Pärtlirajust ja ilmapärimusest

The present study analyses St. Bartholomew’s Day storm – a storm that supposedly takes place near St. Bartholomew’s Day (August 24th) every year – a paroemia that has widely spread on the north-eastern coast of Estonia. The two questions analysed here are: firstly, whether such a storm is proved by the meteorological data and, secondly, whether it is reasonable to study weather paroemias. The present research makes use of the data about wind speed, measured in Jõhvi and Väike-Maarja weather stations during the period from 1966 to 2012. A more detailed analysis is dedicated to wind data around the old St. Bartholomew’s Day – the period around September 7th by the Gregorian calendar (August 12th till September 20th). According to wind speed data, it may be said that in north-eastern Estonia the end of August and beginning of September are rather windless. However, the analysis of the wind measurement data does not give a definitive answer to the question whether such a phenomenon as St. Bartholomew’s Day storm actually exists in nature. Against the background of relatively windless days, every windier day may be seen as stormy. The analysis indicates that the number of windy days around St. Bartholomew’s Day has constantly been decreasing over the last decades. This refers to changes in Estonian landscape, but even more so to changes in the climate. In the olden times, August and September were definitely much windier than today. Further research is needed to find out how windy the north-eastern coast of Estonia was around St. Bartholomew’s Day in the period before 1966. What is the benefit of such an analysis of weather paroemias? Proverb researchers have often viewed weather folklore somewhat condescendingly. It is true that weather paroemias do not qualify as the basis of scientific synoptic meteorology, and many paroemias have been borrowed from other cultures. For example, it is known that the St. Bartholomew’s Day storm tradition is Germanic and has reached Estonia via Finland. Weather paroemias, including weather proverbs, do not often follow the classical proverb rules. This is probably the reason why weather paroemias have been thoroughly studied neither in Estonia nor internationally. A study into weather proverbs potentially gives us knowledge about the worldview of our ancestors, culture distribution mechanisms, and also about the weather in the past. For example, it may be speculated that borrowed paroemias that fitted into the local population’s worldview or helped to better explain local weather phenomena entered common usage. If this is true, then it should be possible to find out from archived data when a particular weather paroemia became popular. This, in turn, enables us to use weather paroemias as proxy data for weather sciences. It may be assumed on the basis of old newspapers that the term St. Bartholomew’s Day storm became widely spread in the 1920s through to 1930s, when several well-documented storms indeed had devastating effects on the coast of north-eastern Estonia. However, this assumption has to be taken as a speculation, as several county newspapers, whose interest in the local weather was great, were established within the same period. A more detailed analysis of the St. Bartholomew’s Day storm tradition and other weather paroemias, however, requires a close cooperation between atmospheric scientists and folklorists

The future climate regions in Estonia

It can be assumed that future climate changes may have diverse impact on different parts of Estonia. For the regionalization of climate change impacts we used outputs of the EURO-CORDEX climate model ensemble and added its relative changes for temperature and precipitation to the values of the model’s reference period 1971–2000. In order to reduce the ambiguity of spatially detailed outputs, the clusters of future climate variables for seasons were produced. We conclude that three climatic regions – the mainland, the coast and the western Estonian islands – will most likely persist in the future climate. Major regional changes may occur at the end of the 21st century according to the pessimistic scenario of Representative Concentration Pathways RCP8.5. In the rest of the cases, although the boundaries of regions may shift, the warming as well as the increase in precipitation will mainly occur within the regions. Partially this will result in the growth of contrasts between regions

An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest

The application of unmanned aerial systems (UAS) in forest research includes a wide range of equipment, systems, and flight settings, creating a need for enhancing data acquisition efficiency and quality. Thus, we assessed the effects of flying altitude and lateral and longitudinal overlaps on digital aerial photogrammetry (DAP) processing and the ability of its products to provide point clouds for forestry inventory. For this, we used 18 combinations of flight settings for data acquisition, and a nationwide airborne laser scanning (ALS) dataset as reference data. Linear regression was applied for modeling DAP quality indicators and model fitting quality as the function of flight settings; equivalence tests compared DAP-and ALS-products. Most of DAP-Digital Terrain Models (DTM) showed a moderate to high agreement (R2 > 0.70) when fitted to ALS-based models; nine models had a regression slope within the 1% region of equivalence. The best DAP-Canopy Height Model (CHM) was generated using ALS-DTM with an R2 = 0.42 when compared with ALS-CHM, indicating reduced similarity. Altogether, our results suggest that the optimal combination of flight settings should include a 90% lateral overlap, a 70% longitudinal overlap, and a minimum altitude of 120 m above ground level, independent of the availability of an ALS-derived DTM for height normalization. We also provided insights into the effects of flight settings on DAP outputs for future applications in similar forest stands, emphasizing the benefits of overlaps for comprehensive scene reconstruction and altitude for canopy surface detection

A Review of Approaches for Automated Habitat Mapping and their Potential Added Value for Biodiversity Monitoring Projects

Habitats are important indicators of biodiversity in their own right, as well as being linked to species, hence their widespread use in reporting on nature conservation planning and policy. For reporting consistent mapping and monitoring habitat extent and change is important. Remote Sensing techniques are becoming an important tool for this. In this paper we describe four examples of methods of semi-automated mapping using Remote Sensing. Because the most effective way of improving the accuracy of the estimation of habitat area is by increasing the sample number, it is important to develop methods for reducing in situ surveys which are expensive. Remote Sensing has the major advantage of comprehensive coverage and the four examples illustrate the potential of extrapolation from semi-automated habitat classifications. The potential for using these methods at national scales is likely to be limited by the need for validation of the automated images and the subsequent calculation of error terms. Existing major national monitoring programs are described, which still use mainly traditional in situ methods. The selection of relatively small numbers of representative samples from environmental classifications to obtain regional estimates reduces the need for large numbers of in situ survey sites and is therefore discussed. The recent development of the use of drones to acquire detailed imagery to support in situ habitat surveys is also covered. Finally, practical problems linked to the methods described in the paper are considered, as in some cases these will override the theoretical benefits of a particular approach. It is concluded that automated methods can enhance existing monitoring systems and should be considered in any biodiversity monitoring system as they represent an opportunity for reducing costs, if integrated with an in situ program

A review of approaches for automated habitat mapping and their potential added value for biodiversity monitoring projects

Habitats are important indicators of biodiversity in their own right, as well as being linked to species, hence their widespread use in reporting on nature conservation planning and policy. For reporting consistent mapping and monitoring habitat extent and change is important. Remote Sensing techniques are becoming an important tool for this. In this paper we describe four examples of methods of semi-automated mapping using Remote Sensing. Because the most effective way of improving the accuracy of the estimation of habitat area is by increasing the sample number, it is important to develop methods for reducing in situ surveys which are expensive. Remote Sensing has the major advantage of comprehensive coverage and the four examples illustrate the potential of extrapolation from semi-automated habitat classifications. The potential for using these methods at national scales is likely to be limited by the need for validation of the automated images and the subsequent calculation of error terms. Existing major national monitoring programs are described, which still use mainly traditional in situ methods. The selection of relatively small numbers of representative samples from environmental classifications to obtain regional estimates reduces the need for large numbers of in situ survey sites and is therefore discussed. The recent development of the use of drones to acquire detailed imagery to support in situ habitat surveys is also covered. Finally, practical problems linked to the methods described in the paper are considered, as in some cases these will override the theoretical benefits of a particular approach. It is concluded that automated methods can enhance existing monitoring systems and should be considered in any biodiversity monitoring system as they represent an opportunity for reducing costs, if integrated with an in situ program.</p