On the uncertainty of stream networks derived from elevation data: the error propagation approach

DEM error propagation methodology is extended to the derivation of vector-based objects (stream networks) using geostatistical simulations. First, point sampled elevations are used to fit a variogram model. Next 100 DEM realizations are generated using conditional sequential Gaussian simulation; the stream network map is extracted for each of these realizations, and the collection of stream networks is analyzed to quantify the error propagation. At each grid cell, the probability of the occurrence of a stream and the propagated error are estimated. The method is illustrated using two small data sets: Baranja hill (30 m grid cell size; 16 512 pixels; 6367 sampled elevations), and Zlatibor (30 m grid cell size; 15 000 pixels; 2051 sampled elevations). All computations are run in the open source software for statistical computing R: package geoR is used to fit variogram; package gstat is used to run sequential Gaussian simulation; streams are extracted using the open source GIS SAGA via the RSAGA library. The resulting stream error map (Information entropy of a Bernoulli trial) clearly depicts areas where the extracted stream network is least precise – usually areas of low local relief and slightly convex (0–10 difference from the mean value). In both cases, significant parts of the study area (17.3% for Baranja Hill; 6.2% for Zlatibor) show high error (H>0.5) of locating streams. By correlating the propagated uncertainty of the derived stream network with various land surface parameters sampling of height measurements can be optimized so that delineated streams satisfy the required accuracy level. Such error propagation tool should become a standard functionality in any modern GIS. Remaining issue to be tackled is the computational burden of geostatistical simulations: this framework is at the moment limited to small data sets with several hundreds of points. Scripts and data sets used in this article are available on-line via the www.geomorphometry.org website and can be easily adopted/adjusted to any similar case study

DIGITAL ELEVATION MODEL VALIDATION WITH NO GROUND CONTROL: APPLICATION TO THE TOPODATA DEM IN BRAZIL

Digital Elevation Model (DEM) validation is often carried out by comparing thedata with a set of ground control points. However, the quality of a DEM can also beconsidered in terms of shape realism. Beyond visual analysis, it can be verified thatphysical and statistical properties of the terrestrial relief are fulfilled. This approachis applied to an extract of Topodata, a DEM obtained by resampling the SRTMDEM over the Brazilian territory with a geostatistical approach. Several statisticalindicators are computed, and they show that the quality of Topodata in terms ofshape rendering is improved with regards to SRTM

Airborne LiDAR for DEM generation: some critical issues

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented

Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale

Soil organic carbon (SOC) plays a major role in the global carbon budget. It can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Improving the tools that model the spatial distributions of SOC stocks at national scales is a priority, both for monitoring changes in SOC and as an input for global carbon cycles studies. In this paper, we compare and evaluate two recent and promising modelling approaches. First, we considered several increasingly complex boosted regression trees (BRT), a convenient and efficient multiple regression model from the statistical learning field. Further, we considered a robust geostatistical approach coupled to the BRT models. Testing the different approaches was performed on the dataset from the French Soil Monitoring Network, with a consistent cross-validation procedure. We showed that when a limited number of predictors were included in the BRT model, the standalone BRT predictions were significantly improved by robust geostatistical modelling of the residuals. However, when data for several SOC drivers were included, the standalone BRT model predictions were not significantly improved by geostatistical modelling. Therefore, in this latter situation, the BRT predictions might be considered adequate without the need for geostatistical modelling, provided that i) care is exercised in model fitting and validating, and ii) the dataset does not allow for modelling of local spatial autocorrelations, as is the case for many national systematic sampling schemes

3D Geology of Lauderdale and Tipton Counties, Tennessee: A Geostatistical Approach

The surface and subsurface geology of Lauderdale and Tipton counties were mapped using seismic reflection line data, borehole data, water well data, previously mapped faults, and Lidar DEM imagery. Geologic structure contour maps, isopach maps, cross sections, and a 3D geologic model were made of Tipton and Lauderdale counties. These maps, cross sections, and 3D model include the tops of the Eocene upper Claiborne Group, Eocene Memphis Sand, Cretaceous strata, and Paleozoic strata. The post-Eocene near-surface geology was also mapped, which includes Pleistocene loess, Pliocene and Pleistocene terrace alluvium, and Holocene floodplain alluvium. The resulting geologic maps and cross sections show the inversion of basement faults, faulting of near-surface upper Claiborne Group, warping of strata above the Covington pluton, windows (breaches) through the Eocene upper Claiborne aquiclude that potentially allows surface water to enter the confined Memphis aquifer, locations, and thickness of Upland Complex sand and gravel resources, and accompanying data to assess the economic potential of the Upland Complex in Tipton and Lauderdale counties

Improvements in groundwater flow modeling through the integration of resistivity logs and hydraulic conductivity and the use of variogram uncertainty

This study developed a coregionalized model to estimate hydraulic conductivity using spatial cross correlation between hydraulic conductivity and borehole geophysical data (a transform of the formation factor). An experimental pseudocross variogram is used instead of a cross variogram because data are not collocated. Experimental variogram uncertainty is investigated using confidence intervals for the experimental variogram calculated assuming variogram sills are lognormally distributed. These intervals are used for sensitivity modeling using kriging, cokriging, simulation and cosimulation. The hydraulic conductivity fields generated by kriging, cokriging, simulation, and cosimulation are then used in a high-resolution groundwater model created using telescopic mesh refinement (TMR) from a regional flow model of the Chicot Aquifer system in southwestern Louisiana. Results are analyzed to assess the significance of adding additional information (i.e., transform of formation factor), the process (i.e., kriging versus simulation and cokriging versus cosimulation) and variogram uncertainty on the groundwater flow model. Spatial images and flow predictions using regionalized models based on sparse conductivity data only are compared with coregionalized models using both conductivity and resistivity data, and the effects on model accuracy and robustness are discussed. Coregionalized model (i.e., cokriging) and simulation process (i.e., cosimulation) significantly affect groundwater flow model prediction. A new approach examines sensitivity of a capture zone groundwater model for the Chicot aquifer parameter uncertainty. Sensitivities to spatial variability of hydraulic conductivity, porosity, and aquifer thickness were investigated. The method calibrated aquifer properties to flow and geophysical data using cosimulation of hydraulic conductivity and formation factor, simulation for porosity, and kriging for aquifer thickness. Geostatistical model uncertainty was analyzed with a Bayesian method. Aquifer property models were scored using integral range to preserve correlation among variogram parameters. Variogram and pseudocrossvariogram models were selected from a lower bound, median, and upper bound of the posterior probability distribution of integral range. A steady-state two-dimensional groundwater flow model of the Chicot aquifer beneath Acadia Parish in Southwestern Louisiana examined capture zone sensitivity to spatial structure of aquifer properties. The capture zone model was insensitive to porosity variability and sensitive to hydraulic conductivity and aquifer thickness. The proposed method demonstrates the importance of model uncertainty compared with fluctuations of a fixed geostatistical model

Geodetic mass balance record with rigorous uncertainty estimates deduced from aerial photographs and lidar data – Case study from Drangajökull ice cap, NW Iceland

In this paper we describe how recent high-resolution digital elevation models (DEMs) can be used to extract glacier surface DEMs from old aerial photographs and to evaluate the uncertainty of the mass balance record derived from the DEMs. We present a case study for Drangajokull ice cap, NW Iceland. This ice cap covered an area of 144 km(2) when it was surveyed with airborne lidar in 2011. Aerial photographs spanning all or most of the ice cap are available from survey flights in 1946, 1960, 1975, 1985, 1994 and 2005. All ground control points used to constrain the orientation of the aerial photographs were obtained from the high-resolution lidar DEM. The lidar DEM was also used to estimate errors of the extracted photogrammetric DEMs in ice-and snow-free areas, at nunataks and outside the glacier margin. The derived errors of each DEM were used to constrain a spherical semivariogram model, which along with the derived errors in ice-and snow-free areas were used as inputs into 1000 sequential Gaussian simulations (SGSims). The simulations were used to estimate the possible bias in the entire glaciated part of the DEM and the 95% confidence level of this bias. This results in bias correction varying in magnitude between 0.03m (in 1975) and 1.66m (in 1946) and uncertainty values between +/- 0.21m (in 2005) and +/- 1.58m (in 1946). Error estimation methods based on more simple proxies would typically yield 2-4 times larger error estimates. The aerial photographs used were acquired between late June and early October. An additional seasonal bias correction was therefore estimated using a degree-day model to obtain the volume change between the start of 2 glaciological years (1 October). This correction was largest for the 1960 DEM, corresponding to an average elevation change of -3.5m or approx. three-quarters of the volume change between the 1960 and the 1975 DEMs. The total uncertainty of the derived mass balance record is dominated by uncertainty in the volume changes caused by uncertainties of the SGSim bias correction, the seasonal bias correction and the interpolation of glacier surface where data are lacking. The record shows a glacier-wide mass balance rate of (B) over dot = -0.26 +/- 0.04m w.e.a(-1) for the entire study period (1946-2011). We observe significant decadal variability including periods of mass gain, peaking in 1985-1994 with (B) over dot = -0.27 +/- 0.11m w.e.a(-1). There is a striking difference when (B) over dot is calculated separately for the western and eastern halves of Drangajokull, with a reduction of eastern part on average similar to 3 times faster than the western part. Our study emphasizes the need for applying rigorous geostatistical methods for obtaining uncertainty estimates of geodetic mass balance, the importance of seasonal corrections of DEMs from glaciers with high mass turnover and the risk of extrapolating mass balance record from one glacier to another even over short distances.This work was carried out within SVALI funded by the Nordic Top-level Research Initiative (TRI) and is SVALI publication number 70. It was also financially supported by alpS GmbH. This work is a contribution to the Rannis grant of excellence project, ANATILS. We thank the National Land Survey of Iceland and Loftmyndir ehf. for acquisition and scanning of the aerial photographs. This study used the recent lidar mapping of the glaciers in Iceland that was funded by the Icelandic Research Fund, the Landsvirkjun Research Fund, the Icelandic Road Administration, the Reykjavik Energy Environmental and Energy Research Fund, the Klima- og Luftgruppen (KoL) research fund of the Nordic Council of Ministers, the Vatnajokull National Park, the organization Friends of Vatnajokull, the National Land Survey of Iceland and the Icelandic Meteorological Office.Peer Reviewe

Ground deformation and source geometry of the 30 October 2016 Mw 6.5 Norcia earthquake (Central Italy) investigated through seismological data, DInSAR measurements, and numerical modelling

We investigate the Mw 6.5 Norcia (Central Italy) earthquake by exploiting seismological data, DInSAR measurements, and a numerical modelling approach. In particular, we first retrieve the vertical component (uplift and subsidence) of the displacements affecting the hangingwall and the footwall blocks of the seismogenic faults identified, at depth, through the hypocenters distribution analysis. To do this, we combine the DInSAR measurements obtained from coseismic SAR data pairs collected by the ALOS-2 sensor from ascending and descending orbits. The achieved vertical deformation map displays three main deformation patterns: (i) a major subsidence that reaches the maximum value of about 98 cm near the epicentral zones nearby the town of Norcia; (ii) two smaller uplift lobes that affect both the hangingwall (reaching maximum values of about 14 cm) and the footwall blocks (reaching maximum values of about 10 cm). Starting from this evidence, we compute the rock volumes affected by uplift and subsidence phenomena, highlighting that those involved by the retrieved subsidence are characterized by significantly higher deformation values than those affected by uplift (about 14 times). In order to provide a possible interpretation of this volumetric asymmetry, we extend our analysis by applying a 2D numerical modelling approach based on the finite element method, implemented in a structural-mechanic framework, and exploiting the available geological and seismological data, and the ground deformation measurements retrieved from the multi-orbit ALOS-2 DInSAR analysis. In this case, we consider two different scenarios: the first one based on a single SW-dipping fault, the latter on a main SW-dipping fault and an antithetic zone. In this context, the model characterized by the occurrence of an antithetic zone presents the retrieved best fit coseismic surface deformation pattern. This result allows us to interpret the subsidence and uplift phenomena caused by the Mw 6.5 Norcia earthquake as the result of the gravitational sliding of the hangingwall along the main fault plane and the frictional force acting in the opposite direction, consistently with the double couple fault plane mechanism

Burgundy Terroir: A Regional GIS Comparison Between the Burgundy and the Willamette Valley Wine Regions

This project examines the concept of Terroir as a wine varietal’s physical habitat. The famous European wine regions were assumed to represent the mother habitat characteristics for optimum varietal growth. This project specifically examined the Burgundy region of France in order to determine the physical characteristics required to grow the Pinot Noir varietal. Once these characteristics were determined, they were used to rate the suitability of the Willamette Valley, Oregon, to grow similar quality Pinot Noir grapes. The test region was found to be suitable, although did not match the source region suitability one hundred percent. Finally, a logit regression model was explored to ascertain the viability of this approach to rate an area as a vineyard or non-vineyard, and to further define the influence of individual physical aspects in rating a varietal area. The results indicated the logit regression model as a viable approach for varietal rating given higher resolution data

A modelling approach for evaluating impacts of hydropeaking in a sub-arctic river

Abstract. The release of pulses of water to increase hydroelectric power production at hydropower dams to meet daily peaks in electricity demands is called hydropeaking. Due to energy supply and demand fluctuations, the energy markets direct hydropower companies to balance load fluctuations through variations in power generation which result in flow regulation. More recently, this regulation is being carried out at shorter time intervals i.e., intra-daily and intra-hourly levels. The hydropeaking phenomenon increases drastically at shorter time intervals, severely impacting the riverine and riparian ecosystem. Social, economic, and ecological impacts arise from short-term hydropeaking. Furthermore, recreational services offered by the river are also impacted. This research develops a novel methodology for assessing these impacts in a strongly regulated sub-arctic river in Finland, i.e., Kemijoki River, Ossauskoski-Tervola reach. The methodology combines assessment of seasonal variations in sub-daily hydropeaking, two-dimensional hydrodynamic modelling, and a high-resolution land cover map developed through supervised land use classification via a machine learning algorithm. The results obtained include; the identification of a zone of influence of hydropeaking at sub-daily levels during each season, the total and class-wise area affected during each peaking event, and vulnerability zonation for water-based recreation in the river reach. The overall area of reach affected by peaking in Winter was (1.05 km2), Spring (0.96 km2), Summer (1.39 km2), and Autumn (0.66 km2). A vulnerability mapping was also carried out for the suitability of water-based recreation in the study reach. The novel methodology developed in this research which defines the vulnerable zone of hydropeaking can be used as the first step in detailed impacts assessment studies such as those for impacts on fish habitat and sediment transport processes in the river. The hydropeaking-influenced zone can be used to set thresholds for ecological flows and ramping rates downstream of power stations and opens avenues for future research, development, and policy endeavors for riparian ecosystem impact assessment and mitigation