Visibility studies in archaeology: a review and case study

This paper describes the history and current state of archaeological visibility studies. The first part is a survey of both GIS (geographic information systems) and non-GIS studies of visibility by archaeologists, which demonstrates how advances in GIS visibility studies have tended to recapitulate, albeit over a compressed timescale, theoretically driven developments in non-GIS studies. The second part presents an example of the kind of methodological development required for the use of GIS to contribute to the agenda set by certain strands of a more humanistic archaeology. An algorithm developed to retrieve various summaries of the inclination at which points on the horizon are visible from a specified viewpoint was applied to nineteen recumbent stone circles in the Grampian region of Scotland. The results suggest that these summaries provide a useful tool for 'unpacking' what archaeologists mean when they claim that the topographic setting of certain stone circles creates an 'impression of circularity'

Hierarchical occlusion culling for arbitrarily-meshed height fields

Many graphics applications today have need for high-speed 3-D visualization of height fields. Most of these applications deal with the display of digital terrain models characterized by a simple, but vast, non-overlapping mesh of triangles. A great deal of research has been done to find methods of optimizing such systems. The goal of this work is to establish an algorithm to efficiently preprocess a hierarchical height field model that enables the real-time culling of occluded geometry while still allowing for classic terrain-rendering frameworks. By exploiting the planar-monotone characteristics of height fields, it is possible to create a unique and efficient occlusion culling method that is optimized for terrain rendering and similar applications. Previous work has shown that culling is possible with certain regularly-gridded height field models, but not until now has a system been shown to work with all height fields, regardless of how their meshes are constructed. By freeing the system of meshing restrictions, it is possible to incorporate a number of broader height field algorithms with widely-used applications such as flight simulators, GIS systems, and computer games

Digital Elevation Models in Geomorphology

This chapter presents place of geomorphometry in contemporary geomorphology. The focus is on discussing digital elevation models (DEMs) that are the primary data source for the analysis. One has described the genesis and definition, main types, data sources and available free global DEMs. Then we focus on landform parameters, starting with primary morphometric parameters, then morphometric indices and at last examples of morphometric tools available in geographic information system (GIS) packages. The last section briefly discusses the landform classification systems which have arisen in recent years

Investigating multiscale meteorological controls and impact of soil moisture heterogeneity on radiation fog in complex terrain using semi-idealised simulations

Coupled surface–atmosphere high-resolution mesoscale simulations were carried out to understand meteorological processes involved in the radiation fog life cycle in a city surrounded by complex terrain. The controls of mesoscale meteorology and microscale soil moisture heterogeneity on fog were investigated using case studies for the city of ¯Otautahi / Christchurch, New Zealand. Numerical model simulations from the synop- tic to microscale were carried out using the Weather Research and Forecasting (WRF) model and the Parallelised Large-Eddy Simulation Model (PALM). Heterogeneous soil moisture, land use, and topography were included. The spatial heterogeneity of soil moisture was derived using Landsat 8 satellite imagery and ground-based me- teorological observations. Nine semi-idealised simulations were carried out under identical meteorological conditions. One contained homogeneous soil moisture of about 0.31 m$^3$ m$^{−3}$, with two other simulations of halved and doubled soil moisture to demonstrate the range of soil moisture impact. Another contained heterogeneous soil moisture derived from Landsat 8 imagery. For the other five simulations, the soil moisture heterogeneity magnitudes were amplified following the observed spatial distribution to aid our understanding of the impact of soil moisture heterogeneity. Analysis using pseudo-process diagrams and accumulated latent heat flux shows significant spatial heterogeneity of processes involved in the simulated fog. Our results showed that soil mois- ture heterogeneity did not significantly change the general spatial structure of near-surface fog occurrence, even when the heterogeneity signal was amplified and/or when the soil moisture was halved and doubled. However, compared to homogeneous soil moisture, spatial heterogeneity in soil moisture can lead to changes in fog duration. These changes can be more than 50 min, although they are not directly correlated with spatial variations in soil moisture. The simulations showed that the mesoscale (10 to 200 km) meteorology controls the location of fog occurrence, while soil moisture heterogeneity alters fog duration at the microscale on the order of 100 m to 1 km. Our results highlight the importance of including soil moisture heterogeneity for accurate spatiotemporal fog forecasting

Artificial intelligence-based software (AID-FOREST) for tree detection: A new framework for fast and accurate forest inventorying using LiDAR point clouds

Forest inventories are essential to accurately estimate different dendrometric and forest stand parameters. However, classical forest inventories are time consuming, slow to conduct, sometimes inaccurate and costly. To address this problem, an efficient alternative approach has been sought and designed that will make this type of field work cheaper, faster, more accurate, and easier to complete. The implementation of this concept has required the development of a specifically designed software called "Artificial Intelligence for Digital Forest (AID-FOREST)", which is able to process point clouds obtained via mobile terrestrial laser scanning (MTLS) and then, to provide an array of multiple useful and accurate dendrometric and forest stand parameters. Singular characteristics of this approach are: No data pre-processing is required either pre-treatment of forest stand; fully automatic process once launched; no limitations by the size of the point cloud file and fast computations.To validate AID-FOREST, results provided by this software were compared against the obtained from in-situ classical forest inventories. To guaranty the soundness and generality of the comparison, different tree spe-cies, plot sizes, and tree densities were measured and analysed. A total of 76 plots (10,887 trees) were selected to conduct both a classic forest inventory reference method and a MTLS (ZEB-HORIZON, Geoslam, ltd.) scanning to obtain point clouds for AID-FOREST processing, known as the MTLS-AIDFOREST method. Thus, we compared the data collected by both methods estimating the average number of trees and diameter at breast height (DBH) for each plot. Moreover, 71 additional individual trees were scanned with MTLS and processed by AID-FOREST and were then felled and divided into logs measuring 1 m in length. This allowed us to accurately measure the DBH, total height, and total volume of the stems.When we compared the results obtained with each methodology, the mean detectability was 97% and ranged from 81.3 to 100%, with a bias (underestimation by MTLS-AIDFOREST method) in the number of trees per plot of 2.8% and a relative root-mean-square error (RMSE) of 9.2%. Species, plot size, and tree density did not significantly affect detectability. However, this parameter was significantly affected by the ecosystem visual complexity index (EVCI). The average DBH per plot was underestimated (but was not significantly different from 0) by the MTLS-AIDFOREST, with the average bias for pooled data being 1.8% with a RMSE of 7.5%. Similarly, there was no statistically significant differences between the two distribution functions of the DBH at the 95.0% confidence level.Regarding the individual tree parameters, MTLS-AIDFOREST underestimated DBH by 0.16 % (RMSE = 5.2 %) and overestimated the stem volume (Vt) by 1.37 % (RMSE = 14.3 %, although the BIAS was not statistically significantly different from 0). However, the MTLS-AIDFOREST method overestimated the total height (Ht) of the trees by a mean 1.33 m (5.1 %; relative RMSE = 11.5 %), because of the different height concepts measured by both methodological approaches. Finally, AID-FOREST required 30 to 66 min per ha-1 to fully automatically process the point cloud data from the *.las file corresponding to a given hectare plot. Thus, applying our MTLS-AIDFOREST methodology to make full forest inventories, required a 57.3 % of the time required to perform classical plot forest inventories (excluding the data postprocessing time in the latter case). A free trial of AID -FOREST can be requested at [email protected]

Numerical simulation of fog and radiation in complex terrain : results from COST-722

Two high resolution numerical 1D models, namely COBEL and PAFOG, have been adapted to compute a probabilistic fog forecast. Major modifications were made to the COBEL model. It was coupled to the NOAH land surface model to take into account the effects of soil and vegetation and furthermore a parameterization of precipitation was added. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification reveals that the probabilistic forecast can significantly improve the current methods used at Z¨rich u Unique airport. The complex topography in Switzerland further complicates fog forecasting. In order to simulate processes like advection, cold air drainage flows and cold air pooling, the NMM 3D model of NOAA/NCEP is modified and extended with detailed fog microphysics. The resulting 3D fog model runs at a horizontal resolution ofkm and a vertical resolution comparable to the 1D models. First results look very promising and are able to reproduce the spatial distribution of fog as it is seen by satellite. With increasing horizontal resolution of numerical weather prediction models, topographical effects on radiation gain importance. With a newly developed parameterization it is possible to consider slope angle, aspect angle, shadows and restricted sky view on the subgrid scale and with negligible computational costs. Verification reveals that RMS and mean error ofm temperature forecasts are generally improved by 0.5 toK

New forecast tools to enhance the value of VRE on the electricity market: Deliverable D4.9

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: The present deliverable was developed as part of the research activities of the TradeRES project Task 4.4 - Enhancing the value of VRE on the electricity markets with advanced forecasting and ramping tools. This report presents the first version of deliverable 4.9, which consists on the description and implementation of the forecasting techniques aiming to identify and explore the time synergies of meteorological effects and electricity market designs in order to maximize the value of variable renewable energy systems and minimize market imbalances. An overview of key aspects that characterize a power forecast system is presented in this deliverable through a literature review. This overview addresses the: i) forecast time horizon; ii) type of approach (physical, statistical or hybrid); iii) data pre-processing procedures; iv) type of forecast output; and v) the most common metrics used to evaluate the performance of the forecast systems.N/