USGS Digital Elevation Models Research Articles

Derivation of spatially detailed lentic habitat map and inventory at a basin scale by integrating multispectral Sentinel-2 satellite imagery and USGS Digital Elevation Models

Aquatic habitats are essential for aquaculture, agriculture, and municipal water supply. The geographical location and quantitative information about the aquatic habitats are critical for inventorying, monitoring, and managing these valuable resources. This paper presents a novel method that integrates the object-oriented image analysis and DEM-based stream network analysis to map and quantify lentic aquatic habitats, using freely available ESA Sentinel-2 multispectral imagery and USGS DEMs. This integrated method has been applied to the entire Mobile River Basin, USA, and lentic habitats at the basin scale have been delineated and inventoried at the 10-m spatial resolution. The surface water pixels of aquatic habitats are extracted through the Iterative Self-Organizing Data Analysis Technique (ISODATA) unsupervised classification of the Sentinel-2 multispectral images. An object-oriented image analysis approach is then used to group contiguous water pixels to form and map discrete aquatic habitat objects. The stream network in the basin has been hydrologically derived from the USGS DEMs at the 10-m spatial resolution. The combination of the geometric and shape information from the object-oriented image analysis and hydrological information from the DEM-based stream network analysis enables the separation of lotic and lentic habitats. A set of planimetric, geometric, and shape attributes has been numerically derived for each lentic habitat object. The abundance pattern, shape pattern, and spatial distribution pattern of the lentic habitats in the basin have been analyzed. The vast majority of the aquatic habitat objects identified in the Mobile River Basin are lentic habitats. The most striking spatial pattern of lentic habitats is that small, oblong lakes and fish ponds are densely distributed along the Black Prairie Belt in the basin, laying the foundation for the catfish aquacultural industry in the regions of mid-west Alabama and east Mississippi. This research demonstrates that the integration of the recently emerged Sentinel-2A/B satellite multispectral imagery with USGS DEMs represents an effective approach to the inventory and environmental investigation of lentic habitats at a basin scale.

Examining Landscape-Scale Fuel and Terrain Controls of Wildfire Spread Rates Using Repetitive Airborne Thermal Infrared (ATIR) Imagery

The objectives of this study are to evaluate landscape-scale fuel and terrain controls on fire rate of spread (ROS) estimates derived from repetitive airborne thermal infrared (ATIR) imagery sequences collected during the 2017 Thomas and Detwiler extreme wildfire events in California. Environmental covariate data were derived from prefire National Agriculture Imagery Program (NAIP) orthoimagery and USGS digital elevation models (DEMs). Active fronts and spread vectors of the expanding fires were delineated from ATIR imagery. Then, statistical relationships between fire spread rates and landscape covariates were analyzed using bivariate and multivariate regression. Directional slope is found to be the most statistically significant covariate with ROS for the five fire imagery sequences that were analyzed and its relationship with ROS is best characterized as an exponential growth function (adj. R2 max = 0.548, min = 0.075). Imaged-derived fuel covariates alone are statistically weak predictors of ROS (adj. R2 max = 0.363, min = 0.002) but, when included in multivariate models, increased ROS predictability and variance explanation (+14%) compared to models with directional slope alone.

Assessment of USGS DEMs for modelling pothole inundation in the prairie pothole region of Iowa

This study aims to compare inundation in two potholes using Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) with three Digital Elevation Models (DEMs): a 1 m DEM prepared from the LiDAR data which is readily available for the state of Iowa, USGS 1/9 arc-second DEM (∼3 m) which covers about 25% of the conterminous U.S. and USGS 1/3 arc-second DEM (∼10 m) which covers the entire USA. In this study, we found that the variations in water depth and presence/absence of ponding in the potholes of size greater than 1 ha can be predicted using USGS DEMs. The estimates of average water depths using USGS 3 m DEM was found to be 6% and 2% lower than the 1 m LiDAR DEM and the estimates of average water depths using USGS 10 m DEM was found to be 7% and 12% higher than the 1 m LiDAR DEM for the Walnut and Bunny potholes, respectively.

Assessing the Effects of DEM Uncertainty on Erosion Rate Estimation in an Agricultural Field

The slope length and steepness (LS) factor is one of the factors in the Revised Universal Soil Loss Equation (RUSLE) needed to estimate average annual erosion rate. The LS factor is often derived from digital elevation models (DEM). DEM errors and uncertainty could affect LS factor estimation and consequently erosion rate estimation. However, DEM uncertainties are not always accounted for, and the effects are not always evaluated in erosion rate estimation. This study compared the erosion rate estimation of a 62.81 ha agricultural crop area using a 7.5 min USGS DEM and DEMs developed using real-time kinematic differential GPS (RTK-DGPS) and dual-frequency DGPS (DF-DGPS) field surveys. Spatial estimation and uncertainty analysis was carried out using sequential Gaussian simulation (SGS). A total of 50 equiprobable DEM realizations were produced using SGS to assess DEM uncertainty and quantify its effect on erosion rate estimation. DEM uncertainty substantially affected the resulting erosion rate estimation. The uncertainty of the average annual erosion rate estimates across the study field was represented using 95% confidence intervals (CI). For the DF-DGPS and USGS DEMs, the percentages of the field area that have erosion rate CIs greater than 11.21 Mg ha-1 year-1 (5 tons acre-1 year-1) were 81% and 85%, respectively, which were substantially larger than that of the RTK DEM (0.41%). The average annual erosion rate map produced using a USGS DEM contained artifacts and underestimated the erosion rate estimation in many areas of the field. The results suggested that higher-accuracy DEMs generated using RTK-DGPS measurements are more appropriate for erosion rate estimation in an agricultural field. Knowledge of DEM uncertainty and its effect on the erosion rate estimation was useful to better judge the reliability of erosion rate estimates.

Effect of Topographic Data Accuracy on Water Storage Environmental Service and Associated Hydrological Attributes in South Florida

AbstractThe effects of digital elevation model (DEM) accuracy on terrain attributes and water storage were evaluated by comparing a standard 10-m U.S. Geological Survey DEM and a 10-m DEM derived from Light Detection and Ranging (LIDAR) data, on a ranchland that participated in a water storage environmental services program in South Florida. Elevation differences between the two DEMs ranged from -0.73 m to 1.6 m with a low (31%) correlation. Frequency analysis showed that 64% of the grids in the study area had elevation differences of 0.8 m or greater. Spatial distribution of wetlands and ditches were better represented by the LIDAR than the USGS DEM. Surface water storage in the study area and an isolated wetland was 49% and 21% more, respectively, for the USGS DEM than LIDAR DEM in wet seasons. In dry seasons, the LIDAR DEM resulted in 28% higher storage than the USGS DEM. Annually, the USGS DEM resulted in an average 44% more storage than LIDAR DEM. For a major rainstorm in 2008, the USGS showed 92% i...

A study on DEM-derived primary topographic attributes for hydrologic applications: Sensitivity to elevation data resolution

Primary topographic attributes play a critical role in determining watershed hydrologic characteristics for water resources modeling with raster-based digital elevation models (DEM). The effects of DEM resolution on a set of important topographic derivatives are examined in this study, including slope, upslope contributing area, flow length and watershed area. The focus of the study is on how sensitive each of the attributes is to the resolution uncertainty by considering the effects of overall terrain gradient and bias from resampling. Two case study watersheds of different gradient patterns are used with their 10 m USGS DEMs. A series of DEMs up to 200 m grid size are produced from the base DEMs using three commonly used resampling methods. All the terrain variables tested vary with the grid size change. It is found that slope angles decrease and contributing area values increase constantly as DEMs are aggregated progressively to coarser resolutions. No systematic trend is observed for corresponding changes of flow path and watershed area. The analysis also suggests that gradient profile of the watershed presents an important factor for the examined sensitivities to DEM resolution.

Relative size of fluvial and glaciated valleys in central Idaho

Quantitative comparisons of the morphometry of glaciated and fluvial valleys in central Idaho were used to investigate the differences in valley relief and width in otherwise similar geologic and geomorphic settings. The local relief, width, and cross-sectional area of valleys were measured using GIS software to extract information from USGS digital elevation models. Hillslope gradients were also measured using GIS software. Power-law relationships for local valley relief, width, and cross-sectional area as a function of drainage area were developed. Local valley relief in glaciated valleys relates to drainage area with a power-law exponent similar to fluvial valleys, but glaciated valleys are deeper for a given drainage area. Local valley width in glaciated valleys is greater than in fluvial valleys, but the exponent of the power-law relationship to drainage area is similar in both valley types. Local valley cross-sectional area in glaciated valleys increases with drainage area with a power-law exponent similar to fluvial valleys, however, glacial valleys have roughly 80% greater cross-sectional area. Steep valley walls in glaciated basins increase the potential for bedrock landsliding relative to fluvial basins. Both the Olympic Mountains of Washington and valleys in central Idaho show relationships in which glaciated valleys are up to 30% deeper than fluvial valleys despite differences in lithology, tectonic setting, and climate.

TOPOGRAPHIC INDICES

Real-time kinematic (RTK) global positioning system (GPS) receivers are being marketed for applications where accurate estimates of elevation are required. Because this technology is fairly expensive, the quality of these data should be considered with respect to that of USGS digital elevation DEMs (digital elevation models) that are freely available for most of the United States at the 30-m grid scale and for some areas on 10-m grids. The objective of this study was to compare terrain attributes calculated from USGS DEMs with those calculated from DEMs developed with precision RTK GPS measurements. At two locations in central Kentucky, intensive elevation measurements were collected with RTK GPS. The USGS and RTK data for these areas were interpolated to create 4-m DEMs for comparing terrain attributes. Correlations between the attributes developed from different DEMs were substantially larger for slope and aspect than for curvature, topographic wetness index, and stream power index values. Although there were large quantitative differences in terrain attribute values, the general spatial patterns were similar for the attributes calculated from the RTK and USGS DEMs. However, landscape features defined using DEMs developed with RTK data were more similar to those observed with 10- than 30-m USGS DEMs. For some course-scale applications, 30-m USGS DEMs may be adequate for identifying important landscape characteristics. However, the resolution of many of these features will be substantially better with 10-m USGS DEMs. However, quantitative estimates of terrain attributes at a specific location may be grossly incorrect. For situations where accuracy is essential, precision elevation surveys will be required.

An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling

Identification and removal of surface depressions is a critical step for automated modelling of surface rainfall runoff based on Digital Elevation Models (DEMs). At present, nearly all GIS and hydrologic software packages employ Jenson and Domingue's method for preparing depressionless DEMs for hydrologic analysis. This conventional method is computationally intensive and time‐consuming. With the growing availability of high‐resolution DEMs produced by airborne LIDAR and InSAR techniques, GIS‐based hydrologic applications often need to handle larger geographic areas at finer resolutions. In the face of high‐resolution DEMs, the conventional method becomes inadequate and deficient. In this paper, we present a new method for efficiently identifying and filling surface depressions in DEMs. This method can simultaneously determine flow paths and spatial partition of watersheds with one pass of processing. A novel concept of spill elevation and the least‐cost search for optimal flow paths are the two cornerstones of our method. The time complexity of our method is in O(Nlog N). It has been implemented using C++ programming language and successfully applied to USGS DEMs and LIDAR DEMs of various sizes. Experiments show that our method outperforms the conventional method by a factor of over 30, in terms of running time.

Comparison of existing triangulation methods for regularly and irregularly spaced height fields

Over the last two decades, the Delaunay triangulation has been the only choice for most geographical information system (GIS) users and researchers to build triangulated irregular networks (TINs). The classical Delaunay triangulation for creating TINs only considers the 2D distribution of data points. Recent research efforts have been devoted to generating data-dependent triangulation which incorporate information on both distribution and values of input data in the triangulation process. This paper compares the traditional Delaunay triangulations with several variant data-dependent triangulations based on Lawson's local optimization procedure (LOP). Two USGS digital elevation models (DEMs) are used in the comparison. It is clear from the experiments that the quality of TINs not only depends on the vertex placement but also on the vertex connection. Traditonal two step processes for TIN construction, which separate point selection from the triangulation, generate far worse results than the methods which iteratively select points during the triangulation process. A pure data-dependent triangulation contains a large amount of slivers and steep triangles, which greatly affect the quality of TINs constructed. Among the triangulation methods tested, the classical Delaunay triangulation is still the most successful technique for constructing TINs for approximating natural terrain surfaces.

Normal fault thermal regimes and the interpretation of low-temperature thermochronometers

Exhumation rates inferred from thermochronometers are dependent on spatial and temporal variations in temperature. In active extensional mountain belts, the temperature field is complicated by tectonic and surface processes including: (1) lateral heat flow across large, range-bounding normal faults due to the juxtaposition of a cool hanging wall and a relatively warmer footwall, (2) uplift and erosion of the footwall, (3) sedimentation and burial of the hanging wall, (4) lateral heat refraction around low thermal conductivity sediments deposited in the hanging wall basin, and (5) 3D temperature variations due to high-relief topography developed on the footwall. We explore these mechanisms through a series of 2D conductive thermal models designed to investigate the effect of tectonics and topography on apatite fission track (AFT) and (U–Th)/He thermochronometer data. Models were tuned to the geometry and kinematics of the Wasatch Mountains, Utah, USA. The principal parameters in our model are exhumation and burial rates ranging from 0.2 to 5.7 mm per year at the range front and decreasing with distance from the fault, surface morphology taken from USGS digital elevation models, and basin geometries inferred from seismic and gravity surveys. Predicted AFT and (U–Th)/He ages were generated using cooling rate dependent annealing and diffusion kinetic models. Results indicate after 10 million years of exhumation, footwall (U–Th)/He and AFT closure temperature isotherms within 10 km of the fault are advected upward 500 and 1000 m, respectively, from their initial position. The upward advection of isotherms and the 2D nature of the thermal regime can result in erroneous exhumation rates calculated from plots of sample elevation versus age using 1D thermal models. For simulations with a uniform vertical uplift rate and canyon and ridge topography, 1D and 2D exhumation rate differences were 20–70% for (U–Th)/He and ∼10% for AFT data. Samples collected perpendicular to fault strike up the range front are sensitive to the exhumation rate and footwall tilt. Differences between 1D and 2D range front exhumation rates were 10–95% for (U–Th)/He and 10–40% for AFT data. Furthermore, 1D thermal models are incapable of deciphering footwall tilt. At simulated exhumation rates of 3, 4, and 5 mm per year predicted (U–Th)/He and AFT ages form closely spaced, near vertical lines on a plot of elevation versus sample age, therefore, suggesting a decreased sensitivity of data to topography and surface processes at high exhumation rates. Despite large errors in previously collected AFT data from the Wasatch mountains, our model suggests a revised fault-adjacent exhumation rate of 0.5–0.6 mm per year compared to previous estimates of 0.7 mm per year based on 1D thermal models.

Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling

The accuracy of digital elevation models (DEM) and DEM-derived products depends on several factors, including the horizontal resolution and vertical precision at which the elevation data are represented, and the source of the elevation data. This accuracy becomes increasingly important as we extend the use of DEM data for spatial prediction of soil attributes. Our objective was to compare terrain attributes and quantitative soil-landscape models derived from grid-based DEM represented at different horizontal resolutions (10 and 30 m), represented at different vertical precisions (0.1 and 1 m), and acquired from different sources. Decreasing the horizontal resolution of the field survey DEM produced lower slope gradients on steeper slopes, steeper slope gradients on flatter slopes, narrower ranges in curvatures, larger specific catchment areas in upper landscape positions, and lower specific catchment areas values in lower landscape positions. Overall, certain landscape features were less discernible on the 30-m DEM than on the 10-m DEM. Decreased vertical precision produced a large proportion of points with zero slope gradient and zero slope curvature, and a large number of steeply sloping and more highly curved areas. Differences among DEM from different sources were more significant, with less accurate representation of depressions and drainage pathways with the USGS DEM as compared to the field survey DEM. Empirical models developed from different DEM included similar predictive terrain attributes, and were equally successful in predicting A-horizon depth (AHD) in the validation data set.

Evaluation of JPEG-LS, the new lossless and controlled-lossy still image compression standard, for compression of high-resolution elevation data

The compression of elevation data is studied. The performance of JPEG-LS, the new international ISO/ITU standard for lossless and near-lossless (controlled-lossy) still-image compression, is investigated both for data from the USGS digital elevation model (DEM) database and the navy-provided digital terrain model (DTM) data. Using JPEG-LS has the advantage of working with a standard algorithm. Moreover, in contrast with algorithms like the popular JPEG-lossy standard, this algorithm permits the completely lossless compression of the data as well as a controlled lossy mode where a sharp upper bound on the elevation error is selected by the user. All these are achieved at a very low computational complexity. In addition to these algorithmic advantages, they show that JPEG-LS achieves significantly better compression results than those obtained with other (nonstandard) algorithms previously investigated for the compression of elevation data. The results here reported suggest that JPEG-LS can immediately be adopted for the compression of elevation data for a number of applications.

Effects of Digital Elevation Model Accuracy on Hydrologic Predictions

The effect of vertical accuracy of digital elevation models (DEMs) on hydrologic prediction accuracy was evaluated by comparing three DEMs and associated streamflow simulations for the 7.2 km 2 USDA-ARS watershed at Mahantango Creek, PA. The DEMs were the standard 30 m USGS 7.5′ DEM, a 5 m product derived from low altitude aerial photography, and a 30 m product derived from interferometric processing of Spaceborne Imaging Radar-C (SIR-C). Statistical analysis of the DEMs showed that the USGS DEM had the typical stipling errors resulting from processing of the source digital contour maps, and a vertical error structure related to the topographic attributes of the watershed. The SIR-C DEM had a vertical offset of about 50 m from the high resolution and USGS DEMs, as well as error features that were somewhat related to topographic features. Inaccuracies in both the USGS and SIR-C DEMs were apparent in the drainage network, as well as in spatial images of elevation, slope, and contributing area. Comparisons of runoff predicted using a hydrologic model based on the three DEMs showed that mean annual predicted runoff volumes were 0.3% and 7.0% larger for the USGS and SIR-C DEMs, respectively, as compared to the reference DEM. Much larger differences were apparent in individual hydrographs; and the USGS and SIR-C DEMs predicted lower peaks, and higher base flows, than did the reference.

The relationships between vegetation type and topography in Lassen Volcanic National Park

The relationships between topographic features and the relative abundances of vegetation types were analyzed for the mid-montane conifer forests, the upper montane conifer forests and the alpine areas of Lassen Volcanic National Park in the Cascade Range of northern California using Landsat Thematic Mapper data. Vegetation types were identified from Landsat Thematic Mapper data and included pine, fir, montane chaparral, willow/alder and herbaceous communities. Topographic features including aspect, slope steepness, slope configuration (i.e., convex, straight or concave) and slope position (i.e., ridge top to valley bottom) were computed from USGS Digital Elevation Models. The distribution of montane chaparral was consistent with its dependency on fire with chaparral communities being more abundant on more xeric aspects and on steeper and more xeric slope configurations. Slope position was important in determining the distribution of willow/alder communities which were more abundant in valley bottom locations. Both slope steepness and slope position affected the abundance of herbaceous communities. Herbaceous communities were: (1) more prevalent on steeper slopes due to the distribution of dicotyledonous forb species; and (2) more abundant in valley bottoms due to the distribution of monocotyledonous species in wet meadows. Pines and firs were separated along a gradient of slope steepness with the firs being more abundant on steep slopes and the pines being more abundant on shallow slopes. In the alpine areas above 2350 m, the abundance of most vegetation types declined with increasingly mesic aspects. This decline may be related to the prolonged persistence of snow in areas of less intense solar radiation.

GIS‐Based Solute Transport Modeling Applications: Scale Effects of Soil and Climate Data Input

AbstractThe Weather Generator (WGEN) and Chemical Movement through Layered Soils (CMLS) computer models were modified and combined with two sets of soil and climate inputs to evaluate the impact of input data map resolution on model predictions. The basic soil and climate inputs required by WGEN and CMLS were acquired from either: (i) the USDA‐NRCS State Soil Geographic Database (STATSGO) database; (ii) the USDA‐NRCS (County) Soil Survey Geographic (SSURGO) database; (iii) the Montana Agricultural Potential System (MAPS) database (which divides Montana into approximately 18 000 twenty square kilometer cells and stores more than 200 different land and climate characteristics for each of these cells); and (iv) a series of fine‐scale monthly climate surfaces developed by the authors (0.55 km2 cell size) using thin‐plate splines, published climate station records, and USGS Digital Elevation Models (DEMs). Fifteen years of daily precipitation and evapotranspiration (ET) values were generated and combined with soil and pesticide inputs in CMLS to estimate the depth of picloram (4‐amino‐3,5,6‐trichloro‐2‐pyridinecarboxylic acid) movement at the end of the growing season for every unique combination (polygon) of soil and climate in a 320 km2 area in Teton County, Montana. Results indicate that: (i) the mean depths of picloram movement predicted for the study area with the SSURGO (county) soils and MAPS (coarse‐scale) climate information and the two model runs using the fine‐scale climate data were significantly different from the values predicted with the STATSGO (state) soils and MAPS climate data (based on a new variable containing the differences between the depths of leaching predicted with the different input data by soil/climate map unit and testing whether the mean difference was significantly different from zero at the 0.01 significance level); and (ii) CMLS identified numerous (small) areas where the mean center of the picloram solute front was likely to leach beyond the root zone when the county soils information was used. This last measure may help to identify areas where potential chemical applications are likely to contaminate groundwaters.

Make Fractal Landscapes and Fly-Through Animations Using USGS Digital Elevation Models (DEM's): ABSTRACT

Make Fractal Landscapes and Fly-Through Animations Using USGS Digital Elevation Models (DEM's): ABSTRACT