The Database of European Forest Insect and Disease Disturbances: DEFID2.

Insect and disease outbreaks in forests are biotic disturbances that can profoundly alter ecosystem dynamics. In many parts of the world, these disturbance regimes are intensifying as the climate changes and shifts the distribution of species and biomes. As a result, key forest ecosystem services, such as carbon sequestration, regulation of water flows, wood production, protection of soils, and the conservation of biodiversity, could be increasingly compromised. Despite the relevance of these detrimental effects, there are currently no spatially detailed databases that record insect and disease disturbances on forests at the pan-European scale. Here, we present the new Database of European Forest Insect and Disease Disturbances (DEFID2). It comprises over 650,000 harmonized georeferenced records, mapped as polygons or points, of insects and disease disturbances that occurred between 1963 and 2021 in European forests. The records currently span eight different countries and were acquired through diverse methods (e.g., ground surveys, remote sensing techniques). The records in DEFID2 are described by a set of qualitative attributes, including severity and patterns of damage symptoms, agents, host tree species, climate-driven trigger factors, silvicultural practices, and eventual sanitary interventions. They are further complemented with a satellite-based quantitative characterization of the affected forest areas based on Landsat Normalized Burn Ratio time series, and damage metrics derived from them using the LandTrendr spectral-temporal segmentation algorithm (including onset, duration, magnitude, and rate of the disturbance), and possible interactions with windthrow and wildfire events. The DEFID2 database is a novel resource for many large-scale applications dealing with biotic disturbances. It offers a unique contribution to design networks of experiments, improve our understanding of ecological processes underlying biotic forest disturbances, monitor their dynamics, and enhance their representation in land-climate models. Further data sharing is encouraged to extend and improve the DEFID2 database continuously. The database is freely available at https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FOREST/DISTURBANCES/DEFID2/.

High-resolution imagery acquired from an unmanned platform to estimate biophysical and geometrical parameters of olive trees under different irrigation regimes.

The experiments were conducted in a fully-productive olive orchard (cv. Frantoio) at the experimental farm of University of Pisa at Venturina (Italy) in 2015 to assess the ability of an unmanned aerial vehicle (UAV) equipped with RGB-NIR cameras to estimate leaf area index (LAI), tree height, canopy diameter and canopy volume of olive trees that were either irrigated or rainfed. Irrigated trees received water 4-5 days a week (1348 m3 ha-1), whereas the rainfed ones received a single irrigation of 19 m3 ha-1 to relieve the extreme stress. The flight altitude was 70 m above ground level (AGL), except for one flight (50 m AGL). The Normalized Difference Vegetation Index (NDVI) was calculated by means of the map algebra technique. Canopy volume, canopy height and diameter were obtained from the digital surface model (DSM) obtained through automatic aerial triangulation, bundle block adjustment and camera calibration methods. The NDVI estimated on the day of the year (DOY) 130 was linearly correlated with both LAI and leaf chlorophyll measured on the same date (R2 = 0.78 and 0.80, respectively). The correlation between the on ground measured canopy volumes and the ones by the UAV-RGB camera techniques yielded an R2 of 0.71-0.86. The monthly canopy volume increment estimated from UAV surveys between (DOY) 130 and 244 was highly correlated with the daily water stress integral of rainfed trees (R2 = 0.99). The effect of water stress on the seasonal pattern of canopy growth was detected by these techniques in correspondence of the maximum level of stress experienced by the rainfed trees. The highest level of accuracy (RMSE = 0.16 m) in canopy height estimation was obtained when the flight altitude was 50 m AGL, yielding an R2 value of 0.87 and an almost 1:1 ratio of measured versus estimated canopy height.

A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization.

Maize crop production is constrained worldwide by nitrogen (N) availability and particularly in poor tropical and subtropical soils. The development of affordable high-throughput crop monitoring and phenotyping techniques is key to improving maize cultivation under low-N fertilization. In this study several vegetation indices (VIs) derived from Red-Green-Blue (RGB) digital images at the leaf and canopy levels are proposed as low-cost tools for plant breeding and fertilization management. They were compared with the performance of the normalized difference vegetation index (NDVI) measured at ground level and from an aerial platform, as well as with leaf chlorophyll content (LCC) and other leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R (2)~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization.