Unveiling the processes that shape biodiversity patterns is a cornerstone of ecology. Land-use diversity (i.e., the variety of land-use categories within an area) is often considered an important environmental factor that promotes species richness at landscape and regional scales by increasing beta-diversity. Still, the role of land-use diversity in structuring global taxonomic and functional richness is unknown. Here, we examine the hypothesis that regional species taxonomic and functional richness is explained by global patterns of land-use diversity by analyzing distribution and trait data for all extant birds. We found strong support for our hypothesis. Land-use diversity predicted bird taxonomic and functional richness in almost all biogeographic realms, even after accounting for the effect of net primary productivity (i.e., a proxy of resource availability and habitat heterogeneity). This link was particularly consistent with functional richness compared to taxonomic richness. In the Palearctic and Afrotropic realms, a saturation effect was evident, suggesting a non-linear relationship between land-use diversity and biodiversity. Our results reveal that land-use diversity is a key environmental factor associated with several facets of bird regional diversity, widening our understanding of key large-scale predictors of biodiversity patterns. These results can contribute to policies aimed at minimizing regional biodiversity loss.

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.

Coastal wetlands provide a range of ecosystem services, yet they are currently under threat from global change impacts. Thus, their monitoring and assessment is vital for evaluating their status, extent and distribution. Remote sensing provides an excellent tool for evaluating coastal ecosystems, whether with small-scale studies using drones or national-/regional-/global-scale studies using satellite-derived data. This study used a fine-scale plant community classification of coastal meadows in Estonia derived from a multispectral camera on board unoccupied aerial vehicles (UAVs) to calculate the plant fractional cover (PFC) in Sentinel-2 MultiSpectral Instrument (MSI) sensor grids. A random forest (RF) algorithm was trained and tested with vegetation indices (VIs) calculated from the spectral bands extracted from the MSI sensor to predict the PFC. Additional RF models were trained and tested after adding a digital elevation model (DEM). After comparing the models, results show that using DEM with VIs can increase the prediction accuracy of PFC up to 2 times (R² 58 %–70 %). This suggests the use of ancillary data such as DEM to improve the prediction of empirical machine learning models, providing an appropriate approach to upscale local studies to wider areas for management and conservation purposes.

Coastal wetlands provide a range of ecosystem services and can support quite high biodiversity as a result of their high productivity. There are a range of techniques applied to monitoring and assessing ecological status and ecosystem service provision, however, traditional techniques can be quite time consuming and costly. In recent years, there has been a strong push to use remotely sensed data to evaluate ecological condition as well as estimate a range of ecosystem services within coastal wetlands.  Unmanned Aerial Vehicles (UAV) platforms have increasingly been used in the field of remote sensing of coastal wetlands because they provide detailed radiometric data to carry out the classification of the high-resolution images. Classifications using supervised Machine Learning algorithms can be performed on those images, providing robust datasets for a range of variables.However, in spite of the flexibility of performing flight plans to monitor coastal wetlands with high accuracy, it is often not feasible to capture large areas using UAV systems. Satellite imagery can be used to undertake evaluations of a wide range of environmental variables in coastal wetlands over much larger areas. Finding synergies between images taken from UAVs and satellite could provide the possibility to extend local observations of plant functional diversity or ecosystem service provision in coastal wetlands to larger areas or to regions. Using validation techniques based on ground-truth data, high-resolution UAV derived images can be used to characterize terrain and ecological features, such as plant communities and then upscale them to satellite resolutions.The present study presents a methodology to compare images taken from a UAV multispectral camera and the freely available Multispectral Instrument (MSI) sensor images from the Sentinel-2 satellite because their spectral bands overlap with those commonly used for plant community assessments in coastal wetlands using drones. First, each pixel of Sentinel-2 image is characterized by the most frequent category of plant communities obtained from a ML supervised classification of high-resolution UAV image. Then, the results of classifying the study areas with the Sentinel-2 image are compared with the previous process by analyzing the differences and similarities of categories in each pixel. By this way, synergies between the UAV and Sentinel-2 images can be found in order to have a reliable upscaling of UAV-based data. Keywords: Remote Sensing, UAV, Machine Learning, Upscaling

Coastal wetlands provide a range of ecosystem services, yet are currently under threat from global change impacts. Thus, monitoring and assessment is vital for evaluating their status, extent and distribution. Remote sensing provides an excellent tool for evaluating coastal ecosystems, whether with small scale studies using drones or national/regional/global scale studies using satellite derived data. This study used a fine-scale plant community classification of coastal meadows in Estonia derived from a multispectral camera on board Unoccupied Aerial Vehicles (UAV) to calculate the Plant Fractional Cover (PFC) in Sentinel-2 MultiSpectral Instrument sensor (MSI) grids. A Random Forest algorithm was trained and tested with vegetation indices (VI) calculated from the spectral bands extracted from the MSI sensor to predict the PFC. Additional RF models were trained and tested after adding a Digital Elevation Model (DEM). After comparing the models, results show that using DEM with VI can increase the prediction accuracy of PFC up to two times (R² 58–70 %). This suggests the use of ancillary data such as DEM to improve the prediction of empirical machine learning models, providing an appropriate approach to upscale local studies to wider areas for management and conservation purposes.