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Abstract Ultrafine particles (UFP) acting as cloud condensation nuclei (CCN) are the driving force behind changing rainfall patterns. Recently observed weather extremes like floods and drought might be due to changing anthropogenic UFP emissions. However, the sources and budgets of anthropogenic primary and secondary particles are not well known. Based on airborne measurements we identified as a major contribution modern fossil fuel flue gas cleaning techniques to cause a doubling of global primary UFP number emissions. The subsequent enhancement of CCN numbers has several side effects. It’s changing the size of the cloud droplets and delays raindrop formation, suppressing certain types of rainfall and increasing the residence time of water vapour in the atmosphere. This additional latent energy reservoir is directly available for invigoration of rainfall extremes. Additionally it’s a further contribution to the column density of water vapour as a greenhouse gas and important for the infrared radiation budget. The localized but ubiquitous fossil fuel related UFP emissions and their role in the hydrological cycle, may thus contribute to regional or continental climate trends, such as increasing drought and flooding, observed within recent decades.

[This corrects the article DOI: 10.1371/journal.pone.0233912.].

It is critical to assess bushfire impact rapidly and accurately because bushfires play a significant role in forest degradation and present a threat to ecosystems and human lives. Over the past decades, several supervised algorithms of burn severity mapping have been proposed, facing the significant drawback of time-consuming labeling. Moreover, there is no robust framework for burn severity mapping through fusing multi-sensor, multi-resolution, and multi-temporal remote sensing imagery from satellite and aerial platforms. Therefore, this paper presents an unsupervised two-step pipeline: processing 2D data followed by 3D data for burn severity mapping, both of which are acquired from either aircraft or satellites. For the 2D data processing, our proposed unsupervised burned area detection (UsBA detection) model enhances burned area mapping accuracy by integrating Ultra-High Resolution (UHR) aerial imagery with bi-temporal medium-resolution PlanetScope imagery, using a Segment Anything Model (SAM)-assisted UNetFormer (pre-trained on the target-style public dataset – LoveDA Rural) for refinement. The model demonstrates superior burned area segmentation, evidenced by improved evaluation metrics calculated from labeled test sites. For the 3D analysis, the burned areas extracted from 2D processing are further assessed using pre- and post-event airborne laser data. We implement a voxel-based workflow, including necessary steps such as ground filtering through Superpoints in RANSAC Planes (SiRP) method and biomass change analysis. The results indicate that the 3D branch provides a reliable lower bound of the actual damage map, because the vegetation growth between two measurements remains, in essence, undetected. The proposed framework offers a more accurate and robust solution for burn severity mapping utilizing combined 2D and 3D data, evaluated on a multi-source dataset from a real bushfire event that occurred in Bushland Park, South Australia. [ABSTRACT FROM AUTHOR]

In 2019, the Deep History of Sea Country (DHSC) Project team found and published two submerged archaeological sites in Murujuga (Dampier Archipelago) Western Australia [[1]]. Please see the authors' respective ORCID iDs here: Author Jo McDonald's ORCID iD is 0000-0002-2701-7406 (https://orcid.org/0000-0002-2701-7406) Author Patrick Morrison's ORCID iD is 0000-0003-0920-3405 (https://orcid.org/0000-0003-0920-3405) Additionally, the contributions for author Patrick Morrison are updated as follows: Data curation, Formal analysis, Methodology, Investigation, Software, Visualization, Writing-original draft, Writing-review & editing. This supersedes the original data in the article, and it is not possible to identify which of the samples constituted the original dataset of 455 terrestrial artefacts. [Extracted from the article]

Point clouds acquired through laser scanning techniques are applied in the three-dimensional modelling of vegetation. They provide the three-dimensional coordinates of geometric surfaces with attributes. However, raw point clouds are unstructured and do not provide semantic, geometric, or topological information about an object. Voxelisation is a method for structuring point clouds. It is a generalisation of point clouds and therefore the voxel size and the voxel neighbourhood play a critical role in the processing. This research explores the influence of voxelisation of point clouds acquired of heathland in Australia and how it influences the three-dimensional modelling and the representation of important heathland structure using different voxel sizes and voxel connectivities. Voxel sizes of 0.4 m, 0.6 m, 1.0 m, 1.2 m and 1.6 m with a voxel neighbourhood connectivity of 6, 18 and 26 are examined for three-dimensional modelling and segmentation of heathland vegetation in Australia. The results indicate that the choice of voxel size and the voxel connectivity influence the representation of important heathland parameters. A smaller voxel size of 0.4 m provides a detailed representation of mallee structure while the the processing time is longer compared to a larger voxel size. While a larger voxel size produces blobs while the processing speed is shorter. The results from the voxel neighbourhood connectivity represent a stronger voxel connectivity of 26-connected voxels suitable for heathland modelling rather than a 6-connected voxels. [ABSTRACT FROM AUTHOR]

Ultrafine particles (UFP) acting as cloud condensation nuclei (CCN) are the driving force behind changing rainfall patterns. Recently observed weather extremes like floods and drought might be due to changing anthropogenic UFP emissions. However, the sources and budgets of anthropogenic primary and secondary particles are not well known. Based on airborne measurements we identified as a major contribution modern fossil fuel flue gas cleaning techniques to cause a doubling of global primary UFP number emissions. The subsequent enhancement of CCN numbers has several side effects. It's changing the size of the cloud droplets and delays raindrop formation, suppressing certain types of rainfall and increasing the residence time of water vapour in the atmosphere. This additional latent energy reservoir is directly available for invigoration of rainfall extremes. Additionally it's a further contribution to the column density of water vapour as a greenhouse gas and important for the infrared radiation budget. The localized but ubiquitous fossil fuel related UFP emissions and their role in the hydrological cycle, may thus contribute to regional or continental climate trends, such as increasing drought and flooding, observed within recent decades. [ABSTRACT FROM AUTHOR]

Bushfires are an intrinsic part of the New South Wales' (NSW) environment in Australia, especially in the Blue Mountains region (11400km2), that is dominated by fire prone vegetation that includes heathland. Many of the Australian native plants in this region are fire-prone and combustible, and many species even require fire to regenerate. The classification of the lateral and vertical distribution of living vegetation is necessary to manage the complexity of bushfires. Currently, interpretation of aerial and satellite images is the prevalent method for the classification of vegetation in NSW. The result does not represent important vegetation structural attributes, such as vegetation height, subcanopy height, and destiny. This paper presents an automated method for the three-dimensional modelling of heathland and important heathland parameters, such as heath shrub height and continuity, and sparse tree and mallee height and density in support of bushfire behaviour modelling. For this study airborne lidar point clouds with a density of 120 points per square meter are used. For the processing and modelling the study is divided into a point cloud processing phase and a voxel-based modelling phase. The point cloud processing phase consists of the normalisation of the height and extraction of the above ground vegetation, while the voxel phase consists of seeded region growing for segmentation, and K-means clustering for the classification of the vegetation into three different canopy layers: a) heath shrubs, b) sparse trees and mallee, c) tall trees. [ABSTRACT FROM AUTHOR]