CREAF

Biodiversity monitoring at simultaneously fine spatial resolutions and large spatial extents is needed but limited by operational trade-offs and costs. Open-access data may be cost-effective to address those limitations. We test the use of open-access satellite imagery (NDVI texture variables) and biodiversity data, assembled from GBIF, to investigate the relative importance of variables of habitat extent and structure as indicators of bird community richness and dissimilarity in the Alentejo region (Portugal). Results show that, at the landscape scale, forest bird richness is better indicated by the availability of tree cover in the overall landscape than by the extent or structure of the forest habitats. Open-land birds also respond to landscape structure, namely to the spectral homogeneity and size of open-land patches and to the presence of perennial vegetation amid herbaceous habitats. Moreover, structure variables were more important than climate variables or geographic distance to explain community dissimilarity patterns at the regional scale. Overall, summer imagery, when perennial vegetation is more discernible, is particularly suited to inform indicators of forest and open-land bird community richness and dissimilarity, while spring imagery appears to be also useful to inform indicators of open-land bird richness. © 2019, The Author(s).

The main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate radiometric correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to radiometrically correct the matching bands of UAS, L8, and S2; and (d) radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroradiometric measurements and the drone data in all evaluated bands (R2 > 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS < 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSENIR > 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2.018%) than the other satellite radiometric correction methods in visible bands (6S-LaSRC (RMSE < 2.680%), Sen2Cor-SNAP (RMSE < 2.192%), and PIA-MiraMon (RMSE < 3.130%), but did not achieve sufficient results in the NIR band (RMSENIR < 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS < 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate radiometric corrections used in local environmental studies or the monitoring of protected areas around the world. © 2018 by the authors.

Aim: Large-scale patterns of limitations in tree recruitment remain poorly described in the Mediterranean Basin, and this information is required to assess the impacts of global warming on forests. Here, we unveil the existence of opposite trends of recruitment limitation between the dominant genera Quercus and Pinus on a large scale and identify the key ecological drivers of these diverging trends. Location: Spain Methods: We gathered data from the Spanish National Forest inventory to assess recruitment trends for the dominant species (Pinus halepensis, Pinus pinea, Pinus pinaster, Pinus nigra, Pinus sylvestris, Pinus uncinata, Quercus suber, Quercus ilex, Quercus petraea, Quercus robur, Quercus faginea and Quercus pyrenaica). We assessed the direct and indirect drivers of recruitment by applying Bayesian structural equation modelling techniques. Results: Severe limitations in recruitment were observed across extensive areas for all Pinus species studied, with recruitment failure affecting 54-71% of the surveyed plots. In striking contrast, Quercus species expanded into 41% of the plots surveyed compared to only 10% for Pinus and had a lower local recruitment failure (29% of Quercus localities compared to 63% for Pinus species). Bayesian structural equation models highlighted the key role of the presence of Q.ilex saplings and the increase in the basal area of Q.ilex in limiting recruitment in five Pinus species. The recruitment of P.sylvestris and P.nigra showed the most negative trends and was negatively associated with the impacts of fire. Main conclusions: This study identified Q.ilex, the most widespread species in this area, as a key driver of recruitment shifts on a large scale, negatively affecting most pine species with the advance of forest succession. These results highlight that the future expansion/contraction of Q.ilex stands with ongoing climate change will be a key process indirectly controlling the demographic responses of Pinus species in the Mediterranean Basin. © 2013 John Wiley & Sons Ltd.

Radiometric correction is a prerequisite for generating high-quality scientific data, making it possibleto discriminate between product artefacts and real changes in Earth processes as well as accuratelyproduce land cover maps and detect changes. This work contributes to the automatic generation of surfacereflectance products for Landsat satellite series. Surface reflectances are generated by a new approachdeveloped from a previous simplified radiometric (atmospheric + topographic) correction model. Theproposed model keeps the core of the old model (incidence angles and cast-shadows through a digitalelevation model [DEM], Earth-Sun distance, etc.) and adds new characteristics to enhance and automatizeground reflectance retrieval. The new model includes the following new features: (1) A fitting model basedon reference values from pseudoinvariant areas that have been automatically extracted from existingreflectance products (Terra MODIS MOD09GA) that were selected also automatically by applying qualitycriteria that include a geostatistical pattern model. This guarantees the consistency of the internal andexternal series, making it unnecessary to provide extra atmospheric data for the acquisition date and time,dark objects or dense vegetation. (2) A spatial model for atmospheric optical depth that uses detailedDEM and MODTRAN simulations. (3) It is designed so that large time-series of images can be processedautomatically to produce consistent Landsat surface reflectance time-series. (4) The approach can handlemost images, acquired now or in the past, regardless of the processing system, with the exception ofthose with extremely high cloud coverage. The new methodology has been successfully applied to aseries of near 300 images of the same area including MSS, TM and ETM+ imagery as well as to differentformats and processing systems (LPGS and NLAPS from the USGS; CEOS from ESA) for different degreesof cloud coverage (up to 60%) and SLC-off. Reflectance products have been validated with some exampleapplications: time series robustness (for a pixel in a pseudoinvariant area, deviations are only 1.04% onaverage along the series), spectral signatures generation (visually coherent with the MODIS ones, butmore similar between dates), and classification (up to 4 percent points better than those obtained withthe original manual method or the CDR products). In conclusion, this new approach, that could also beapplied to other sensors with similar band configurations, offers a fully automatic and reasonably goodprocedure for the new era of long time-series of spatially detailed global remote sensing data. © 2014 The Authors.