Coll M., Penuelas J., Ninyerola M., Pons X., Carnicer J. (2013) Multivariate effect gradients driving forest demographic responses in the Iberian Peninsula. Forest Ecology and Management. 303: 195-209.EnllaçDoi: 10.1016/j.foreco.2013.04.010
A precise knowledge of forest demographic gradients in the Mediterranean area is essential to assess future impacts of climate change and extreme drought events. Here we studied the geographical patterns of forest demography variables (tree recruitment, growth and mortality) of the main species in Spain and assessed their multiple ecological drivers (climate, topography, soil, forest stand attributes and tree-specific traits) as well as the geographical variability of their effects and interactions. Quantile modeling analyses allowed a synthetic description of the gradients of multiple covariates influencing forest demography in this area. These multivariate effect gradients showed significantly stronger interactions at the extremes of the rainfall gradient. Remarkably, in all demographic variables, qualitatively different levels of effects and interactions were observed across tree-size classes. In addition, significant differences in demographic responses and effect gradients were also evident between the dominant genus Quercus and Pinus. Quercus species presented significantly higher percentage of plots colonized by new recruits, whereas in Pinus recruitment limitation was significantly higher. Contrasting positive and negative growth responses to temperature were also observed in Quercus and Pinus, respectively. Overall, our results synthesize forest demographic responses across climatic gradients in Spain, and unveil the interactions between driving factors operating in the drier and wetter edges. © 2013 Elsevier B.V.
Garcia Millan V.E., Sanchez Azofeifa G.A., Malvarez G.C., More G., Pons X., Yamanaka-Ocampo M. (2013) Effects of topography on the radiometry of CHRIS/PROBA images of successional stages within tropical dry forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6: 1584-1595.EnllaçDoi: 10.1109/JSTARS.2013.2259471
In the present paper, the effect of shadows in the classification of three successional stages of a tropical dry forest (TDF) in Mexico, using hyperspectral and multi-angular CHRIS/PROBA images, is evaluated. An algorithm based on the cosine of the angle of solar incidence on the terrain is applied to correct the effect of topography on CHRIS/PROBA reflectances. Previous to the removal of shadows caused by topography, CHRIS/PROBA images were atmospherically corrected in BEAM software. Vegetation maps of the study site were generated using non-parametric decision trees, defining four main classes: late, intermediate and early stages of forest succession within a tropical dry forest, and riparian forests. By comparing the vegetation maps before and after shadow removal in CHRIS/PROBA spectral data, it was observed that the late stage of succession and riparian forests are overestimated for the non-corrected images while intermediate and early stages of succession are underestimated. Errors in classification are more important for the large CHRIS/PROBA viewing angles. Therefore, the removal of shadows caused by topography is necessary for an accurate classification of successional stages in tropical dry forests. © 2013 IEEE.
Marcer A., Saez L., Molowny-Horas R., Pons X., Pino J. (2013) Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation. Biological Conservation. 166: 221-230.EnllaçDoi: 10.1016/j.biocon.2013.07.001
Range maps provide important information in species conservation management, specially in the case of rare species of conservation interest. For the vast majority of cases, this information can only be estimated by means of species distribution modelling. When absence data is unavailable, modelled distribution maps represent the spatial variation of the degree of suitability for the species rather than their realised distribution. Although discerning potentially suitable areas for a given species is an important asset in conservation, it is necessary to estimate current distributions in order to preserve current populations. This work explores the use of species distribution modelling (Maxent) for species of conservation interest when their Extent of Occurrence (EOO) is well-known and there is quality occurrence data. In this case, derived binary maps of potentially suitable areas can be obtained and used to assess the conservation and protection status of a given species in combination with the EOO and existing protected area networks. Seven species, which are rare and endemic to the Western Mediterranean, have been used as an example. Valuable information for conservation assessment such as potentially suitable areas, EOO, Areas of Occupancy (AOO) and degree of protection is provided for this set of species. In addition, the existing informal view that among experts these species have range sizes much smaller than their potentially suitable area is confirmed. This could probably be attributed to important but currently unknown predictor variables and to historical phylogeographic factors. © 2013 Elsevier Ltd.
Pesquer L., Domingo C., Pons X. (2013) A geostatistical approach for selecting the highest quality MODIS daily images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7887 LNCS: 608-615.EnllaçDoi: 10.1007/978-3-642-38628-2_72
The aim of this work was to develop a new methodology for automatic selection of the highest quality MODIS daily images, MOD09GA Surface Reflectance product. The methodology developed here complements the quality assessment of MODIS products with a geostatistical analysis of spatial pattern images based on variogram tools. The resulting selection is formed by 26 high-quality images (from an initial dataset of 365) from throughout 2007. Most images with geometric distortion problems, such as the bow-tie effect, were rejected. The automatic selection was validated by comparing it to manual selection, which showed that it achieved an overall accuracy of 71.4%. © 2013 Springer-Verlag.
Pesquer L., Pons X., Cortes A., Serral I. (2013) Spatial pattern alterations from JPEG2000 lossy compression of remote sensing images: Massive variogram analysis in high performance computing. Journal of Applied Remote Sensing. 7: 0-0.EnllaçDoi: 10.1117/1.JRS.7.073595
We evaluate the implications of JPEG2000 lossy compression of remote sensing images for spatial analytical purposes. The main issue is to identify which cases and conditions in geostatistical studies are suitable for using lossy compressed images. For these purposes, an extensive test using Landsat, compact airborne spectrographic imager (CASI), and moderate resolution imaging spectroradiometer (MODIS) image series has been analyzed, through applying and comparing two-dimensional and three-dimensional (spectral and time domains) compression methods with a wide range of compression ratios for several dates, different landscape regions, and spectral bands. Due to the massive test bed and consequently to the high time consuming executions, a parallel solution was specifically developed. Variogram analyses showed that all the compression ratios maintain the variogram shapes, but high compression ratios (>20:1) degrade the spatial patterns of the remote sensing images. These alterations are lower for the three-dimensional compression method, which was a considerable improvement (25%) on the two-dimensional method for large three-dimensional series (CASI, MODIS). However, the two methods behave similarly in the Landsat case. Finally, the parallel solution in a distributed environment demonstrates that high performance computing offers a suitable scientific platform for highly demanding time execution applications, such as geostatistical analyses of remote sensing images. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
Zabala A., Riverola A., Serral I., Diaz P., Lush V., Maso J., Pons X., Habermann T. (2013) Rubric-Q: Adding quality-related elements to the GEOSS clearinghouse datasets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6: 1676-1687.EnllaçDoi: 10.1109/JSTARS.2013.2259580
Geospatial data have become a crucial input for the scientific community for understanding the environment and developing environmental management policies. The Global Earth Observation System of Systems (GEOSS) Clearinghouse is a catalogue and search engine that provides access to the Earth Observation metadata. However, metadata are often not easily understood by users, especially when presented in ISO XML encoding. Data quality included in the metadata is basic for users to select datasets suitable for them. This work aims to help users to understand the quality information held in metadata records and to provide the results to geospatial users in an understandable and comparable way. Thus, we have developed an enhanced tool (Rubric-Q) for visually assessing the metadata quality information and quantifying the degree of metadata population. Rubric-Q is an extension of a previous NOAA Rubric tool used as a metadata training and improvement instrument. The paper also presents a thorough assessment of the quality information by applying the Rubric-Q to all dataset metadata records available in the GEOSS Clearinghouse. The results reveal that just 8.7% of the datasets have some quality element described in the metadata, 63.4% have some lineage element documented, and merely 1.2% has some usage element described. © 2013 IEEE.
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