Search

Landslide thickness is a key parameter in various types of models used to simulate landslide susceptibility. In this study, we developed a model providing improved predictions of potential shallow landslide thickness in Switzerland. We tested three machine learning models based on random forests, generalized additive model, and linear regression and compared the results to three existing models that link soil thickness to slope and elevation. The models were calibrated using data from two field inventories in Switzerland ('HMDB' with 709 records and 'KtBE' with 517 records). We explored 37 different covariates including metrics on terrain, geomorphology, vegetation height, and lithology at three different cell sizes. To train the machine learning models, 21 variables were chosen based on the variable importance derived from random forest models and expert judgement. Our results show that the machine learning models consistently outperformed the existing models by reducing the mean absolute error by at least 17 %. The random forests models produced a mean absolute error of 0.25 m for the HMDB and 0.19 m for the KtBE data. Models based on machine learning substantially improve the prediction of landslide thickness, offering refined input for enhancing the performance of slope stability simulations. [ABSTRACT FROM AUTHOR]

The Oostvaardersplassen nature reserve in the Netherlands is grazed by large herbivores. Due to their increasing numbers, the area became dominated by short grazed grasslands and biodiversity decreased. From 2018, the numbers are controlled to create a diverse landscape. Fine-scale mapping and monitoring of the aboveground biomass is a tool to evaluate management efforts to restore a heterogeneous and biodiverse area. We developed a random forest model that describes the correlation between field-based samples of aboveground biomass and fifteen height-related vegetation metrics that were calculated from high-density point clouds collected with a handheld LiDAR. We found that two height-related metrics (maximum and 75th percentile of all height points) produced the best correlation with an R2 of 0.79 and a root-mean-square error of 0.073 kg/m2. Grassland segments were mapped by applying a segmentation routine on the normalized grassland's digital surface model. For each grassland segment, the aboveground biomass was mapped using the point cloud and the random forest AGB model. Visual inspection of video recordings of the scanned trajectories and field observations of grassland patterns suggest that drift and stretch effects of the point cloud influence the map. We recommend optimizing data collection using looped trajectories during scanning to avoid point cloud drift and stretch, test horizontal vegetation metrics in the model development and include seasonal influence of the vegetation status. We conclude that handheld LiDAR is a promising technique to retrieve detailed height-related metrics in grasslands that can be used as input for semi-automated spatio-temporal modelling of grassland aboveground biomass for supporting management decisions in nature reserves. [ABSTRACT FROM AUTHOR]

We present a method of digital geomorphological mapping of mountainous areas with a legend consisting of a three-tiered nested hierarchy using two case study areas from Vorarlberg, Austria. Users can easily visualize maps in a geographical information system (GIS) at the finest level with a legend of 33 morphogenetic domains. Reclassification of the morphogenetic classes in an automated GIS-workflow generates the medium and high levels of hierarchy, and each tier is accompanied by suggested scale ranges for visualization. A variety of high-resolution input data (LiDAR-derived data, geomorphological and geological raster maps) supports the mapping method, which also strongly benefits from field knowledge. The method facilitates analysis, interpretation, visualization and application of geomorphological data at a large range of scales and corresponding information densities within one database. The structure of the legend allows for inclusion of additional morphogenetic classes and for application and adaptation in other environments. [ABSTRACT FROM AUTHOR]

Mapping 3D vegetation structure in wetlands is important for conservation and monitoring. Openly accessible country‐wide Airborne Laser Scanning (ALS) data—using light detection and ranging (lidar) technology—are increasingly becoming available and allow us to quantify 3D vegetation structures at fine resolution and across broad spatial extents. Here, we develop a new, open‐source workflow for classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide ALS data. We developed a case study in the Netherlands with a workflow consisting of four routines: (1) pre‐processing of ALS data, (2) calculation of lidar metrics (i.e. 31 features representing cover, 3D shape, vertical variability, horizontal variability and height of vegetation as well as microtopography), (3) assessing feature importance of lidar metrics for classifying wetland habitats, and (4) applying a Random Forest algorithm for mapping and prediction. We used an expert‐based vegetation map for annotation and generated 100, 500 and 1000 annotation points for each class. Using a three‐level hierarchical approach, we differentiated at level 1 planar surfaces (e.g. roads and agricultural fields) from wetland vegetation with 82% mean overall accuracy, using predominantly height and horizontal variability metrics. At level 2, we classified wetland vegetation into four land cover types (forest, grassland, reedbeds, shrubs) with 71% mean overall accuracy, using lidar metrics related to vegetation height and horizontal and vertical variability. At level 3, we differentiated two types of land reed as well as water reed with 78% mean overall accuracy, using predominantly vertical variability metrics. Our results demonstrate that lidar metrics (related to vegetation height, cover, vertical and horizontal variability) derived from country‐wide ALS data can differentiate land cover types and habitats within wetlands at high resolution. Given appropriate annotation data, our workflow can be up‐scaled to a country‐wide extent to allow the comprehensive mapping and monitoring of wetlands at national scales. [ABSTRACT FROM AUTHOR]

The δ18O data for the last 8000 years in the Greenland NGRIP1, GRIP, DYE-3 and GISP2 ice cores have been analyzed stratigraphically in search of potentially meaningful boundaries and units. Pattern matching of the profiles is supported by using graphical display enhancements, calculating spectral trend curves and generating a compound profile. Techniques routinely used in subsurface geology have been applied in correlating the profiles. Four major stratigraphic units are identified (8.1-4.9, 4.9-3.3, 3.3-1.9 and 1.9-0.1 ka b2k), resulting in an improved understanding of the climate change after the Holocene Climate Optimum. Correlatable higher-order boundaries are identified within these units. The layers between the boundaries show δ18O patterns which generally are similar in character, the differences being ascribed to lateral variations in the factors that control the isotope content of the ice. The layering forms a series of short-lived low-amplitude aperiodic oscillations on a centennial time scale. The suggestion is that these higher-order boundaries and δ18O oscillations have climatic significance. Equivalent units are tentatively identified in ice-core data from the Agassiz and Renland ice caps. Comparison with other climate proxies or stratigraphies from the Northern Hemisphere is expected to render support for the here proposed scheme. It will then serve to guide and constrain the analysis of the dynamics of the climatic fluctuations for the study period. [ABSTRACT FROM AUTHOR]

The lack of high-resolution measurements of 3D ecosystem structure across broad spatial extents impedes major advancements in animal ecology and biodiversity science. We aim to fill this gap by using Light Detection and Ranging (LiDAR) technology to characterize the vertical and horizontal complexity of vegetation and landscapes at high resolution across regional to continental scales. The newly LiDAR-derived 3D ecosystem structures will be applied in species distribution models for breeding birds in forests and marshlands, for insect pollinators in agricultural landscapes, and songbirds at stopover sites during migration. This will allow novel insights into the hierarchical structure of animal-habitat associations, into why animal populations decline, and how they respond to habitat fragmentation and ongoing land use change. The processing of these massive amounts of LiDAR point cloud data will be achieved by developing a generic interactive eScience environment with multi-scale object-based image analysis (OBIA) and interpretation of LiDAR point clouds, including data storage, scalable computing, tools for machine learning and visualisation (feature selection, annotation/segmentation, object classification, and evaluation), and a PostGIS spatial database. The classified objects will include trees, forests, vegetation strata, edges, bushes, hedges, reedbeds etc. with their related metrics, attributes and summary statistics (e.g. vegetation openness, height, density, vertical biomass distribution etc.). The newly developed eScience tools and data will be available to other disciplines and applications in ecology and the Earth sciences, thereby achieving high impact. The project will foster new multi-disciplinary collaborations between ecologists and eScientists and contribute to training a new generation of geo-ecologists. [ABSTRACT FROM AUTHOR]

Modernization of agricultural land use across Europe is responsible for a substantial decline of linear vegetation elements such as tree lines, hedgerows, riparian vegetation, and green lanes. These linear objects have an important function for biodiversity, e.g., as ecological corridors and local habitats for many animal and plant species. Knowledge on their spatial distribution is therefore essential to support conservation strategies and regional planning in rural landscapes but detailed inventories of such linear objects are often lacking. Here, we propose a method to detect linear vegetation elements in agricultural landscapes using classification and segmentation of high-resolution Light Detection and Ranging (LiDAR) point data. To quantify the 3D structure of vegetation, we applied point cloud analysis to identify point-based and neighborhood-based features. As a preprocessing step, we removed planar surfaces such as grassland, bare soil, and water bodies from the point cloud using a feature that describes to what extent the points are scattered in the local neighborhood. We then applied a random forest classifier to separate the remaining points into vegetation and other. Subsequently, a rectangularity-based region growing algorithm allowed to segment the vegetation points into 2D rectangular objects, which were then classified into linear objects based on their elongatedness. We evaluated the accuracy of the linear objects against a manually delineated validation set. The results showed high user's (0.80), producer's (0.85), and total accuracies (0.90). These findings are a promising step towards testing our method in other regions and for upscaling it to broad spatial extents. This would allow producing detailed inventories of linear vegetation elements at regional and continental scales in support of biodiversity conservation and regional planning in agricultural and other rural landscapes. [ABSTRACT FROM AUTHOR]

The Amazon is the largest drainage basin on Earth and contains a wide variety of abiotic landscape features. In spite of this, the geodiversity in this basin has not yet been objectively evaluated. We address this knowledge gap by combining a meta-analysis of an existing global geodiversity map and its components with a systematic literature review, to identify the key characteristics of geodiversity in the Amazon drainage basin (ADB). We also evaluate how these global geodiversity component maps, that are based on the geology, geomorphology, soils and hydrology, could be refined to better reflect geodiversity in the basin. Our review shows that geology-through lithological diversity and geological structures-and hydrology-through hydrological processes that influence geomorphology and soil diversity-are the main determinants of geodiversity. Based on these features, the ADB can be subdivided into three principal regions: (i) the Andean orogenic belt and western Amazon, (ii) the cratons and eastern Amazon, and (iii) the Solimões-Amazon river system. Additional methods to map geomorphological and hydrological diversity have been identified. Future research should focus on investigating the relationship between the geodiversity components and assess their relationship with biodiversity. Such knowledge can enhance conservation plans for the ADB. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

The aim of UNESCO Global Geoparks (UGGs) is to protect globally significant geoheritage and geodiversity, but quantitative evidence on the global representativeness of geodiversity components (i.e. geology, soils, geomorphology and hydrology) in these geoparks is in short supply. Here, we provide a first assessment by deriving a global map of geodiversity to test whether the presence of geodiversity components in UGGs is representative for the global availability and distribution of geodiversity. Using openly accessible global datasets and a newly developed workflow, we have calculated metrics for each geodiversity component and a global geodiversity index; we then quantified whether UGGs represent global geodiversity and then compared their components to a randomized spatial distribution of geoparks. Our results show that lithological and topographical diversity are more represented in UGGs than outside these sites, while soil type and hydrological diversity are not significantly different. Furthermore, individual soil types and lithological classes are under-represented and unevenly distributed in Asian and European UGGs. This is probably caused by the concentration of geoparks in Asian and European mountains. To better conserve geodiversity, we suggest an initiative to consider the protection and representation of all geodiversity components in their global context. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Geodiversity is a topical concept in earth and environmental sciences. Geodiversity information is needed to conserve nature, use ecosystem services and achieve sustainable development goals. Despite the increasing demand for geodiversity data, there exists no comprehensive system for categorizing geodiversity. Here, we present a hierarchically structured taxonomy that is potentially applicable in mapping and quantifying geodiversity across different regions, environments and scales. In this taxonomy, the main components of geodiversity are geology, geomorphology, hydrology and pedology. We propose a six-level hierarchical system where the components of geodiversity are classified at progressively lower taxonomic levels based on their genesis, physical-chemical properties and morphology. This comprehensive taxonomy can be used to compile geodiversity information for scientific research and various applications of value to society and nature conservation. Ultimately, this hierarchical system is the first step towards developing a global geodiversity taxonomy. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

The third Dutch national airborne laser scanning flight campaign (AHN3, Actueel Hoogtebestand Nederland) conducted between 2014 and 2019 during the leaf-off season (October-April) across the whole Netherlands provides a free and open-access, country-wide dataset with ∼700 billion points and a point density of ∼10(-20) points/m 2 . The AHN3 point cloud was obtained with Light Detection And Ranging (LiDAR) technology and contains for each point the x, y, z coordinates and additional characteristics (e.g. return number, intensity value, scan angle rank and GPS time). Moreover, the point cloud has been pre-processed by 'Rijkswaterstraat' (the executive agency of the Dutch Ministry of Infrastructure and Water Management), comes with a Digital Terrain Model (DTM) and a Digital Surface Model (DSM), and is delivered with a pre-classification of each point into one of six classes (0: Never Classified, 1: Unclassified, 2: Ground, 6: Building, 9: Water, 26: Reserved [bridges etc.]). However, no detailed information on vegetation structure is available from the AHN3 point cloud. We processed the AHN3 point cloud (∼16 TB uncompressed data volume) into 10 m resolution raster layers of ecosystem structure at a national extent, using a novel high-throughput workflow called 'Laserfarm' and a cluster of virtual machines with fast central processing units, high memory nodes and associated big data storage for managing the large amount of files. The raster layers (available as GeoTIFF files) capture 25 LiDAR metrics of vegetation structure, including ecosystem height (e.g. 95 th percentiles of normalized z), ecosystem cover (e.g. pulse penetration ratio, canopy cover, and density of vegetation points within defined height layers), and ecosystem structural complexity (e.g. skewness and variability of vertical vegetation point distribution). The raster layers make use of the Dutch projected coordinate system (EPSG:28992 Amersfoort / RD New), are each ∼1 GB in size, and can be readily used by ecologists in a geographic information system (GIS) or analytical open-source software such as R and Python. Even though the class '1: Unclassified' mainly includes vegetation points, other objects such as cars, fences, and boats can also be present in this class, introducing potential biases in the derived data products. We therefore validated the raster layers of ecosystem structure using >180,000 hand-labelled LiDAR points in 100 randomly selected sample plots (10 m × 10 m each) across the Netherlands. Besides vegetation, objects such as boats, fences, and cars were identified in the sampled plots. However, the misclassification rate of vegetation points (i.e. non-vegetation points that were assumed to be vegetation) was low (∼0.05) and the accuracy of the 25 LiDAR metrics derived from the AHN3 point cloud was high (∼90%). To minimize existing inaccuracies in this country-wide data product (e.g. ships on water bodies, chimneys on roofs, or cars on roads that might be incorrectly used as vegetation points), we provide an additional mask that captures water bodies, buildings and roads generated from the Dutch cadaster dataset. This newly generated country-wide ecosystem structure data product provides new opportunities for ecology and biodiversity science, e.g. for mapping the 3D vegetation structure of a variety of ecosystems or for modelling biodiversity, species distributions, abundance and ecological niches of animals and their habitats., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Competing Interests: The authors declare no conflict of interest.