Laboratory for Essential Biodiversity Variables (EBV) Concepts – The “Data Pool Initiative for the Bohemian Forest Ecosystem”

Forest ecosystems respond very sensitively to climate and atmospheric changes. Feedback mechanisms can be measured via changes in albedo, energy balance and carbon storage. The Bavarian Forest National Park is a unique forest ecosystem with large non-intervention zones, which promote a large scale re-wilding process with low human interference. It provides important ecosystem services of clear water, carbon sequestration and recreation, and has fragile habitats with endangered forest species. The national park is therefore a very suitable field of research to study natural and near natural ecosystem processes. Under the leadership of the national park authority, experts from various European research institutions have joined forces to systematically establish a remote sensing data pool on the Bavarian Forest as a resource for their research. This collaborative effort provides an opportunity to combine various methodological approaches and data and to optimize products by sharing knowledge and expertise. The first objective of the data pool is to develop methods for the establishment of Essential Biodiversity Variables (EBV) based on a very sound and comprehensive data base. The recent advances in tighter collaboration of remote sensing and biodiversity science, especially with regard to the newly established EBV and RS-EBV concepts will help to improve the interdisciplinary research. However, such concepts and especially the underlying remote sensing data need to be developed, adapted and validated against biodiversity patterns. Such process needs an extensive set of in-situ and remotely sensed data in order to allow a thorough analysis. The Bavarian data pool fits these requirements through the commitment of all members and hence provides a variety of remote sensing data sets such as hyperspectral, Lidar as well as CIR and multispectral data, as well as a wealth of in-situ data of zoological and botanical transects. This combination allows setting sensor-specific, as well as species-specific analysis on different aspects, i.e. different processes between managed and natural forest, impact of climate change or species distribution mapping. The second objective is to develop concepts for EBV using Sentinel mission data combined with data from future contributing hyperspectral missions such as EnMAP. Spaceborne hyperspectral data has been identified by the remote sensing related biodiversity community as an important data source. However, the acquisition of airborne data is very expensive for regular coverage of forest stands and the entire forest ecosystem. This drawback will be overcome by the launch of the space-borne imaging spectroscopy mission EnMAP. It is a contributing mission to the Copernicus program and will be launched in 2018. EnMAP is expected to provide high quality imaging spectroscopy data on an operational basis and will be suitable for the retrieval of high resolution plant traits at local scales. First studies within the data pool have been focused on e.g. derivation of plant traits like chlorophyll, LAI and nitrogen and tree species classification with a special focus on rare species within the national park, just to name a few. Objective, purpose and content of the data pool will be shown as well as first selective developments

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Detection of forest parameters using imaging spectroscopy

The main tree species in Bavaria is spruce, which is being strongly affected by the climate change, because of its anthropogenic influenced distribution in non-typical site ranges. Through climate change, biotic and abiotic factors, such as bark beetles, fungi, storm, snow and water stress are occurring more often and are stressing spruce in these unsuitable sites. Due to these changing conditions it is getting more important to detect forest types as a feature of its own, but also other tree species and spruce in particular. This thesis presents a spectral analysis of detecting deciduous and coniferous forest as forest type and independently European beech, European fir and Norway spruce as tree species. The analysis was carried out using hyperspectral HySpex VNIR and multispectral Worldview-2 images, each with 2 m ground resolution from a heterogeneous and stratified temperate forest in southern Germany. This type of forest exhibits characteristics of sustainable prospective temperate forests. A total of 2008 and 2009 reference spectra from HySpex VNIR and Worldview-2 were extracted and analysed with Principal Component Analysis for possible discrimination. A combination of eight uncorrelated bands, that were optimised for forest type discrimination and another eight bands, also optimised for tree species discrimination were extracted from HySpex VNIR imagery, using a Genetic Algorithm. The extracted eight bands from HySpex VNIR and the eight bands from W orldview-2 served as input for Linear Discriminant Analysis. The overall accuracies achieved from HySpex VNIR are 94.4 % for forest type and 88.3 % for tree species discrimination. Worldview-2 achieved an overall accuracy of 87.8 % for forest type and 86.7 % for tree species discrimination. The successful spectral based classification for forest type and tree species were transferred onto the corresponding images for spatial description of their occurrence. The promising results of this thesis confirms the advantage of airborne hyperspectral images for forest type and species detection. The transferability of this approach to spaceborne multispectral images with spectral information, relevant for vegetation purposes seems feasible

Early Detection of Vitality Changes of Multi-Temporal Norway Spruce Laboratory Needle Measurements - The Ring-Barking Experiment

The focus of this analysis is on the early detection of forest health changes, specifically that of Norway spruce (Picea abies L. Karst.). In this analysis, we planned to examine the time (degree of early detection), spectral wavelengths and appropriate method for detecting vitality changes. To accomplish this, a ring-barking experiment with seven subsequent laboratory needle measurements was carried out in 2013 and 2014 in an area in southeastern Germany near Altötting. The experiment was also accompanied by visual crown condition assessment. In total, 140 spruce trees in groups of five were ring-barked with the same number of control trees in groups of five that were selected as reference trees in order to compare their development. The laboratory measurements were analysed regarding the separability of ring-barked and control samples using spectral reflectance, vegetation indices and derivative analysis. Subsequently, a random forest classifier for determining important spectral wavelength regions was applied. Results from the methods are consistent and showed a high importance of the visible (VIS) spectral region, very low importance of the near-infrared (NIR) and minor importance of the shortwave infrared (SWIR) spectral region. Using spectral reflectance data as well as indices, the earliest separation time was found to be 292 days after ring-barking. The derivative analysis showed that a significant separation was observed 152 days after ring-barking for six spectral features spread through VIS and SWIR. A significant separation was detected using a random forest classifier 292 days after ring-barking with 58% separability. The visual crown condition assessment was analysed regarding obvious changes of vitality and the first indication was observed 302 days after ring-barking as bark beetle infestation and yellowing of foliage in the ring-barked trees only. This experiment shows that an early detection, compared with visual crown assessment, is possible using the proposed methods for this specific data set. This study will contribute to ongoing research for early detection of vitality changes that will support foresters and decision makers

Analysis of temporal statistics and long term climate observations for deriving the predisposition of forests to stress events

Forest ecosystems are affected by stress induced changes in various ways. Environmental factors that affect trees negatively can be distinguished between biotic and abiotic factors. Abiotic factors are non-living such as drought, storm, frost, etc. Biotic factors are of living kind such as fungi or insects. Tree species react to stress in terms of activating their repair process and/or long-term adaptation of their morphology and metabolism. Depending on the strength of stress events this can lead to resistance and repair or severe damages and even plant death. However, with regard to water or nutrient supply, tree species respond very differently. Especially for coniferous tree species bark beetle infestations are a consequence of primary damage in form of drought and unfavourale conditions for trees. Therefor it is crucial to analyse the predisposition of forests to stress events. Long-term temporal statistics of Landsat data will be analysed for change of forest state and linked to temporal climate records and soil-moisture data. Especially longterm and repeated drought periods result in lower vitality of forests prolonging for several years after the drought event. The preliminary results of a case study will be presented and an outlook for further research will be given

Erstellung von Fichten- und Kiefernanteilskarten auf Basis von Satellitendaten für Bayern

Der fortschreitende Klimawandel erhöht das Gefährdungspotenzial von Wäldern zunehmend. Eine forstwirtschaftliche Nutzung von Wäldern ist oftmals nur durch den Wechsel auf Baumarten mit größerer Flexibilität gegenüber den Auswirkungen des Klimawandels langfristig sichergestellt. Für derartige Waldumbauvorhaben auf großer Fläche benötigen die Akteure aktuelle Verbreitungskarten einzelner Baumarten in einer räumlichen Aufl ösung von z. B. 1 Hektar. Um eine regelmäßige Überwachung und Aktualisierung zu ermöglichen, sind die Kosten für derartige Kartenprodukte ein weiterer wichtiger Faktor, der zu berücksichtigen ist. Der vorliegende Beitrag beschreibt und validiert einen fernerkundungsbasierten Ansatz für die Kartierung von einzelnen Baumarten (Fichte und Kiefer) mit einer innovativen Kombination von kommerziellen, sehr hoch aufgelösten Satellitendaten und frei verfügbar Landsat-Zeitreihen. Die Methodik beinhaltet dabei die überwachte Klassifi kation von WorldView-2 Daten ausgewählter Testgebiete, gefolgt von einem „Upscaling“ dieser Referenzinformationen auf große Flächen mit multispektralen und multitemporalen Landsat-Daten. Für die Modellierung wurde mit Random Forest (RF) ein auf Entscheidungsbäumen basierender Ansatz gewählt. Mit der entwickelten Methode konnten für ganz Bayern konsistente und genaue Karten der Fichtenund Kiefernverbreitung (kontinuierliche Anteile) mit einer Aufl ösung von 1 ha erstellt werden. Eine Validierung mit 3798 unabhängigen Referenzzellen ergab für Fichte bzw. Kiefer einen Root-Mean-Square Error (RMSE) von 11 und 14 %, und ein Bestimmtheitsmaß (R²) von 0.74 bis 0.79. Zwischen 76 und 85 % der Validierungspunkte wurden besser modelliert als die angenommene Unsicherheitsmarge von ±15 % der Referenzinformation (aus manueller Bildinterpretation von Orthophotos)

Mapping of spruce and pine fractional coverage at 1 ha resolution for entire Bavaria

In Central Europe spruce and pine are severely affected by the impacts of climate change. In several regions a significant decline in their distribution is observed. To cope with this threat, the Bavarian State Institute of Forestry produces climate risk maps for the next decades. To be effective however, locational information of the two tree species is required. Such basic information is not yet available at the necessary spatial resolution. The aim of this study is to generate distribution maps for spruce and pine for entire Bavaria (70.500 km2) at a resolution of about one hectare. For each hectare cell, the fraction coverage of the two tree species is to be specified as well as associated uncertainties. In order to meet these user-defined requirements, a two-step methodology combining satellite imagery at metric to deca-metric resolution was developed. In a first step, tree species maps with a high level of detail were generated from 8-band multispectral WorldView-2 data with 0.5 to 2.0 m spatial resolution. As reference, inventory data from the Bavaria State Forest enterprise was used. Where necessary, additional reference samples were derived from stereo interpretation of aerial images. From this data, detailed tree species maps were generated for roughly 40 sites (each about 100 km2 large) well distributed across Bavaria. For the object-based mapping, spectral information and textural indices were used. The textural measures were generated at several scales with a discrete stationary wavelet transformation (using Red, Near Infrared and NDVI as inputs). The classification itself was performed using Random Forest (RF). Features used in the classification were selected by means of RF’s importance measures. The generated tree species maps were used in a second step as reference information (targets) to generate the fractional coverage maps for the entire country using neural nets. For the upscaling, Landsat multi-temporal data complemented by high resolution RapidEye imagery was used as predictor variables. From the sites with detailed tree species maps, data from these two satellite sensors were extracted and used to train a neural network for estimating the fractional coverage of the two tree species. After network training, the models were applied to the entire area

Visualizing and Analyzing 3D Metal Nanowire Networks for Stretchable Electronics

Composites based on conductive nanowires embedded in elastomers are popular in a wide range of stretchable electronics applications where the requirements are either a stable or a highly increasing electrical resistance upon strain. Despite the widespread use of such composites, their production is not based in solid theoretical grounds but rather in empirical observations. The lack of such a framework is due to limitations in the methods for studying nanowire meshes, in particular the lack of knowledge on the spatial distribution of the nanowires and the change of their position under strain. This hurdle is overcome by collecting 3D reconstructed X-ray tomographies of silver nanowires embedded in polydimethylsiloxane (PDMS) under variable deformations and the missing structural information of the nanomaterial is obtained by unsupervised artificial intelligence image analysis. This allowed to reveal the precise assembly mechanisms of nanowire systems and derive a precise analytical formula for the piezoresistive response of the composite and finally to simulate the behavior of arbitrary samples in-silico