Conference Agenda

Session
C45: Advances in Environmental GIS
Time:
Thursday, 09/July/2020:
1:00pm - 1:45pm

Session Chair: Gudrun Wallentin
Location: C - Sessions

Presentations

Interannual Variability of Lake Ice Backscatter Anomalies on Lake Neyto, Yamal, Russia

Georg Pointner1,2, Annett Bartsch1,2

1b.geos, Korneuburg, Austria; 2Austrian Polar Research Institute, Vienna, Austria

Backscatter anomalies in Synthetic Aperture Radar (SAR) imagery of lake ice on lake Neyto on the Yamal Peninsula in Russia have been described in the literature for multiple years and possible suggested causes are the formation of eddies or the release of gas through the lake sediments, which could both lead to local thinning of the ice layer and alter radar backscatter. In order to gain a better understanding of the phenomenon, we assess the spatial variability of patterns of low backscatter among the years 2015 to 2019. We introduce an image classification algorithm developed with state-of-the-art open-source image processing tools and calculate polygon distances, polygon intersections and cumulative pixel counts deduced from the classification results. Our results show large spatial variations, but also always some overlap between different years. Changes in locations do not seem smaller between consecutive years when compared to other years. Some spatial properties of clusters of low backscatter may support the explanation of gas release as the primary cause of the observed patterns.



Land suitability analysis of Alvar grassland vegetation in Estonia using Random Forest

Irada Ismayilova1, Evelyn Uuemaa2, Aveliina Helm2, Christian Röger1, Sabine Timpf1

1University of Augsburg, Germany; 2University of Tartu, Estonia

Alvar grasslands are one of the most species rich habitats in Estonia. The anthropogenic pressure in the form of land use change decreased grassland’s persistence. Therefore, their conservation and restoration issue is becoming more and more relevant. Many attempts at their restoration have already been made. However, a land suitability analysis, using the machine learning technique Random Forest (RF), was performed for the first time in this study. The aim of the study was to assess the use of RF for alvar grassland vegetation suitability analysis. As a result, RF predicted/identified 610.91 km2 of suitable areas for restoring alvar grasslands or creating alvar-like habitats in Estonia. We discuss the suitability analysis and found it reasonable for subsequent restoration or creation of new sites of Alvars.



Towards Predicting Vine Yield: Conceptualization of 3D Grape Models and Derivation of reliable Physical and Morphological Parameters

Thomas Andreas Schneider, Gernot Paulus, Karl-Heinrich Anders

Carinthia University of Applied Sciences, Österreich

In viticulture, yield prediction plays an important role, helping winegrowers to predict the start of the next growth stage of vines and to improve vineyard management decision-making. To predict a vineyard’s yield, it is necessary to gather accurate local information about the vine’s phenology and morphology, such as the volume of individual grapes. Traditional collection of these data and yield prediction rely on resource- and time-intensive direct visual and manual in-field work by viticulturists. Thus, only limited sampling in the vineyards is possible, carried out by humans. Automated procedures utilizing sensor-based systems reduce the data acquisition time and enable the collection of high-resolution data from the entire vineyard. Large-scale 3D models of vineyards can be generated from these data and used to analyse, for example, the vineyard’s yield or the vegetative stage of individual vines.

We propose a concept for a 3D model that uses close-range photogrammetry. In a laboratory experiment, we tested the acquisition of multi-view image datasets from grapes using close-range photogrammetry and derived physical and morphological parameters from 3D grape models. The results could contribute to the design and implementation of a large-scale in-field experiment.



Characterizing agricultural landscapes using landscape metrics and cluster analysis in Brandenburg, Germany

Saskia Wolff, Tobia Lakes

Humboldt-Universität zu Berlin, Deutschland

An increasing demand for agricultural products in recent years has led to agricultural intensification. Agricultural land use patterns can have various impacts on the provision of ecosystem services. We used the plot-based data provided by the Integrated Administration and Control System (IACS) to identify different types of agricultural landscapes and their spatial distribution in Brandenburg, Germany. By calculating a set of landscape metrics to characterise agricultural land use, we were able to identify six types of agricultural landscape through a Two-Step cluster analysis for a hexagonal grid. Most of Brandenburg’s agricultural land is characterised by a high proportion of cropland but different degrees of fragmentation. By providing a framework using landscape metrics derived from IACS data, the approach of clustering to identify typologies is highly transferable to other regions within the EU, which aims to support and develop sustainable food production systems. Our framework may be an important asset in offering new units of analysis for better-tailored environmental and agricultural planning depending on local and regional characteristics.