Remote sensing has become the most important data source for the digital elevation model (DEM) generation. DEM analyses can be applied in various fields and many of them require appropriate DEM visualization support. Analytical hill-shading is the most frequently used relief visualization technique. Although widely accepted, this method has two major drawbacks: identifying details in deep shades and inability to properly represent linear features lying parallel to the light beam. Several authors have tried to overcome these limitations by changing the position of the light source or by filtering. This paper proposes a new relief visualization technique based on diffuse, rather than direct, illumination. It utilizes the sky-view factor—a parameter corresponding to the portion of visible sky limited by relief. Sky-view factor can be used as a general relief visualization technique to show relief characteristics. In particular, we show that this visualization is a very useful tool in archaeology as it improves the recognition of small scale features from high resolution DEMs.

Visualization products computed from a raster elevation model still form the basis of most archaeological and geomorphological enquiries of lidar data. We believe there is a need to improve the existing visualizations and create meaningful image combinations that preserve positive characteristics of individual techniques. In this paper, we list the criteria a good visualization should meet, present five different blend modes (normal, screen, multiply, overlay, luminosity), which combine various images into one, discuss their characteristics, and examine how they can be used to improve the visibility (recognition) of small topographical features. Blending different relief visualization techniques allows for a simultaneous display of distinct topographical features in a single (enhanced) image. We provide a “recipe” and a tool for a mix of visualization techniques and blend modes, including all the settings, to compute a visualization for archaeological topography that meets all of the criteria of a good visualization.

Aerial mapping and remote sensing takes another step forward with this method of modelling lidar data. The usual form of presentation, hill shade, uses a point source to show up surface features. Sky-view factor simulates diffuse light by computing how much of the sky is visible from each point. The result is a greatly improved visibility — as shown here by its use on a test site of known topography in Slovenia.

This guide provides an insight into a range of visualization techniques for high-resolution digital elevation models (DEMs). It is provided in the context of investigation and interpretation of various types of historical and modern, cultural and natural small-scale relief features and landscape structures. It also provides concise guidance for selecting the best techniques when looking at a specific type of landscape and/or looking for particular kinds of forms. The three main sections – descriptions of visualization techniques, guidance for selection of the techniques, and visualization tools – accompany examples of visualizations, exemplar archaeological and geomorphological case studies, a glossary of terms, and a list of references and recommendations for further reading. The structure facilitates people of different academic background and level of expertise to understand different visualizations, how to read them, how to manipulate the settings in a calculation, and choose the best suited for the purpose of the intended investigation. Monografija nudi vpogled v nabor tehnik prikaza visokoločljivih modelov višin. Napisana je v kontekstu preučevanja in interpretacije različnih tipov zgodovinskih in modernih, kulturnih in naravnih majhnih reliefnih oblik. Daje jedrnate napotke za izbiro najboljših tehnik prikaza določenih tipov pokrajine in izrazitih oblik. Tri glavna poglavja – opis tehnik prikazovanja digitalnih modelov višin, napotki za njihovo izbiro in orodja za izračun prikazov –, spremljajo izbrani primeri tipičnih arheoloških in geomorfoloških študij, slovarček pojmov ter seznam literature in priporočenega branja. Posameznikom z različnih znanstvenih področij in z različnim predznanjem o tematiki je struktura v pomoč pri razumevanju različnih tehnik prikazov, kako jih brati, kako izbrati prave nastavitve pri njihovem izračunu in kako prepoznati najbolj primerne za namen zasnovane raziskave.

The Pannonian Basin in southeastern Europe is heavily used for rain-fed agriculture. The region experienced several droughts in the last years, causing major yield losses. Ongoing climate change, characterised by increasing temperatures and potential evapotranspiration, and by changes in precipitation distribution will likely increase the frequency and intensity of drought episodes in the future. Hence, ongoing monitoring of droughts and estimation of their impact on agriculture is necessary to adapt agricultural practices to changing weather and climate extremes. Several regional initiatives, projects and online tools have been established to facilitate drought monitoring and management in the Pannonian Basin. However, reliable systems to forecast potential drought impacts on plant productivity and agricultural yields at monthly to seasonal scales are only in their infancy, as plant response to climatic extremes is still poorly understood. With the increasing availability of high-resolution and long-term Earth Observation (EO) data and recent progress in machine learning and artificial intelligence, further improvements in drought monitoring and impact prediction capacities are expected. Here we review the current state of drought monitoring in the Pannonian Basin, identify EO-based variables to potentially improve regional drought impact monitoring and outline future perspectives for seasonal forecasts of drought impacts on agriculture.