Highlights:-Plant communities in coastal wetlands are at risk due to the impacts of global change-Knowing the distribution of plant communities is essential for nature conservation-Communities distribution maps were produced using a UAV-based multispectral sensor-The Random Forest classifier yielded the highest classification accuracy-Species diversity and aboveground biomass affect the classification performance ABSTRACT Coastal meadows worldwide are subjected to habitat degradation due to abandonment, intensification and the impacts of global change. In order to protect and restore these habitats and ensure the supply of valuable ecosystem services, it is necessary to know the extent and location of plant communities in coastal meadows. In this study, five plant communities were mapped at very high resolution in three different study sites in West Estonia. A fixed wing UAV was used to obtain multispectral images and derive a set of vegetation indices. Two different image classification techniques were used to cluster the vegetation indices maps and produce plant community distribution maps. The highest classification accuracy was obtained using a Random Forest classifier and 13 vegetation indices. Additionally, the spectral characteristics of the training samples were correlated with aboveground biomass and species diversity. Both biomass and species diversity were positively correlated with the spectral diversity of training samples and are thus likely to have an effect on the classification accuracy. The results of this study highlight the need to utilize a wide array of vegetation indices and assess the spectral characteristics of training samples in order to obtain high classification accuracies and understand the nature of misclassification errors. The resulting maps provide a solid foundation for global change impact assessment and habitat management and restoration in coastal meadows.

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High-resolution images obtained by multispectral cameras mounted on Unmanned Aerial Vehicles (UAVs) are helping to capture the heterogeneity of the environment in images that can be discretized in categories during a classification process. Currently, there is an increasing use of supervised machine learning (ML) classifiers to retrieve accurate results using scarce datasets with samples with non-linear relationships. We compared the accuracies of two ML classifiers using a pixel and object analysis approach in six coastal wetland sites. The results show that the Random Forest (RF) performs better than K-Nearest Neighbors (KNN) algorithm in the classification of pixels and objects and the classification based on pixel analysis is slightly better than the object-based analysis. The agreement between the classifications of objects and pixels is higher in Random Forest. This is likely due to the heterogeneity of the study areas, where pixel-based classifications are most appropriate. In addition, from an ecological perspective, as these wetlands are heterogeneous, the pixel-based classification reflects a more realistic interpretation of plant community distribution.

Studies involving various aspects of the biology and ecology of sea turtles have successfully applied stable isotope analysis. In many of these studies, the chemical extraction of

This data article ranks 294 countries worldwide with more potential available, of cereal based agricultural residues for bioenergy production. Nine different cereal-based agricultural waste products (barley, wheat, millet, oat, rice, and rye straw, sorghum straw/stalk, and maize cob) are used. The tables and figures are grouped by the most prevalent Köppen-Geiger climate classification (tropical/megathermal, dry (desert and semi-arid), temperate/mesothermal, continental/microthermal), continent and region. The data was collected by the authors from FAO bioenergy and food security rapid appraisal tool (excel-based tools) that uses crop yields and production with 10 years (2005–2014) average annual production to estimate the residue yield (t/ha), by feedstock.