基于RTK的高程数据对比分析

以内蒙古扎赉特旗吉日根林场为研究区,通过采集区内高精度的RTK高程数据作为参考,比较区域内的Google Earth、SRTM-1和ASTER GDEM V2高程数据的垂直精度。研究结果表明,Google Earth和ASTER GDEM V2数据在地形起伏度小的区域有明显抖动。坡度与地貌不同时,SRTM-1数据的绝对均值误差、标准差和均方根误差均较小。相较而言,SRTM-1表达地形更为准确,和采集的RTK实验点基本相同。     

复杂山区地形条件下ERA5再分析地表气温降尺度方法

高时空分辨率的气温栅格数据是多种地学模型和气候模型的重要输入。山区地形复杂,气温空间异质性强,如何获取高时空分辨率的山区地表气温数据一直是研究热点与难点。本文选择地形复杂的河北省张家口市作为试验区,基于局部薄盘样条函数对ERA5再分析日均近地表气温（2 m高度）进行空间插值,并利用随机森林算法,结合少量气象站观测气温数据、地形地表参数数据构建日均气温订正模型和气温逐时化模型,实现空间分辨率由0.1°（约11 km）到30 m的逐时气温降尺度,最后将该模型拓展应用于其他时间与区域,检验本文发展的降尺度方法在没有站点观测数据条件下的时空移植性。结果显示,本文降尺度方法得到的高时空分辨率山区气温数据精度较高,1月均方根误差（RMSE）平均值为2.4℃,明显优于气象站点插值结果,且气温相对高低的空间分布更为合理、纹理更加丰富;将该方法应用到其他时间与区域的RMSE平均值分别为2.9℃与2.5℃,均小于再分析资料直接插值所产生的误差。研究结果总体表明,在气象站点较少甚至没有时,可利用本文方法通过ERA5再分析气温准确获取复杂地形条件下的山区高时空分辨率气温数据。     

Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data

The knowledge of biomass stocks in tropical forests is critical for climate change and ecosystem services studies. This research was conducted in a tropical rain forest located near the city of Libreville (the capital of Gabon), in the Akanda Peninsula. The forest cover was stratified in terms of mature, secondary and mangrove forests using Landsat-ETM data. A field inventory was conducted to measure the required basic forest parameters and estimate the aboveground biomass (AGB) and carbon over the different forest classes. The Shuttle Radar Topography Mission (SRTM) data were used in combination with ground-based GPS measurements to derive forest heights. Finally, the relationships between the estimated heights and AGB were established and validated. Highest biomass stocks were found in the mature stands (223 ± 37 MgC/ha), followed by the secondary forests (116 ± 17 MgC/ha) and finally the mangrove forests (36 ± 19 MgC/ha). Strong relationships were found between AGB and forest heights (R² > 0.85).     

中国流域的SRTM30数据提取与计算

本研究基于SRTM30数字高程模型数据,在运用D8算法计算流向的基础上,采用两种不同的方案分别提取中国的数字流域。一种是以中国国界为流出边界提取的详细的中国流域图,另外一种是将与中国接壤的周边地区包括在内提取出中国及邻近地区的流域分布图。结果表明,将与中国接壤的周边地区包含在提取范围内,得出的流域分布更能反映真实情况,对于国际间流域分布研究有着很重要的参考意义。而以中国国界提取出来的数字流域,虽然为国内相关研究广泛应用,但这种方法割裂了流域在地理上的天然联系。经过计算,采取第二种方法提取的中国范围内面积大于50 000km²的独立流域一共有23个,占总的流域面积的82.09%,与传统的中国流域划分不同的是,内陆河流域是中国第一大流域,由于处于干旱少雨的地区,该流域为人们所忽视,紧随其后的是长江、黄河、松花江、嫩江等主要流域。面积的估算理论误差在0.25%到1.73%之间。SRTM30数据质量高,1km的分辨率很适合像中国地区这样大范围的流域提取。并且可以采用更高分辨率的数据,继续进行水系的提取及其分级,从而提取出更精细的流域边界和水系分布。     

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基于SRTM的SNOW—TOPMODEL模型在玛纳斯河流域的应用

基于分辨率90m网格的玛河流域SRTM数据,提取了玛纳斯河流域的河网和地形指数,生成的河网与实际河网基本接近。将加入融雪模块TOPMODE应用于玛河流域,对模型计算结果进行误差评价并分析误差产生的原因。由玛纳斯河流域应用加入融雪模块的TOPMODEL模型可以看出,该模型在玛纳斯河流域有较好的适应性,可以将此模型应用于玛纳斯河流域的洪水预报。     

青藏高原河流地貌定量研究的基础——SRTM3空白数据填充方法剖析及数据填充质量评价

河流地貌和侵蚀是构造地貌研究的核心内容。90m分辨率的SRTM3数字高程模型被广泛运用于构造地貌学的研究,但数据空白区是其在河流地貌定量研究中的瓶颈。对SRTM3数据与中国1:25万DEM数据的对比分析表明,1:25万DEM数据在生成数字河网和河流纵剖面方面不如SRTM3数据,尤其是其在平原区数字河网提取中易发生错误,但在高山峡谷地区数据质量较好,而这正是SRTM3数据空洞的主要区域。对比国内外各种SRTM空洞填补方法发现,相对于算法的改进,高精度数据的参与是填充SRTM3数据空洞的关键。因此,利用SRTM3与1:25万DEM数据的互补性,对SRTM3进行数据优化,是现阶段青藏高原河流地貌定量研究的基础。详细介绍了SRTM3空洞充填的步骤,具有实际参考价值。     

Spatial relationship of groundwater arsenic distribution with regional topography and water-table fluctuations in the shallow aquifers in Bangladesh

The present study has examined the relationship of groundwater arsenic (As) levels in alluvial aquifers with topographic elevation, slope, and groundwater level on a large basinal-scale using high-resolution (90 m × 90 m) Shuttle Radar Topography Mission (SRTM) digital elevation model and water-table data in Bangladesh. Results show that high As (>50 μg/l) tubewells are located in low-lying areas, where mean surface elevation is approximately 10 m. Similarly, high As concentrations are found within extremely low slopes (<0.7°) in the country. Groundwater elevation (weekly measured by Bangladesh Water Development Board) was mapped using water-table data from 950 shallow (depth <100 m) piezometers distributed over the entire country. The minimum, maximum and mean groundwater elevation maps for 2003 were generated using Universal Kriging interpolation method. High As tubewells are located mainly in the Ganges–Brahmaputra–Meghna delta, Sylhet Trough, and recent floodplains, where groundwater elevation in shallow aquifers is low with a mean value of 4.5 m above the Public Works Datum (PWD) level. Extremely low groundwater gradients (0.01–0.001 m/km) within the GBM delta complex hinder groundwater flow and cause slow flushing of aquifers. Low elevation and gentle slope favor accumulation of finer sediments, As-carrying iron-oxyhydroxide minerals, and abundant organic matter within floodplains and alluvial deposits. At low horizontal hydraulic gradients and under reducing conditions, As is released in groundwater by microbial activity, causing widespread contamination in the low-lying deltaic and floodplain areas, where As is being recycled with time due to complex biogeochemical processes.     

Carpentaria ‐ Mt Isa Zinc Belt: Basement framework,chronostratigraphy and geodynamic evolution of Proterozoic successions

This study investigates the quality (in terms of elevation accuracy and systematic errors) of three recent publicly available elevation model datasets over Australia: (i) the 9 arc second national GEODATA DEM-9S ver3 from Geoscience Australia and the Australian National University; (ii) the 3 arc second SRTM ver4.1 from CGIAR-CSI; and (iii) the 1 arc second ASTER-GDEM ver1 from NASA/METI. The main features of these datasets are reported from a geodetic point of view. Comparison at about 1 billion locations identifies artefacts (e.g. residual cloud patterns and stripe effects) in ASTER. For DEM-9S, the comparisons against the space-collected SRTM and ASTER models demonstrate that signal omission (due to the ～270 m spacing) may cause errors of the order of 100–200 m in some rugged areas of Australia. Based on a set of geodetic ground control points over Western Australia, the vertical accuracy of DEM-9S is ～9 m, SRTM ～6 m and ASTER ～15 m. However, these values vary as a function of the terrain type and shape. Thus, CGIAR-CSI SRTM ver4.1 may represent a viable alternative to DEM-9S for some applications. While ASTER GDEM has an unprecedented horizontal resolution of ～30 m, systematic errors present in this research-grade version of the ASTER GDEM ver1 will impede its immediate use for some applications.     

资源三号卫星DEM数据与全球开放DEM数据的误差对比

全球开放DEM数据为数字地形分析提供了重要数据源。与已有的全球开放DEM数据相比,资源三号卫星具有更高的空间分辨率、更大的覆盖范围和更好的现势性。将资源三号卫星生成的DEM数据与全球开放DEM数据进行误差对比则为基于资源三号卫星的全球DEM数据研制提供科学依据。本文以山西省中部太原市为研究区,基于高精度激光点云数据生成DEM为参考数据,对资源三号卫星影像生成的DEM数据与全球典型的开放DEM数据(AW3D30、SRTM1和ASTER GDEM)的误差进行了对比分析,并获得了其在不同坡度等级下绝对误差与相对误差的平均值、平均绝对值、均方根值和标准偏差值。研究结果表明:①4种DEM数据的误差分布均具有较好的对称性。同时,平均误差接近于0 m,SRTM1和ASTER GDEM数据更是如此。因此均方根误差值与标准偏差值近似一致;②资源三号DEM具有最高的精度,误差最小(均方根误差4.6 m)。其次为AW3D30数据(均方根误差5.6 m)和SRTM1数据(均方根误差8.8 m)。ASTER GDEM数据误差最大(均方根误差12.6 m),精度最差;③资源三号DEM、SRTM1和ASTER GDEM数据的误差均随坡度的变大而增大,而AW3D30数据误差随着坡度增加呈现先减小后增大的趋势。总体上,与其他3种DEM数据相比,资源三号DEM在所有坡度范围均具有最小的误差值。