Using MODIS images to examine the surface extents and variations derived from the DEM and laser altimeter data in the Danjiangkou Reservoir,China

We used publicly available digital spatial datasets to study the area extents and their horizontal variations of two water bodies within the Danjiangkou Reservoir, China. Between 2003 and 2005, the water levels varied from 140 to 149 m above mean sea level as measured by the Geoscience Laser Altimeter System (GLAS). Detailed procedures to derive the horizontal extents and variations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) coupled with GLAS data and to verify the extents and variations spatially were provided. For the water bodies on the north and west, the surface water extents derived from four MODIS images varied between 174 and 218 km² and from 96 to 135 km², respectively. The extents by inundating the DEM using the GLAS data were 178–212 km² for the water body on the north and 104–118 km² for the water body on the west. The spatial verifications of surface water extents derived from the MODIS images versus DEM coupled with GLAS data agreed 83–93%. Within the ring areas between water/land boundaries at elevations of 140 and 147 m, and 140 and 149 m, the spatial agreement was 52–75%.     

Estimating floodwater from MODIS time series and SRTM DEM data

Real-time flood mapping with an automatic flood-detection technique is important in emergency response efforts. However, current mapping technology still has limitations in accurately expressing information on flood areas such as inundation depth and extent. For this reason, the authors attempt to improve a floodwater detection method with a simple algorithm for a better discrimination capacity to discern flood areas from turbid floodwater, mixed vegetation areas, snow, and clouds. The purpose of this study was to estimate a flood area based on the spatial distribution of a nationwide flood from the Moderate Resolution Imaging Spectroradiometer (MODIS) time series images (8-day composites, MOD09A1, 500-m resolution) and a digital elevation model (DEM). The results showed the superiority of the developed method in providing instant, accurate flood mapping by using two algorithms, which modified land surface water index from MODIS image and eight-direction tracking algorithm based on DEM data.     

基于三维GIS的地形可视化研究及实现

给出了一个适用于水利领域的三维可视化系统的组成,介绍了解决超大数据量数字高程模型(DEM)快速显示的算法,将地物在地形中进行显示,研究了基于三维场景空间分析的理论,最后对系统的实现作了简要介绍。实践表明该系统能在PC机上流畅地运行,图形的生成速度和质量令人满意。     

Estimating vertical error of SRTM and map-based DEMs using ICESat altimetry data in the eastern Tibetan Plateau

The Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud and land Elevation Satellite (ICESat) provides elevation data with very high accuracy which can be used as ground data to evaluate the vertical accuracy of an existing Digital Elevation Model (DEM). In this article, we examine the differences between ICESat elevation data (from the 1064 nm channel) and Shuttle Radar Topography Mission (SRTM) DEM of 3 arcsec resolution (90 m) and map-based DEMs in the Qinghai-Tibet (or Tibetan) Plateau, China. Both DEMs are linearly correlated with ICESat elevation for different land covers and the SRTM DEM shows a stronger correlation with ICESat elevations than the map-based DEM on all land-cover types. The statistics indicate that land cover, surface slope and roughness influence the vertical accuracy of the two DEMs. The standard deviation of the elevation differences between the two DEMs and the ICESat elevation gradually increases as the vegetation stands, terrain slope or surface roughness increase. The SRTM DEM consistently shows a smaller vertical error than the map-based DEM. The overall means and standard deviations of the elevation differences between ICESat and SRTM DEM and between ICESat and the map-based DEM over the study area are 1.03 ± 15.20 and 4.58 ± 26.01 m, respectively. Our results suggest that the SRTM DEM has a higher accuracy than the map-based DEM of the region. It is found that ICESat elevation increases when snow is falling and decreases during snow or glacier melting, while the SRTM DEM gives a relative stable elevation of the snow/land interface or a glacier elevation where the C-band can penetrate through or reach it. Therefore, this makes the SRTM DEM a promising dataset (baseline) for monitoring glacier volume change since 2000.     

地形三维可视化系统的设计与实现

给出了一个适用于水利领域的三维可视化系统(YJJT3D系统)的组成,提出了解决超大数据量数字高程模型(DEM)快速显示的算法,将地物在地形中进行显示,研究了基于三维场景空间分析的理论,最后对系统的实现作了简要介绍。     

Analysis of the water volume,length, total area and inundated area of the Three Gorges Reservoir,China using the SRTM DEM data

Using the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data covering the region of the Three Gorges Reservoir, Changjiang, China, we have computed the water volume, length, and total and inundated areas of the reservoir, with the assumption that the water surface within the reservoir is flat. When the reservoir's surface water level is 175 m above the mean sea level, the computed values may be comparable to the official data published by the Chinese government.     

Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach

FROM-GLC (Fine Resolution Observation and Monitoring of Global Land Cover) is the first 30 m resolution global land-cover map produced using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Due to the lack of temporal features as inputs in producing FROM-GLC, considerable confusion exists among land-cover types (e.g. agriculture lands, grasslands, shrublands, and bareland). The Moderate Resolution Imaging Spectrometer (MODIS) provides high-temporal frequency information on surface cover. Other auxiliary bioclimatic, digital elevation model (DEM), and world maps on soil-water conditions are possible sources for improving the accuracy of FROM-GLC. In this article, a segmentation-based approach was applied to Landsat imagery to down-scale coarser-resolution MODIS data (250 m) and other 1 km resolution auxiliary data to the segment scale based on TM data. Two classifiers (support vector machine (SVM) and random forest (RF)) and two different strategies for use of training samples (global and regional samples based on a spatial temporal selection criterion) were performed. Results show that RF based on the global use of training samples achieves an overall classification accuracy of 67.08% when assessed by test samples collected independently. This is better than the 64.89% achieved by FROM-GLC based on the same set of test samples. Accuracies for vegetation cover types are most substantially improved.     

Determination of land surface temperature and its lapse rate in the Satluj River basin using NOAA data

Temperature lapse rate (TLR), an essential parameter for snowmelt runoff analysis, was determined for the Satluj River basin in the Western Himalayas. National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) data sets were used to determine the land surface temperature (LST) of the region using the split‐window algorithm proposed by Coll and Caselles (Journal of Geophysical Research, 1997, 102, pp. 16697–16713). The LST was correlated with the elevation values obtained from a US Geological Survey digital elevation model (USGS‐DEM) of the same area and the trend showed an inverse relationship between LST and elevation. The TLRs for the study area on 2 February, 1 March, 26 March, 16 October, 1 November and 20 November 2004 were in the range 0.6–0.74°C/100 m. The results obtained were compared with lapse rates determined using Moderate Resolution Imaging Spectroradiometer (MODIS) LST maps. TLR determination in the past was based on air temperature data available from meteorological stations that are sparsely located in rugged terrain such as the Himalayas. As these measurements were point data and had been measured manually, they may have led to erroneous results. Satellite data, however, provide continuous and potentially unbiased recording provided an accurate radiometric calibration and atmospheric correction can be achieved. A previous TLR calculation using air temperature from meteorological stations for the western Himalayan region was found to be 0.65°C/100 m. Air temperature and LST from NOAA‐AVHRR and MODIS‐Terra data were found to be in good agreement. This type of study will be useful for snowmelt runoff modelling studies for the Himalayan region.     

一种机载InSAR水体阴影的提取和识别方法

为了解决机载InSAR DEM中水体和阴影区域质量不佳需要区分修复的问题,提出一种综合利用机载InSAR数据源自动提取水体和阴影并加以识别的方法。首先基于InSAR DEM进行粗差点检测,利用粗差点作为种子点在SAR图像中区域生长,提取完整的水体和阴影区域;然后利用沿斜距向高程差和雷达俯角构造约束条件自动识别两者。通过对实测的机载高分辨率InSAR数据进行处理,水体阴影的识别率达到92%以上,其中水体和地形阴影的识别较好,而受制于DEM内在噪声等因素的影响,由树木造成的小块阴影容易造成误分。     

多尺度小波分析用于DEM网格数据综合

数字高程模型(DEM)虽已广泛应用于地学、景观以及区域规划等工作，然而在不同尺度下，不同的系统对其数据量的要求是不同的，且数据量将影响系统运行及分析的速度；其次等高线的制图综合也可直接通过数字高程模型数据的综合来实现，为了加快DEM数据综合的速度和保持综合前后DEM数据特征一致性，提出了一种采用小波多尺度分析的方法来进行DEM网格数据综合的方案，并给出了数字高程模型网格数据的综合基本模型。同时对综合以后的可靠性分别从数据量、断面图和曲面面积变化等方面进行了分析。具体实例说明，将多尺度小波分析用于DEM网格数据综合是有效的。     

Modelling of Gangotri glacier thickness and volume using an artificial neural network

The volume of glaciers in a glacierized basin is an important characteristic for the existence of the glaciers and their evolution. Knowledge of glacier volume motivates scientific interest for two main reasons. First, the volumes of individual glaciers are monitored to estimate future water and sea level rises. Second, glaciers in the Indian Himalayas have been recognized as important water storage systems for municipal, industrial, and hydroelectric power generation purposes. Therefore, estimation of glacier volume is desired to estimate sea level rise accurately. The problem of deriving volume and glacier ice thickness is solved by developing an artificial neural network (ANN) approach that requires glacier boundaries, central branch lines, width-wise lines, digital elevation model (DEM), and slope information. Two geomorphic assumptions were taken in this investigation after testing, and strong relationships were found between elevation values of the frontal ice-denuded area of the Gangotri glacier and ice thickness derived from an ANN.     

基于改进混合像元方法的MODIS影像水体提取研究

基于MODIS的反射率数据,利用改进的线性混合像元分解方法提取水体,并结合MODIS温度产品和SRTM的DEM数据校正阴影对提取结果的影响。通过和利用ETM+影像的水体提取结果对比分析得出:该方法结果较好的体现出河流和湖泊的细节特点,同时在很大程度上消除了阴影影响,面积提取结果具有较高的精度。     

利用DWG数据建立DEM关键技术研究

DWG数据的图形和属性分离特性,使GIS软件难以直接基于DWG数据生成DEM.首先阐述利用DWG数据提取并基于阈值+最小距离与人工辅助技术匹配高程点和高程注记,进而建立DEM的关键技术.设计了WzjGIS系统,实现了高程信息提取与匹配、DEM生成与显示及3D分析一体化.最后以长春市鹿乡镇DWG数据为例,建立了DEM并进行了3D分析.     

基于MODIS产品的区域土壤遥感分类研究——以新疆为例

以新疆维吾尔自治区全境为研究区域，采用中高分辨率MODIS遥感数据和地形数据，在第二次全国土壤普查数据库的支持下，采用自动分类方法，探讨了遥感技术在常规土壤调查工作受限制的干旱地区进行土壤调查的效果和适用性。研究中使用了MODIS地表反射率、植被指数、地表昼夜温度等数据产品，提取了多种图像特征，并结合了DEM生成的地形参数。研究区土壤分类系统在发生学分类的基础上集合遥感信息特征进行了调整，形成了具有26个土壤类型及特殊地表覆被的土壤遥感分类系统。经分类试验，总体精度为70%左右。
     

基于无人机影像的山地人工林景观DEM构建

山地人工林景观的DEM构建是对景观地形信息进行描述的基础的研究内容,也是人工林景观面积、结构、蓄积量等信息提取的重要因子,具有重要的研究意义。通过无人机平台获取影像,采用立体像对拼接的方式生成正射影像并提取DEM信息,并与GPS测量数据、ASTER GDEM、SRTM数据进行比较分析。结果表明:在该区域无人机影像构建的DEM与实测高程差距最小(RMSE=8.96),具有比ASTER GDEM(RMSE=13.68)和SRTM(RMSE=11.81)更高的精度;在每个样方内的最大高程差值与最大树高最为接近(RMSE=1.813),说明无人机DEM能够反映出更多的冠层与地面分层信息,在山地人工林景观DEM构建中表现出较大潜力。     

Using MODIS data to characterize seasonal inundation patterns in the Florida Everglades

Information regarding the spatial extent and timing of flooding in the world's major wetlands is important to a wide range of research questions including global methane models, water management, and biodiversity assessments. The Florida Everglades is one of the largest wetlands in the US, and is subject to substantial development and pressures that require intensive hydrological modeling and monitoring. The Moderate Resolution Imaging Spectrometer (MODIS) is a global sensor with high frequency repeat coverage and significant potential for mapping wetland extent and dynamics at moderate spatial resolutions. In this study, empirical models to predict surface inundation in the Everglades were estimated using MODIS data calibrated to water stage data from the South Florida Water Management District for the calendar year 2004. The results show that hydropatterns in the Florida Everglades are strongly correlated to a Tasseled Cap wetness index derived from MODIS Nadir Bidirectional Reflectance Function Adjusted Reflectance data. Several indices were tested, including the Normalized Difference Wetness Index and the diurnal land surface temperature difference, but the Tasseled Cap wetness index showed the strongest correlation to water stage data across a range of surface vegetation types. Other variables included in the analysis were elevation and percent tree cover present within a pixel. Using logistic regression and ensemble regression trees, maps of water depth and flooding likelihood were produced for each 16-day MODIS data period in 2004. The results suggest that MODIS is useful for dynamic monitoring of flooding, particularly in wetlands with sparse tree cover.     

Cross-validation as a means of investigating DEM interpolation error

Studies of the detailed characteristics of DEM error have been hampered by the difficulty in obtaining a large sample of error values for a DEM. The approach proposed in this paper is to resample a DEM to a lower resolution and then reinterpolate back to the original resolution which produces a large sample of error values well distributed across the DEM. This method is applied to a sample area from Scotland, which contains a variety of terrain types. The results show that the standard measure of error, the root mean square error (RMSE) of elevation, shows only moderate correlation with a visual assessment of the quality of DEMs produced by a range of interpolation methods. The frequency distribution and strength of spatial autocorrelation are shown to vary with the initial data density and interpolation method. When the source data density is low, the error has strong spatial autocorrelation and a distribution that is close to being Gaussian. However, as the data density increases, levels of spatial autocorrelation drop and the distribution becomes leptokurtic with values very strongly clustered around zero. At the level of the individual DEM point, elevation error is shown to be a poor predictor of error in slope derivatives which depend on the spatial pattern of elevation errors around the point and are also sensitive to differences in terrain. At the level of a whole DEM, however, RMSE of elevation is a good predictor of RMSE in gradient and aspect but not of curvature.     

Estimation of average DEM accuracy under linear interpolation considering random error at the nodes of TIN model

The concept of a digital elevation model (DEM) can be used for a digital representation of any single‐valued surface such as a terrain relief model (digital terrain model, DTM). DEMs are widely used in remote sensing, geographical information systems (GIS), and virtual reality. Estimating the accuracy of a DEM is an essential issue in the acquisition of spatial data, particularly for applications that require a highly accurate DEM, such as engineering applications. The accuracy of a DEM is subject to many factors such as the number of sampling points, the spatial distributions of the sampling points, the methods used for interpolating surface elevations, the propagated error from the source data, and other factors. Of these factors, this study will focus on estimating the mean elevation error in a DEM surface that is caused by errors of component nodes in a triangulated irregular network (TIN). This paper will present a newly derived mathematical formula, with the details of the procedure used to derive this formula, to study the relationship between the errors at the TIN nodes and the propagated mean elevation error of a DEM surface that is linearly constructed from the TIN. We have verified the analytical formula by numerical simulation. The experimental results confirm the theoretical derivation of the formula.     

A semi-automated method to create a lidar-based hydro-flattened DEM

The response of water surfaces to light detection and ranging (lidar) pulses is unpredictable, which results in sparse lidar point density with varying intensity values. Due to the sparseness of the point cloud and lack of natural breaklines, lidar-derived digital elevation model (DEM) can produce unnatural surface over waterbodies. Such surfaces are not cartographically pleasing and can cause issues in the hydrologic and hydraulic modelling of a river. Hydro-flattening is the process of creating a lidar-derived DEM in which water surfaces appear and behave as they would in traditional topographic DEMs generated from photogrammetric digital terrain models. Hydro-flattened DEMs, created using lidar data, exclude the lidar points over waterbodies and include three-dimensional (3D) bank shorelines. In this article, a semi-automated method is presented for extracting bank shorelines for the purpose of creating lidar-derived hydro-flatten DEMs. Lidar point cloud and an approximate stream centreline are the primary data for this process. In the first step, a continuous bare ground surface (CBGS) is created by eliminating non-ground lidar points and by adding artificial underwater points. In the second step, the lowest elevation from the lidar point cloud within a radius distance from the river centreline is used to create a virtual water surface (VWS). This VWS is revised to consider water surface undulations such as ripples or waves, protruding underwater objects, etc. The revised VWS is then intersected with the CBGS to locate the two-dimensional (2D) bank shorelines. The 2D shorelines are assigned the elevations of the VWS and are used to produce a hydro-flattened DEM. The planimetric absolute mean separation of 0.94, 0.69, and 0.63 m for the three water surfaces is observed between the bank shoreline extracted using raw lidar points and a GPS (global positioning system) survey. The mean separation using vendor classified lidar points is 0.74, 0.67, and 0.64 m which are very similar to those using raw lidar.     

基于机载LiDAR 技术的典型水库水量变化估算

针对传统外业测量水库库容估算方法易受地形、气象条件影响,存在危险性高、效率低等问题,以仑山水库为研究对象,采用机载 LiDAR 技术估算水库面积及水量变化。通过实地数据采集,利用 KD- 树算法剔除点云中的粗差点,并采用渐进加密不规则三角网(PTIND)滤波分离出地面点,完成 LiDAR 点云数据的预处理;根据点云数据建立精细化数字高程模型(DEM),提取不同水位的水库水面面积;最终利用积分和传统棱台体积估算方法对比分析不同水位水量差值变化。结果表明：与高程实测值相比,DEM 高程反演值满足高程精度要求,基于机载 LiDAR 的水量估算受库底坡度变化的影响,估算值低于传统估算方法的估算值,估算结果更为精确,可为监测水库水量变化提供科学依据。