Shuttle Radar Topographic Mission Elevation Research Articles

Geospatial Assessment and Mapping of Suitable Sites for a Utility-scale Solar PV Farm

Geospatial and multi-criteria decision-making techniques are applied to process and analyse datasets for determining suitable areas for multiple utilityscale solar photovoltaic farms in the city of Akure, Ondo State, southwestern Nigeria. Data processed include local electric power distribution system data, Shuttle Radar Topographic Mission elevation data, Landsat 8 and solar global horizontal irradiance. Multi-criteria decision-making techniques adopted are the analytical hierarchy process, reclassification, and overlay. These techniques were carried out considering criteria for siting solar photovoltaic farms. Criteria considered in this study are climate, topography, economic, environmental impact operational and technical while sub-criteria are solar global horizontal irradiance, slope, proximity and land cover. The outcome of the study shows that the study area covering a total extent of ~33,200 ha, 15%, 8%, 13% and 64% are highly suitable, suitable, moderately suitable, and unsuitable respectively for siting utility-scale solar photovoltaic farms within the study area. The study reveals the potential of multiple utility-scale solar photovoltaic farms in the study area. However, the proportions of areas suitable for solar photovoltaic farms are quite lower compared to findings from similar studies conducted in northwestern Nigeria. The study recommends solar photovoltaic sources as an alternative energy source in and around the study area.

Generating and analyzing Terrain characteristics from Shuttle Radar Topographic Mission (SRTM), DEM

Terrain analysis is the quantitative analysis of topographic surfaces. The purpose of a digital terrain system is to provide the digital representation of terrain so that environmental problem like soil erosion may be approached accurately and efficiently through automated means. Traditionally this was (and still is!) being done manually by using topographic/contour maps. With the availability of Digital Elevation Models (DEM) and GIS tools, watershed properties can be extracted by using automated procedures. Remote Sensing and Digital elevation models (DEMs) are known to be very useful data sources for the automated delineation of flow paths, sub watersheds and flow networks for hydrologic modelling and watershed characterization. The digital terrain model was extracted from a 90m resolution Shuttle Radar Topographic Mission (SRTM) of the study area. The SRTM data was corrected by removing voids, striping, tree offsets and random noise. The SRTM DEM data was projected from geographic coordinate WGS 84 to UTM zone 32 of the study area. The 3-D analysis tool of the ArcGIS 10.1 was used for this process. The DEM was processed to obtain the Slope, Contour, Flow direction, Flow accumulation, Flow length, Stream power Index of the study area. The study proved that SRTM elevation dataset has the ability to obviate the lack of terrain data for hydrologic modelling using ArcGIS where appropriate data for terrain modelling and simulation of hydrological processes is unavailable.

Comparative Analysis of Ellipsoidal Height and Shuttle Radar Topographic Mission Elevation

Ellipsoidal elevation represents a precise geospatial data type within the analysis and modelling of various hydrological and ecological phenomenon required in preserving the human environment. Likewise, Shuttle Radar Topographic Mission (SRTM) has created an unparalleled data set of global elevations that are freely available for modelling ubiquitous environmental applications. This research aims to carry out a comparative analysis of ellipsoidal heights and SRTM heights with the following objectives: downloading DEM’s (SRTM) data covering the study area, determining the spot heights within the boundary in conventional method, extract DEM’S heights within the boundary of the study area, and compared the heights in the conventional method with DEM’S heights. South GPS and Leica Total Station were used to acquire data for control extension and spot heightening respectively while the elevation of SRTM data was obtained by transforming the X and Y data from GPS observationto Longitude and Latitude before using ArcGIS 10.6 to extract the elevation of the boundary pillar and all the spot heights which were relatively compared in terms of its products- heights, contour, 3-D wireframe, 3-D surface model, and overlaid of contour on shaded relief. The results of the study showed that vertical difference using conventional method and SRTM dataset ranges between -2.345m to 11.026m. Also, the hypothesis tested using a two-tail student t-test and F-test revealed that one mean is not significantly different from the other at 95% confidence level. The research recommends that the products obtained for the two systems can be used interchangeably.
 Keywords: Shuttle radar topographic mission, Ellipsoidal elevation, contour, 3D wireframe, 3D surface model

Application of Remote Sensing, GIS and Hydrogeophysics to Groundwater Exploration in Ogun State: A case study of OGD-Sparklight Estate

With the increase in population of Ogun state, the necessity to provide water to the populace has become a disturbing problem. In this study, a systematic approach to delineate the groundwater potential zones of the state was carried out using Remote Sensing, Geographic Information Systems (GIS) and Hydrogeophysics as a tool. Vertical Electrical Sounding (VES) observations were also carried out in OGD Sparklight Estate to validate the results obtained from the integrated remote sensing and GIS observation and also determine the aquifer depth and possible pollution. The various thematic maps such as: soil map, land use/Land, geological map, rainfall map, lineaments map were obtained from enhanced satellite imagery and Slope map was generated from Shuttle Radar Topographic Mission elevation model (SRTM DEM). These maps were overlaid in terms of weighed overlay method using Spatial Analysis tool in Arc GIS 10.4. During weighed overlay analysis, different ranks were given to each individual parameter of each thematic map and weights were assigned according to their influence. The groundwater potential map obtained from the study area showed that 47% of the total study area (Ogun state) lie within the “very high” potential zone, 15% of the area falls within the “high”, 30% lies within the of “moderate” zone, 5% lies within the “low “potential zone while “2% “ lies within the very low potential zone. The very high potential areas lie within the sedimentary zone in the southern part of the study area with high alluvial deposits, while the “very low” prospect zone lies majorly within the basement complex zone in the northern part of the study area. The boreholes susceptible to salt water intrusion were identified and the best drilling point with respect to depth were also determined.

Groundwater Recharge Estimation Using WetSpass Modeling in Upper Bilate Catchment, Southern Ethiopia

The study area, upper Bilate catchment, is strongly dependent on groundwater like other rural catchments in the country. The main objective of this work is to quantify the amount of groundwater recharge in upper Bilate catchment. Recharge was estimated using physically based distributed recharge model called WetSpass. As input to the model precipitation, potential evapotranspiration, temperature and wind speed were estimated using data collected from meteorological stations located within the catchment and nearby areas. The physical environmental data including land use, soil type, and groundwater depth were collected from field and existing maps. Slope and topography map were generated from Shuttle Radar Topographic Mission elevation data. Using the model the mean annual recharge of the catchment was found to be 9.4 % of the precipitation whereas the direct runoff was found to be 20 % of the precipitation. The study area is characterized by lower groundwater recharge relative to surface runoff due to the effect of impermeable soils, morphology of the land and land use/land cover of the study area. The western and northern parts of the area are identified as recharging zone and the central and southern part are discharge zone. The groundwater recharge zoning map was validated using base flow separation method and also compared with previous groundwater recharge works of the study area. This study revealed that the groundwater recharge estimation using WetSpass model is reasonable and useful for quantification of annual groundwater recharge with spatial and seasonal variation and also capable in the identification of groundwater recharge zones in the area under study. Keywords: Runoff; Evapotranspiration; Groundwater; WetSpass; Bilate catchment; Ethiopia.

Mapping boreal forest biomass from a SRTM and TanDEM-X based on canopy height model and Landsat spectral indices

We propose a method for mapping above-ground biomass (AGB) (Mg ha−1) in boreal forests based predominantly on Landsat 8 images and on canopy height models (CHM) generated using interferometric synthetic aperture radar (InSAR) from the Shuttle Radar Topographic Mission (SRTM) and the TanDEM-X mission. The original SRTM digital elevation model (DEM) was corrected by modelling the respective effects of landform and land cover on its errors and then subtracted from a TanDEM-X DSM to produce a SAR CHM. Among all the landform factors, the terrain curvature had the largest effect on SRTM elevation errors, with a r2 of 0.29. The NDSI was the best predictor of the residual SRTM land cover error, with a r2 of 0.30. The final SAR CHM had a RMSE of 2.45 m, with a bias of 0.07 m, compared to a lidar-based CHM. An AGB prediction model was developed based on a combination of the SAR CHM, TanDEM-X coherence, Landsat 8 NDVI, and other vegetation indices of RVI, DVI, GRVI, EVI, LAI, GNDVI, SAVI, GVI, Brightness, Greenness, and Wetness. The best results were obtained using a Random forest regression algorithm, at the stand level, yielding a RMSE of 26 Mg ha−1 (34% of average biomass), with a r2 of 0.62. This method has the potential of creating spatially continuous biomass maps over entire biomes using only spaceborne sensors and requiring only low-intensity calibration.

A practical method for SRTM DEM correction over vegetated mountain areas

Digital elevation models (DEMs) are essential to various applications in topography, geomorphology, hydrology, and ecology. The Shuttle Radar Topographic Mission (SRTM) DEM data set is one of the most complete and most widely used DEM data sets; it provides accurate information on elevations over bare land areas. However, the accuracy of SRTM data over vegetated mountain areas is relatively low as a result of the high relief and the penetration limitation of the C-band used for obtaining global DEM products. The objective of this study is to assess the performance of SRTM DEMs and correct them over vegetated mountain areas with small-footprint airborne Light Detection and Ranging (Lidar) data, which can develop elevation products and vegetation products [e.g., vegetation height, Leaf Area Index (LAI)] of high accuracy. The assessing results show that SRTM elevations are systematically higher than those of the actual land surfaces over vegetated mountain areas. The mean difference between SRTM DEM and Lidar DEM increases with vegetation height, whereas the standard deviation of the difference increases with slope. To improve the accuracy of SRTM DEM over vegetated mountain areas, a regression model between the SRTM elevation bias and vegetation height, LAI, and slope was developed based on one control site. Without changing any coefficients, this model was proved to be applicable in all the nine study sites, which have various topography and vegetation conditions. The mean bias of the corrected SRTM DEM at the nine study sites using this model (absolute value) is 89% smaller than that of the original SRTM DEM, and the standard deviation of the corrected SRTM elevation bias is 11% smaller.

Avaliação da qualidade de ortoimagens do sensor PRISM/ALOS-2: estudo de caso para São Gabriel, RS, Brasil

The focus of this study was the generation and evaluation of ALOS/PRISM orthoimages using DEMs (Digital Elevation Models) derived from distinct combinations of sensor viewing from a 1B1 image processing level. The studied area chosen was a rural one located in São Gabriel municipality, in the State of Rio Grande do Sul, Brazil, which is nearly flat (average altimetry 160 m). In the first stage the DEMs were generated using a combination of the three viewing and control points obtained through field survey. Among other parameters, the minimum amount of control points needed that could generate the MDE, the processing level and the window size were tested. Next, two results were compared: the extraction with ASTER (GDEM) Digital Elevation Models and SRTM (Shuttle Radar Topographic Mission), which was generated from the Project TOPODATA from the National Institute for Space Research (INPE, 2009). Finally, the DEMs with the checkpoints were evaluated and the best DEMs for orthorectification were selected using statistical analysis. The results showed that for this specific study all DEMs combining views Backward-Forward generated from the control points would be approved, since they have the lowest values of RMS (Mean Square Error between 2.94 and 3m). Moreover, it was shown that when using DEMs from other combinations, the orthoimages obtained also comply with the Cartographic Accuracy Standard (PEC) for Class A scale 1:25,000 as well as the orthoimages generated from Global SRTM Elevation Models and GDEM. The flatness of the studied area seems to be the main reason for the good performance achieved by SRTM together with the PRISM generated DEMs.Key words: ALOS/PRISM orthoimages, 1B1 processing level, Digital Elevation Models, orthorectification.

Intelligent estimation of spatially distributed soil physical properties

Spatial analysis of soil samples is often times not possible when measurements are limited in number or clustered. To obviate potential problems, we propose a new approach based on the self-organizing map (SOM) technique. This approach exploits underlying nonlinear relation of the steady-state geomorphic concave–convex nature of hillslopes (from hilltop to bottom of the valley) to spatially limited soil textural data. The topographic features are extracted from Shuttle Radar Topographic Mission elevation data; whereas soil textural (clay, silt, and sand) and hydraulic data were collected in 29 spatially random locations (50 to 75cm depth). In contrast to traditional principal component analysis, the SOM identifies relations among relief features, such as, slope, horizontal curvature and vertical curvature. Stochastic cross-validation indicates that the SOM is unbiased and provides a way to measure the magnitude of prediction uncertainty for all variables. The SOM cross-component plots of the soil texture reveals higher clay proportions at concave areas with convergent hydrological flux and lower proportions for convex areas with divergent flux. The sand ratio has an opposite pattern with higher values near the ridge and lower values near the valley. Silt has a trend similar to sand, although less pronounced. The relation between soil texture and concave–convex hillslope features reveals that subsurface weathering and transport is an important process that changed from loss-to-gain at the rectilinear hillslope point. These results illustrate that the SOM can be used to capture and predict nonlinear hillslope relations among relief, soil texture, and hydraulic conductivity data.