GIS and Remote Sensing Applications in Geomorphology

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Remote Sensing (RS) and Geographic Information Systems (GIS) are useful tools in hydrological analysis and natural resource management. The application of RS and GIS techniques lends to estimate soil loss based on different parameters. RUSLE (Revised Universal Soil Loss Equation) model is used for soil loss estimation. Different parameters, namely the rainfall and runoff factor (R), soil erodibility factor (K), slope length and steepness factor (LS), crop management factor (C) and conservation practice factor (P), that are the mandatory inputs to RUSLE, have been either derived from remote sensing data or through conventional data collection systems. These parameters are obtained from monthly and annual rainfall data, soil map of the region, Digital Elevation Model (DEM), RS techniques (with use of Normalized Difference Vegetation Index) and land use/land cover map, respectively. This experiential study is carried out on the Kaliaghai river basin under Purbo and Paschim Medinipur district of West Bengal. Soil loss is very high in the river basin area, calculated as 1927779 tons/year using RUSLE model. Thus, the RUSLE model integrated with RS and GIS technologies has great potential for producing accurate and inexpensive erosion and sediment yield assessment map in the Kaliaghai river basin. Keywords: Remote Sensing, Geographical Information System, Soil loss and RUSLE model

The morphological changes of rivers and lakes continue as a result of several factors, including natural and human. These changes include rivers' pathways, deposition of sediment, erosion, and flooding due to water level fluctuation. It is therefore necessary to understand these changes, which are the basis for the sustainable management of water resources. The Remote Sensing (RS) technique is often regarded as very efficient for monitoring natural events, including the morphological characteristics of river basins. This study has addressed the worldwide morphological changes of rivers and lakes. It has monitored them using modern techniques such as remote sensing techniques and its effectiveness in assessing these changes. Remote sensing technology is an advanced tool brought about a significant shift in the field of studying the morphological changes of rivers and lakes. This technology has provided the capability to collect and analyze data on a large scale, offering precise and up-to-date information about morphological changes not only for aquatic systems but also for landforms and physical alterations. This technology relies on multiple applications for assessing morphological changes, including satellite imaging, LiDAR (Light Detection and Ranging) techniques, and radar systems. The results show the effectiveness of remote sensing techniques and geographic information systems in analyzing and monitoring the morphological changes of rivers, lakes, and the aquatic environment in general, as these techniques save effort, time, and accuracy compared to traditional methods and fieldwork. The findings of this research indicate that remote sensing technology and geographic information systems are highly effective in monitoring morphological changes in rivers and lakes. These methods provide precise results in a significantly shorter timeframe compared to conventional approaches. Additionally, they offer precise and dependable outcomes capable of encompassing extensive regions and gathering crucial and varied information such as terrain heights, water movement, sediment dispersion, and early identification of geological problems.

Integrated remote sensing and Geographic Information System provides an aid to find presence of metallic and non-metallic minerals. This approach was used to explore Iron deposits in districts of Gandaki and Lumbini province of Nepal in which 8 districts were identified as the reserves of iron ores namely Nawalpur, Palpa, Baglung, Parbat, Syangja, Tanahu, Arghakhanchi and Gulmi. These districts fall under various geological formation such as Suntar formation, Melpani formation. These formations are found to be rich in metallic minerals. Remote Sensing (RS) and Geographic Information Systems (GIS) offers a cost effective and attractive strategy in mineral prospection, notably for a mineral exploration project. Landsat 8 satellite images, processed through band rationing method highlights the hydrothermal alterations and Iron Oxide containing region. False Color Composite and Principal Component Analysis method highlights sedimentary rocks. Each method exaggerates spectral signatures which highlights the surfaces by different colors indicating presence of iron ores. Lineaments are also extracted from Landsat images combining with Digital Elevation Model (DEM).Lineaments are those structures that provide information about fault and fractures on the surface and helps to identify mineral deposition zones. The result produced through different RS techniques were introduced in GIS environment. Potential iron content region were identified by using Suitability analysis i.e. multi criteria decision analysis (MCDA). Map showing the potential iron ore deposit map was generated by integrating the results together reclassified into a common scale and overlaid with suitable weightage. Final output/map indicates the best possible sites for detailed study of iron ores. The study demonstrates the usefulness and effectiveness of remote sensing and GIS in iron and other mineral mapping.

Lava flows have severe impacts on the Earth’s surface, communities, and natural habitats. Accurate modeling of lava flow paths is crucial for volcanic hazard assessment and risk mitigation. This research integrated remote sensing, geographic information systems, geomorphological techniques, and hydrologic analysis to numerically determine the optimal ground sample distance (GSD) of digital elevation models (DEMs) for delineating lava flow paths. Two volcanic regions were analyzed: the Afar Region of Ethiopia (Alu, Dalaffilla, and Erta Ale volcanoes) and the Reykjanes Peninsula of Iceland (Fagradalsfjall volcano). Forward and backward modeling methods were used to simulate potential lava flow paths. Remote sensing data, including multispectral and thermal images as well as DEMs, were utilized for analysis. A large-scale mean-shift image segmentation method was applied to identify lava objects and extract the main centerline for analysis. The forward model was applied to the fissures between the Alu and Dalaffilla volcanoes. The main lava centerline was derived from a 2008 ASTER image, while flow paths were simulated using the 2000 SRTM DEM. An optimal GSD of 35 m resulted in the lowest root mean square error (RMSE) of 146.89 m. This was validated using the 2017 Erta Ale eruption, confirming 35 m as a suitable GSD for delineating a major lava polygon. In the backward model, hydrologic analysis on resampled DEMs using TauDEM identified an optimal GSD of 38 m with an RMSE of 123.44 m. In the study of the Fagradalsfjall volcano, an optimal GSD of 71 m with an RMSE of 98.84 m was obtained, while the backward model yielded an optimal GSD of 33 m with an RMSE of 173.56 m. Statistical analyses were conducted to validate the results. This research contributed to volcanic hazard assessment by providing a framework that integrated geospatial technologies into lava flow modeling. Initiating simulation from the observed lava outlet, the forward model addressed a key limitation of the backward model. Results indicate that lava flow path accuracy was sensitive to the GSD of a DEM. The findings further demonstrate that the forward model may aid emergency response planning and risk mitigation strategies in volcanic regions.

Automated feature extraction based on prototypes is only partially successful when applied to remotely sensed imagery of natural scenes due to the complexity and unpredictability of the shape and geometry of natural features. Here, a new method is developed for extracting the locations of treetops by applying GIS (Geographical Information System) overlay techniques and morphological functions to high spatial resolution airborne imagery. This method is based on the geometrical and spatial properties of tree crowns. Airborne data of the study site in the New Forest, UK included colour aerial photographs, LIDAR (Light Detection And Ranging) and ATM (Airborne Thematic Mapper) imagery. A DEM (Digital Elevation Model) was generated from LIDAR data and then subtracted from the original LIDAR image to create a Canopy Height Model (CHM). A set of procedures using image contouring and the manipulation of the resulting polygons was implemented to extract treetops from the aerial photographs and the CHM. Criteria were developed and threshold values were set using a supervised approach for the acceptance or rejection of features based on field knowledge. Tree species were mapped by classifying the ATM data and these data were co‐registered with the treetop layer. For broadleaved deciduous plantations the success of treetop extraction using aerial photographs was 91%, but was much lower using LIDAR data. For semi‐natural forests, the LIDAR produced better results than the aerial photographs with a success of 80%, which was considered high, given the complexity of these uneven aged stands. The methodology presented here is easy to apply as it is implemented within a GIS and the final product is an accurate map with information about the location, height and species of each tree.

The use of 1D, 2D, and 1D/2D modelling techniques to identify flood prone areas is a critical component of any flood hazard management project (e.g., APDF – action plan for dam failure) in the proximity of big dams and reservoirs. In this work, we manage to computed flood hazard models using 2D HEC-RAS module based on Digital Elevation Models (DEM’s) derived from Light Detection and Ranging (LiDAR) data and pre- and post-processed using Geographic Information Systems (GIS)-based software (e.g., ArcGIS, HEC-RAS). Therefore, to produce urban flood hazard (FH) maps downstream of the Strâmtori-Firiza reservoir (S-Fr) in NW Romania, a multi-scenario approach based on LiDAR-derived DEM integration, 2D hydraulic modeling, and remote sensing (RS) data validation is provided. In this context, to assess the flood control capacity (FCC) of the S-Fr hydro-technical system, three flood tests based on S-Fr flow rate with 5% (167 m3/s), 1% (270 m3/s) and 0.1% (447 m3/s) return periods were performed. The flood impact within the urban area of Baia Mare located downstream of S-Fr was achieved for each flood scenarios using four spatial data derived from the RAS Mapper module: flood extent (FE), flood depth (FD), flood velocity (FV) and flood hazard (FH). The results indicate that a large area of Baia Mare city can be affected by a potential flood caused by a dam failure and also contribute to the APDF update of S-Fr dam.

Solar radiation significantly affects many processes on Earth. In situ measurements are demanding and require a dense network of sensors. A suitable alternative solution could be the modelling of potential solar radiation based on a digital elevation model (DEM) in geographic information systems. The key issue of this study is to determine the influence of the terrain parameters and the spatial resolution of a DEM on the calculation of potential solar radiation. The area of study is the Tatra Mountains (the highest mountains of the Carpathians). The DEM determined from light detection and ranging (LiDAR) was used. To determine the influence of the terrain, the following terrain parameters were applied: slope; aspect, represented by northness and eastness; elevation; and topographical position index using six different circular neighbourhoods (10 m, 30 m, 50 m, 100 m, 500 m, and 1000 m). The results indicate a moderate correlation (0.32–0.46) between the solar radiation calculation errors and the absolute values of the topographic position indices with small neighbourhoods (10 m–100 m). To show the impact of the spatial resolution, the calculation was performed based on four different DEM resolutions, namely 5 m, 10 m, 30 m, and 90 m. Mutual differences in potential solar radiation were quantified concerning the topographic position index. The result is also a model of potential annual solar radiation in the Tatra Mountains, calculated at a resolution of 5 m or 2 m.

This Special Issue collates seven papers regarding the assessment or monitoring of hydrological disasters such as droughts and flood using remote sensing and geography information system (GIS) techniques.. The new published research focused on evaluations and models of various hydrological hazards such as droughts and floods. Furthermore, we include two original scientific articles addressing the subject of water quality. This Special Issue received investigations based on different techniques such as remote sensing, GIS, machine learning and monitoring. All papers present findings characterized as unconventional, provocative, innovative and methodologically new. Scientific findings presented in this Special Issue highlight how a combination of various modern analysis techniques (e.g., remote sensing, GIS) can improve our understanding of complex hydrological hazards such as droughts and floods. We hope that the research contained within this Special Issue is useful to the scientific community, policymakers and stakeholders at large in the field of hydrological hazards.

Groundwater is one of the most valuable natural resources, which supports human health, economic development and ecological diversity. Overexploitation and unabated pollution of this vital resource is threatening our ecosystems and even the life of future generations. With the advent of powerful personal computers and the advances in space technology, efficient techniques for land and water management have evolved of which RS (remote sensing) and GIS (geographic information system) are of great significance. These techniques have fundamentally changed our thoughts and ways to manage natural resources in general and water resources in particular. The main intent of the present paper is to highlight RS and GIS technologies and to present a comprehensive review on their applications to groundwater hydrology. A detailed survey of literature revealed six major areas of RS and GIS applications in groundwater hydrology: (i) exploration and assessment of groundwater resources, (ii) selection of artificial recharge sites, (iii) GIS-based subsurface flow and pollution modeling, (iv) groundwater-pollution hazard assessment and protection planning, (v) estimation of natural recharge distribution, and (vi) hydrogeologic data analysis and process monitoring. Although the use of these techniques in groundwater studies has rapidly increased since early nineties, the success rate is very limited and most applications are still in their infancy. Based on this review, salient areas in need of further research and development are discussed, together with the constraints for RS and GIS applications in developing nations. More and more RS- and GIS-based groundwater studies are recommended to be carried out in conjunction with field investigations to effectively exploit the expanding potential of RS and GIS technologies, which will perfect and standardize current applications as well as evolve new approaches and applications. It is concluded that both the RS and GIS technologies have great potential to revolutionize the monitoring and management of vital groundwater resources in the future, though some challenges are daunting before hydrogeologists/hydrologists.

Geographic Information Systems (GIS) and Remote Sensing (RS) applications are becoming an important issue for the territorial management, governmental and research projects, and for many fields of our society. A characteristic of such applications is the displaying of successive layers of information that, in some cases, may overlap areas of the displayed images that are eventually never showed to the final user of the application. Even though these overlapped areas are of null interest, the coding of these images considers the complete area of the image, and thus the coding performance of the compression system is penalized. This paper introduces a novel use of the Region Of Interest (ROI) coding techniques to overcome the drawbacks of the map overlapping in GIS and RS applications. The proposed approach is based on a ROI coding method defined for the JPEG2000 standard that efficiently improves the coding performance and keeps JPEG2000 compliance.

Light Detection and Ranging (LIDAR) has been used for years with a variety of applications, including the efficient creation of digital terrain models (DTMs) for large-scale, high- accuracy mapping. LIDAR technology offers fast, real-time collection of 3-D points so that allows the production of higher-accuracy orthophotos than traditional stereo-compilation methods. The technology is moving quickly toward offering more efficient collection techniques for applications such as city modelling and true orthophoto production. Traditional orthophoto production suffers from the fact that buildings (and any other objects above ground) are not correctly placed in the orthophoto. True orthophoto production overcomes these deficiencies by taking the digital surface model (DSM) into account. However, the resulting true orthophotos still suffer from occluded areas and unsharp edges of buildings and roads. The experimental investigations with different data sets show that it is essential to use high quality DSMs to produce high quality true orthophotos. In this paper, aspects of the improvement of true orthophoto production will be discussed: (a) in the first approach which has been widely used in photogrammetry, the DSM is used for generating true orthophotos and (b) in comparison to the DSM based method, semi-automatically collected vector data of buildings are superimposed on DTM and are used as input data for generating true orthophotos in the second approach. The processes and problems of both true orthophoto production procedures will be investigated in detail and results include the comparison of true orthophoto productions based on DSM (from LIDAR data) and DTM plus vector data. For the experimental investigation, a LIDAR data set for downtown Stuttgart is used.

The detailed representation of the topography is an important aspect when dealing with any flood simulation model. A Digital Surface Model (DSM) with fine spatial resolution can capture topographic object boundaries with a high level of detail. Light detection and ranging (LiDAR) is a technology that can rapidly collect elevation data of a relatively large topographic area with fine spatial resolution (< 0.5m ∼1m). Information about physical objects on the DSM is critical for storm water management as the propagation of flood water is influenced by both the terrain and the objects on the terrain. The processing of LiDAR data is an important task for generating an accurate DSM suitable to running a flood simulation model. Filtering is the first step in processing LiDAR data. In the LiDAR community, the term “filtering” means the task of separating LiDAR data into ground (bare earth) and non-ground points (buildings and trees). However, the choice of appropriate filtering algorithm is crucial in order to detect micro objects such as boundary walls, fences, and thin embankments that have significant impact on the surface flow pattern. This study offers an innovative approach for developing a LiDAR filtering algorithm that is flexible in nature and able to detect not only buildings and trees but also the micro objects. The filtering algorithm is applied to two study areas, and the output suggests close correspondence to reality.

This study estimates the equivalent continuous sound pressure level (Leq) during peak daily periods (‘rush hour’) along the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia, using a land use regression (LUR) model based on machine learning, statistical regression, and geographical information systems (GIS). The research utilises two types of soft computing methods including machine learning (i.e., decision tree, random frost algorithms) and statistical regression (i.e., linear regression, support vector regression algorithms) to determine the best approach to create a prediction Leq map at the NKVE in Shah Alam, Malaysia. The selection of the best algorithm is accomplished by considering correlation, correlation coefficient, mean-absolute-error, mean-square-error, root-mean-square-error, and mean absolute percentage error. Traffic noise level was monitored using three sound level meters (TES 52A), and a traffic tally was done to analyse the traffic flow. Wind speed was gauged using a wind speed meter. The study relied on a variety of noise predictors including wind speed, digital elevation model, land use type (specifically, if it was residential, industrial, or natural reserve), residential density, road type (expressway, primary, and secondary) and traffic noise average (Leq). The above parameters were fed as inputs into the LUR model. Additional noise influencing factors such as traffic lights, intersections, road toll gates, gas stations, and public transportation infrastructures (bus stop and bus line) are also considered in this study. The models utilised parameters derived from LiDAR (Light Detection and Ranging) data, and various GIS (Geographical Information Systems) layers were extracted to produce the prediction maps. The results highlighted the superior performances by the machine learning (random forest) models compared to the statistical regression-based models.

Digital Elevation Model (DEM) is main input for watershed modelling. Recently, (DEM) is available online for free in different accuracies, and spatial resolutions as a product of several remote sensing satellites. Hence, it is necessary to find out which one is the best for watershed modeling in the study area. In this study, the different accuracies 30m spatial resolution DEMs of (Copernicus, SRTM, and ASTER) can be examined by using Remote sensing (RS) and Geographic Information systems (GIS) techniques to delineate and calculate the topographic characteristics for five different size and topography watersheds (Swaidy, Garlond, Khuwayr Hirah, Naqab, and Kalak) located on both sides of the Mosul reservoir in the northeastern part of Iraq. The analysis results can be led to find that the Copernicus (GLO-30) 30 m resolution DEM is the optimum and most accurate DEM in the selected study area at the vertical accuracy (1.3521 m) and with 95% confidence level is (2.6502 m) represented by the minimum Root Mean Square Error (RMSE) of the elevations differences between check points (MOWR elevations and LIDAR DEM 1 m resolution) and the used DEMs. The watershed delineation and calculated topographic characteristics (watersheds boundary, elevations, area, perimeter and slope areas) are affected by DEM accuracy. Where the considerable accuracy of the differences is with the (Copernicus-SRTM) DEMS at the minimum RMSE of watersheds characteristics.