<?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><authors><author><style face="normal" font="default" size="100%">Quintano, C.</style></author><author><style face="normal" font="default" size="100%">Fernández-Manso, A.</style></author><author><style face="normal" font="default" size="100%">Calvo, L.</style></author><author><style face="normal" font="default" size="100%">Marcos, E.</style></author><author><style face="normal" font="default" size="100%">Valbuena, L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Land surface temperature as potential indicator of burn severity in forest Mediterranean ecosystems</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Applied Earth Observation and Geoinformation</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">burn severity</style></keyword><keyword><style  face="normal" font="default" size="100%">Composite Burn Index (CBI)</style></keyword><keyword><style  face="normal" font="default" size="100%">Land surface temperature (LST)</style></keyword><keyword><style  face="normal" font="default" size="100%">Landsat</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Elsevier B.V.</style></publisher><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">1-12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Forest fires are one of the most important causes of environmental alteration in Mediterranean countries. Discrimination of different degrees of burn severity is critical for improving management of fire-affected areas. This paper aims to evaluate the usefulness of land surface temperature (LST) as potential indicator of burn severity. We used a large convention-dominated wildfire, which occurred on 19–21 September, 2012 in Northwestern Spain. From this area, a 1-year series of six LST images were generated from Landsat 7 Enhanced Thematic Mapper (ETM+) data using a single channel algorithm. Further, the Composite Burn Index (CBI) was measured in 111 field plots to identify the burn severity level (low, moderate, and high). Evaluation of the potential relationship between post-fire LST and ground measured CBI was performed by both correlation analysis and regression models. Correlation coefficients were higher in the immediate post-fire LST images, but decreased during the fall of 2012 and increased again with a second maximum value in summer, 2013. A linear regression model between post-fire LST and CBI allowed us to represent spatially predicted CBI (R-squaredadj &gt; 85%). After performing an analysis of variance (ANOVA) between post-fire LST and CBI, a Fisher’s least significant difference test determined that two burn severity levels (low-moderate and high) could be statistically distinguished. The identification of such burn severity levels is sufficient and useful to forest managers. We conclude that summer post-fire LST from moderate resolution satellite data may be considered as a valuable indicator of burn severity for large fires in Mediterranean forest ecosytems.</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%">Senf, Cornelius</style></author><author><style face="normal" font="default" size="100%">Leitão, Pedro J.</style></author><author><style face="normal" font="default" size="100%">Pflugmacher, Dirk</style></author><author><style face="normal" font="default" size="100%">van der Linden, Sebastian</style></author><author><style face="normal" font="default" size="100%">Hostert, Patrick</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery</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%">image classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Landsat</style></keyword><keyword><style  face="normal" font="default" size="100%">Mediterranean</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenology</style></keyword><keyword><style  face="normal" font="default" size="100%">Pseudo-steppe</style></keyword><keyword><style  face="normal" font="default" size="100%">STARFM</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">156</style></volume><pages><style face="normal" font="default" size="100%">527-536</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Low-intensity farming systems are of great importance for biodiversity in Europe, but they are often affected by soil degradation or economic pressure, leading to either land abandonment or intensification of agriculture. These changes in land use influence local biodiversity patterns and require annual monitoring of land cover. To accuratelymap landcover in such spatio-temporal complex landscapes, it isimportant to capture their phenolog- ical dynamics andfine spatial heterogeneity.Multi-seasonal analyses using optical sensorswith amediumspatial resolution from10 to 60m(e.g. Landsat) have been used for this task, but data availability can be scarce due to cloud cover, sub-optimal acquisition schedules and data archive access restrictions. Combining coarse spatial res- olution data fromtheMODerate-resolution Imaging Spectroradiometer(MODIS) and Landsat provides opportu- nities to close these gaps by simulating Landsat-like images atMODIS temporal resolution. In this study,we test whether and by what degree land cover maps of complex Mediterranean landscapes improve by integrating multi-seasonal Landsat imagery, as well aswhether STARFM-simulated imagery can be usedwhenever original multi-seasonal Landsat observations are unavailable. Therefore, we develop different classification scenarios based on seasonally varying data availability and based on original and simulated Landsat data. Results show that multi-seasonal Landsat data from spring and early autumn are crucial for achieving satisfying mapping accuracies (overall accuracy 74.5%). Using synthetic Landsat imagery increases classification accuracy compared to using single-date Landsat data, but accuracieswere never as good as a classification based on original data.We conclude thatmulti-seasonal data is essential for mapping complex Mediterranean landscapes and that STARFM can be used to compensate for missing Landsat observations. However, if Landsat data availability is sufficient to cover all phenologically important dates, we suggest relying solely on Landsat.</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%">Vlassova, Lidia</style></author><author><style face="normal" font="default" size="100%">Perez-Cabello, Fernando</style></author><author><style face="normal" font="default" size="100%">Nieto, Hector</style></author><author><style face="normal" font="default" size="100%">Martín, Pilar</style></author><author><style face="normal" font="default" size="100%">Riaño, David</style></author><author><style face="normal" font="default" size="100%">de la Riva, Juan</style></author></authors></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><keywords><keyword><style  face="normal" font="default" size="100%">land surface temperature</style></keyword><keyword><style  face="normal" font="default" size="100%">Landsat</style></keyword><keyword><style  face="normal" font="default" size="100%">multitemporal</style></keyword></keywords><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><authors><author><style face="normal" font="default" size="100%">Pons, X</style></author><author><style face="normal" font="default" size="100%">Pesquer, L</style></author><author><style face="normal" font="default" size="100%">Cristóbal, J</style></author><author><style face="normal" font="default" size="100%">González-Guerrero, O</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Applied Earth Observation and Geoinformation</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Landsat</style></keyword><keyword><style  face="normal" font="default" size="100%">MODIS</style></keyword><keyword><style  face="normal" font="default" size="100%">Pseudoinvariant area</style></keyword><keyword><style  face="normal" font="default" size="100%">Radiometric correction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">243-254</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Radiometric correction is a prerequisite for generating high-quality scientific data, making it possible to discriminate between product artefacts and real changes in Earth processes as well as accurately produce land cover maps and detect changes. This work contributes to the automatic generation of surface reflectance products for Landsat satellite series. Surface reflectances are generated by a new approach developed from a previous simplified radiometric (atmospheric+topographic) correction model. The proposed model keeps the core of the old model (incidence angles and cast-shadows through a digital elevation model [DEM], Earth–Sun distance, etc.) and adds new characteristics to enhance and automatize ground reflectance retrieval. The new model includes the following new features: (1) A fitting model based on reference values from pseudoinvariant areas that have been automatically extracted from existing reflectance products (Terra MODIS MOD09GA) that were selected also automatically by applying quality criteria that include a geostatistical pattern model. This guarantees the consistency of the internal and external 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 detailed DEM and MODTRAN simulations. (3) It is designed so that large time-series of images can be processed automatically to produce consistent Landsat surface reflectance time-series. (4) The approach can handle most images, acquired now or in the past, regardless of the processing system, with the exception of those with extremely high cloud coverage. The new methodology has been successfully applied to a series of near 300 images of the same area including MSS, TM and ETM+ imagery as well as to different formats and processing systems (LPGS and NLAPS from the USGS; CEOS from ESA) for different degrees of cloud coverage (up to 60%) and SLC-off. Reflectance products have been validated with some example applications: time series robustness (for a pixel in a pseudoinvariant area, deviations are only 1.04% on average along the series), spectral signatures generation (visually coherent with the MODIS ones, but more similar between dates), and classification (up to 4 percent points better than those obtained with the original manual method or the CDR products). In conclusion, this new approach, that could also be applied to other sensors with similar band configurations, offers a fully automatic and reasonably good procedure for the new era of long time-series of spatially detailed global remote sensing data.</style></abstract></record></records></xml>