34 research outputs found
Drone-Based Vegetation Assessment in Arid Ecosystems
Proof of long-term vegetation change in arid rangelands is often insufficient to influence policy, even when the change is clear to ecologists. Drones provide a way to collect unbiased evidence of plant spatiotemporal distribution at a dramatically reduced cost for the scales needed in these habitats. Assessment of phytomass spatial distribution by drone has become a routine, but further analysis requires advanced skills in data collection and post-flight processing. Accurate assessment of phytomass temporal change will require protocols to be developed for data collection and analysis. Biodiversity assessment by drone is unreliable, but there is potential for assessing phytomass change within and among taxonomic groups in arid rangelands, by repeatedly sampling areas in which perennial plants have been classified manually
Towards scalable estimation of plant functional diversity from Sentinel-2: in-situ validation in a heterogeneous (semi-)natural landscape
Environmental BiologyConservation Biolog
A Range of Earth Observation Techniques for Assessing Plant Diversity
AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS
Not Available
Not AvailableIn the field of the efficiency of very shallow geothermal energy systems, there is still a
significant need for research activity. To ensure the proper exploitation of this energy resource,
the decisive geophysical parameters of soil must be well-known. Within this study, thermal
conductivity, as a fundamental property for evaluating the geothermal potential of very shallow
geothermal systems, was analyzed and measured with a TK04 device. A dataset, consisting of various
geophysical parameters (thermal conductivity, bulk density, water content, and porosity) determined
for a large range of different textural soil classes, was collated. In a new approach, the geophysical
properties were visualized covering the complete grain size range. The comparison between the
measured and calculated thermal conductivity values enabled an investigation with respect to the
validity of the different Kersten equations. In the course of this comparison, the influence of effective
bulk density was taken into account. In conclusion, both Kersten formulas should be used as
recommended and regular bulk density corresponded better to the reference dataset representing the
outcomes of the TK04 laboratory measurement. Another objective was to visualize the relation of
thermal conductivities within their corresponding textural classes and the validity of Kersten formulas
for various bulk densities, depths, and soils. As a result, the accessibility to information for expedient
recommendations about the feasibility of very shallow geothermal systems will be improved. Easy,
accessible know-how of the fundamentals is important for a growing renewable energy sector where
very shallow geothermal installations can also cover heating and cooling demands.Not Availabl
Satellite remote sensing to monitor species diversity: potential and pitfalls
Assessing the level of diversity in plant communities from field-based data is difficult for a number of practical reasons: (1) establishing the number of sampling units to be investigated can be difficult; (2) the choice of sample design can impact on results; and (3) defining the population of concern can be challenging. Satellite remote sensing (SRS) is one of the most cost-effective approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field-based methods, it allows for complete spatial coverages of the Earth's surface under study over a short period of time. Furthermore, SRS provides repeated measures, thus making it possible to study temporal changes in biodiversity. Here, we provide a concise review of the potential of satellites to help track changes in plant species diversity, and provide, for the first time, an overview of the potential pitfalls associated with the misuse of satellite imagery to predict species diversity. Our work shows that, while the assessment of alpha-diversity is relatively straightforward, calculation of beta-diversity (variation in species composition between adjacent locations) is challenging, making it difficult to reliably estimate gamma-diversity (total diversity at the landscape or regional level). We conclude that an increased collaboration between the remote sensing and biodiversity communities is needed in order to properly address future challenges and development
Development of mixers and Local Oscillators for THz Heterodyne Instruments at Observatoire de Paris - LERMA
This article presents current R&D activities at Observatoire de Paris â Laboratoire d'Etude du Rayonnement
et de la MatiĂšre en Astrophysique in the fields of low noise mixers and local oscillators for heterodyne instruments dedicated to astrophysics, planetology and the sciences of the atmosphere
Appendix A. Supplementary methods for estimation of classification model accuracy, community analysis, and field data comparison.
Supplementary methods for estimation of classification model accuracy, community analysis, and field data comparison