SRTM Elevation Data Research Articles

Advancing Flood Risk Assessment through Integrated Hazard Mapping: A Google Earth Engine-Based Approach for Comprehensive Scientific Analysis and Decision Support

This study utilises a comprehensive, multi-layered approach to assess flooding susceptibility in a specific area, integrating diverse environmental datasets such as JRC Global Surface Water, Landsat 8 images, and SRTM elevation data. Employing the GEE FMA, a powerful tool leveraging Google Earth Engine capabilities, the analysis covers water occurrence, permanent water, elevation, distance to water, topographic hazard score, and vegetation indices (NDVI and NDWI). The Water Occurrence layer establishes a foundational understanding of water-body distribution’s correlation with flood vulnerability, while Permanent Water refines this understanding. Distance to Water measures proximity for targeted risk evaluation, and Elevation identifies vulnerable regions based on topography. The GEE FMA synthesises these layers into a Flood Hazard Susceptibility map, categorising vulnerability into Very Low, Low, Medium, High, and Very High. This nuanced understanding is crucial for prioritising interventions. The GEE FMA’s rapid processing speed makes it an invaluable tool for short-term decision support in flood hazard disaster management, offering insights for informed decision-making and resilient infrastructure development. The Topographic Hazard Score provides information on how topography influences flood risk, while the Wetness Hazard Score categorises moisture conditions for identifying flood-prone locations. Decision-makers rely on these values for quick and precise flood susceptibility assessments. In an era of climate uncertainties and urbanisation, the GEE FMA emerges as a reliable tool for decision-making, mitigating flood impacts, and developing effective flood risk management strategies.

Machine Learning-Based Rice Field Mapping in Kulon Progo using a Fusion of Multispectral and SAR Imageries

The land-conversion of rice fields can reduce rice production and negatively impact food security. Consequently, monitoring is essential to prevent the loss of productive agricultural land. This study uses a combination of Sentinel-2 MSI, Sentinel-1 SAR, along with SRTM (elevation and slope data) to monitor rice fields land-conversion. NDVI, NDBI and NDWI indices are transformed from the annual median composite Sentinel-2 MSI images used to identify different rice fields with another object. A monthly median composite of SAR images from Sentinel-1 data are used to identify cropping patterns of rice fields in the inundation phase. The classification is performed by using the Random Forest machine learning algorithm in the Google Earth Engine (GEE) platform. Random Forest classification is run using 1000 trees, with a 70:30 ratio of training and testing data from sample features extracted by visual interpretation of high resolution Google Earth imagery. In this study, Random Forest classification is effective in computing a high amount of multi-temporal and multi-sensory data to map rice-field land conversion with an accuracy rate of 96.16% (2021) and 95.95% (2017) for mapping paddy fields. From the multitemporal rice field maps in 2017—2021, a conversion of 826.66 hectares of rice-fields to non-rice fields was identified. Based on the spatial distribution, the conversion from rice-field to non-rice field is higher at the area near the roads, built area and Yogyakarta International Airport. Therefore, it is important to assess and ensure that National Strategic Projects are managed with due regard to environmental impacts and food security.

Automatic built-up area extraction by feature-level fusion of Luojia 1–01 nighttime light and Sentinel satellite imageries in Google Earth Engine

Today, with the non-stop expansion of urbanization, mapping urban areas and monitoring their dynamic changes have become challenges for governments and also a hot topic for researchers. Remote sensing imageries play a key role in urban studies, the extraction of urban built-up areas, and monitoring their changes. A variety of studies have proposed methods for the extraction of regional, national, and global built-up areas. However, the majority of them used limited features and applied a manual sample selection strategy for classification, leading to time-consuming and low-efficient algorithms. This paper proposes a fully automatic procedure to real-time extract built-up areas by integrating the Luojia 1–01 nighttime lights (NTL) images, Sentinel-2 multispectral data, Sentinel-1 Radar images, and SRTM elevation data in cloud-computing Google Earth Engine. Firstly, potential built-up areas (PBA) and non-built-up areas (NBA) are obtained by applying Otsu and multi-level thresholding to some of the extracted spectral-textural-spatial (STS) features and by applying logical rules. Secondly, built-up and non-built-up samples are automatically selected and are used to train a Support Vector Machine (SVM) supervised classifier and to classify the hybrid feature set so that a preliminary classified map (PCM) can be obtained. Thirdly, the PCMs are automatically corrected using the non-built-up area, and morphological operations in the so-called post-classification to provide a refined classified map (RCM) and final built-up map. Four study areas in Northern America, Europe (Scandinavia), the Middle East, and Eastern Asia were selected to test the proposed method. Also, five state-of-the-art built-up products, accompanied by Google Earth images, were used as the reference data. The results indicate that the proposed method can accurately and automatically select samples and map built-up areas with a spatial resolution of 10 m. Its performance is validated with an average overall accuracy of 94.4% and an average Kappa coefficient of 0.89 and by visual comparison of our method results with other reference data. The proposed method has significant potential to be used in real-time extracting built-up areas and in monitoring their dynamic changes on national and global scales.

Impact of Topography on Rural Cycling Patterns: Case Study of Bugesera District, Rwanda

Rural mobility in Africa is an under-researched issue. Rural communities have often suffered from reduced mobility that has hampered their access to essential services and facilities such as education, health care, food, and clean water. In many rural communities, a more affordable mobility option, such as non-motorized mobility (cycling and walking), is the preferred way for people to travel. Apart from its well-known advantages, little is known about the impact of topography and routes on the mobility options adopted by rural communities. Therefore, this study aims to use Digital Elevation Models (DEMs) to analyze the impact of topography and routes on rural mobility patterns at the level of formal and informal cycle track networks in the Bugesera District, focusing on the Nyamata and Mayange sectors, Rwanda. This study used GPS devices given to 50 participants to collect mobility patterns in the two previously mentioned sectors. Then, the study imposed a 30-m buffer on the official road networks of Rwanda collected by the Rwanda Transport Development Agency (RTDA). These data were joined to GPS tracks to highlight official and unofficial roads (tracks that did not fall within the 30-m buffer). In addition, Digital Elevation Models were applied to analyze the SRTM (30 m resolution) and ALOS PALSAR (12.5 m resolution) elevation data of the Bugesera region. The findings revealed an elevation range of 1333 to 1535 for SRTM and 1323 to 1641 for ALOS PALSAR. The study calculated the slope to find the slope percentage (m) and length (m). The findings from the DEMs and the slope calculation revealed that Bugesera has a relatively flat surface, favorable for cycling. The slope percentage was further classified into five levels of slope ranging from steep to very steep. And the Van Zuidam classification results confirmed that Bugesera has “a flat or nearly flat surface, without significant denudation processes”. With a favorable topography and a higher bicycle ownership ratio, residents of the Bugesera district favor cycling in their daily activities more than any district in Rwanda. Cycling in Nyamata or Mayange links residents to areas with higher social, educational, administrative, and economic activities.

The 2016 flood of Bihar, India: an analysis of its causes

Flood is common in the sub-Himalayan plains of the Indian state of Bihar. During the massive flood of the Ganga between 18 and 31 August 2016, 15 (out of 38) districts and 3459 villages of the state were affected. The flood inundated 6006 km2 of area, harmed 8.82 million people, and caused damages worth US$ 73.99 million. To investigate the factors behind the event, we utilised SRTM elevation data and Landsat-8 OLI images to extract drainage networks and reservoirs of the region. Inundated area was delineated using MNDWI on MODIS data of 25-Aug-2016. Different hydrometeorological parameters employed in the study include percentage departure of monthly rainfall, discharge data of selected dams and barrages, and gauge data from six stations along a 526 km stretch of the Ganga: Buxar, Gandhi Ghat (Patna), Hathidah, Munger, Kahalgaon, and Manikchak Ghat. It was found that the flood was mainly caused by rapid release of water from the Bansagar Dam on the Son from 18 to 20-Aug-2016, as a tropical storm traversed through its upper catchment. Contributions from the Ganga’s Himalayan tributaries and upstream of the flooded area were negligible. The flood peak (50.52 m), recorded at Gandhi Ghat on 21 August 2016, passed downstream, and reached Manikchak Ghat (25.78 m) on 27–28 August 2016. The upstream extension of flooding due to the highest recorded flood level of 26.66 m of the Ganga at Farakka can only reach up to 6 km above Kahalgaon. Therefore, impoundment of the Ganga by the Farakka Barrage cannot have any impact on inundation of the areas above that limit and did not cause the 2016 floods.

Spatial Characteristics of the Southern Al Jabal Al Akhdar Watersheds: Remote Sensing Approach

The present study is to evaluate the spatial characteristics of the watersheds located in Northeast Libya, which is vital since the area is promising for surface water investment in rain-fed agriculture and pastoral activities. The study conducted using Geographical Information System (GIS) and Remote Sensing (RS) data sets: SRTM elevation data, Landsat 8 (OLI) imagery and Global Climate Monitor (GCM) data. SRTM data processed in ArcGIS, where elevations show a progressive decrease towards the South and eleven watersheds delineated (17721km2). Moreover, the perimeter, slope, aspect, and stream length of the watersheds also calculated. The hydrologic divide bounds the watersheds in the North delineated; it extends from the Southwest to the East with a length of 470km. Also, the outlets of the watersheds, which are mostly temporary lakes, those locally known as Balat assessed spatially. Landsat 8 imagery classified by Quantum GIS (QGIS), where five classes identified (alluvial plains, spreading zones, forest, grassland and bare rocks). Furthermore, precipitation and temperature data from the GCM was mapped, where the precipitation shows the highest rates in the North and gradual decrease to the South, unlike the temperature values indicate an increase towards the South and drop in the North.

Large lake gauging using fractional imagery

Large floodplain lakes provide riparian habitat, are sediment and nutrient sinks, help control flow connectivity and flooding along rivers, and are both used by humans and strongly impacted by human activity. However, water level in many remote large floodplain lakes, especially in developing countries, is often monitored inconsistently or not at all. In this study, a novel method for estimating large lake water level using passive, optical remote sensing data combined with any digital elevation model (DEM) is presented. The method obtains water level estimates at 30 m2 resolution using Landsat, in this case in conjunction with SRTM elevation data, nested within a 240 m2 grid “fishnet”. A probabilistic mean of elevation values for all water-designated pixels (between 5% and 95% filled within each grid) produces lake water levels often accurate to within ±50 cm of gauged reference data on Lake Curuai in the Amazon River and Tonle Sap Lake along the Mekong River. The method is relatively insensitive to cloud cover, especially as lake size increases. This study is the first to use solely passive optical remote sensing data for water level estimation and thus could be used to produce accurate, long-term estimations of water level in many large lakes globally. The use of optical sensors to obtain lake water level is both an important complement and potential alternative to methods that use active sensors.

Flood and Substance Transportation Analysis Using Satellite Elevation Data: A Case Study in Dhaka City, Bangladesh

This study investigated the vertical accuracy of satellite elevation data and its effect on flood and substance transportation analysis by using a two-dimensional flood simulation model. SRTM, AW3D, and ASTER GDEM satellite elevation data for East Dhaka, Bangladesh were used for evaluating the vertical accuracy and conducting numerical analyses. A case study in 2007 was simulated for the flooding analysis. The results showed that AW3D had the highest applicability because its vertical accuracy for low-lying areas was better than that of the other products. According to the differences in the flood extent of each satellite elevation data, the simulation results of the substance transportation analysis showed different spreading conditions. Furthermore, differences in the flood velocity and direction affected the distribution of the deposited substance.

Rimaal: A Sand Buried Structure of Possible Impact Origin in the Sahara: Optical and Radar Remote Sensing Investigation

This work communicates the discovery of a sandy buried 10.5 km diameter near-circular structure in the eastern part of the Great Sahara in North Africa. Rimaal, meaning “sand” in Arabic, is given as the name for this structure since it is largely concealed beneath the Sahara Aeolian sand. Remote sensing image fusion and transformation of multispectral data (from Landsat-8) and synthetic aperture radar (from Sentinel-1 and ALOS PALSAR), of dual wavelengths (C and L-bands) and multi-polarization (HV, VV, HH, and HV), were adopted in this work. The optical and microwave hybrid imagery enabled the combining of surface spectral properties and subsurface roughness information for better understanding of the Rimaal structure. The long wavelength of the radar, in particular, enabled the penetration of desert sands and the revealing of the proposed structure. The structure exhibits a clear outer rim with traces of concentric faults, an annular flat basin and an inner ring surrounding remnants of a highly eroded central peak. Radar imagery clearly shows the interior wall of the structure is incised with radial pattern gullies that originate at or near the crater periphery, implying a much steeper rim wall in the past. In addition, data reveals a circumferential of a paleoriver course that flows along a curved path parallel to the crater’s western margin indicating the plausible presence of a concentric ring graben related to the inferred structure. The defined crater boundary is coincident with a shallow semi-circular-like basin in the SRTM elevation data. The structure portrays considerable modifications by extensive long-term Aeolian and fluvial erosion. Residing in the Cretaceous Nubian Sandstone formation suggests an old age of ≤65 Ma for the structure. If proven to be of an impact origin, the Rimaal structure could help in understanding the early evolution of the landscape of the Eastern Sahara and holds promise for hosting economically valuable ore deposits and hydrocarbon resources in the region.

Comparison and Suitability of SRTM and ASTER Digital Elevation Data for Terrain Analysis and Geomorphometric Parameters: Case Study of Sungai Patah Subwatershed (Baram River, Sarawak, Malaysia)

Determination of suitability of satellite-derived elevation data sets in terrain characterisation in a tropical region was carried out on the Sungai Patah subwatershed in the interior of Sarawak, East Malaysia. The goal was to facilitate rapid assessment of topographic variables and spatial parameters related to the morphometric aspects of the region. The freely available SRTM (90 m) and ASTER (30 m) elevation data sets were compared and used to generate spatial and aspatial parameters. The cross-validation of SRTM and ASTER elevation surfaces with toposheet-derived elevation for 200 random points shows root mean squared errors (RMSE) of ± 35.08 m and ± 44 m, respectively. The spatial and aspatial parameters derived show certain major and minor variations in the outputs, which can be attributed to the differences in spatial and spectral resolutions of the data acquisition systems. The results and the findings of the present study suggest that both SRTM and ASTER elevation data sets can be used for terrain characterisation in regions similar to the study area, by replacing the traditional toposheet-derived elevation surfaces. However, minor errors are present when either set is used independently. This can be avoided by the concurrent use of SRTM and ASTER elevation data sets, which will reduce data errors and artefacts in both data sets and improve the accuracy of terrain variables and watershed parameters derived from them. DOI: http://dx.doi.org/10.5755/j01.erem.71.3.12566

Openness: A visualization parameter for two-dimensional relief

As one of visualization parameters for two-dimensional relief,Openness does not require light source and is not affected by the shadow of adjacent terrain. The parameter places more emphasis on the concave and convex of the terrain,which improves the intuitive stereoscopic impression of images. Openness can be calculated from multiple DEM sources,and can analyze the landform characteristics at different scales as well. The estuary of the Minjiang Estuary has various types of landform structure; therefore,the authors chose the area as a study area and calculated its Openness parameter on the basis of SRTM( 90 m) elevation data so as to study the initial application of the parameter in terms of topography. The results show that,in comparison with traditional two-dimensional visualization methods such as shaded relief,Openness could display a better image which highlights the concave and convex of the terrain and reflects more details of the topography.

PERFORMANCE ANALYSIS OF DIGITAL ELEVATION MODELS - A CASE STUDY OF BASIN LOW MIDDLE SÃO FRANCISCO RIVER

Corresponding author: madson_tavares@hotmail.comThe performance assessment of digital elevation models, from the handling of the mission SRTM elevation data , ASTER imager instrument , and project TOPODATA compared to elevation data obtained in the field from GPS was the aim of this work, in view the adequacy of the best model for use in studies of morphometric analysis of watersheds , as well as applications in hydrological models . To conduct this study we used the ArcGIS 9.3 software. The results demonstrate that the elevation data extracted from SRTM , ASTER and TOPODATA are extremely satisfactory for presenting high correlation with the data collected by GPS (r = 0.97 , 0.96 and 0.97 , respectively) , was also observed that the smallest errors associated were obtained for the SRTM DEM , being observed PBIAS equal to 2.71 % and MAE of 11.45 m . Finally from the index c Camargo & Sentelhas be excellent in the criterion of model performance. In conclusion it was observed that the DEM SRTM showed the best results, possibly due to the lower number of cells to compose your image, thus generating more homogeneous areas than other models.

Prediction of china’s submerged coastal areas by sea level rise due to climate change

Based on the simulation with the Ocean-Atmosphere Coupled Model CCSM and Ocean Model POP under the greenhouse gas emission scenario of the IPCC SRES A2 (IPCC, 2001), and on the earth crust subsidence and glacier melting data, the relative sea level change is obtained along the coast of China in the 21st century. Using the SRTM elevation data the submergence of coastal low land is calculated under the extreme water level with a 100-year return period. The total flooding areas are 98.3×103 and 104.9×103 km2 for 2050 and 2080, respectively. For the three regions most vulnerable to extreme sea level rise, i.e., the coast of Bohai Bay, the Yangtze River Delta together with neighboring Jiangsu Province and northern Zhejiang Province, and the Pearl River Delta, the flooded areas are 5.0×103, 64.1×103 and 15.3×103 km2 in 2050 and 5.2×103, 67.8×103 and 17.2×103 km2 in 2080, respectively.

Landscape-scale variation in structure and biomass of Amazonian seasonally flooded and unflooded forests

Accurate estimates of current forest carbon stocks are required for efforts to reduce emissions from tropical deforestation and forest degradation. The relative contributions of different vegetation types to carbon stocks and potential emissions are poorly understood in highly heterogeneous forest mosaics, and further field-based measurements are necessary from severely undersampled regions and forest types to improve regional scale extrapolations based on remote sensing. We assessed the aboveground biomass (AGB) of two contiguous western Brazilian Amazonian protected areas totalling 886,176ha, which contain vast expanses of seasonally flooded várzea (VZ) forest along the floodplain of the Juruá river and adjacent terra firme (TF) forest farther inland. Estimates were based on equations incorporating wood specific gravity (WSG) and tree height in addition to DBH, and derived from a network of 200 forest plots of 0.1ha (=20ha) sampled across adjacent areas of flooded and unflooded forest. A large number of small plots stratified by forest type allowed a more representative sample, encompassing the considerable variation in forest structure and composition both within and between forest types. Mean basal area per plot was higher in várzea forest plots than in terra firme plots (VZ: 37.6±1.2m2ha−1; TF: 32.4±0.9m2ha−1) but AGB was lower in várzea (VZ: 281.9±12.0Mgha−1; TF: 358.4±14.4Mgha−1) due to lower WSG and tree height. Linear mixed effects models showed the overriding effect of forest type on AGB, and the roles of water stress and a historical signature of selective logging pressure, particularly within várzea forests. ALOS ScanSAR generated categories of flood duration provided a more relevant description of water stress than SRTM elevation data; AGB within várzea forest was higher in plots subjected to longer flood duration. Várzea forests store significant levels of forest carbon despite their lighter-wooded trees and lower canopy stature, and yet are heavily settled by rural Amazonians, and are increasingly vulnerable to deforestation and logging. This study helps understand how baseline environmental gradients and human disturbances in these unique forests affect their carbon storage value, and highlights their importance both within and outside existing protected areas.

Investigating error structure of shuttle radar topography mission elevation data product

An attempt was made to experimentally assess the instrumental component of error of the C‐band SRTM (SRTM). This was achieved by comparing elevation data of 302 runways from airports all over the world with the shuttle radar topography mission data product (SRTM). It was found that the rms of the instrumental error is about ±1.55 m. Modeling of the remaining SRTM error sources, including terrain relief and pixel size, shows that downsampling from 30 m to 90 m (1 to 3 arc‐sec pixels) worsened SRTM vertical accuracy threefold. It is suspected that the proximity of large metallic objects is a source of large SRTM errors. The achieved error estimates allow a pixel‐based accuracy assessment of the SRTM elevation data product to be constructed. Vegetation‐induced errors were not considered in this work.

Electrical resistivity image of the Jingsutu Graben at the NE margin of the Ejina Basin (NW China) and implications for the basin development

Magnetotelluric and transient electromagnetic data reveal the Graben geometry of a NNE striking depression within crystalline basement at the NE margin of the Ejina Basin. We interpret this structure as a Late Mesozoic pull‐apart basin, which is delineated by NNE‐striking normal faults to the east and west. Satellite imagery, SRTM elevation data and previously published geophysical data of our work group suggest that the Graben is part of normal‐fault system that continues at least 140 km farther to the south, thus forming the eastern margin of the Ejina Basin. NNE striking normal faults are atypical in a regional context, where NW striking and ENE striking fault systems prevail. Thus, this feature marks a distinct difference of the Ejina Basin when compared to other basin structures in the China‐Mongolia border region.

Evaluation of the Horizontal Resolution of SRTM Elevation Data

The SRTM dataset is available at the UsGs seamless website with one arc-second pixel spacing for the U.S. Recently, the value for horizontal resolution has been questioned. One paper (Smith and Sandwell, 2003) suggests that 60 meters may be more accurate, implying that the resolution is twice the provided spacing. For users of this data, the horizontal resolution is very important for their analyses. Hence, this paper addresses this important question by using two different approaches: coherence spectra and step-response. The coherence spectra approach uses statistical techniques to compare the SRTM dataset against a more accurate one, while the step response approach uses the observed step response in many areas of the dataset to estimate the width of the averaging function used to produce the SRTM data. Results from this study show that the resolution is between 1 and 1.6 pixels, depending on the local variability of the elevation data; with higher resolution near sharp edges and corners, and lower resolution in smoother areas.

Regional scale modelling of hillslope sediment delivery with SRTM elevation data

A spatially distributed soil erosion and sediment delivery model (WATEM/SEDEM) was applied to the Scheldt River Basin (19,000 km 2) using SRTM elevation data with a 3″ resolution, and CORINE Land Cover data which are available at a resolution of 100 m. Transport capacity coefficients in the model were first calibrated using observed sediment yield data and WATEM/SEDEM predictions made with a higher resolution DEM derived from contour maps. When optimal transport capacity values are used, the calibrated model with SRTM data has an overall model efficiency of 0.79 for area-specific sediment yield and 0.95 for total sediment yield. R-square values between observed and predicted sediment yields are > 0.8. Optimal calibration values are much smaller than those obtained from a higher resolution model, illustrating the need for recalibrating distributed models when input data with different accuracies or resolution are used. Application of the calibrated model to the Scheldt River Basin estimated the total sediment supply from hillslopes to the river channels in the basin at 1.9 × 10 6 t year − 1 . Model results indicate a large spatial variability in hillslope sediment delivery, with the major sediment sources situated in the upper parts of the river basin. It is shown that the decrease in area-specific sediment yield with increasing catchment area can already be explained by the increasing importance of lower slope gradients in the lower parts of the river basin, without taking into account of floodplain sediment storage.

How Complementary are SRTM-X and -C Band Digital Elevation Models?

Two different digital elevation models (DEM) were derived during the 2000 Shuttle Radar Topography Mission from C- and X-band interferometric radar data. While these two DEMs share several of their properties, they were processed independently. Here, we investigate what can be gained by merging the two DEMs into a single composite DEM for four different test areas. Based on an analysis of the relative differences and the deviations from an absolute reference in one test area, we propose an algorithm for combining the two DEMs optimally. We then compare the composite DEM with both individual DEMs and with a reference of a large number of precise GPS profiles in one test area in southern Germany. We find that in our test areas, the area of missing values is reduced significantly in the composite DEM. Even compared with the more complete C-band DEM, the number of void pixels can be reduced by 22 percent to 53 percent. Also, outlier values resulting from errors in the interferometric phase unwrapping can often be identified and removed in the merging. The deviations of both C- and X-band DEMs from the GPS reference are very similar and well within the accuracy specifications of the global data set. The standard deviation of the difference between the composite DEM and the reference is about 14 percent below that of the original values. Depending on the requirements for completeness and accuracy, merging the two SRTM elevation data sets may provide an important improvement above either of the original DEMs.