PyShoreVolume 1.0.0: A Python based Shoreline Change and beach Volumetric Change Analysis tool

Shoreline Change Analysis (SCA) and Volumetric Change Analysis (VCA) are of growing importance to coastal managers throughout the world. The volume, resolution and accuracy of shoreline configurations are gradually improving, which demands tools for efficient processing and analysis. The limited number of systems that combine the two analysis types have lengthy workflows or require commercial software licences, with no current tool that automates VCA from a time series of Digital Elevation Models (DEM’s). We present a new, dedicated and open-source package for automating SCA and VCA in the Python environment. It is designed with a user-friendly interface and workflow, delivers efficient processing speeds, automatically generates map based graphical outputs and computes a full range of positional statistics. We verify the package delivers equivalent outputs to existing tools (AMBUR, DSAS) and demonstrate strong performance by reconstructing erosion and accretion over the last twenty years along the beaches of the Taw and Torridge Estuary, North Devon, UK, a region known to have a complex sediment and morphometric dynamics

Supervised classification of landforms in Arctic mountains

Erosional and sediment fluxes from Arctic mountains are lower than for temperate mountain ranges due to the influence of permafrost on geomorphic processes. As permafrost extent declines in Arctic mountains, the spatial distribution of geomorphic processes and rates will change. Improved access to high‐quality remotely sensed topographic data in the Arctic provides an opportunity to develop our understanding of the spatial distribution of Arctic geomorphological processes and landforms. Utilizing newly available Arctic digital topography data, we have developed a method for geomorphic mapping using a pixel‐based linear discriminant analysis method that could be applied across Arctic mountains. We trained our classifier using landforms within the Adventdalen catchment in Svalbard and applied it to two adjacent catchments and one in Alaska. Slope gradient, elevation–relief ratio and landscape roughness distinguish landforms to a first order with >80% accuracy. Our simple classification system has a similar overall accuracy when compared across our field sites. The simplicity and robustness of our classification suggest that it is possible to use it to understand the distribution of Arctic mountain landforms using extant digital topography data and without specialized classifications. Our preliminary assessments of the distribution of geomorphic processes within these catchments demonstrate the importance of post‐glacial hillslope processes in governing sediment movement in Arctic mountains

Marine snow as vectors for microplastic transport: Multiple aggregation cycles account for the settling of buoyant microplastics to deep-sea sediments

Many studies have reported the paradoxical observation of high concentrations of low-density microplastics (plastic particles < 5 mm) in deep-sea sediments despite their buoyancy. The incorporation of buoyant microplastics into marine snow has been observed to enhance microplastic settling. Previous studies on the vertical movement of buoyant microplastics have been unable to theoretically account for these ocean observations and no study has comprehensively elucidated microplastic transport pathways in the ocean from the surface to seafloor. Here, we establish a one-dimensional theoretical model, that embraces key elements of the flocculation process, to explain how marine snow acts as a vector to transport buoyant microplastics to deep water and the ocean bottom. Microplastics reach the ocean floor through multiple cycles of aggregation, settling, and disaggregation between marine snow and microplastics. Each settling cycle results in a net settling of 200–400 m. We demonstrate that microplastics with different sizes show distinct vertical settling behaviors and only microplastics less than 100 μm in diameter can reach the ocean bottom. This theoretical model refines our ability to predict and understand the global and long-term fate, transport, and inventory of microplastics in the ocean interior, the influence of microplastics on the biological carbon pump and the efficacy of plastic management policies

Aridity is expressed in river topography globally.

It has long been suggested that climate shapes land surface topography through interactions between rainfall, runoff and erosion in drainage basins1-4. The longitudinal profile of a river (elevation versus distance downstream) is a key morphological attribute that reflects the history of drainage basin evolution, so its form should be diagnostic of the regional expression of climate and its interaction with the land surface5-9. However, both detecting climatic signatures in longitudinal profiles and deciphering the climatic mechanisms of their development have been challenging, owing to the lack of relevant global data and to the variable effects of tectonics, lithology, land surface properties and human activities10,11. Here we present a global dataset of 333,502 river longitudinal profiles, and use it to explore differences in overall profile shape (concavity) across climate zones. We show that river profiles are systematically straighter with increasing aridity. Through simple numerical modelling, we demonstrate that these global patterns in longitudinal profile shape can be explained by hydrological controls that reflect rainfall-runoff regimes in different climate zones. The most important of these is the downstream rate of change in streamflow, independent of the area of the drainage basin. Our results illustrate that river topography expresses a signature of aridity, suggesting that climate is a first-order control on the evolution of the drainage basin

The CAIRN method: Automated, reproducible calculation of catchment-averaged denudation rates from cosmogenic radionuclide concentrations

The use of cosmogenic radionuclides to calculate catchment-averaged denudation rates has become a widely adopted technique in the last two decades, yet the methodology varies between studies and is not always reproducible. We report a new program for calculating catchment-averaged denudation rates from cosmogenic radionuclide concentrations. The method (Catchment-Averaged denudatIon Rates from cosmogenic Nuclides: CAIRN) bundles previously reported production scaling and topographic shielding algorithms. In addition, it calculates production and shielding on a pixel-by-pixel basis. We explore the sampling frequency across both azimuth (&amp;Delta;&lt;i&gt;&amp;theta;&lt;/i&gt;) and altitude (&amp;Delta;&lt;i&gt;&amp;phi;&lt;/i&gt;) angles for topographic shielding and show that in high relief terrain a relatively high sampling frequency is required, with a good balance achieved between accuracy and computational expense at &amp;Delta;&lt;i&gt;&amp;theta;&lt;/i&gt; = 8&amp;deg; and &amp;Delta;&lt;i&gt;&amp;phi;&lt;/i&gt; = 5&amp;deg;. The method includes both internal and external uncertainty analysis, and is packaged in freely available software in order to facilitate easily reproducible denudation rate estimates. CAIRN calculates denudation rates but also automates catchment averaging of shielding and production, and thus can be used to provide reproducible input parameters for the CRONUS family of online calculators.s. Simon Marius Mudd and Marie-Alice Harel are funded by US Army Research Office contract number W911NF-13-1-0478 and Simon Marius Mudd and Stuart W. D. Grieve are funded by NERC grant NE/J009970/1. Shasta M. Marrero is funded by NERC grant NE/I025840/1

Low-Cost Tree Crown Dieback Estimation Using Deep Learning-Based Segmentation

The global increase in observed forest dieback, characterised by the death of tree foliage, heralds widespread decline in forest ecosystems. This degradation causes significant changes to ecosystem services and functions, including habitat provision and carbon sequestration, which can be difficult to detect using traditional monitoring techniques, highlighting the need for large-scale and high-frequency monitoring. Contemporary developments in the instruments and methods to gather and process data at large-scales mean this monitoring is now possible. In particular, the advancement of low-cost drone technology and deep learning on consumer-level hardware provide new opportunities. Here, we use an approach based on deep learning and vegetation indices to assess crown dieback from RGB aerial data without the need for expensive instrumentation such as LiDAR. We use an iterative approach to match crown footprints predicted by deep learning with field-based inventory data from a Mediterranean ecosystem exhibiting drought-induced dieback, and compare expert field-based crown dieback estimation with vegetation index-based estimates. We obtain high overall segmentation accuracy (mAP: 0.519) without the need for additional technical development of the underlying Mask R-CNN model, underscoring the potential of these approaches for non-expert use and proving their applicability to real-world conservation. We also find colour-coordinate based estimates of dieback correlate well with expert field-based estimation. Substituting ground truth for Mask R-CNN model predictions showed negligible impact on dieback estimates, indicating robustness. Our findings demonstrate the potential of automated data collection and processing, including the application of deep learning, to improve the coverage, speed and cost of forest dieback monitoring.M. J. A. was supported by the UKRI Centre for Doctoral Training in Application of Artificial Intelligence to the study of Environmental Risks [EP/S022961/1]. E. R. L. and S. W. D. G. were funded by a UKRI Future Leaders Fellowship awarded to E. R. L. [MR/T019832/1]. PRB was supported by the Community of Madrid Region under the framework of the multi-year Agreement with the University of Alcalá (Stimulus to Excellence for Permanent University Professors, EPUINV/ 2020/010) . PRB and ERL are supported by the Science and Innovation Ministry (subproject LARGE and REMOTE, N° PID2021-123675OB-C41 and PID2021-123675OB-C42)

UAV-derived greenness and within-crown spatial patterning can detect ash dieback in individual trees

Ash Dieback (ADB) has been present in the UK since 2012 and is expected to kill up to 80% of UK ash trees. Detecting and quantifying the extent of ADB in individual tree crowns (ITCs), which is crucial to understanding resilience and resistance, currently relies on visual assessments which are impractical over large scales or at high frequency. The improved imaging capabilities and declining cost of consumer UAVs, together with new remote sensing methods such as structure from motion photogrammetry (SfM) offers potential to quantify the fine-scale structural and spectral metrics of ITCs that are indicative of ADB, rapidly, and at low-cost. We extract high-resolution 3D RGB point clouds derived from SfM of canopy ash trees taken monthly throughout the growing season at Marden Park, Surrey, UK, a woodland impacted by ADB. We segment ITCs, extract green chromatic coordinate (gcc), and test the relationship with visual assessments of crown health. Next, we quantify spatial patterning of dieback within ITCs by testing the relationship between internal variation of gcc and path length, a measure of the distance from foliage to trunk, for small clusters of foliage. We find gcc correlates with visual assessments of crown health throughout the growing season, but the strongest relationships are in measurements taken after peak greenness, when the effects of ADB on foliage are likely to be most prevalent. We also find a negative relationship between gcc and path length in infected trees, indicating foliage loss is more severe at crown extremities. We demonstrate a new method for identifying ADB at scale using a consumer-grade 3D RGB UAV system and suggest this approach could be adopted for widespread rapid monitoring. We recommend the optimum time of year for data acquisition, which we find to be an important factor for detecting ADB. Although here applied to ADB, this framework is applicable to a multitude of drivers of crown dieback, presenting a method for identifying spectral-structural relationships which may be characteristic of disturbance type

UAV-derived greenness and within-crown spatial patterning can detect ash dieback in individual trees

Publication status: PublishedAbstract Ash Dieback (ADB) has been present in the UK since 2012 and is expected to kill up to 80% of UK ash trees. Detecting and quantifying the extent of ADB in individual tree crowns (ITCs), which is crucial to understanding resilience and resistance, currently relies on visual assessments which are impractical over large scales or at high frequency. The improved imaging capabilities and declining cost of consumer UAVs, together with new remote sensing methods such as structure from motion photogrammetry (SfM) offers potential to quantify the fine‐scale structural and spectral metrics of ITCs that are indicative of ADB, rapidly, and at low‐cost. We extract high‐resolution 3D RGB point clouds derived from SfM of canopy ash trees taken monthly throughout the growing season at Marden Park, Surrey, UK, a woodland impacted by ADB. We segment ITCs, extract green chromatic coordinate (gcc), and test the relationship with visual assessments of crown health. Next, we quantify spatial patterning of dieback within ITCs by testing the relationship between internal variation of gcc and path length, a measure of the distance from foliage to trunk, for small clusters of foliage. We find gcc correlates with visual assessments of crown health throughout the growing season, but the strongest relationships are in measurements taken after peak greenness, when the effects of ADB on foliage are likely to be most prevalent. We also find a negative relationship between gcc and path length in infected trees, indicating foliage loss is more severe at crown extremities. We demonstrate a new method for identifying ADB at scale using a consumer‐grade 3D RGB UAV system and suggest this approach could be adopted for widespread rapid monitoring. We recommend the optimum time of year for data acquisition, which we find to be an important factor for detecting ADB. Although here applied to ADB, this framework is applicable to a multitude of drivers of crown dieback, presenting a method for identifying spectral‐structural relationships which may be characteristic of disturbance type. </jats:p