<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-Grass Ecosystem Modeling</style></title><secondary-title><style face="normal" font="default" size="100%">Remote Sensing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">4345-4368</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global change) and FLUXPEC (Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean ―dehesa‖ ecosystem) projects LST retrieved from Landsat data is required to integrate ground-based observations of energy, water, and carbon fluxes with multi-scale remotely-sensed data and assess water and carbon balance in ecologically fragile heterogeneous ecosystem of Mediterranean wooded grassland (dehesa). Thus, three methods based on the Radiative Transfer Equation were used to extract LST from a series of 2009–2011 Landsat-5 TM images to assess the applicability for temperature input generation to a Landsat-MODIS LST integration. When compared to surface temperatures simulated using MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) with atmospheric profiles inputs (LSTref), values from Single-Channel (SC) algorithm are the closest (root-mean-square deviation (RMSD) = 0.50 °C); procedure based on the online Radiative Transfer Equation Atmospheric Correction Parameters Calculator (RTE-ACPC) shows RMSD = 0.85 ° C; Mono-Window algorithm (MW) presents the highest RMSD (2.34 °C) with systematical LST underestimation (bias = 1.81 ° C). Differences between Landsat-retrieved LST and MODIS LST are in the range of 2 to 4 ° C and can be explained mainly by differences in observation geometry, emissivity, and time mismatch between Landsat and MODIS overpasses. There is a seasonal bias in Landsat-MODIS LST differences due to greater variations in surface emissivity and thermal contrasts between landcover components.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Understanding the optical responses of leaf nitrogen in Mediterranean Holm oak (Quercus ilex) using field spectroscopy</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Applied Earth Observation and Geoinformation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">105-118</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The direct estimation of nitrogen (N) in fresh vegetation is challenging due to its weak influence on leaf reflectance and the overlaps with absorption features of other compounds. Different empirical models relate in this work leaf nitrogen concentration ([N]Leaf) on Holm oak to leaf reflectance as well as derived spectral indices such as normalized difference indices (NDIs), the three bands indices (TBIs) and indices previously used to predict leaf N and chlorophyll. The models were calibrated and assessed their accuracy, robustness and the strength of relationship when other biochemicals were considered. Red edge was the spectral region most strongly correlated with [N]Leaf, whereas most of the published spectral indexes did not provide accurate estimations. NDIs and TBIs based models could achieve robust and acceptable accuracies (TBI1310,1720,730: R2 = 0.76, [0.64,0.86]; RMSE (%) = 9.36, [7.04,12.83]). These models sometimes included indices with bands close to absorption features of N bonds or nitrogenous compounds, but also of other biochemicals. Models were independently and inter-annually validated using the bootstrap method, which allowed discarding those models non-robust across different years. Partial correlation analysis revealed that spectral estimators did not strongly respond to [N]Leaf but to other leaf variables such as chlorophyll and water, even if bands close to absorption features of N bonds or compounds were present in the models.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">García, Mariano</style></author><author><style face="normal" font="default" size="100%">Riaño, David</style></author><author><style face="normal" font="default" size="100%">Chuvieco, Emilio</style></author><author><style face="normal" font="default" size="100%">Salas, Javier</style></author><author><style face="normal" font="default" size="100%">Danson, F. Mark</style></author><author><style face="normal" font="default" size="100%">García, Mariano</style></author><author><style face="normal" font="default" size="100%">Riaño, David</style></author><author><style face="normal" font="default" size="100%">Chuvieco, Emilio</style></author><author><style face="normal" font="default" size="100%">Salas, Javier</style></author><author><style face="normal" font="default" size="100%">Danson, F. Mark</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multispectral and LiDAR data fusion for fuel type mapping using Support Vector Machine and decision rules</style></title><secondary-title><style face="normal" font="default" size="100%">REMOTE SENSING OF ENVIRONMENT</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Data fusion</style></keyword><keyword><style  face="normal" font="default" size="100%">Decision rules</style></keyword><keyword><style  face="normal" font="default" size="100%">Fuel types</style></keyword><keyword><style  face="normal" font="default" size="100%">LiDAR</style></keyword><keyword><style  face="normal" font="default" size="100%">Prometheus Classification System</style></keyword><keyword><style  face="normal" font="default" size="100%">Support Vector Machine</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011///</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1016/j.rse.2011.01.017</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">115</style></volume><pages><style face="normal" font="default" size="100%">1369 - 1379</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents a method for mapping fuel types using LiDAR and multispectral data. A two-phase classification method is proposed to discriminate the fuel classes of the Prometheus classification system, which is adapted to the ecological characteristics of the European Mediterranean basin. The first step mapped the main fuel groups, namely grass, shrub and tree, as well as non-fuel classes. This phase was carried out using a Support Vector Machine (SVM) classification combining LiDAR and multispectral data. The overall accuracy of this classification was 92.8% with a kappa coefficient of 0.9. The second phase of the proposed method focused on discriminating additional fuel categories based on vertical information provided by the LiDAR measurements. Decision rules were applied to the output of the SVM classification based on the mean height of LiDAR returns and the vertical distribution of fuels, described by the relative LiDAR point density in different height intervals. The final fuel type classification yielded an overall accuracy of 88.24% with a kappa coefficient of 0.86. Some confusion was observed between fuel types 7 (dense tree cover presenting vertical continuity with understory vegetation) and 5 (trees with less than 30% of shrub cover) in some areas covered by Holm oak, which showed low LiDAR pulses penetration so that the understory vegetation was not correctly sampled. (C) 2011 Elsevier Inc. All rights reserved.</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><notes><style face="normal" font="default" size="100%">The following values have no corresponding Zotero field:&lt;br/&gt;pub-location: 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA&lt;br/&gt;publisher: ELSEVIER SCIENCE INC</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">García, Mariano</style></author><author><style face="normal" font="default" size="100%">Riaño, David</style></author><author><style face="normal" font="default" size="100%">Chuvieco, Emilio</style></author><author><style face="normal" font="default" size="100%">Danson, F. Mark</style></author><author><style face="normal" font="default" size="100%">García, Mariano</style></author><author><style face="normal" font="default" size="100%">Riaño, David</style></author><author><style face="normal" font="default" size="100%">Chuvieco, Emilio</style></author><author><style face="normal" font="default" size="100%">Danson, F. Mark</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data</style></title><secondary-title><style face="normal" font="default" size="100%">REMOTE SENSING OF ENVIRONMENT</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biomass fractions</style></keyword><keyword><style  face="normal" font="default" size="100%">Carbon content</style></keyword><keyword><style  face="normal" font="default" size="100%">Intensity</style></keyword><keyword><style  face="normal" font="default" size="100%">LiDAR</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010///</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1016/j.rse.2009.11.021</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">114</style></volume><pages><style face="normal" font="default" size="100%">816 - 830</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Biomass fractions (total aboveground, branches and foliage) were estimated from a small footprint discrete-return LiDAR system in an unmanaged Mediterranean forest in central Spain. Several biomass estimation models based on LiDAR height, intensity or height combined with intensity data were explored. Raw intensity data were normalized to a standard range in order to remove the range dependence of the intensity signal. In general terms, intensity-based models provided more accurate predictions of the biomass fractions. Height models selected were mainly based on a percentile of the height distribution. Intensity models selected included variables that consider the percentage of the intensity accumulated at different height percentiles, which implicitly take into account the height distribution. The general models derived considering all species together were based on height combined with intensity data. These models yielded R(2) values greater than 0.58 for the different biomass fractions considered and RMSE values of 28.89, 18.28 and 1.51 Mg ha(-1) for aboveground, branch and foliage biomass, respectively. Results greatly improved for species-specific models using the main species present in each plot, with R(2) values greater than 0.85, 0.70 and 0.90 for black pine, Spanish juniper and Holm oak, respectively, and with lower RMSE for the biomass fractions. Reductions in WAR point density had only a small effect on the results obtained, except for those models based on a variation of the Canopy Reflection Sum, which was weighted by the mean point density. Based on the species-specific equations derived, Holm oak dominated plots showed the highest average carbon contained by aboveground biomass and branch biomass 44.66 and 31.42 Mg ha(-1) respectively, while for foliage biomass carbon, Spanish juniper showed the highest average value (3.04 Mg ha(-1)). (C) 2009 Elsevier Inc. All rights reserved.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><notes><style face="normal" font="default" size="100%">The following values have no corresponding Zotero field:&lt;br/&gt;pub-location: 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA&lt;br/&gt;publisher: ELSEVIER SCIENCE INC</style></notes></record></records></xml>