Remote sensing and GIS based ecological modelling of potential red deer habitats in the test site region DEMMIN (TERENO)

Introduction: The destruction of habitats has not only reduced biological diversity but also affected essential ecosystem services of the Central European cultural landscape. Therefore, in the further development of the cultural landscape and in the management of natural resources, special importance must be attached to the habitat demands of species and the preservation of ecosystem services. The study of ecosystem services has extended its influence into spatial planning and landscape ecology, the integration of which can offer an opportunity to enhance the saliency, credibility, and legitimacy of landscape ecology in spatial planning issues. Objective: This paper proposes a methodology to detect red deer habitats for e.g. huntable game. The model is established on remote sensing based value-added information products, the derived landscape structure information and the use of spatially and temporally imprecise in-situ data (e.g. available hunting statistics). In order to realize this, four statistical model approaches were developed and their predictive performance assessed. Methods: Altogether, our results indicate that based on the data mentioned above, modeling of habitats is possible using a coherent statistical model approach. All four models showed an overall classification of > 60% and in the best case 71,4%. The models based on logistic regression using preference data derived from 5-year hunting statistics, which has been interpreted as habitat suitability. The landscape metrics (LSM) will be calculated on the basis of the Global Forest Change dataset (HANSEN et al. 2013b ). The interpolation of landcover data into landscape-level was made with the software FRAGSTAT and the moving window approach. Correlation analysis is used to identify relevant LSM serving as inputs; logistic regression was used to derive a final binary classifier for habitat suitability values. Three model variations with different sets of LSM are tested using the unstandardized regression coefficient. Results lead to an insight of the effect of each LSM but not on the strength of the effect. Furthermore, the predicted outcome is rather difficult to interpret as different units and scales for each LSM are used. Hence, we calculated the fourth model using the standardized regression coefficient. It harmonized the measurement units of the LSM and thus allowed a better comparison, interpretation, and evaluation.Conclusion: Our research reveals that applying a statistical model using coarse data is effective to identify potential red deer habitats in a significant qualitative manner. The presented approach can be analogously applied to other mammals if the relevant structural requirements and empirical habitat suitability data (e.g. home range, biotopes, and food resources) are known. The habitat preferences of red deer are best described by LSM concerning area-relation and wildlifeedge relations. Most important are edges between meadows, pastures or agricultural field and forest, as well as short paths between those elements for food resources. A large proportion of forest is important for species survival and positively influences the occurrence of red deer. Outcomes help to understand species habitat relation and on which scale wildlife perceives the landscape. In addition, they support the practical habitat management and thus the overall species diversity

ARDD 2020: from aging mechanisms to interventions

Aging is emerging as a druggable target with growing interest from academia, industry and investors. New technologies such as artificial intelligence and advanced screening techniques, as well as a strong influence from the industry sector may lead to novel discoveries to treat age-related diseases. The present review summarizes presentations from the 7th Annual Aging Research and Drug Discovery (ARDD) meeting, held online on the 1st to 4th of September 2020. The meeting covered topics related to new methodologies to study aging, knowledge about basic mechanisms of longevity, latest interventional strategies to target the aging process as well as discussions about the impact of aging research on society and economy. More than 2000 participants and 65 speakers joined the meeting and we already look forward to an even larger meeting next year. Please mark your calendars for the 8th ARDD meeting that is scheduled for the 31st of August to 3rd of September, 2021, at Columbia University, USA

Automatisiertes Forstinventurverfahren basierend auf mobiler terrestrischer Fernerkundung

Vorstellung eines automatisierten Forstinventurverfahren basierend auf mobiler terrestrischer Fernerkundung mittel Integrated Positioning System (IPS

Monitoring and Quantification of Floating Biomass on Tropical Water Bodies. GI_Forum 2014 – Geospatial Innovation for Society|

Water hyacinth, or Eichhornia crassipes (Mart.) Solms, is an invasive and free floating water plant, native to South America that has often been marked as one of the world’s worst invasive aquatic species. It grows into large dense vegetable carpets that block sun energy transmission into shallow waters or even the lake bottom. This study focuses on two different water bodies in the tropical/subtropical zone, where water hyacinth infestation is already an issue. A parallel research approach was chosen in order to compare regional results with regards to operability and suitability of the chosen method. The first area studied is the southeastern area of the Inle Lake in Central Myanmar, and the second region is the Winam Gulf on Lake Victoria in Kenya. The Winam Gulf has been clogged by Eichhornia crassipes for decades, in contrast to a few reports about Water hyacinth infestation on the Inle lake waters. As in many other countries in the tropics, the current local monitoring and control measures of invasive aquatic vegetation in Myanmar and Kenya mostly rely on simple observations and mechanical methods. The monitoring and quantification of spatialtemporal variation of floating vegetation using satellite images has been tested in many approaches as being useful for analysing the spatial extent and temporal abundance of the macrophytes on tropical water bodies (SCHOUTEN 1999 et al., ALBRIGHT 2004 et al., NASA 2007, LANEVE 2010 et al., WINSTANLEY 2011 et al. ). The objective of the presented study was to test a multi-sensor approach, combining several remote sensing methods at two different locations, with particular ecological and land use systems, in order to detect and monitor floating biomass, and assess the abundance and quantity of Eichhornia crassipes and similar floating macrophytes using MERIS, MODIS and Landsat 7-ETM imagery. The successful multi sensor approach resulted in temporal spatial patterns of floating biomass, enabling transformable quantifications for the detected floating biomass. The quantified and harvestable biomass can be used further as a permanent source of bioenergy, or a resource for a chemical process named Hydrothermal Carbonization (HTC), converting biomass into an alternative energy source (hydrochar) or into solid or liquid fertilizer (LIBRA et al. 2011)

Innovatives Inspektionssystem für die Forstwirtschaft

Die Hochschule für nachhaltige Entwicklung Eberswalde (HNEE) forscht in Kooperation mit dem Deutschen Zentrum für Luft- und Raumfahrt e.V. (DLR) an einem neuartigen, mobilen und automatisierten Nahbereichserkundungsverfahren zur Erfassung und Vermessung von Einzelbäumen für die Forstwirtschaft. Um die großen Potentiale des Systems der Wirtschaft zugänglich zu machen hat sich das Gründungsteam VINS zusammengefunden. Mit dem interdisziplinären Hintergrund des Teams wird die Überführung der Forschungsergebnisse in die Produktentwicklung und Vermarktung des neuartigen Waldmesssystems sichergestellt

UAV Workflow Optimization for the Acquisition of High-Quality Photogrammetric Point Clouds in Forestry . GI_Forum|GI_Forum 2016, Volume 1 – open:spatial:interfaces|

Three-dimensional modelling using photogrammetric point clouds derived from UAV-based aerial imagery is currently a popular topic in the scientific community. In particular, the use of image-based point clouds to enhance and update LiDAR DSMs is of growing interest in forest environments, i.e. as a future forest inventory method. Thanks to very high resolution imagery acquired via low-altitude UAV optical sensor payloads, very dense and accurate photogrammetric point clouds can be reconstructed through a triangulation process by means of photogrammetry software. In order to validate the use of image-based point clouds for their potential use in operational forestry, further comparison studies with LiDAR DSMs are being carried out by various research institutions. The acquisition of UAV-based aerial imagery, with the aim of producing accurate photogrammetric point clouds, though cost-effective, is not without its challenges. Due to constraints regarding power capacity and fair weather windows, we came to develop an effective image acquisition workflow with an emphasis on precision flight planning. The aim of this paper is to explore the process of UAV-based aerial imagery acquisition for the purpose of producing photogrammetric point clouds, as well as to give an overview of the initial stages of our research. With the aid of an image acquisition workflow that is adaptable to various field conditions, technical failures and precision flight planning, we estimate that the acquisition of aerial imagery for point cloud production will become more efficient as well as more precise, and in turn influence the accuracy of the 3D-modelling of forested areas

Forschungen zur automatischen, multisensor-ge stützten Forstinventur

Automatisierungsroutinen sowie semi-automatische echtzeitbasierte Erfassungsmethoden wichtiger forstlicher Bestandsparameter haben heute eine hohe Relevanz, insbesondere in Bezug auf die Verwendung der räumlich erhobenen Attributdaten innerhalb von modernen 3D und 4D Waldwachstumsmodellen, wie z. B. „B-Win-Pro“ oder dem „Waldplaner“. Ein Großteil forstlich relevanter Objektparameter können mittels der Verknüpfung verschiedener, autonomer Sensorplattformen IPS (Integrated Positioning System) und anderer opto-elektronischer Scanner in nahezu Echtzeit erfasst werden. Die Lage- und Objektdaten werden in internetfähigen Geodatenbanken als individuelle Geo-Objekte und Sachdatenbestände (Attribute) nutzerfreundlich verwaltet

Performance test of tree segmentation algorithms for WLS point clouds

1042362394German Federal Ministry of Food and Agriculture (BMEL

UAV Workflow Optimization for the Acquisition of High-Quality Photogrammetric Point Clouds in Forestry . GI_Forum|GI_Forum 2016, Volume 1 – open:spatial:interfaces|

Three-dimensional modelling using photogrammetric point clouds derived from UAV-based aerial imagery is currently a popular topic in the scientific community. In particular, the use of image-based point clouds to enhance and update LiDAR DSMs is of growing interest in forest environments, i.e. as a future forest inventory method. Thanks to very high resolution imagery acquired via low-altitude UAV optical sensor payloads, very dense and accurate photogrammetric point clouds can be reconstructed through a triangulation process by means of photogrammetry software. In order to validate the use of image-based point clouds for their potential use in operational forestry, further comparison studies with LiDAR DSMs are being carried out by various research institutions. The acquisition of UAV-based aerial imagery, with the aim of producing accurate photogrammetric point clouds, though cost-effective, is not without its challenges. Due to constraints regarding power capacity and fair weather windows, we came to develop an effective image acquisition workflow with an emphasis on precision flight planning. The aim of this paper is to explore the process of UAV-based aerial imagery acquisition for the purpose of producing photogrammetric point clouds, as well as to give an overview of the initial stages of our research. With the aid of an image acquisition workflow that is adaptable to various field conditions, technical failures and precision flight planning, we estimate that the acquisition of aerial imagery for point cloud production will become more efficient as well as more precise, and in turn influence the accuracy of the 3D-modelling of forested areas

Monitoring and Quantification of Floating Biomass on Tropical Water Bodies. GI_Forum 2014 – Geospatial Innovation for Society|

Water hyacinth, or Eichhornia crassipes (Mart.) Solms, is an invasive and free floating water plant, native to South America that has often been marked as one of the world’s worst invasive aquatic species. It grows into large dense vegetable carpets that block sun energy transmission into shallow waters or even the lake bottom. This study focuses on two different water bodies in the tropical/subtropical zone, where water hyacinth infestation is already an issue. A parallel research approach was chosen in order to compare regional results with regards to operability and suitability of the chosen method. The first area studied is the southeastern area of the Inle Lake in Central Myanmar, and the second region is the Winam Gulf on Lake Victoria in Kenya. The Winam Gulf has been clogged by Eichhornia crassipes for decades, in contrast to a few reports about Water hyacinth infestation on the Inle lake waters. As in many other countries in the tropics, the current local monitoring and control measures of invasive aquatic vegetation in Myanmar and Kenya mostly rely on simple observations and mechanical methods. The monitoring and quantification of spatialtemporal variation of floating vegetation using satellite images has been tested in many approaches as being useful for analysing the spatial extent and temporal abundance of the macrophytes on tropical water bodies (SCHOUTEN 1999 et al., ALBRIGHT 2004 et al., NASA 2007, LANEVE 2010 et al., WINSTANLEY 2011 et al. ). The objective of the presented study was to test a multi-sensor approach, combining several remote sensing methods at two different locations, with particular ecological and land use systems, in order to detect and monitor floating biomass, and assess the abundance and quantity of Eichhornia crassipes and similar floating macrophytes using MERIS, MODIS and Landsat 7-ETM imagery. The successful multi sensor approach resulted in temporal spatial patterns of floating biomass, enabling transformable quantifications for the detected floating biomass. The quantified and harvestable biomass can be used further as a permanent source of bioenergy, or a resource for a chemical process named Hydrothermal Carbonization (HTC), converting biomass into an alternative energy source (hydrochar) or into solid or liquid fertilizer (LIBRA et al. 2011)