Vegetation traits of pre-Alpine grasslands in southern Germany.

The data set contains information on aboveground vegetation traits of > 100 georeferenced locations within ten temperate pre-Alpine grassland plots in southern Germany. The grasslands were sampled in April 2018 for the following traits: bulk canopy height; weight of fresh and dry biomass; dry weight percentage of the plant functional types (PFT) non-green vegetation, legumes, non-leguminous forbs, and graminoids; total green area index (GAI) and PFT-specific GAI; plant water content; plant carbon and nitrogen content (community values and PFT-specific values); as well as leaf mass per area (LMA) of PFT. In addition, a species specific inventory of the plots was conducted in June 2020 and provides plot-level information on grassland type and plant species composition. The data set was obtained within the framework of the SUSALPS project ("Sustainable use of alpine and pre-alpine grassland soils in a changing climate"; https://www.susalps.de/ ) to provide in-situ data for the calibration and validation of remote sensing based models to estimate grassland traits.

Remote sensing monitoring of land restoration interventions in semi-arid environments with a before-after control-impact statistical design.

Restoration interventions to combat land degradation are carried out in arid and semi-arid areas to improve vegetation cover and land productivity. Evaluating the success of an intervention over time is challenging due to various constraints (e.g. difficult-to-access areas, lack of long-term records) and the lack of standardised and affordable methodologies. We propose a semi-automatic methodology that uses remote sensing data to provide a rapid, standardised and objective assessment of the biophysical impact, in terms of vegetation cover, of restoration interventions. The Normalised Difference Vegetation Index (NDVI) is used as a proxy for vegetation cover. Recognising that changes in vegetation cover are naturally due to environmental factors such as seasonality and inter-annual climate variability, conclusions about the success of the intervention cannot be drawn by focussing on the intervention area only. We therefore use a comparative method that analyses the temporal variations (before and after the intervention) of the NDVI of the intervention area with respect to multiple control sites that are automatically and randomly selected from a set of candidates that are similar to the intervention area. Similarity is defined in terms of class composition as derived from an ISODATA classification of the imagery before the intervention. The method provides an estimate of the magnitude and significance of the difference in greenness change between the intervention area and control areas. As a case study, the methodology is applied to 15 restoration interventions carried out in Senegal. The impact of the interventions is analysed using 250-m MODIS and 30-m Landsat data. Results show that a significant improvement in vegetation cover was detectable only in one third of the analysed interventions, which is consistent with independent qualitative assessments based on field observations and visual analysis of high resolution imagery. Rural development agencies may potentially use the proposed method for a first screening of restoration interventions.

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform.

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.

Pedogeochemistry in NE-Brazil - compared to Australia and Europe.

A soil geochemical dataset (major and minor elements), based on low-density sampling, is provided for NE-Brazil (ca. 1.7 million km²). It covers an area from about 2°S to 12°S, and from 34°W to 49°W, and refers to top (TOP: 0-20 cm) and bottom (BOT: 30-50 cm) mineral soils. Results are put in perspective using two recent and comparable studies, the National Geochemical Survey of Australia (NGSA) and the European Geochemical Mapping of Agricultural Soils (GEMAS). All median element concentrations in the Brazilian samples are depleted compared to World Soil Averages (WSA), except for Al2O3 and SiO2, which are respectively similar to WSA and enriched in Brazil. While the depletion is moderate for Fe2O3, MnO, P2O5, and TiO2, it reaches an order of magnitude and more for K2O, MgO, CaO, and Na2O. The difference between TOP and BOT concentrations is lower than the variation of either TOP or BOT concentrations between sample sites. Similar spatial distribution and the high correlation between TOP and BOT concentrations suggest that (1) similar processes and parameters are of general relevance for the geochemical composition of TOP and BOT samples, and (2) topsoil and subsoil are not decoupled. Cluster analysis revealed similar results for TOP and BOT samples, yielding three groups of elements/oxides displaying similar behavior: Gr.1 comprising Al2O3, Fe2O3, TiO2, and P2O5; Gr.2 comprising CaO, K2O, MgO, MnO, and Na2O; and Gr.3 being SiO2. Weathering indicators are significantly positively correlated and show similar spatial distributions in TOP and BOT samples. All elements deliver similar mass removal times (time to export all material from a 10 cm soil layer) and clearly discern between the regions: Europe with the fastest "depletion" (12,200 ± 300 years), followed by Australia (33,200 ± 3000 years) and Brazil (86,700 ± 3000 years). Similar results emerge when calculating denudation rates, using independent fluvial denudation data in large basins.