Robert S. Nuske

Using Unmanned Aerial Vehicles (UAV) to Quantify Spatial Gap Patterns in Forests

Gap distributions in forests reflect the spatial impact of man-made tree harvesting or naturally-induced patterns of tree death being caused by windthrow, inter-tree competition, disease or senescence. Gap sizes can vary from large (>100 m2) to small (<10 m2), and they may have contrasting spatial patterns, such as being aggregated or regularly distributed. However, very small gaps cannot easily be recorded with conventional aerial or satellite images, which calls for new and cost-effective methodologies of forest monitoring. Here, we used an unmanned aerial vehicle (UAV) and very high-resolution images to record the gaps in 10 temperate managed and unmanaged forests in two regions of Germany. All gaps were extracted for 1-ha study plots and subsequently analyzed with spatially-explicit statistics, such as the conventional pair correlation function (PCF), the polygon-based PCF and the mark correlation function. Gap-size frequency was dominated by small gaps of an area <5 m2, which were particularly frequent in unmanaged forests. We found that gap distances showed a variety of patterns. However, the polygon-based PCF was a better descriptor of patterns than the conventional PCF, because it showed randomness or aggregation for cases when the conventional PCF showed small-scale regularity; albeit, the latter was only a mathematical artifact. The mark correlation function revealed that gap areas were in half of the cases negatively correlated and in the other half independent. Negative size correlations may likely be the result of single-tree harvesting or of repeated gap formation, which both lead to nearby small gaps. Here, we emphasize the usefulness of UAV to record forest gaps of a very small size. These small gaps may originate from repeated gap-creating disturbances, and their spatial patterns should be monitored with spatially-explicit statistics at recurring intervals in order to further insights into forest dynamics.

Density estimation based on k-tree sampling and point pattern reconstruction.

In k-tree sampling, also referred to as point-to-tree distance sampling, the k nearest trees are measured. The problem associated with k-tree sampling is its lack of unbiased density estimators. Th...

Will climate change increase irrigation requirements in agriculture of Central Europe? A simulation study for Northern Germany

BackgroundBy example of a region in Northern Germany (County of Uelzen), this study investigates whether climate change is likely to require adaption of agricultural practices such as irrigation in Central Europe. Due to sandy soils with low water retention capacity and occasional insufficient rainfall, irrigation is a basic condition for agricultural production in the county of Uelzen. Thus, in the framework of the comprehensive research cluster Nachhaltiges Landmanagement im Norddeutschen Tiefland (NaLaMa-nT), we investigated whether irrigation might need to be adapted to changing climatic conditions. To this end, results from regionalised climate change modelling were coupled with soil- and crop-specific evapotranspiration models to calculate potential amounts of irrigation to prevent crop failures. Three different runs of the climate change scenario RCP 8.5 were used for the time period until 2070.ResultsThe results show that the extent of probable necessary irrigation will likely increase in the future. For the scenario run with the highest temperature rise, the results suggest that the amount of ground water presently allowed to be extracted for irrigation might not be sufficient in the future to retain common agricultural pattern.ConclusionsThe investigation at hand exemplifies data requirements and methods to estimate irrigation needs under climate change conditions. Restriction of ground water withdrawal by German environmental regulation may require an adaptation of crop selection and alterations in agricultural practice also in regions with comparable conditions.

Challenges in Climate-Driven Decision Support Systems in Forestry

The history of Decision Support Systems in forestry is quite long as well as the list of created systems and reviews summarizing their merits and flaws. It is generally recognized that a modern decision support system (DSS) should address simultaneously as many economical and ecological issues as possible without becoming overly complex and still remain understandable for users (Reynolds et al., 2008). The ongoing global change including the climate change sets new boundary conditions for decision makers in the forestry sector. The changing growth conditions (Albert & Schmidt, 2010) and expected increasing number of weather extremes like storms force forest owners to make decisions on how to replace the damaged stands and/or how to mitigate the damages. This decision making process requires adequate information on the future climate as well as on complex climate-forest interactions which could be provided by an appropriate climate-driven decision support tool. Both the damage factors and the forest management (e.g. harvesting) result in changes of the structure of forest stands. The structural changes result in immediate changes of albedo and roughness of land surface as well as of microclimatological conditions within the stand and on the soil surface. The consequences are manifold. The changed stand density and leaf area index trigger energy and water balance changes which in turn increase or decrease the vulnerability of the remaining stand to abiotic and biotic damage factors like droughts or insect attacks. A change of the microclimatic conditions might strengthen the forest against drought, but at the same time reduce its resistance to windthrow. The sign and extent of vulnerability changes depend on complex interactions of the effective climatic agents, aboveand belowground forest structure, and soil. There are many DSS that are capable of assessing one or several risk factors; however there are few that are able to assess the additional increase or decrease of risks triggered by modification of forest structure resulting from previous damage or forest management activities. Disregarding these effects will inevitably lead user to either underor overestimation of the potential damages. The question arises whether these additional risks are significant enough to be considered in a DSS. In this chapter we present a new DSS developed according to the above mentioned requirements and capable to provide decision support taking into account economical and ecological considerations under the conditions of changing climate the Decision Support