Comparació de models que prediuen les respostes de la vegetació ibèrica al canvi global

Les prediccions de la resposta de la vegetació ibèrica al canvi global es basen en dos tipus de models, uns de base matemàtica (models fisiològics) que reprodueixen el funcionament de les plantes i uns altres de base estadística (models correlatius) que es basen en establir correlacions amb les condicions ambientals actuals.Un estudi mostra les congruències i incongruències entre aquests dos tipus de models i la importància de les interaccions entre organismes. També destaca la importància de comparar diversos models per entendre millor els factors que condicionen la distribució de la vegetació.Las predicciones de la respuesta de la vegetación ibérica al cambio global se basan en dos tipos de modelos, unos de base matemática (modelos fisiológicos) que reproducen el funcionamiento de las plantas y otros de base estadística (modelos correlativos) que se basan en establecer correlaciones con las condiciones ambientales actuales. Un estudio muestra las congruencias e incongruencias entre estos dos tipos de modelos y la importancia de las interacciones entre organismos. También destaca la importancia de comparar diversos modelos para entender mejor los factores que condicionan la distribución de la vegetación

Cartografía de usos y cubiertas del suelo del sureste de la Península Ibérica a partir de la clasificación de imágenes Landsat en el quinquenio 2000-2004

Se presenta la metodología utilizada en la obtención de la cartografía de usos y cubiertas del suelo para la zona oriental de Andalucía (ámbito de 29.259 km²) en el quinquenio 2000-2004, empleando el clasificador híbrido y utilizando imágenes Landsat junto con las variables auxiliares. Las áreas de entrenamiento se han obtenido de manera semiautomática a partir del SIOSE y depuradas con umbrales de NDVI. Se han ejecutado bancos de pruebas en función de las variables incluidas en el clasificador. Los mejores resultados, en cuanto a porcentaje de acierto y área clasificada,se han obtenido excluyendo la radiación solar de invierno y la banda 1. El proceso se ha realizado por separado para cubiertas naturales y urbanas, con un grado de acierto global superior al 88%, y para cultivos, con un acierto superior al 86%

Combining remote sensing and GIS climate modelling to estimate daily forest evapotranspiration in a Mediterranean mountain area

Evapotranspiration monitoring allows us to assess the environmental stress on forest and agricultural ecosystems. Nowadays, Remote Sensing and Geographical Information Systems (GIS) are the main techniques used for calculating evapotranspiration at catchment and regional scales. In this study we present a methodology, based on the energy balance equation (B-method), that combines remote sensing imagery with GIS-based climate modelling to estimate daily evapotranspiration (ETd) for several dates between 2003 and 2005. The three main variables needed to compute ETd were obtained as follows: (i) Land surface temperature by means of the Landsat-5 TM and Landsat-7 ETM+ thermal band, (ii) air temperature by means of multiple regression analysis and spatial interpolation from meteorological ground stations data at satellite pass, and (iii) net radiation by means of the radiative balance. We calculated ETd using remote sensing data at different spatial and temporal scales (Landsat-7 ETM+, Landsat-5 TM and TERRA/AQUA MODIS, with a spatial resolution of 60, 120 and 1000 m, respectively) and combining three different approaches to calculate the B parameter, which represents an average bulk conductance for the daily-integrated sensible heat flux. We then compared these estimates with sap flow measurements from a Scots pine (Pinus sylvestris L.) stand in a Mediterranean mountain area. This procedure allowed us to better understand the limitations of ETd modelling and how it needs to be improved, especially in heterogeneous forest areas. The method using Landsat data resulted in a good agreement, R2 test of 0.89, with a mean RMSE value of about 0.6 mm day−1 and an estimation error of ±30 %. The poor agreement obtained using TERRA/AQUA MODIS, with a mean RMSE value of 1.8 and 2.4 mm day−1 and an estimation error of about ±57 and 50 %, respectively. This reveals that ETd retrieval from coarse resolution remote sensing data is troublesome in these heterogeneous areas, and therefore further research is necessary on this issue. Finally, implementing regional GIS-based climate models as inputs in ETd retrieval have has provided good results, making possible to compute ETd at regional scales

Phenological sensitivity and seasonal variability explain climate-driven trends in Mediterranean butterflies

Although climate-driven phenological shifts have been documented for many taxa across the globe, we still lack knowledge of the consequences they have on populations. Here, we used a comprehensive database comprising 553 populations of 51 species of north-western Mediterranean butterflies to investigate the relationship between phenology and population trends in a 26-year period. Phenological trends and sensitivity to climate, along with various species traits, were used to predict abundance trends. Key ecological traits accounted for a general decline of more than half of the species, most of which, surprisingly, did not change their phenology under a climate warming scenario. However, this was related to the regional cooling in a short temporal window that includes late winter and early spring, during which most species concentrate their development. Finally, we demonstrate that phenological sensitivity-but not phenological trends-predicted population trends, and argue that species that best adjust their phenology to inter-annual climate variability are more likely to maintain a synchronization with trophic resources, thereby mitigating possible negative effects of climate change. Our results reflect the importance of assessing not only species' trends over time but also species' abilities to respond to a changing climate based on their sensitivity to temperature

Bioclimatological mapping tackling uncertainty propagation: application to mainland Portugal

We present a bioclimatological diagnosis of mainland Portugal, namely the thermotype and ombrotype maps following Rivas-Martínez’s worldwide bioclimatic classi$cation system. In order to obtain this diagnosis, we produced maps of bioclimatological indices using, as base data, geostatistical interpolations of air temperature and precipitation.We performed uncertainty propagation obtaining uncertainty measures for the produced maps: mean absolute errors and root mean squared errors. For the non-linear indices, besides the usual approximation using Taylor expansion, we devised error formulae, for which we showed that the propagated uncertainties are upper bounds on the true uncertainty measures. We compared the obtained uncertainty measures to those reported on a previously published work, which used a different methodological framework to obtain the same diagnosis. Although the approach we used here implies a great number of interpolations and subsequent calculation steps, it permitted the use of a large amount of data relative to precipitation. An F-test showed that the estimated mean squared errors for the maps of ombrothermic indices were signi$cantly lower than those produced by the former methodological framework.info:eu-repo/semantics/publishedVersio

Aligning citizen science and remote sensing phenology observations to characterize climate change impact on vegetation

Phenology observations are essential indicators to characterize the local effects of climate change. Citizen participation in the collection of phenological observations is a potential approach to provide data at both high temporal scale and fine grain resolution. Traditional observation practices of citizen science (CS), although precise at the species scale, are limited to few observations often closely located to an observer's residence. These limitations hinder coverage of the great variability of vegetation phenology across biomes and improvement of the knowledge of vegetation changes due to climate change impacts. This study presents a new approach to overcome these limitations by improving CS guidance and feedback as well as expanding phenology report sites and observations across different habitats and periods to contribute to monitoring climate change. This approach includes: (a) a new methodology focused on harmonizing remote sensing phenology products with traditional CS phenology observations to direct volunteers to active phenology regions and, (b) a new protocol for citizen scientists providing tools to guide them to specific regions to identify, collect and share species phenological observations and their phenophases. This approach was successfully tested, implemented and evaluated in Catalonia with more than 5000 new phenologically interesting regions identified and more than 200 observations collected and Sentinel-2 derived phenometrics were demonstrated as of good quality.info:eu-repo/semantics/publishedVersio

Environmental and socioeconomic factors of abandonment of rainfed and irrigated crops in northeast Spain

Changes over the last century in the economic model of European countries and the development of the market economy have led to intense shifts in land occupation patterns. Agricultural abandonment is an important consequence of these processes and has modified natural and cultural landscapes, involving side-effects for society. Understanding how environmental and socio-economic factors influence the abandonment process can provide useful insights for managing investments, whether from the public or the private sector. In Spain, the Pyrenees and the Ebro Depression are two differentiated areas in terms of land-use dynamics, particularly in terms of the agricultural model carried out. In this paper we have analyzed the agricultural abandonment in these areas during the 1987-2012 period in relation to several potential explanatory factors. The analysis focuses on the abandonment of rainfed and irrigated herbaceous crops in order to derive specific explanations according to the crop type and geographical region. Crop covers were classified from four Landsat scenes, and conditions were described by topographic variables, human factors and drought occurrence. Boosted regression trees (BRT) were used to identify the most important variables and to describe the relationships between agricultural abandonment and key factors. Topography derived variables were found to be the main determinants, except for irrigated crops in the Ebro Basin, where locational factors play a more important role. BRT models allowed us to identify other significant patterns such as: the vulnerability of irrigated crops to drought; the higher dependence of agricultural activity in the Pyrenees on internal networks; pattern shifts of land abandonment in the analyzed sub-periods, and; evidence of the importance of economic diversification for maintaining cropland