Application of multi-temporal DEM data in calculating the Earth's surface deformation

This paper study the effect of the Digital Elevation Model (DEM) data parameters ( type, source, and resolution) on a slope unit and landslide susceptibility analysis using Geographic Information System (GIS). The Landslide susceptibility analysis and slope unit delineation is done using six DEM data from three different sources (Bige Map, SRTM, and Wietu) at varying resolutions and pixel sizes. The Frequency Ratio Method is then used to analyze Ghana’s susceptibility to seismic landslides whiles; a pseudo-static critical slip method (Factor of safety method) is used to check the accuracy and also indicate the specific slopes to fail at specific seismic loading. The research aimed to achieve the goal since the method allowed for the attainment of an optimum slope unit and landslide map for each resolution of data regardless of the source. The procedure permitted and informed a decision making on the best DEM DATA and resolution for slope unit and Landslide analysis. The result showcases a DEM Resolution of 18 and 30 m and pixel 30m*30m size to be the best DEM to be used for landslide susceptibility mapping. The results further prove Bige Map and STRM DEM to give a more precise effect than weitu.

Flood incidents that often occur in Karawang regency need to be mitigated. These expectations exist on technologies that can predict, anticipate and reduce disaster risks. Flood modeling techniques using Digital Elevation Model (DEM) data can be applied in mitigation activities. High accuracy DEM data used in modeling, will result in better flooding flood models. The result of high accuracy DEM data processing will yield information about surface morphology which can be used to identify indication of flood hazard area. The purpose of this study was to identify and describe flood hazard areas by identifying wetland areas using DEM data and Landsat-8 images. TerraSAR-X high-resolution data is used to detect wetlands from landscapes, while land cover is identified by Landsat image data. The Topography Wetness Index (TWI) method is used to detect and identify wetland areas with basic DEM data, while for land cover analysis using Tasseled Cap Transformation (TCT) method. The result of TWI modeling yields information about potential land of flood. Overlay TWI map with land cover map that produces information that in Karawang regency the most vulnerable areas occur flooding in rice fields. The spatial accuracy of the flood hazard area in this study was 87%.

Because large amount of ground water have been excessively withdrawn within the lowlands along the southwestern area in Taiwan, the land subsidence becomes a serious problem that property damages and life losses in the subsidence area will be caused by the poor drainage and serious flooding arise during the periods of typhoon and heavy rainfall. Therefore, how to monitor and analyze the land subsidence effectively will be an important study issue. In this study, many kinds of geospatial information are firstly collected and analyzed for subsidence monitoring. The error adjustment of the multi-temporal Digital Elevation Models (DEMs) data is implemented using the Least Squares Collocation (LSC) method proposed in our previous research works. Then the HEC-RAS and FLO-2D models are used to simulate the flood hazards to test the performance of the LSC adjustment method. The results are also used to analyze the influence of the land subsidence for flood hazards.

Since hurricane Katrina, developer and planners are looking at a tools and sustainable ways to minimize vulnerabilities in events of disasters especially along the coast. In this setting, remote sensing and Geographic information systems (GIS) have the capacity to provide valuable and timely information about coastal resources, and form an important basis for sustainable planning for land management and decision making. This paper focuses on the design of appropriate coastal databases in six counties in Southern Mississippi using remote sensing and GIS technology. The intent is to enable planners and policy-makers contribute to improved land administration and coastal resources management in order to enhance their competence in decision-making. In order to achieve these objectives, satellite image and digital elevation models (DEMs) data were downloaded from the United States Geological Survey (USGS) seamless data warehouse National Elevation Dataset (NED). From there, the DEM data was co-registered to the satellite image to facilitate draping of the image over the DEM to create terrain models. Result reveals greater part of the three counties along the coast lies less than 10 meters above mean sea level with exposure to coastal disaster vulnerability. In the context of the objectives of the research anchored on the applications of GIS and remote sensing towards efficient land administration and coastal resource management in six coastal counties in an ecologically fragile area already dubbed the epicenter of coastal disasters. Considering the changes that took place in the six counties after Hurricane Katrina debacle, the findings in this study not only stand out, but they remain highly beneficial to decision makers made up of planners and policy makers in several ways. Just as the study injected elements of novelty by identifying digital elevation model information for the six counties in low lying areas, revealing the steeper areas in the north portion of the study stands out as a major finding and study contribution in a way beneficial to decision makers in the region. With that they are now better informed in sharing and cautioning and pinpointing to the public the hidden critical pathways to coastal vulnerability that were previously unknown to ordinary people.

Subgrid variability of solar downward radiation at the surface can be important in estimating subgrid variability of other radiation‐driven variables, such as snowmelt and soil temperature. However, this information is ignored in current hydrological and weather prediction models as only the mean downward solar radiation of model grid is used. In this study, a parameterization for estimating subgrid variability of downward solar radiation from the model grid mean using high‐resolution digital elevation model (DEM) data is proposed. This scheme considers aspect and slope effects on the subgrid variability. The advantage of this scheme is that computations are performed at the same resolution as the considered hydrological or weather prediction model, and subgrid topographic properties derived from high‐resolution DEM data are used as static inputs. This proposed scheme has been verified in mountainous and flat areas, respectively. It is found that the scheme can well estimate the subgrid variability of downward solar radiation. Also, effects of the DEM resolution on the calculated subgrid variability and the spatial correlation of downward solar radiation are studied. Results show that modeled subgrid variability highly depends on the resolution of the DEM, while the spatial correlation is negligibly time dependent. The proposed scheme can be used in any hydrological and weather prediction model to estimate subgrid variability of downward solar radiation. For example, it is planned to be tested in future NOAA regional and global weather models to account for the effects of the subgrid variability of downward solar radiation on the snow model of the land‐surface component.

Using low resolution digital elevation model (DEM) data, reconstruction of cartographic contours is focused on. With a small number of control points, we aim to draw a smooth curve as well as to compute the area and perimeter of a closed contour accurately. The input data forms an oriented contour, each control point of which consists of three values: x, y, and tangent angle. An interpolation method based on arc spline is developed. The method can reproduce G1 continuous curves,in which the exact area and perimeter can be calculated. In a numerical experiment for analytically defined curves, accurate computation of the area and perimeter was achieved with a smaller number of control points. In another experiment using a DEM data, the reconstructed contours were smoother than those by a conventional method.

The drainage pattern of a watershed is an important parameter in nonpoint-source water quality modeling. Manual preparation of this pattern from topographic maps is time–consuming and sometimes subjective. Digital Elevation Model (DEM) data are fundamental cartographic data stored in a uniform grid system which can be easily processed by the computer. Using DEM data and a set of pre-defined rules, the drainage pattern can be automatically determined. Six grid-based methods have been developed, and three of them have been tested for a case study, an area enclosing a subwatershed of Chin-Mei River, Taipei County, Taiwan, R. O. C. The results are comparable to the manually prepared drainage pattern, although several complexities exist for areas such as depression and highly irregular areas. Several refined rules based on the drainage patterns in adjacent areas are utilized to modify the drainage patterns in these areas. Compared with the conventional manual method, the proposed automated methods are much more efficient for regional water quality studies.

Employing K-means clustering to reconstruct missing water surface elevations in LiDAR digital elevation models for hydrodynamic simulation.

Landsat 8, Sentinel 2, Aster, RapidEye and PlanetScope data and Sentinel 1- and Advanced Land Observing Satellite (ALOS)-Phased Array type L-band Synthetic Aperture Radar (PALSAR)-radar images have been evaluated for a karst feature inventory in the Black Hills area in Wyoming/South Dakota, USA. The GeoInformation System (GIS) integrated evaluation of the different satellite data included as well World Imagery files of ESRI and Bing Maps high resolution satellite data of Microsoft. The satellite data revealed several types of circular features related to karst such as enclosed depressions and collapsed dolines as well as traces of tectonic/structural features (visualized by lineament analysis) cutting through youngest sediments, influencing karstification processes. The origin of the circular features is complex and partly unknown, needing further investigations. Digital Elevation Model (DEM) data, such as Aster- and Shuttle Radar Topography Mission (SRTM) DEM data with 30 m and ALOS PASAR DEM with 12.5 m spatial resolution contributed to the detection of depressions, partly related to karst phenomena (sinkholes). Time series of satellite data reveal seasonal changes of the landscape and provide a data base for the documentation of the impact of climate change.

Spatial application of Random Forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data

The availability of watershed delineation that has been generated from DEM data is difficult to obtain and the accessibility of DEM data which are unrestricted and precise are hard to obtain in Malaysia. The aim of this study is to examine the accuracy of watershed delineation between Digital Elevation Model (DEM) from ASTER and SRTM in Johor State. In this study, free online data sources from USGS website are used to delineate watershed from ASTER and SRTM satellite imageries. The hydrological modelling tool namely ArcSWAT is utilized to delineate watersheds for both DEM datasets. Both DEM data that had been mosaiced using ERDAS imagine and their DEM is generated using ArcGIS. Watershed boundary for the whole Johor State is then being delineated by using ArcSWAT. ASTER and SRTM accuracies were verified using correlation analysis and mean center distance with data from Department of Irrigation and Drainage (DID). Study indicated that DID watershed area is correlated to ASTER and SRTM at 67.60% and 67.85%. In addition, total mean center distance for ASTER and SRTM are 148.485 and 200.200 where it shows the total mean center distance of ASTER is almost close with DID. The results from this study have successfully indicated that both ASTER and SRTM DEMs are suitable for watershed delineation for Johor State at free and reliable source.

With the increasing availability of high-resolution digital elevation model (DEM) data, a need has emerged for new processing techniques. Topographic variables such as slope and curvature are relevant on length scales far larger than the pixel resolution of modern DEM data sets. We propose an approach for computing slope and curvature that uses standard regression coefficients over large windows while generating output on the full resolution of the original data, without adding substantially to the computation time. In our approach, to which we will refer as Iterative Aggregation of Regression Terms (IART), aggregates for fitting a quadratic function are computed iteratively from the DEM data in a process that scales logarithmically with the window size. We show that the IART algorithm produces results of much higher-quality than the two-step process of applying neighborhood operations like focal statistics followed by small-window topographic computations, at comparable computational cost.

In atmospheric modeling, sub-grid parameterization is an important method for studying the topographic effects of solar radiation using high-resolution Digital Elevation Model (DEM) data. For reducing the amount of computation, some approximate methods that can lead to errors are used in existing sub-grid parameterization schemes for clear-sky direct solar radiation (SPS-CSDSR). The lack of a high-precision fast terrain occlusion algorithm (HPFTOA) remains one of the biggest constraints in this field. This study proposed an HPFTOA. It mainly uses two kinds of acceleration algorithms. One method is to use a dynamic, lossless, and fast occlusion search radius. Another way is to use the rectangular grid for calculations within the accuracy of DEM data to avoid coordinate projection conversions. The test results indicate that the HPFTOA can carry out large-scale computation based on DEM data with a resolution of 90 m. Because it rarely uses approximation algorithms and considers the curvature of the Earth, SPS-CSDSR can achieve unprecedented precision. The HPFTOA can also be used in the fields of mountain solar energy assessment, remote sensing, and telemetry, including terrain-obscuring the probe. As computer performance improves and algorithms and execution code are optimized, the application prospects will be very broad.

Biomass carbon stocks and stock changes in managed hedgerows

Texture and its analysis methods are crucial for image feature extraction and classification. Digital elevation model (DEM) is the most important data source of digital terrain analysis and landform classification, and considerable research values are gained from texture feature extraction and analysis from DEM data. In this research, on the basis of optimal texture feature extraction, the hilly area in Shandong, China, was selected as the study area, and DEM data with a resolution of 500 m were used as the experimental data for landform classification. First, second-order texture measures and texture image were extracted from DEM data by using a gray level cooccurrence matrix (GLCM). Second, the variation characteristics of each texture measure were analyzed, and the optimal feature parameters, such as direction, gray level, and texture window, were determined. Meanwhile, the texture feature value, combined with maximum information, was calculated, and the multiband texture image was obtained by resolving three optimal texture measure images. Finally, a support vector machine (SVM) method was adopted to classify landforms on the basis of the multiband texture image. Results indicated that the texture features of DEM data can be sufficiently represented and measured via the quantitative GLCM method. However, the feature parameters during the texture feature value calculation required further optimization. Based on the image texture from DEM data, efficient classification accuracy and ideal classification effect were achieved.