Publications

Citizens are increasingly becoming involved in data collection, whether for scientific purposes, to carry out micro-tasks, or as part of a gamified, competitive application. In some cases, volunteered data collection overlaps with that of mapping agencies, e.g., the citizen-based mapping of features in OpenStreetMap. LUCAS (Land Use Cover Area frame Sample) is one source of authoritative in-situ data that are collected every three years across EU member countries by trained personnel at a considerable cost to taxpayers. This paper presents a mobile application called FotoQuest Austria, which involves citizens in the crowdsourcing of in-situ land cover and land use data, including at locations of LUCAS sample points in Austria. The results from a campaign run during the summer of 2015 suggest that land cover and land use can be crowdsourced using a simple protocol based on LUCAS. This has implications for remote sensing as this data stream represents a new source of potentially valuable information for the training and validation of land cover maps as well as for area estimation purposes. Although the most detailed and challenging classes were more difficult for untrained citizens to recognize, the agreement between the crowdsourced data and the LUCAS data for basic high level land cover and land use classes in homogeneous areas (ca. 80%) shows clear potential. Recommendations for how to further improve the quality of the crowdsourced data in the context of LUCAS are provided so that this source of data might one day be accurate enough for land cover mapping purposes.

The impact of the climate on the Arctic plays a crucial role for Finland's, as well as other Nordic countries', current and future climatic conditions. Far-reaching and multi-faceted changes are taking place in the Arctic, which have profound consequences for the region's economic and political significance in international relations. The review analyses the effects of climate change and likely climate abatement policies on the accessibility and value of natural resources in Northern Europe in the Arctic Sea area and on the logistical position of Northern Europe with a special emphasis on Finland.

No abstract is provided for this article.

The global spatial extent of croplands is a crucial input to global and regional agricultural monitoring and modeling systems. Although many new remotely-sensed products are now appearing due to recent advances in the spatial and temporal resolution of satellite sensors, there are still issues with these products that are related to the definition of cropland used and the accuracies of these maps, particularly when examined spatially. To address the needs of the agricultural monitoring community, here we have created a hybrid map of global cropland extent at a 500 m resolution by fusing two of the latest high resolution remotely-sensed cropland products: the European Space Agency’s WorldCereal and the cropland layer from the University of Maryland. We aggregated the two products to a common resolution of 500 m to produce percentage cropland and compared them spatially, calculating two kinds of disagreement: density disagreement, where the two maps differ by more than 80%, and absence-presence of cropland disagreement, where one map indicates the presence of cropland while the other does not. Based on these disagreements, we selected continuous areas of disagreement, referred to in the paper as hotspots of disagreement, for manual correction by experts using the Geo-Wiki land cover application. The hybrid map was then validated using a stratified random sample based on the disagreement layer, where the sample was visually interpreted by a different set of experts using Geo-Wiki. The results show that the hybrid product improves upon the overall accuracy statistics in the areas where the underlying cropland layer from the University of Maryland was improved with the WorldCereal product, but more importantly, it represents an improved spatially explicit cropland mask for early warning and food security assessment purposes.

. Processes that describe the distribution of vegetation and ecosystem succession after disturbance are an important component of dynamic global vegetation models (DGVMs). The vegetation dynamics module (ORC-VD) within the process-based ecosystem model ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) has not been updated and evaluated since many years and is known to produce unrealistic results. This study presents a new parameterization of ORC-VD for mid- to high-latitude regions in the Northern Hemisphere, including processes that influence the existence, mortality and competition between tree functional types. A new set of metrics is also proposed to quantify the performance of ORC-VD, using up to five different data sets of satellite land cover, forest biomass from remote sensing and inventories, a data-driven estimate of gross primary productivity (GPP) and two gridded data sets of soil organic carbon content. The scoring of ORC-VD derived from these metrics integrates uncertainties in the observational data sets. This multi-data set evaluation framework is a generic method that could be applied to the evaluation of other DGVM models. The results of the original ORC-VD published in 2005 for mid- to high-latitudes and of the new parameterization are evaluated against the above-described data sets. Significant improvements were found in the modeling of the distribution of tree functional types north of 40° N. Three additional sensitivity runs were carried out to separate the impact of different processes or drivers on simulated vegetation distribution, including soil freezing which limits net primary production through soil moisture availability in the root zone, elevated CO2 concentration since 1850, and the effects of frequency and severity of extreme cold events during the spin-up phase of the model.

This dataset contians supporting data and code for the manuscript: A global map of root biomass across the world's forests. <br> Folder data_orin include raw data and references. Python script data_merge_fill.py merges different raw databases, removes repeated entries and fills data gaps in environmental factors. Final output of data_merge_fill.py is site_filled_1kmv1.csv. Python scripts in fig folder include scripts for calculation and producing figures in the manuscript. pred_1km_15cover_1000_99.py is an example code to train a random forest model and to make a global forest root biomass map with a unit of weight biomass per tree. pred_1km_15cover_allometry_1000_99.py generates data used in Supplementary Figure 7 which estimates global forest root biomass through allometric equations. Folder budget_uncertainty contains code and calculations of the uncertainty of global and biome scale root biomass budget. <br> The pergridarea_agb.nc contains global forest aboveground biomass, pergridarea_bgb.nc is the belowground biomass, err_bgb_15_99_ncomm_revise.nc is uncertainty of belowground biomass maps with a resolution of 1km and the unit of Mg/ha (gridcell area).

Aim To infer a forest carbon density map at 0.01° resolution from a radar remote sensing product for the estimation of carbon stocks in Northern Hemisphere boreal and temperate forests. Location The study area extends from 30° N to 80° N , covering three forest biomes – temperate broadleaf and mixed forests ( TBMF ), temperate conifer forests ( TCF ) and boreal forests ( BFT ) – over three continents ( N orth A merica, E urope and A sia). Methods This study is based on a recently available growing stock volume ( GSV ) product retrieved from synthetic aperture radar data. Forest biomass and spatially explicit uncertainty estimates were derived from the GSV using existing databases of wood density and allometric relationships between biomass compartments (stem, branches, roots, foliage). We tested the resultant map against inventory‐based biomass data from R ussia, Europe and the USA prior to making intercontinent and interbiome carbon stock comparisons. Results Our derived carbon density map agrees well with inventory data at regional scales ( r 2 = 0.70–0.90). While 40.7 ± 15.7 petagram of carbon ( P g C ) are stored in BFT , TBMF and TCF contain 24.5 ± 9.4 P g C and 14.5 ± 4.8 P g C , respectively. In terms of carbon density, we found 6.21 ± 2.07 kg C m −2 retained in TCF and 5.80 ± 2.21 kg C m −2 in TBMF , whereas BFT have a mean carbon density of 4.00 ± 1.54 kg C m −2 . Indications of a higher carbon density in Europe compared with the other continents across each of the three biomes could not be proved to be significant. Main conclusions The presented carbon density and corresponding uncertainty map give an insight into the spatial patterns of biomass and stand as a new benchmark to improve carbon cycle models and carbon monitoring systems. In total, we found 79.8 ± 29.9 P g C stored in northern boreal and temperate forests, with Asian BFT accounting for 22.1 ± 8.3 P g C .