Detection of grassland mowing frequency using time series of vegetation indices from Sentinel-2 imagery

5openInternationalItalian coauthor/editorManagement intensity deeply influences meadow structure and functioning, therefore affecting grassland ecosystem services. Conservation and management measures, including European Common Agricultural Policy subsidies, should therefore be based on updated and publicly available data about management intensity. The mowing frequency is a crucial trait to describe meadows management intensity, but the potential of using vegetation indices from Sentinel-2 imagery for its retrieval has not been fully exploited. In this work we developed on the Google Earth Engine platform a four-phases algorithm to identify mowing frequency, including i) vegetation index time-series computing, ii) smoothing and resampling, iii) mowing detection, and iv) majority analysis. Mowing frequency during 2020 of 240 ha of grassland fields in the Italian Alps was used for algorithm optimization and evaluation. Six vegetation indexes (EVI, GVMI, MTCI, NDII, NDVI, RENDVI783.740) were tested as input to the proposed algorithm. The Normalized Difference Infrared Index (NDII) showed the best performance, resulting in mean absolute error of 0.07 and 93% overall accuracy on average at the four sites used for optimization, at pixel resolution. A slightly lower accuracy (mean absolute error = 0.10, overall accuracy = 90%) was obtained aggregating the maps to management parcels. The algorithm showed a good generalization ability, with a similar performance between global and local optimization and an average mean absolute error of 0.12 and an overall accuracy of 89% on average on the sites not used for parameters optimization. The lowest accuracies occurred in intensively managed grasslands surveyed by one satellite orbit only. This study demonstrates the suitability of the proposed algorithm to monitor very fragmented grasslands in complex mountain ecosystems. Google Earth Engine was used to develop the model and will enable researchers, agencies and practitioners to easily and quickly apply the code to map grassland mowing frequency for extensive grasslands protection and conservation, for mowing event verification, or for forage system characterization.openAndreatta, Davide; Gianelle, Damiano; Scotton, Michele; Vescovo, Loris; Dalponte, MicheleAndreatta, D.; Gianelle, D.; Scotton, M.; Vescovo, L.; Dalponte, M

Assessing plant trait diversity as an indicators of species α and β-diversity in a subalpine grassland of the Italian Alps

As the need for ecosystem biodiversity assessment increases within the climatecrisis framework, more and more studies using spectral variation hypothesis(SVH) are proposed to assess biodiversity at various scales. The SVH impliesoptical diversity (also called spectral diversity) is driven by light absorptiondynamics associated with plant traits (PTs) variability (which is an indicator offunctional diversity) which is, in turn, determined by biodiversity. In this study,we examined the relationship between PTs variability, optical diversity andα-andβ-diversity at different taxonomic ranks at the Monte Bondone grasslands,Trentino province, Italy. The results of the study showed that the PTs variabil-ity, at theαscale, was not correlated with biodiversity. On the other hand, theresults observed at the community scale (β-diversity) showed that the variationof some of the investigated biochemical and biophysical PTs was associatedwith theβ-diversity. We used the Mantel test to analyse the relationshipbetween the PTs variability and speciesβ-diversity. The results showed a corre-lation coefficient of up to 0.50 between PTs variability and speciesβ-diversity.For higher taxonomic ranks such as family and functional groups, a slightlyhigher Spearman’s correlation coefficient of up to 0.64 and 0.61 was observed,respectively. The SVH approach was also tested to estimateβ-diversity and wefound that spectral diversity calculated by Spectral Angle Mapper showed to bea better proxy of biodiversity in the same ecosystem where the spectral diversityapproach failed to estimateα-diversity. These findings suggest that optical andPTs diversity approaches can be used to predict species diversity in the grass-lands ecosystem where the species turnover is high

Assessing across-scale optical diversity and productivity relationships in grasslands of the Italian alps

The linearity and scale-dependency of ecosystem biodiversity and productivity relationships (BPRs) have been under intense debate. In a changing climate, monitoring BPRs within and across different ecosystem types is crucial, and novel remote sensing tools such as the Sentinel-2 (S2) may be adopted to retrieve ecosystem diversity information and to investigate optical diversity and productivity patterns. But are the S2 spectral and spatial resolutions suitable to detect relationships between optical diversity and productivity? In this study, we implemented an integrated analysis of spatial patterns of grassland productivity and optical diversity using optical remote sensing and Eddy Covariance data. Across-scale optical diversity and ecosystem productivity patterns were analyzed for different grassland associations with a wide range of productivity. Using airborne optical data to simulate S2, we provided empirical evidence that the best optical proxies of ecosystem productivity were linearly correlated with optical diversity. Correlation analysis at increasing pixel sizes proved an evident scale-dependency of the relationships between optical diversity and productivity. The results indicate the strong potential of S2 for future large-scale assessment of across-ecosystem dynamics at upper levels of observation

Effects of land use and climate on carbon and nitrogen pool partitioning in European mountain grasslands

European mountain grasslands are increasingly affected by land-use changes and climate, which have been suggested to exert important controls on grassland carbon (C) and nitrogen (N) pools. However, so far there has been no synthetic study on whether and how land-use changes and climate interactively affect the partitioning of these pools amongst the different grassland compartments. We analyzed the partitioning of C and N pools of 36 European mountain grasslands differing in land-use and climate with respect to above- and belowground phytomass, litter and topsoil (top 23 cm). We found that a reduction of management intensity and the abandonment of hay meadows and pastures increased above-ground phytomass, root mass and litter as well as their respective C and N pools, concurrently decreasing the fractional contribution of the topsoil to the total organic carbon pool. These changes were strongly driven by the cessation of cutting and grazing, a shift in plant functional groups and a related reduction in litter quality. Across all grasslands studied, variation in the impact of land management on the topsoil N pool and C/N-ratio were mainly explained by soil clay content combined with pH. Across the grasslands, below-ground phytomass as well as phytomass- and litter C concentrations were inversely related to the mean annual temperature; furthermore, C/N- ratios of phytomass and litter increased with decreasing mean annual precipitation. Within the topsoil compartment, C concentrations decreased from colder to warmer sites, and increased with increasing precipitation. Climate generally influenced effects of land use on C and N pools mainly through mean annual temperature and less through mean an- nual precipitation. We conclude that site-specific conditions need to be considered for understanding the effects of land use and of current and future climate changes on grassland C and N pools.Peer reviewe

Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies

This paper reviews the currently available optical sensors, their limitations and opportunities for deployment at Eddy Covariance (EC) sites in Europe. This review is based on the results obtained from an online survey designed and disseminated by the Co-cooperation in Science and Technology (COST) Action ESO903—“Spectral Sampling Tools for Vegetation Biophysical Parameters and Flux Measurements in Europe” that provided a complete view on spectral sampling activities carried out within the different research teams in European countries. The results have highlighted that a wide variety of optical sensors are in use at flux sites across Europe, and responses further demonstrated that users were not always fully aware of the key issues underpinning repeatability and the reproducibility of their spectral measurements. The key findings of this survey point towards the need for greater awareness of the need for standardisation and development of a common protocol of optical sampling at the European EC sites

Feeding management of dairy cattle affect grassland dynamics in an alpine pasture

5This work was supported by the Autonomous Province of Trento (Provincia Autonoma di Trento) [Progetto ‘Forma’] (Italy) with the ‘Forma’ project.restrictedInternationalItalian coauthor/editorThe effect of different supplementary feeding rates for grazing cattle on high-altitude pastures dynamics was evaluated. A field experiment was carried out during three years in a subalpine pasture area of the Eastern Alps. The investigated pasture area was 40 ha, located between 1820 and 2230 m a.s.l. Two paddocks were chosen in the experiment and two herds of 12 cattle each were kept in the two enclosures for 5 weeks. For the first herd (HS), the supplementary feeding rate was 4.8 kg OM head−1 per day, while for the second herd (LS), the rate was 1.6 kg OM head−1 per day. The amount of herbage consumed by each cattle was determined using the n-alkane technique. To monitor the pasture vegetation dynamics, eight metal exclusion cages were placed in each paddock to determine herbage growth, utilization rates, vegetation composition and animal grazing selectivity. Grazing behaviour of dairy cattle, in terms of herbage intake and species selection was affected by the different feeding rates. Cattle grazing Paddock HS consumed 1.9 kg OM day−1 of herbage less than Paddock LS. In the LS paddock, cattle grazed higher phytomass rates. When the animals were fed by higher concentrate rates, a more selective grazing seemed to significantly increase the pasture necromass component. The lower grazing selectivity favoured the development of species as Nardus stricta and Deschampsia caespitosa, which are well known for their low palatability. Distinct vegetation dynamic patterns were observed, with a reduction of hair grass and an increase of legumes in the Paddock LS.restrictedGianelle, D.; Romanzin, A.; Clementel, F.; Vescovo, L.; Bovolenta, S.Gianelle, D.; Romanzin, A.; Clementel, F.; Vescovo, L.; Bovolenta, S

Ecosystem Carbon Fluxes and Canopy Spectral Reflectance of a Mountain Meadow

Proximal and remote sensing measurements were used to calculate different vegetation indices that were applied as predictors of gross primary production (GPP), total ecosystem respiration (TER), net ecosystem production (NEP) and leaf area index (LAI). Reflectance data and carbon fluxes were collected during the 2005 growing season at a mountain grassland site in the Italian Alps. Significant relationships were found between GPP, TER, NEP, LAI and the most commonly used spectral vegetation indices, the Normalized Difference Vegetation Index (NDVI) and Green-NDVI. Saturation of the spectral indices was evident in the estimation of both biophysical and ecophysiological parameters. Among the different indices, Green-NDVI was less affected by saturation on both a spatial and a temporal basis. Therefore, the use of an additional green-band sensor for spectral measurements at eddy covariance grassland sites is recommended. Concerning the bandwidth for the calculation of the indices, the highest predictive capacities among the sensor simulations included in the analysis were those of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the high-resolution hyperspectral instrument Hyperion, indicating the advantage of narrow bands for the prediction of plant parameters. Further analyses are, however, required to investigate the relationships between NEP, GPP and vegetation indices retrieved from satellite platforms, using the bands available on MODIS and Hyperion sensors.JRC.H.2-Air and Climat