ICESAT data, on and around the glaciers. A horizontal shift of 50 m is found between the HRS and SRTM DEMs and is attributed to errors in the SRTM DEM. Over ice-free areas, HRS elevations are 7 m higher than those of SRTM, with a standard deviation of ± 25 m for the difference between the two DEMs. The 7-m difference is partly attributed to the differential penetration of the electromagnetic waves (visible for HRS; microwave for SRTM) in snow and vegetation.

We also report on the application of sequential DEMs (SRTM DEM in February 2000 and HRS DEM in May 2004) for the monitoring of glacier elevation changes. We map the topographic changes induced by a surge of one tributary of Ferris Glacier. Maximum surface lowering of 42 (± 10) m and rising of 77 (± 10) m are observed in the 4 years time interval. Thinning rates up to 10 (± 2.5) m/yr are observed at low altitudes and confirm the ongoing wastage of glaciers in South-East Alaska.