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Abstract Filtering approaches on Global Ecosystem Dynamics Investigation (GEDI) data differ considerably across existing studies and it is yet unclear which method is the most effective. We conducted an in‐depth analysis of GEDI's vertical accuracy in mapping terrain and canopy heights across three study sites in temperate forests and grasslands in Spain, California, and New Zealand. We started with unfiltered data (2,081,108 footprints) and describe a workflow for data filtering using Level 2A parameters and for geolocation error mitigation. We found that retaining observations with at least one detected mode eliminates noise more effectively than sensitivity. The accuracy of terrain and canopy height observations depended considerably on the number of modes, beam sensitivity, landcover, and terrain slope. In dense forests, a minimum sensitivity of 0.9 was required, while in areas with sparse vegetation, sensitivity of 0.5 sufficed. Sensitivity greater than 0.9 resulted in an overestimation of canopy height in grasslands, especially on steep slopes, where high sensitivity led to the detection of multiple modes. We suggest excluding observations with more than five modes in grasslands. We found that the most effective strategy for filtering low‐quality observations was to combine the quality flag and difference from TanDEM‐X, striking an optimal balance between eliminating poor‐quality data and preserving a maximum number of high‐quality observations. Positional shifts improved the accuracy of GEDI terrain estimates but not of vegetation height estimates. Our findings guide users to an easy way of processing of GEDI footprints, enabling the use of the most accurate data and leading to more reliable applications.

Abstract Global mapping of forest height is an extremely important task for estimating habitat quality and modeling biodiversity. Recently, three global canopy height maps have been released, the global forest canopy height map (GFCH), the high‐resolution canopy height model of the Earth (HRCH), and the global map of tree canopy height (GMTCH). Here, we assessed their accuracy and usability for biodiversity modeling. We examined their accuracy by comparing them with the reference canopy height models derived from airborne laser scanning (ALS). Our results show considerable differences between the evaluated maps. The root mean square error ranged between 10 and 18 m for GFCH, 9–11 m for HRCH, and 10–17 m for GMTCH, respectively. GFCH and GMTCH consistently underestimated the height of all canopies regardless of their height, while HRCH tended to overestimate the height of low canopies and underestimate tall canopies. Biodiversity models using predicted global canopy height maps as input data are sufficient for estimating simple relationships between species occurrence and canopy height, but their use leads to a considerable decrease in the discrimination ability of the models and to mischaracterization of species niches where derived indices (e.g., canopy height heterogeneity) are concerned. We showed that canopy height heterogeneity is considerably underestimated in the evaluated global canopy height maps. We urge that for temperate areas rich in ALS data, activities should concentrate on harmonizing ALS canopy height maps rather than relying on modeled global products.

Abstract Improved phenotyping in pneumonia is necessary to strengthen risk assessment. Via a feasible and multidimensional approach with basic parameters, we aimed to evaluate the effect of host response at admission on severity stratification in COVID-19 and community-acquired pneumonia (CAP). Three COVID-19 and one CAP multicenter cohorts including hospitalized patients were recruited. Three easily available variables reflecting different pathophysiologic mechanisms—immune, inflammation, and respiratory—were selected (absolute lymphocyte count [ALC], C-reactive protein [CRP] and, SpO2/FiO2). In-hospital mortality and intensive care unit (ICU) admission were analyzed as outcomes. A multivariable, penalized maximum likelihood logistic regression was performed with ALC ( 60 mg/L), and, SpO2/FiO2 (

Globally, there is a significant trend in the loss of native forests, including those of the Polylepis genus, which are essential for soil conservation across the Andes Mountain range. These forests play a critical role in regulating water flow, promoting soil regeneration, and retaining essential nutrients and sediments, thereby contributing to the soil conservation of the region. In Ecuador, these forests are often fragmented and isolated in areas of high cloud cover, making it difficult to use remote sensing and spectral vegetation indices to detect this forest species. This study developed twelve scenarios using medium- and high-resolution satellite data, integrating datasets such as Sentinel-2 and PlanetScope (optical), Sentinel-1 (radar), and the Sigtierras project topographic data. The scenarios were categorized into two groups: SC1–SC6, combining 5 m resolution data, and SC7–SC12, combining 10 m resolution data. Additionally, each scenario was tested with two target types: multiclass (distinguishing Polylepis stands, native forest, Pine, Shrub vegetation, and other classes) and binary (distinguishing Polylepis from non-Polylepis). The Recursive Feature Elimination technique was employed to identify the most effective variables for each scenario. This process reduced the number of variables by selecting those with high importance according to a Random Forest model, using accuracy and Kappa values as criteria. Finally, the scenario that presented the highest reliability was SC10 (Sentinel-2 and Topography) with a pixel size of 10 m in a multiclass target, achieving an accuracy of 0.91 and a Kappa coefficient of 0.80. For the Polylepis class, the User Accuracy and Producer Accuracy were 0.90 and 0.89, respectively. The findings confirm that, despite the limited area of the Polylepis stands, integrating topographic and spectral variables at a 10 m pixel resolution improves detection accuracy.

Este artículo cuestiona uno de los principales axiomas de la guerra civil española, que Franco deliberadamente condujo una guerra lenta con dos objetivos: implementar un minucioso proceso represivo para eliminar más efectivamente a la anti-España; y consolidarse en el poder. Varios de los principales historiadores de la guerra civil han defendido esta idea, siendo reproducida en sucesivos trabajos. Nuestro objetivo es cuestionar esa interpretación mediante el estudio de la documentación militar del ejército sublevado, lo que permitirá incorporar un enfoque bélico que no se ha tenido en cuenta hasta ahora, al menos extensiva y sistemáticamente. Con ello, buscamos poner en relación lo militar con lo político, lo social y lo simbólico, considerando todas estas dimensiones como igualmente relevantes para entender cómo se construyó el franquismo. Por último, queremos también subrayar la importancia que ha tenido la renovación de los estudios de la guerra en ese proceso de actualización de la historiografía sobre la guerra civil.

Unmanned aerial vehicles (UAVs) equipped with RGB, multispectral, or thermal cameras have demonstrated their potential to provide high-resolution data before, during, and after wildfires and prescribed burns. Pre-burn point clouds generated through the photogrammetric processing of UAV images contain geometrical and spectral information of vegetation, while active fire imagery allows for deriving fire behavior metrics. This paper focuses on characterizing the relationship between the fire rate of spread (RoS) in prescribed burns and a set of independent geometrical, spectral, and neighborhood variables extracted from UAV-derived point clouds. For this purpose, different flights were performed before and during the prescribed burning in seven grasslands and open forest plots. Variables extracted from the point cloud were interpolated to a grid, which was sized according to the RoS semivariogram. Random Forest regressions were applied, obtaining up to 0.56 of R2 in the different plots studied. Geometric variables from the point clouds, such as planarity and the spectral normalized blue–red difference index (NBRDI), are related to fire RoS. In analyzing the results, the minimum value of the eigenentropy (Eigenentropy_MIN), the mean value of the planarity (Planarity_MEAN), and percentile 75 of the NBRDI (NBRDI_P75) obtained the highest feature importance. Plot-specific analyses unveiled distinct combinations of geometric and spectral features, although certain features, such as Planarity_MEAN and the mean value of the grid obtained from the standard deviation of the distance between points (Dist_std_MEAN), consistently held high importance across all plots. The relationships between pre-burning UAV data and fire RoS can complement meteorological and topographic variables, enhancing wildfire and prescribed burn models.

IntroductionThere are no data on the association of type of pneumonia and long-term mortality by the type of pneumonia (COVID-19 or community-acquired pneumonia [CAP]) on long-term mortality after an adjustment for potential confounding variables. We aimed to assess the type of pneumonia and risk factors for long-term mortality in patients who were hospitalized in conventional ward and later discharged.MethodsRetrospective analysis of two prospective and multicentre cohorts of hospitalized patients with COVID-19 and CAP. The main outcome under study was 1-year mortality in hospitalized patients in conventional ward and later discharged. We adjusted a Bayesian logistic regression model to assess associations between the type of pneumonia and 1-year mortality controlling for confounders.ResultsThe study included a total of 1,693 and 2,374 discharged patients in the COVID-19 and CAP cohorts, respectively. Of these, 1,525 (90.1%) and 2,249 (95%) patients underwent analysis. Until 1-year follow-up, 69 (4.5%) and 148 (6.6%) patients from the COVID-19 and CAP cohorts, respectively, died (p = 0.008). However, the Bayesian model showed a low probability of effect (PE) of finding relevant differences in long-term mortality between CAP and COVID-19 (odds ratio 1.127, 95% credibility interval 0.862–1.591; PE = 0.774).ConclusionCOVID-19 and CAP have similar long-term mortality after adjusting for potential confounders.

Crop yield prediction at plot scale is a vitally important magnitude for farmers at the socio-economic level. This study aims to quantify rice yield using phenological metrics from a normalized difference vegetation index (NDVI) time series derived from Sentinel-2 imagery, with yield data collected from 32 plots with an area of 36 ha in the Ferreñafe District of the Lambayeque region, Peru. Three different rice yield models were obtained, the best linear regression models were obtained for the SVM classification, with R2 of 0.69, MAE = 1.01 and RMSE = 1.23 t ha−1; and MRL with R2 of 0.61, MAE = 1.10 and RMSE = 1.38 t ha−1; RF with R2 of 0.44, MAE = 1.23 and RMSE = 1.66 t ha−1. The models obtained open the possibility to generate more robust models using a larger number of samples, which would be useful for farmers as well as for management and planning decisions for food and economic security.

The inclusion of technological innovation and the development of remote sensing tools in wine production are an efficient and productive factor that supports the production and improves the quality of the wine produced. In this study we explored models based on Sentinel-2 image bands and spectral indices to estimate key wine quality variables, such as phenols (TP), anthocyanins (TA) and color intensity (CI), providing different sensory characteristics of wine. Two Cabernet Sauvignon wine harvest seasons were studied, 2017 and 2018, and models with coefficients of determination (R2) higher than 60% were obtained for color intensity and total anthocyanins during the first season, both in a period very close to harvest during the first days of April, so the high periodicity of Sentinel 2 becomes strategic. In addition, homogeneous sectors can be identified in the plots for selective harvesting and thus the winery space can be programmed appropriately. These results suggest further work on the number of samples in order to transform it into a useful tool with the potential to define a differentiated harvest and estimate the accumulation of phenolic compounds and the intensity of wine color, key elements in the final quality of the wine.

Live fuel moisture content (LFMC) is an input factor in fire behavior simulation models highly contributing to fire ignition and propagation. Developing models capable of accurately estimating spatio-temporal changes of LFMC in different forest species is needed for wildfire risk assessment. In this paper, an empirical model based on multivariate linear regression was constructed for the forest cover classified as shrublands in the central part of the Valencian region in the Eastern Mediterranean of Spain in the fire season. A sample of 15 non-monospecific shrubland sites was used to obtain a spatial representation of this type of forest cover in that area. A prediction model was created by combining spectral indices and meteorological variables. This study demonstrates that the Normalized Difference Moisture Index (NDMI) extracted from Sentinel-2 images and meteorological variables (mean surface temperature and mean wind speed) are a promising combination to derive cost-effective LFMC estimation models. The relationships between LFMC and spectral indices for all sites improved after using an additive site-specific index based on satellite information, reaching a R2adj = 0.70, RMSE = 8.13%, and MAE = 6.33% when predicting the average of LFMC weighted by the canopy cover fraction of each species of all shrub species present in each sampling plot.

To monitor the ripeness and composition of wine grape berries and establish an optimal harvest date, the determination of °Brix and pH is vital. This research studies two harvest seasons of Cabernet Sauvignon wine grapes: 2017 and 2018. Field data were periodically collected to follow the phenological state of the fruits. In parallel, eight bands and four spectral indices from Sentinel-2 image time series were used, which are directly related to the foliage properties and activity, and indirectly to the fruit evolution. They were related to the variables measured from field samples: °Brix and pH. The °Brix models obtained with the spectral indices presented an R2 of 69% and 73% in the 2017 and 2018 seasons, respectively. In pH modeling, the 2017 season had low R2 results, reaching 43%, improving considerably in the 2018 season, reaching 63.8%. Estimated Brix and pH maps were obtained, expressing the spatial variability in the evolution of the fruit, which is useful for zoning the plots and to improve the sampling task prior to harvest. They are therefore a valuable tool to monitor the maturation, to improve the efficiency of harvest and subsequently, the quality of the wine.

Abstract Background Limited data are available regarding fit and healthy patients with pneumonia at different ages. We evaluated the association of age with clinical presentation, serotype and outcomes among healthy and well-functioning patients hospitalized for bacteremic pneumococcal community–acquired pneumonia. Methods We performed a prospective cohort study of consecutive healthy and well-functioning patients hospitalized for this type of pneumonia. Patients were stratified into younger (18 to 64 years) and older (≥65 years) groups. Results During the study period, 399 consecutive patients were hospitalized with bacteremic pneumococcal pneumonia. We included 203 (50.8%) patients who were healthy and well-functioning patients, of whom 71 (35%) were classified as older. No differences were found in antibiotic treatment, treatment failure rate, antibiotic resistance, or serotype, except for serotype 7F that was less common in older patients. In the adjusted multivariate analysis, the older patients had higher 30-day mortality (OR 6.83; 95% CI 1.22–38.22; P = 0.028), but were less likely to be admitted to the ICU (OR 0.14; 95% CI 0.05–0.39; P

En el presente estudio se aborda la problemática de la arqueología de la Guerra Civil en Toledo. La ingente bibliografía histórica se ha centrado prácticamente en exclusiva en el estudio del episodio del Alcázar, olvidando la constitución de un frente al Sur del río Tajo, uno de los más importantes de la contienda española. Los trabajos de prospección se han centrado en la documentación exhaustiva de este Frente Sur del Tajo como una sola unidad, incluyendo todos los sistemas de fortificación. Por otro lado, el estudio de la cultura material recogida permite asignar las posiciones a uno y otro bando, ya que no se trató de un frente estable, sino que se vivieron diferentes episodios de cambios de posiciones entre el inicio y el final de la contienda.

The rise of the student as prosumer (producer-consumer) of educational content is a novel development that has hitherto been the subject of very little research, especially in relation to the generation of digital contents and materials for online and hybrid education in particular. This article analyses whether there are patterns of behaviour and different perceptions associated with different groups of students in their role as producers and/or users in the field of active learning in hybrid university education systems. To this end, the research has been conducted with a group of engineering students at one of the largest blended learning universities in Europe. The results indicate higher levels of involvement in, and appreciation of, the experience in content producers compared with mere content consumers, but the students' environment and personal attitudes (such as their availability and degree of professional dedication) in relation to this type of education, the profile of which is often quite distinct from that of traditional learning, may differentiate their interest and appreciation of these activities, which are more creative and probably more demanding.

Manifold socio-economic processes shape the built and natural elements in urban areas. They thus influence both the living environment of urban dwellers and sustainability in many dimensions. Monitoring the development of the urban fabric and its relationships with socio-economic and environmental processes will help to elucidate their linkages and, thus, aid in the development of new strategies for more sustainable development. In this study, we identified empirical and significant relationships between income, inequality, GDP, air pollution and employment indicators and their change over time with the spatial organization of the built and natural elements in functional urban areas. We were able to demonstrate this in 32 countries using spatio-temporal metrics, using geoinformation from databases available worldwide. We employed random forest regression, and we were able to explain 32% to 68% of the variability of socio-economic variables. This confirms that spatial patterns and their change are linked to socio-economic indicators. We also identified the spatio-temporal metrics that were more relevant in the models: we found that urban compactness, concentration degree, the dispersion index, the densification of built-up growth, accessibility and land-use/land-cover density and change could be used as proxies for some socio-economic indicators. This study is a first and fundamental step for the identification of such relationships at a global scale. The proposed methodology is highly versatile, the inclusion of new datasets is straightforward, and the increasing availability of multi-temporal geospatial and socio-economic databases is expected to empirically boost the study of these relationships from a multi-temporal perspective in the near future.

In the last decade, full-waveform airborne laser scanning (ALSFW) has proven to be a promising tool for forestry applications. Compared to traditional discrete airborne laser scanning (ALSD), it is capable of registering the complete signal going through the different vertical layers of the vegetation, allowing for a better characterization of the forest structure. However, there is a lack of ALSFW software tools for taking greater advantage of these data. Additionally, most of the existing software tools do not include radiometric correction, which is essential for the use of ALSFW data, since extracted metrics depend on radiometric values. This paper describes and presents a software tool named WoLFeX for clipping, radiometrically correcting, voxelizing the waves, and extracting object-oriented metrics from ALSFW data. Moreover, extracted metrics can be used as input for generating either classification or regression models for forestry, ecology, and fire sciences applications. An example application of WoLFeX was carried out to test the influence of the relative radiometric correction and the acquisition scan angle (1) on the ALSFW metric return waveform energy (RWE) values, and (2) on the estimation of three forest fuel variables (CFL: canopy fuel load, CH: canopy height, and CBH: canopy base height). Results show that radiometric differences in RWE values computed from different scan angle intervals (0°−5° and 15°−20°) were reduced, but not removed, when the relative radiometric correction was applied. Additionally, the estimation of height variables (i.e., CH and CBH) was not strongly influenced by the relative radiometric correction, while the model obtained for CFL improved from R2 = 0.62 up to R2 = 0.79 after applying the correction. These results show the significance of the relative radiometric correction for reducing radiometric differences measured from different scan angles and for modelling some stand-level forest fuel variables.

Información sobre los créditos, índice y editorial del Nº 46 de Revista de Teledetección [RAET]

In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.

An archaeological work that implied the clear of a great extension of land, permitted to document in Villaverde Madrid, for the first time in the Iberian Peninsula the existence of a habitat formed for two large hut or long houses similar to the longhouses of Central Europe. This fact opens new perspectives for the study of the settlement at the start of the Iron Age in the Middle Tajo Valley, moment that the analyses of C14 permit us to carry to the full century IX b.C. Among the materials found emphasizes a graphite that could be interpreted as copy of a Phoenician letter, which, next to other elements of oriental inspiration, seem to indicate us the existence of a commercial traffic already very flowed, in whose circuits begins to verify in some way, the Phoenician presence in the peninsular south.

Multi-temporal analysis is one of the main applications of remote sensing, and Landsat imagery has been one of the main resources for many years. However, the moderate spatial resolution (30 m) restricts their use for high precision applications. In this paper, we simulate Landsat scenes to evaluate, by means of an exhaustive number of tests, a subpixel registration process based on phase correlation and the upsampling of the Fourier transform. From a high resolution image (0.5 m), two sets of 121 synthetic images of fixed translations are created to simulate Landsat scenes (30 m). In this sense, the use of the point spread function (PSF) of the Landsat TM (Thematic Mapper) sensor in the downsampling process improves the results compared to those obtained by simple averaging. In the process of obtaining sub-pixel accuracy by upsampling the cross correlation matrix by a certain factor, the limit of improvement is achieved at 0.1 pixels. We show that image size affects the cross correlation results, but for images equal or larger than 100 × 100 pixels similar accuracies are expected. The large dataset used in the tests allows us to describe the intra-pixel distribution of the errors obtained in the registration process and how they follow a waveform instead of random/stochastic behavior. The amplitude of this waveform, representing the highest expected error, is estimated at 1.88 m. Finally, a validation test is performed over a set of sub-pixel shorelines obtained from actual Landsat-5 TM, Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI (Operation Land Imager) scenes. The evaluation of the shoreline accuracy with respect to permanent seawalls, before and after the registration, shows the importance of the registering process and serves as a non-synthetic validation test that reinforce previous results.

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