GIS-Based Digital Terrain Analysis of Assam-Meghalaya Foothills in Kamrup District, Assam

Terrain characteristics and their evaluation usually come under geomorphological study and more particularly the applied geomorphological study (Prasad & Sarkar 2011). Foothills are a geographically defined zone having a gradual increase in elevation at the base of a mountain or hill range. Detailed assessment of the present terrain parameters of the study area using GIS is significant as it shows the influence on the landscape of the area. It is a prerequisite in effective management of the impact of transition upon the landscape and its natural resources for sustainable management. In the study, an attempt has been made to delineate the foothill belt of the Assam-Meghalaya border in Kamrup District, Assam using Geographical Information system (GIS), and remote sensing techniques. Datasets available from USGS Earth Explorer, i.e. Shuttle Radar Topographic Mission (SRTM) and Digital Elevation Model (DEM) are used for analyzing the elevation, contour, slope, and terrain characteristics. The present study aims at getting an information archive of the geomorphological and terrain characteristics of the Assam-Meghalaya foothills in Kamrup District, Assam, and its spatio-temporal variation.

Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool

Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (20m) and Landsat 8 imagery (30m) using either a single date of imagery or two dates of imagery for mapping land cover type. The two-date model includes 11 land cover type classes, whereas the single-date model contains 6 classes. The models’ overall accuracies were 84% (Sentinel-2 single date), 82% (Sentinel-2 two date), and 86% (Landsat 8 two date) across the continental United States. The three different models were built into an ArcGIS Pro Python toolbox to enable a semi-automated workflow for end users to generate their own land cover type maps on demand. The toolboxes were built using parallel processing and image-splitting techniques to enable faster computation and for use on less-powerful machines.

Dynamic Intervisibility Analysis of 3D Point Clouds

With the popularity of ground and airborne three-dimensional laser scanning hardware and the development of advanced technologies for computer vision in geometrical measurement, intelligent processing of point clouds has become a hot issue in artificial intelligence. The intervisibility analysis in 3D space can use viewpoint, view distance, and elevation values and consider terrain occlusion to derive the intervisibility between two points. In this study, we first use the 3D point cloud of reflected signals from the intelligent autonomous driving vehicle’s 3D scanner to estimate the field-of-view of multi-dimensional data alignment. Then, the forced metrics of mechanical Riemann geometry are used to construct the Manifold Auxiliary Surface (MAS). With the help of the spectral analysis of the finite element topology structure constructed by the MAS, an innovative dynamic intervisibility calculation is finally realized under the geometric calculation conditions of the Mix-Planes Calculation Structure (MPCS). Different from advanced methods of global and interpolation pathway-based point clouds computing, we have removed the 99.54% high-noise background and reduced the computational complexity by 98.65%. Our computation time can reach an average processing time of 0.1044 s for one frame with a 25 fps acquisition rate of the original vision sensor. The remarkable experimental results and significant evaluations from multiple runs demonstrate that the proposed dynamic intervisibility analysis has high accuracy, strong robustness, and high efficiency. This technology can assist in terrain analysis, military guidance, and dynamic driving path planning, Simultaneous Localization And Mapping (SLAM), communication base station siting, etc., is of great significance in both theoretical technology and market applications.

Accuracy Assessment of TanDEM-X 90 and CartoDEM Using ICESat-2 Datasets for Plain Regions of Ratlam City and Surroundings

The spaceborne LiDAR dataset from the Ice, Cloud, and Land Elevation Satellite (ICESat-2) provides highly accurate measurements of heights for the Earth’s surface, which helps in terrain analysis, visualization, and decision making for many applications. TanDEM-X 90 (90 m) and CartoDEM V3R1 (30 m) elevation are among the high-quality openly accessible DEM datasets for the plain regions in India. These two DEMs are validated against the ICESat-2 elevation datasets for the relatively plain areas of Ratlam City and its surroundings. The mean error (ME), mean absolute error (MAE), and root mean square error (RMSE) of TanDEM-X 90 DEM are 1.35 m, 1.48 m, and 2.19 m, respectively. The computed ME, MAE, and RMSE for CartoDEM V3R1 are 3.05 m, 3.18 m, and 3.82 m, respectively. The statistical results reveal that TanDEM-X 90 performs better in plain areas than CartoDEMV3R1. The study further indicates that these DEMs and spaceborne LiDAR datasets can be useful for planning various works requiring height as an important parameter, such as the layout of pipelines or cut and fill calculations for various construction activities. The TanDEM-X 90 can assist planners in quick assessments of the terrain for infrastructural developments, which otherwise need time-consuming traditional surveys using theodolite or a total station.

Quantitative and Qualitative Terrain Analysis Based on Digital Terrain Model

Within the Digital Terrain Models (DTM) processing and consequently qualitative and quantitative analysis, it is possible to gain a credible imagination of real terrain shape. In order to obtain an appropriate DTM, it is necessary to decrease the influence of the gross errors that have negative effects on the final DTM. These gross errors may degrade and in the worst case also ruin the calculations and the final outputs. The gross errors have a greater impact and are harder to define in complicated terrain and pointing out these types of errors depends on the editor’s experiences and terrain knowledge.

The analysis of morphometric parameters in hydrological modeling using GIS

The analysis of the physical-geographical conditions determines and influences the formation and the regime of the water resources from a hydrographic basin. This paper aims to analyse spatial data based on raster models, more precisely the terrain analysis, later used in hydrological modelling. For the elaboration of the digital model of the terrain, methods of interpolation of certain data are used - the level curves - after which, by running the ArcGIS program will result the structure of the irregular triangulation network (TIN). Next, based on the TIN model, a set of analyses is obtained regarding the morphology of the terrain: the slope map; slope exposure map, etc. The slope is one of the most important factors for controlling surface and intermediate water runoff. The exposure of the slopes depends very much on the direction of the slope of the land. With the help of the analysis of the slope and slope exposure it is possible to: calculate the solar lighting for each location in a region; find all slopes in the southern part of a mountainous region to identify locations where the snowmelt process will start earlier than in other areas, thus avoiding the danger of flooding due to runoff from the slopes and the danger of soil erosion; the value and speed of surface runoff; identify the spread and abundance of flora and fauna, precipitation; identify the productivity classes of the land; find all the north-facing slopes on a mountain as part for the search of the best ski slopes.