Farmland abandonment has been a widespread land-use change in the Iberian Peninsula since the second half of the 20th century, leading to the establishment of secondary forests across the region. In this study, we aimed to address changes in the recent (1985–2014) emergence patterns of these forests and examine how environmental factors affected their growth by considering differences in leaf-habit types. We used a combination of Landsat-derived land-cover maps and aboveground biomass (AGB) maps from the European Space Agency to assess the secondary forest establishment and growth, respectively, in the study region. We also obtained a set of topographic, climatic and landscape variables from diverse GIS layers and used them for determining changes over time in the environmental drivers of forest establishment and AGB using general linear models. The results highlight that secondary forest cover was still increasing in the Iberian Peninsula at a rate above the European average. Yet, they also indicate a directional change in the emergence of secondary forests towards lower and less steep regions with higher water availability (mean rainfall and SPEI) and less forest cover but are subjected to greater drought events. In addition, these environmental factors differentially affect the growth of forests with different leaf-habit types: i.e., needleleaf secondary forests being less favoured by high temperature and precipitation, and broadleaf deciduous forests being most negatively affected by drought. Finally, these spatial patterns of forest emergence and the contrasting responses of forest leaf-habits to environmental factors explained the major development of broadleaf evergreen compared to broadleaf deciduous forests and, especially, needleleaf secondary forests. These results will improve the knowledge of forest dynamics that have occurred in the Iberian Peninsula in recent decades and provide an essential tool for understanding the potential effects of climate warming on secondary forest growth.

Remote Sensing (RS) digital classification techniques require sufficient, accurate and ubiquitously distributed ground truth (GT) samples. GT is usually considered “true” per se; however, human errors, or differences in criteria when defining classes, among other reasons, often undermine this veracity. Trusting the GT is so crucial that protocols should be defined for making additional quality checks before passing to the classification stage. Fortunately, the nature of RS imagery allows setting a framework of quality controls to improve the confidence in the GT areas by proposing a set of filtering rules based on data from the images themselves. In our experiment, two pre-existing reference datasets (rDS) were used to obtain GT candidate pixels, over which inconsistencies were identified. This served as a basis for inferring five key filtering rules based on NDVI data, a product available from almost all RS instruments. We evaluated the performance of the rules in four temporal study cases (under backdating and updating scenarios) and two study areas. In each case, a set of GT samples was extracted from the rDS and the set was used both unfiltered (original) and filtered according to the rules. Our proposal shows that the filtered GT samples made it possible to solve usual problems in wilderness and agricultural categories. Indeed, the confusion matrices revealed, on average, an increase in the overall accuracy of 10.9, a decrease in the omission error of 16.8, and a decrease in the commission error of 14.0, all values in percent points. Filtering rules corrected inconsistencies in the GT samples extracted from the rDS by considering inter-annual and intra-annual differences, scale issues, multiple behaviours over time and labelling misassignments. Therefore, although some intrinsic limitations have been detected (as in mixed forests), the protocol allows a much better Land Cover mapping thanks to using more robust GT samples, something particularly important in a multitemporal context in which accounting for phenology is essential.

This study analyzes the spatio-temporal dynamics of the drivers of forest expansion in the Iberian Peninsula for the periods 1987–2002–2017 through a 185 km-wide north–south Landsat scene transect. The analysis has considered a variety of biogeographical regions [0–3500 m.a.s.l, annual rainfalls 150–2200 mm] and 30 explanatory variables. A rigorous map production at 30 m resolution, including detailed filtering methods and uncertainty management at pixel scale, provided high-quality land cover maps. The main forest expansion trajectories were related to explanatory variables using boosted regression trees. Proximity to previous forests was a key common factor for forest encroachment in all forest types, with other factors being distance to the hydrographic network, temperature and precipitation for broadleaf deciduous forests (BDF), precipitation, temperature and solar radiation for broadleaf evergreen forests (BEF) and precipitation, distance to province capitals, and solar radiation for needleleaf evergreen forests (NEFs). Results also showed contrasting forest expansion trajectories and drivers per biogeographic region, with a high dynamism of grasslands towards new forest in the Eurosiberian and the mountainous Mediterranean regions, a high importance of croplands as land cover origin of new forest in the Mesomediterranean, and increasing importance over time of socioeconomic drivers (such as those employed in the industry sector and the utilized agricultural area) in the Supramediterranean region but the opposite pattern in the Southern Mesomediterranean. Lower precipitation rates favored new NEFs from shrublands in the Thermomediterraean region which, together with the Northern Mesomediterranean, exhibited the highest relative rates of new forests. These findings provide reliable insights to develop policies considering the ecological and social impacts of land abandonment and subsequent forest expansion.

La utilización de Modelos Digitales de Alturas derivados de datos lidar en los procesos de clasificación automática de imágenes de teledetección destinadas a la generación de cartografía de usos y cubiertas del suelo no es nueva, pero normalmente se ha incorporado como covariable en dichos procesos. Por otra parte, las leyendas en dichos mapas suelen incluir clases como “bosque” (o sus subtipos), “matorral”, y categorías similares. Sin embargo, la concreción exacta de lo que significan semánticamente estas categorías es un terreno que se suele dejar al resultado de la propia clasificación, con lo que se echa en falta la posibilidad de una aproximación más cuantitativa. Dicha aproximación es altamente deseable en un contexto en que cada vez es más valorado el control de la fiabilidad y coherencia durante el propio proceso de generación, más allá de los indicadores generales de calidad que resulten de la verificación de resultados en la última fase. Además, cuando el objetivo último es la obtención de mapas en series temporales que permitan el estudio de dinámicas de cubiertas y/o de sus funcionalidades, este rigor garantiza la eliminación de gran parte del ruido en las trayectorias de cambio consideradas. En este trabajo mostramos los criterios que se han seguido para la generación de los Mapas de Usos y Cubiertas quinquenales en el NE peninsular durante las últimas décadas a partir de datos Landsat y Sentinel-2, y se proporciona una primera cuantificación de los beneficios obtenidos.

INTERCOMPARACIÓN DE LAS VERSIONES 5 Y 6 DEL PRODUCTO BRDF/ALBEDO DE MODIS