The Arctic Greening Database v0.1 is an open access database created as part of the ETH+ project "Unraveling biogeochemical, microbial and vegetation feedbacks driving soil development and Arctic greening under a warming climate". The database contains data on soil, vegetation, microbial, and environmental properties from 14 active-layer tundra sites sampled in 2022 and 2023 on Svalbard. The spatially-explicit field observations, field and laboratory measurements provides an interdisciplinary collection of data from a remote and data-poor region to study linkages between vegetation, microbiome and pedogenesis in the context of Arctic Greening.
The database is structured hierarchically with four connected levels: site, plot, sample, and species. At the site level, aggregated data are provided (e.g. GHG fluxes). This is followed by plot-level data (e.g. plant functional type cover) that connects to sample-level data (soil organic matter content) and species-level data. Tables at the same level are connected via one-to-one relationships, from a broader to finer level one-to-many relationships are in place. Sampling and measurement procedures are described in Section 2 of the database description. The metadata file accompanying a specific .csv file provides further information on data creation, sample processing and units. The current version of the dataset consists of a reduced set of tables that will be updated soon with more curated data from Svalbard and Northern Norway (Finnmark). A more extensive overview of the data will be published as a data paper in the future.
Version History:29 April 2020: Release of Version 0.3This is an updated version of Reyer et al., (2019, V. 0.1.12, http://doi.org/10.5880/PIK.2019.008). All changes and updates are documented in the changelog available via the data download section.Current process-based vegetation models are complex scientific tools that require proper evaluation of the different processes included in the models to prove that the models can be used to integrate our understanding of forest ecosystems and project climate change impacts on forests. The PROFOUND database (PROFOUND DB) described here aims to bring together data from a wide range of data sources to evaluate vegetation models and simulate climate impacts at the forest stand scale.It has been designed to fulfill two objectives:- Allow for a thorough evaluation of complex, process-based vegetation models using multiple data streams covering a range of processes at different temporal scales- Allow for climate impact assessments by providing the latest climate scenario data.Therefore, the PROFOUND DB provides general a site description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level and remote sensing data for 9 forest stands spread throughout Europe. Moreover, for a subset of 5 sites, also time series of carbon fluxes, energy balances and soil water are available. The climate and nitrogen deposition data contains several datasets for the historic period and a wide range of future climate change scenarios following the Representative Emission Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5).In addition, we also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND Database is available freely but we incite users to respect the data policies of the individual datasets as provided in the metadata of each data file. The database can also be accessed via the PROFOUND R-package, which provides basic functions to explore, plot and extract the data.The data (PROFOUND DB) are provided in two different versions (ProfoundData.sqlite download as ProfoundData.zip, ProfoundData_ASCII.zip) accompanied by a change-log to the previous published version (changelog_Profound-DB_v03.pdf), auxiliary data of reconstructed single tree data at the site Sorø (Soroe_DBH_H_AGE_20200428.zip) and documented by the three explanatory documents:(1) PROFOUNDdatabase.pdf: describes the structure, organisation and content of the PROFOUND DB.(2) PROFOUNDsites.pdf: displays the main data of the PROFOUND DB for each of the 9 forest sites in tables and plots.(3) ProfoundData.pdf: explains how to use the PROFOUND R-Package "ProfoundData" to access the PROFOUND DB and provides example scripts on how to apply it.
Current process-based vegetation models are complex scientific tools that require proper evaluation of the different processes included in the models to prove that the models can be used to integrate our understanding of forest ecosystems and project climate change impacts on forests. The PROFOUND database (PROFOUND DB) described here aims to bring together data from a wide range of data sources to evaluate vegetation models and simulate climate impacts at the forest stand scale.It has been designed to fulfill two objectives:- Allow for a thorough evaluation of complex, process-based vegetation models using multiple data streams covering a range of processes at different temporal scales- Allow for climate impact assessments by providing the latest climate scenario data.Therefore, the PROFOUND DB provides general a site description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level and remote sensing data for 9 forest stands spread throughout Europe. Moreover, for a subset of 5 sites, also time series of carbon fluxes, energy balances and soil water are available. The climate and nitrogen deposition data contains several datasets for the historic period and a wide range of future climate change scenarios following the Representative Emission Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5).In addition, we also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND Database is available freely but we incite users to respect the data policies of the individual datasets as provided in the metadata of each data file. The database can also be accessed via the PROFOUND R-package, which provides basic functions to explore, plot and extract the data.The data (PROFOUND DB) are provided in two different versions (ProfoundData.sqlite download as ProfoundData.zip, ProfoundData_ASCII.zip) and documented by the following three documents:(1) PROFOUNDdatabase.pdf: describes the structure, organisation and content of the PROFOUND DB.(2) PROFOUNDsites.pdf: displays the main data of the PROFOUND DB for each of the 9 forest sites in tables and plots.(3) ProfoundData.pdf: explains how to use the PROFOUND R-Package "ProfoundData" to access the PROFOUND DB and provides example scripts on how to apply it.
The dataset is composed of Neo HySpex (VNIR & SWIR) and Telops Hyper-Cam (LWIR) hyperspectral imagery acquired during the MOSES GFZ/FUB/UFZ airborne campaign on August 8th, 2020 over the test area Oschersleben covering parts of the Bode catchment in the northern foreland of the Harz Mountain, Central Germany. The study area covers an ecological transect including three TERENO climate stations/flux towers ranging from forest sites (Hohes Holz) to lowland meadows (Grosses Bruch) to intensively used agricultural land (Hordorf). The survey was conducted within the frame of the Helmholtz program MOSES (Modular Observation Solutions for Earth Systems) heatwave event chain, which overall objective is to monitor heat extremes and drought events. In particular, the 2020 MOSES heatwave campaign over the Oschersleben test site aimed at an GFZ/UFZ intercalibration comparison measurements between different hyperspectral instruments flown on same day with different platforms and altitude, and test impact of different workflows on resulting data. This publication contains the GFZ VNIR-SWIR-LWIR hyperspectral dataset. It includes 1) 17 HySpex cloud-free flight lines already mosaicked in orthorectified reflectance, covering the VNIR to SWIR wavelength regions (0.4-2.5 µm) with 408 spectral bands, and 2) a composite of Hyper-Cam 1956 frames processed to surface temperature and spectral emissivity covering the LWIR (7.7 – 11.7 µm) in 125 bands. The dataset also includes Level 2A EnMAP-like reflectance imagery simulated using the end-to-end Simulation tool (EeteS). Associated field data and UFZ hyperspectral data are included in related publications of this campaign.