83 research outputs found
UNMANNED AERIAL VEHICLE LASER SCANNING FOR EROSION MONITORING IN ALPINE GRASSLAND
With this contribution we assess the potential of unmanned aerial vehicle (UAV) based laser scanning for monitoring shallow erosion in Alpine grassland. A 3D point cloud has been acquired by unmanned aerial vehicle laser scanning (ULS) at a test site in the subalpine/alpine elevation zone of the Dolomites (South Tyrol, Italy). To assess its accuracy, this point cloud is compared with (i) differential global navigation satellite system (GNSS) reference measurements and (ii) a terrestrial laser scanning (TLS) point cloud. The ULS point cloud and an airborne laser scanning (ALS) point cloud are rasterized into digital surface models (DSMs) and, as a proof-of-concept for erosion quantification, we calculate the elevation difference between the ULS DSM from 2018 and the ALS DSM from 2010. For contiguous spatial objects of elevation change, the volumetric difference is calculated and a land cover class (bare earth, grassland, trees), derived from the ULS reflectance and RGB colour, is assigned to each change object. In this test, the accuracy and density of the ALS point cloud is mainly limiting the detection of geomorphological changes. Nevertheless, the plausibility of the results is confirmed by geomorphological interpretation and documentation in the field. A total eroded volume of 672 m3 is estimated for the test site (48 ha). Such volumetric estimates of erosion over multiple years are a key information for improving sustainable soil management. Based on this proof-of-concept and the accuracy analysis, we conclude that repeated ULS campaigns are a well-suited tool for erosion monitoring in Alpine grassland
Direct observation of a single nanoparticleâubiquitin corona formation
The advancement of nanomedicine and the increasing applications of nanoparticles in consumer products have led to administered biological exposure and unintentional environmental accumulation of nanoparticles, causing concerns over the biocompatibility and sustainability of nanotechnology. Upon entering physiological environments, nanoparticles readily assume the form of a nanoparticle-protein corona that dictates their biological identity. Consequently, understanding the structure and dynamics of nanoparticle-protein corona is essential for predicting the fate, transport, and toxicity of nanomaterials in living systems and for enabling the vast applications of nanomedicine. Here we combined multiscale molecular dynamics simulations and complementary experiments to characterize the silver nanoparticle-ubiquitin corona formation. Notably, ubiquitins competed with citrates for the nanoparticle surface, governed by specific electrostatic interactions. Under a high protein/nanoparticle stoichiometry, ubiquitins formed a multi-layer corona on the particle surface. The binding exhibited an unusual stretched-exponential behavior, suggesting a rich binding kinetics. Furthermore, the binding destabilized the α-helices while increasing the ÎČ-sheets of the proteins. This study revealed the atomic and molecular details of the structural and dynamic characteristics of nanoparticle-protein corona formation
Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain
In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 Ă 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance
Engineered Nanoparticles Interact with Nutrients to Intensify Eutrophication in a Wetland Ecosystem Experiment
Despite the rapid rise in diversity and quantities of engineered nanomaterials produced, the impacts of these emerging contaminants on the structure and function of ecosystems have received little attention from ecologists. Moreover, little is known about how manufactured nanomaterials may interact with nutrient pollution in altering ecosystem productivity, despite the recognition that eutrophication is the primary water quality issue in freshwater ecosystems worldwide. In this study, we asked two main questions: (1) To what extent do manufactured nanoparticles affect the biomass and productivity of primary producers in wetland ecosystems? (2) How are these impacts mediated by nutrient pollution? To address these questions, we examined the impacts of a citrateâcoated gold nanoparticle (AuNPs) and of a commercial pesticide containing Cu(OH)2 nanoparticles (CuNPs) on aquatic primary producers under both ambient and enriched nutrient conditions. Wetland mesocosms were exposed repeatedly with low concentrations of nanoparticles and nutrients over the course of a 9âmonth experiment in an effort to replicate realistic field exposure scenarios. In the absence of nutrient enrichment, there were no persistent effects of AuNPs or CuNPs on primary producers or ecosystem productivity. However, when combined with nutrient enrichment, both NPs intensified eutrophication. When either of these NPs were added in combination with nutrients, algal blooms persisted for \u3e 50 d longer than in the nutrientâonly treatment. In the AuNP treatment, this shift from clear waters to turbid waters led to large declines in both macrophyte growth and rates of ecosystem gross primary productivity (average reduction of 52% ± 6% and 92% ± 5%, respectively) during the summer. Our results suggest that nutrient status greatly influences the ecosystemâscale impact of two emerging contaminants and that synthetic chemicals may be playing an underâappreciated role in the global trends of increasing eutrophication. We provide evidence here that chronic exposure to Au and Cu(OH)2 nanoparticles at low concentrations can intensify eutrophication of wetlands and promote the occurrence of algal blooms
Primary succession and its driving variables â a sphere-spanning approach applied in proglacial areas in the upper Martell Valley (Eastern Italian Alps)
Climate change and the associated glacier retreat lead to
considerable enlargement and alterations of the proglacial systems. The
colonisation of plants in this ecosystem was found to be highly dependent on
terrain age, initial site conditions and geomorphic disturbances. Although
the explanatory variables are generally well understood, there is little
knowledge on their collinearities and resulting influence on proglacial
primary succession. To develop a sphere-spanning understanding of vegetation
development, a more interdisciplinary approach was adopted. In the
proglacial areas of FĂŒrkeleferner, Zufallferner and Langenferner (Martell
Valley, Eastern Italian Alps), in total 65 plots of 5Ă2âm were
installed to perform the vegetation analysis on vegetation cover, species
number and species composition. For each of those, 39 potential explanatory
variables were collected, selected through an extensive literature review.
To analyse and further avoid multicollinearities, 33 of the explanatory
variables were clustered via principal component analysis (PCA) to five
components. Subsequently, generalised additive models (GAMs) were used to
analyse the potential explanatory factors of primary succession. The results
showed that primary succession patterns were highly related to the first
component (elevation and time), the second component (solar radiation),
the third component (soil chemistry), the fifth component
(soil physics) and landforms. In summary, the analysis of all explanatory
variables together provides an overview of the most important influencing
variables and their interactions; thus it provides a basis for the debate on future
vegetation development in a changing climate.</p
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