Jae Ogilvie

Bio: Jae Ogilvie is an academic researcher from University of New Brunswick. The author has contributed to research in topics: Soil water & Digital elevation model. The author has an hindex of 13, co-authored 27 publications receiving 862 citations.

Topographic modelling of soil moisture conditions: a comparison and verification of two models

Abstract: Summary Topography, as captured by a digital elevation model (DEM), can be used to model soil moisture conditions because water tends to flow and accumulate in response to gradients in gravitational potential energy. A widely used topographic index, the soil wetness index (SWI), was compared with a new algorithm that produces a cartographic depth-to-water (DTW) index based on distance to surface water and slope. Both models reflect the tendency for soil to be saturated. A 1 m resolution Light Detection and Ranging (LiDAR) DEM and a 10 m conventional photogrammetric DEM were used and results were compared with field-mapped wet soil areas for a 193 ha watershed in Alberta, Canada, for verification. The DTW model was closer to field-mapped conditions. Values of Kmatch90 (areal correspondence, smaller values indicating better performance) were 7.8% and 12.3% for the LiDAR and conventional DEM DTW models, respectively, and 88.5% and 86.7% for the SWI models. The two indices were poorly correlated spatially. Both DEMs were found to be useful for modelling soil moisture conditions using the DTW model, but the LiDAR DEM produced the better results. All major wet areas and flow connectivity were reproduced and a threshold value of 1.5 m DTW accounted for 71% of the observed wet areas. The poor performance of the SWI model is probably because of its over-dependence on flow accumulation. Incorporation of a flow accumulation algorithm that replicates the effects of dispersed flow showed some improvement in the SWI model for the conventional DEM but it still failed to replicate the full areal extent of wet areas. Local downslope topography and hydrologic conditions seemed to be more important in determining soil moisture conditions than is taken account of by the SWI. The DTW model has potential for application in distributed hydrologic modelling, precision forestry and agriculture and implementation of environmental soil management practices.

Stream network modelling using lidar and photogrammetric digital elevation models : a comparison and field verification

Abstract: A conventional, photogrammetrically derived digital elevation model (DEM; 10 m resolution) and a light detection and ranging (lidar)-derived DEM (1 m resolution) were used to model the stream network of a 193 ha watershed in the Swan Hills of Alberta, Canada. Stream networks, modelled using both hydrologically corrected and uncorrected versions of the DEMs and derived from aerial photographs, were compared. The actual network, mapped in the field, was used as verification. The lidar DEM-derived network was the most accurate representation of the field-mapped network, being more accurate even than the photo-derived network. This was likely due to the greater initial point density, accuracy and resolution of the lidar DEM compared with the conventional DEM. Lidar DEMs have great potential for application in land-use planning and management and hydrologic modelling. The network derived from the hydrologically corrected conventional DEM was more accurate than that derived from the uncorrected one, but this was not the case with the lidar DEM. Copyright © 2007 John Wiley & Sons, Ltd.

Evaluating digital terrain indices for soil wetness mapping – a Swedish case study

Abstract: Trafficking wet soils within and near stream and lake buffers can cause soil disturbances, i.e. rutting and compaction. This - in turn - can lead to increased surface flow, thereby facilitating the leaking of unwanted substances into downstream environments. Wet soils in mires, near streams and lakes have particularly low bearing capacity and are therefore more susceptible to rutting. It is therefore important to model and map the extent of these areas and associated wetness variations. This can now be done with adequate reliability using a high-resolution digital elevation model (DEM). In this article, we report on several digital terrain indices to predict soil wetness by wet-area locations. We varied the resolution of these indices to test what scale produces the best possible wet-areas mapping conformance. We found that topographic wetness index (T-WI) and the newly developed cartographic depth-to-water index (D-TW) were the best soil wetness predictors. While the T-WI derivations were sensitive to scale, the D-TW derivations were not and were therefore numerically robust. Since the D-TW derivations vary by the area threshold for setting stream flow initiation, we found that the optimal threshold values for permanently wet areas varied by landform within the Krycklan watershed, e.g. 1-2 ha for till-derived landforms versus 8-16 ha for a coarse-textured alluvial floodplain.

Mapping wetlands: a comparison of two different approaches for new brunswick, canada

Abstract: Wetlands have an important role in ecosystem function and biodiversity. Effective management of wetlands requires accurate and comprehensive spatial information on location, size, classification, and connectivity in the landscape. Using a GIS, two provincial wetland maps were compared with regard to their areal correspondence across different ecoregions of New Brunswick. The first consisted of discrete wetland units (vector data) derived from aerial photo interpretation. The second consisted of wet areas modeled by a newly developed depth-to-water index with continuous coverage across the landscape (raster data). This index was derived from a digital elevation model and hydrographic data. The relative advantages and disadvantages of the two approaches were assessed. The two maps were generally consistent with most discrete wetland areas (51%–67%) embedded in the 0– 10 cm depth-to-water class, verifying the continuous modeling approach. The continuous model identified a larger wetland area. Much of this additional area consisted of riparian zones and numerous small wetlands (< 1 ha) that were not captured by aerial photo interpretation. Unlike the discrete map, the continuous model showed the hydrological connectivity of wetlands across the landscape. Both approaches revealed that topography was a major control on wetland distribution between ecoregions, with more wetland in ecoregions with flatter topography.

The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning

Abstract: Airborne Laser Scanning (ALS), also known as Light Detection and Ranging (LiDAR) enables an accurate three-dimensional characterization of vertical forest structure. ALS has proven to be an information-rich asset for forest managers, enabling the generation of highly detailed bare earth digital elevation models (DEMs) as well as estimation of a range of forest inventory attributes (including height, basal area, and volume). Recently, there has been increasing interest in the advanced processing of high spatial resolution digital airborne imagery to generate image-based point clouds, from which vertical information with similarities to ALS can be produced. Digital airborne imagery is typically less costly to acquire than ALS, is well understood by inventory practitioners, and in addition to enabling the derivation of height information, allows for visual interpretation of attributes that are currently problematic to estimate from ALS (such as species, health status, and maturity). At present, there are two limiting factors associated with the use of image-based point clouds. First, a DEM is required to normalize the image-based point cloud heights to aboveground heights; however DEMs with sufficient spatial resolution and vertical accuracy, particularly in forested areas, are usually only available from ALS data. The use of image-based point clouds may therefore be limited to those forest areas that already have an ALS-derived DEM. Second, image-based point clouds primarily characterize the outer envelope of the forest canopy, whereas ALS pulses penetrate the canopy and provide information on sub-canopy forest structure. The impact of these limiting factors on the estimation of forest inventory attributes has not been extensively researched and is not yet well understood. In this paper, we review the key similarities and differences between ALS data and image-based point clouds, summarize the results of current research related to the comparative use of these data for forest inventory attribute estimation, and highlight some outstanding research questions that should be addressed before any definitive recommendation can be made regarding the use of image-based point clouds for this application.

Impact of DEM accuracy and resolution on topographic indices

Abstract: Topography is an important land-surface characteristic that affects most aspects of the water balance in a catchment, including the generation of surface and sub-surface runoff; the flow paths followed by water as it moves down and through hillslopes and the rate of water movement. All of the spatially explicit fully distributed hydraulic and hydrological models use topography (represented by the DEM of the area modelled) to derive bathymetry. DEM is also used to derive some other key information critical in fully distributed hydraulic and hydrological models. With high-resolution DEMs such as LiDAR (Light Detection and Ranging) becoming more readily available and also with the advancements in computing facilities which can handle these large data sets, there is a need to quantify the impact of using different resolution DEMs (e.g. 1 m against 10 m or 25 m) on hydrologically important variables and the loss of accuracy and reliability of the results as we move from high resolution to coarser resolution. The results from statistical analysis carried out to compare field survey elevations with the LiDAR DEM-derived elevations, show that there are small differences between the two data sets but LiDAR DEM is a reasonably good representation of the actual ground surface compared to other commonly used DEMs derived from contour maps. The results from the analysis clearly show that the accuracy and resolution of the input DEM have serious implications on the values of the hydrologically important spatial indices derived from the DEM. The result also indicates that the loss of details by re-sampling the higher resolution DEM to coarser resolution are much less compared to the details captured in the commonly available coarse resolution DEM derived from contour maps. Topographic indices based on contour derived DEMs should be used with caution and where available, the higher resolution DEM should be used instead of the coarse resolution one.

A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach

Abstract: A best practices guide for the use of airborne laser scanning data (ALS; also referred to as Light Detection and Ranging or LiDAR) in forest inventory applications is now available for download from the Canadian Forest Service bookstore (White et al ., 2013; http://cfs.nrcan.gc.ca/publications?id= 34887 ). The guide, produced by the Canadian Forest Service, Natural Resources Canada, brings together state-of-the-art approaches, methods, and data to enable readers interested in using ALS data to characterize large forest areas in a costeffective manner. The best practices presented in the guide are based on more than 25 years of scientific research on the application of ALS data to forest inventory. The guide describes the entire process for generating forest inventory attributes from ALS data and recommends best practices for each step of the process—from ground sampling through to metric generation and model development. The collection of ground plot data for model calibration and validation is a crit ical component of the recommended approach and is described in detail in the guide. Appendices to the guide pro vide additional details on ALS data acquisition and metric generation. The area-based approach is typically accomplished in two steps (Fig. 1). In the first step, ALS data are acquired for the entire area of interest (wall-to-wall coverage), tree-level meas ures are acquired from sampled ground plots and summa rized to the plot level, and predictive models are developed (e.g., using regression or non-parametric methods). For the purposes of model development, the ALS data is clipped to correspond to the area and shape of each ground plot. A set of descriptive statistics (referred to as “metrics”) are calculated from the clipped ALS data and include measures such as mean height, height percentiles, and canopy cover (Woods et al . 2011). Inventory attributes of interest are either measured by ground crews (i.e., height, diameter) or modelled (i.e., vol ume, biomass) for each ground plot. It is critical that ground plots represent the full range of variability in the attribute(s) of interest and to accomplish this, the use of a stratified sampling approach is recommended, preferably with strata that are defined using the ALS metrics themselves. Thus, the ALS data must be acquired and processed prior to ground sampling. Finally, predictive models are constructed using the ground plot attributes as the response variable and the ALS-derived metrics as predictors. In the second step of the area-based approach, models that were developed using co-located ground plots and ALS data are then applied to the entire area of interest to generate the desired wall-to-wall estimates and maps of specific forest inventory attributes. The same metrics that are calculated for the clipped ALS data (as described above) are generated for the wall-to-wall ALS data and the predictive equations devel oped from the modelling in the first step are applied to the entire area of interest using the wall-to-wall metrics. The pre diction unit for this application is a grid cell, the size of which relates to the size of the ground-measured plot. Once the pre dictive equations are applied to the wall-to-wall ALS data, each grid cell will have an estimate for the attribute of interest. The primary advantage of the area-based approach is hav ing complete (i.e., wall-to-wall) spatially explicit measures of canopy height, associated metrics, and all modelled attributes for an area of interest (Fig. 2). The area-based approach described in the guide also enables more precise estimates of certain forest variables and the calculation of confidence intervals for stand-level estimates (Woods et al . 2011).