Impacts of extreme wind speeds and other factors on vegetation disturbances in the German railway network

<p><span>Winter windstorms are among the most dangerous and costly natural hazards in Central Europe. Their ability to cause tree and branch fall leads to disruptions and damages along railway systems. </span><span>Along the German railway network </span><span>the Deutsche Bahn is </span>preventively<span> removing about 30.000 trees per year. </span><span>Still</span><span>, each year </span><span>a multiplicity of</span><span> disturbances </span><span>occur</span> <span>which may lead to delays, economic damages or even train collisions.</span></p><p><span>A</span><span> data set with vegetation disturbance events between 2017 and 2020 along the German railway system </span><span>is provided by the Deutsche Bahn</span><span>. </span><span>The aim of this study is</span><span> to use exploratory statistics as well as machine learning methods like regression techniques or decision trees to explore the relationship between vegetation damages and meteorological parameters like wind gusts, precipitation or temperature. Additionally, tree related factors</span> <span>and surrounding conditions like </span><span>ground frost and soil moisture </span><span>will be taken into account. Finally, we want to derive critical thresholds and combinations of weather parameters leading to significant damage risk. </span></p><p><span>We find </span><span>a positive relationship between vegetation disturbance and wind speeds. Especially strong winter storms leave a very clear signal in the disturbance time series. </span><span>For example, t</span><span>he highest numbers of vegetation disturbances occurred during the winter storms Sabine (10.02.2020, 515 events) and Friderike (18.01.2018, 360 events). During winter storm days the majority of events occurs in th</span><span>ose</span><span> areas </span><span>affected by</span><span> high wind speeds. Tree fall </span><span>disturbance</span><span>s</span><span> pe</span><span>a</span><span>k during the winter storm season between January and March, while branch fall </span><span>disturbance</span><span>s</span><span> peak between June and August. </span><span>However</span><span>, a high number of events occurs </span><span>also </span><span>during times of low wind speeds. </span><span>Additionally, high wind speeds do not necessarily lead to vegetation disturbances.</span><span> It is clear that other meteorological and tree related factors need to be taken into account. Compound events as well as previous weather and soil conditions are expected to affect wind throw risks.</span></p>

Timing and Pace of Neolithisation in the Dutch Wetlands (c. 5000–3500 cal. BC)

This article presents an overview of the current evidence on the process of Neolithisation in the Dutch wetlands. Over the years, several models have been proposed with different perspectives on the timing and pace of the process: a long transition, an early short transition, and a late short transition. The applicability of any of these models is, of course, dependent of the evidence. In this article, we briefly discuss recently obtained data from the Netherlands on vegetation disturbance (woodland clearing), soil disturbance (tillage), cereal cultivation, animal husbandry, and the use of ceramics. The data discussed involve palynological, sedimentary, micromorphological, archaeobotanical, zooarchaeological, as well as lipid analyses. Hence, it is concluded that from the mid fifth millennium cal. BC onwards, various aspects of a more “Neolithic lifestyle” become apparent in the archaeological record, including cereal cultivation on a structural, but small-scale basis in wetland environments. However, despite the “gradual” tendency that can be observed, the evidence is as yet inconclusive with regard to any of the models, due to persisting limitations of the datasets, potential regional variability, and aspects of scale. A new project, the Emergence of Domestic Animals in the Netherlands (EDAN), aims at a better understanding of animal husbandry through aDNA and isotope analyses, within a framework of statistical chronological modelling. We expect this project to enhance the debate greatly.

Spatial Agreement among Vegetation Disturbance Maps in Tropical Domains Using Landsat Time Series

Detecting disturbances in native vegetation is a crucial component of many environmental management strategies, and remote sensing-based methods are the most efficient way to collect multi-temporal disturbance data over large areas. Given that there is a large range of datasets for monitoring, analyzing, and detecting disturbances, many methods have been well-studied and successfully implemented. However, factors such as the vegetation type, input data, and change detection method can significantly alter the outcomes of a disturbance-detection study. We evaluated the spatial agreement of disturbance maps provided by the Breaks For Additive Season and Trend (BFAST) algorithm, evaluating seven spectral indices in three distinct vegetation domains in Brazil: Atlantic forest, savanna, and semi-arid woodland, by assessing levels of agreement between the outputs. We computed individual map accuracies based on a reference dataset, then ranked their performance, while also observing their relationships with specific vegetation domains. Our results indicated a low rate of spatial agreement among index-based disturbance maps, which itself was minimally influenced by vegetation domain. Wetness indices produced greater detection accuracies in comparison to greenness-related indices free of saturation. The normalized difference moisture index performed best in the Atlantic forest domains, yet performed poorest in semi-arid woodland, reflecting its specific sensitivity to vegetation and its water content. The normalized difference vegetation index led to high disturbance detection accuracies in the savanna and semi-arid woodland domains. This study offered novel insight into vegetation disturbance maps, their relationship to different ecosystem types, and corresponding accuracies. Distinct input data can produce non-spatially correlated disturbance maps and reflect site-specific sensitivity. Future research should explore algorithm limitations presented in this study, as well as the expansion to other techniques and vegetation domains across the globe.

ANALISIS PROFIL RESORT CAGAR ALAM DOLOK SIBUAL-BUALI SEBAGAI PENENTU STRATEGI PENGELOLAAN KAWASAN

Dolok Sibual Buali Nature Reserve with area’s large + 5000 hectares is one of the five resort models resort based management scope Hall of the North Sumatera’s Natural Resources Conservation. Before implement resort based management, must be known the resort profile. This research aims to known the resort profile of Dolok Sibual Buali Nature Reserve based the cover of vegetation and disturbance area by human activity. This research was conducted in the Nature Reserve Dolok Sibual Buali South Tapanuli on May-June 2013. The methods of data collection are observation and interviews. The data analysis consist of maps analysis and interview results analysis. The cover of vegetation in Dolok Sibual Buali Nature Reserve included safe category with the secondary forest area’s large 4624.84 hectares or about 92.26 % of the total area. The other cover of vegetation are mixed farms, dry land farms, shrubs and rice fields. The form of disturbance area that comes from villages around the area are illegal logging, land conversion, wildlife hunting, theft of non-timber forest products and ecotourism. The conflicts that often occur between communities and area manager are conflicts about area boundaries. Communities also felt anxious caused the wildlife damaged their gardens. The management strategies that should be done are improved the area security and optimization the function of biodiversity preservation by apply the resort based management. Keywords: Cover of vegetation, disturbance area, Dolok Sibual Buali nature reserve, resort based management, resort profile