Evaluating the effects of generalisation approaches and DEM resolution on the extraction of terrain indices in KwaZulu Natal, South Africa

Digital elevation model (DEM) data are elemental in deriving primary topographic attributes which serve as input variables to a variety of hydrologic and geomorphologic studies. There is however still varied consensus on the effect of DEM source and resolution on the application of these topographic attributes to landscape characterisation. While elevation data for South Africa are available from several major sources and resolutions: Shuttle Radar Topographic Mission (SRTM), Earth ENV and Stellenbosch University DEM (SUDEM). Limited research has been conducted in a local context comparing the extraction of terrain attributes to high resolution Digital Terrain Data (DTM) such as LiDAR (Light Detection and Ranging) that are becoming increasing available. However, the utility of LiDAR to topographic analyses presents its own challenges in terms of operational-relevant resolution, processing demands and limited spatial coverage. There is a need to quantify the impact that generalisation approaches have on simplifying detailed DEMs and to compare the accuracy and reliability of results between high resolution and coarse resolution data on the extraction of localized topographic variables. In this regional study, we analyse the accuracy on selected local terrain attributes: elevation, slope and topographic wetness index derived from DEMs from varying sources, at different spatial resolutions and using three generalisation algorithms, namely: mean cell aggregation, nearest neighbour and hydrological corrected topo-to-raster. We show that topographic variable extraction is highly dependent on DEM source and generalisation approach and while higher resolution DEMs may represent the “true“ surface more accurately, they do not necessarily offer the best results for all extracted variables. Our results highlight the caveats of selecting DEMs not “fit-for-purpose” for topographic analysis and offer a simple yet effective solution for reconciling the selection of DEMs based on neighbourhood size resolution prior to terrain analyses and topographic feature characterization

MAPPING AND DECOMPOSING SCALE-DEPENDENT SOIL MOISTURE VARIABILITY WITHIN AN INNER BLUEGRASS LANDSCAPE

There is a shared desire among public and private sectors to make more reliable predictions, accurate mapping, and appropriate scaling of soil moisture and associated parameters across landscapes. A discrepancy often exists between the scale at which soil hydrologic properties are measured and the scale at which they are modeled for management purposes. Moreover, little is known about the relative importance of hydrologic modeling parameters as soil moisture fluctuates with time. More research is needed to establish which observation scales in space and time are optimal for managing soil moisture variation over large spatial extents and how these scales are affected by fluctuations in soil moisture content with time. This research fuses high resolution geoelectric and light detection and ranging (LiDAR) as auxiliary measures to support sparse direct soil sampling over a 40 hectare inner BluegrassKentucky (USA) landscape. A Veris 3100 was used to measure shallow and deep apparent electrical conductivity (aEC) in tandem with soil moisture sampling on three separate dates with ascending soil moisture contents ranging from plant wilting point to near field capacity. Terrain attributes were produced from 2010 LiDAR ground returns collected at ≤1 m nominal pulse spacing. Exploratory statistics revealed several variables best associate with soil moisture, including terrain features (slope, profile curvature, and elevation), soil physical and chemical properties (calcium, cation exchange capacity, organic matter, clay and sand) and aEC for each date. Multivariate geostatistics, time stability analyses, and spatial regression were performed to characterize scale-dependent soil moisture patterns in space with time to determine which soil-terrain parameters influence soil moisture distribution. Results showed that soil moisture variation was time stable across the landscape and primarily associated with long-range (~250 m) soil physicochemical properties. When the soils approached field capacity, however, there was a shift in relative importance from long-range soil physicochemical properties to short-range (~70 m) terrain attributes, albeit this shift did not cause time instability. Results obtained suggest soil moisture’s interaction with soil-terrain parameters is time dependent and this dependence influences which observation scale is optimal to sample and manage soil moisture variation

Multiscale Landforms Classification Based on UAV Datasets

The advance uses of Unmanned Aerial Vehicles (UAV) in geosciences by producing very high spatial resolution Digital Surface Models (DSMs), the various UAV flight altitudes led to different scales DSM. In this paper, we analyzed terrain forms using Topographic Position Index (TPI), landforms extracted by Iwahashi and Pike method and morphometric features of three different spatial resolutions DSM processed from different UAV flights height datasets of the same study area.Topographic Position Index (TPI) is an algorithm for measuring topographic slope positions and to automate landform classi?cations, Iwahashi and Pike had developed an unsupervised method for classification of Landforms and we have used the techniques developed by Peuker and Douglas, a method classifying terrain surfaces into 7 classes.Landforms extracted from the three indices listed above at the three flight heights of 120, 240 and 360 meters and compared with each other to understand the generalization of different scale and to highlight which landforms are more affected by the scale changes

Strategies for Handling Spatial Uncertainty due to Discretization

Geographic information systems (GISs) allow users to analyze geographic phenomena within areas of interest that lead to an understanding of their relationships and thus provide a helpful tool in decision-making. Neglecting the inherent uncertainties in spatial representations may result in undesired misinterpretations. There are several sources of uncertainty contributing to the quality of spatial data within a GIS: imperfections (e.g., inaccuracy and imprecision) and effects of discretization. An example for discretization in the thematic domain is the chosen number of classes to represent a spatial phenomenon (e.g., air temperature). In order to improve the utility of a GIS an inclusion of a formal data quality model is essential. A data quality model stores, specifies, and handles the necessary data required to provide uncertainty information for GIS applications. This dissertation develops a data quality model that associates sources of uncertainty with units of information (e.g., measurement and coverage) in a GIS. The data quality model provides a basis to construct metrics dealing with different sources of uncertainty and to support tools for propagation and cross-propagation. Two specific metrics are developed that focus on two sources of uncertainty: inaccuracy and discretization. The first metric identifies a minimal?resolvable object size within a sampled field of a continuous variable. This metric, called detectability, is calculated as a spatially varying variable. The second metric, called reliability, investigates the effects of discretization on reliability. This metric estimates the variation of an underlying random variable and determines the reliability of a representation. It is also calculated as a spatially varying variable. Subsequently, this metric is used to assess the relationship between the influence of the number of sample points versus the influence of the degree of variation on the reliability of a representation. The results of this investigation show that the variation influences the reliability of a representation more than the number of sample points

Multi-Scale Analysis of the Spatial Distribution of Soil Organic Carbon Stocks in Permafrost-Affected Soils in West Greenland

Soils of the northern circumpolar region are a key organic carbon storage strained by global warming. Thawing of permafrost-affected soils from global warming increases greenhouse-gas emissions whose quantification is limited by sparse, uncertain and spatially diverse data of soil organic carbon stocks (SOCS) across the Arctic region, especially in Greenland. The accurate assessment of the effects of global warming requires better understanding of environmental interactions and feedbacks on SOCS which, however, vary spatially and across scales in Arctic environments. Therefore, different scales were selected to identify scale-dependent effects of environmental factors and processes on the SOCS distribution in permafrost-affected soils in Arctic environments, exemplified by two study areas in West Greenland. Three controlling factors (vegetation, landscape, aspect) were used as representation of spatial varying environmental conditions to investigate the spatial SOCS distribution over short distances separately in both areas on the local scale and over a long distance between both areas on the regional scale. Further, the spatial SOCS distribution was analyzed using a set of multi-scale terrain and spatial features representing environmental processes acting parallel but differing in their intensity on the moraine, valley and catchment scale. The soil data set comprises of SOCS from 140 locations distributed over a study area at the coast and at the ice margin of West Greenland being characterized by oceanic and continental climate. On the local scale, the SOCS distribution was best explained by vegetation and aspect as both reflect the importance of wind and solar radiation in both areas. Furthermore, aspect and curvature best mapped the SOCS distribution shaped by water-driven relocation processes on the moraine and valley scale in SISI and wind-induced processes acting parallel on the moraine, valley and catchment scale in RUSS. On the regional scale, differences in the SOCS distribution result from contrasting climate conditions between the coast and the ice margin which both are reflected by differences in the importance of relevant terrain features and scales and vegetation units between both study areas. Consequently, it is recommended to apply multi-scale terrain features in combination with vegetation to address scale-dependent soil-landscape interrelations being essential for spatial analysis of SOCS in West Greenland

Scripting methods in topographic data processing on the example of Ethiopia

This study evaluates the geomorphometric parameters of the topography in Ethiopia using scripting cartographic methods by applying R languages (packages 'tmap' and 'raster') and Generic Mapping Tools (gmt) for 2D and 3D topographic modelling. Data were collected from the open source repositories on geospatial data with high resolution: gebco with 15 arc-second and etopo1 with 1 arc-minute resolution and embedded dataset of srtm 90 m in 'raster' library of R. The study demonstrated application of the programming approaches in cartographic data visualization and mapping for geomorphometric analysis. This included modelling of slope steepness, aspect and hillshade visualized using dem srtm90 to derive geomorphometric parameters of slope, aspect and hillshade of Ethiopia and demonstrate contrasting topography and variability climate setting of Ethiopia. The topography of the country is mapped, including Great Rift Valley, Afar Depression, Ogaden Desert and the most distinctive features of the Ethiopian Highlands. A variety of topographical zones is demonstrated on the presented maps. The results include 6 new maps made using programming console-based approach which is a novel method of cartographic visualization compared to traditional gis software. The most important fragments of the codes are presented and technical explanations are provided. The presented series of 6 new maps contributes to the cartographic data on Ethiopia and presents the methodology of scripting mapping techniques

A differential equation for specific catchment area

Analysis of the behavior of specific catchment area in a stream tube leads to a simple nonlinear differential equation describing the rate of change of specific catchment area along a flow path. The differential equation can be integrated numerically along a flow path to calculate specific catchment area at any point on a digital elevation model without requiring the usual estimates of catchment area and width. The method is more computationally intensive than most grid-based methods for calculating specific catchment area, so its main application is as a reference against which conventional methods can be tested. This is the first method that provides a benchmark for more approximate methods in complex terrain with both convergent and divergent areas, not just on simple surfaces for which analytical solutions are known. Preliminary evaluation of the D8, M8, digital elevation model networks (DEMON), and D methods indicate that the D method is the best of those methods for estimating specific catchment area, but all methods overestimate in divergent terrain