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Abstract Background Transfers of nursing home (NH) residents to the emergency department (ED) is frequent. Our main objective was to assess the cost of care pathways 6 months before and after the transfer to the emergency department among NH residents, according to the type of transfer (i.e. appropriate or inappropriate). Methods This was a part of an observational, multicenter, case-control study: the Factors associated with INappropriate transfer to the Emergency department among nursing home residents (FINE) study. Sixteen public hospitals of the former Midi-Pyrénées region participated in recruitment, in 2016. During the inclusion period, all NH residents arriving at the ED were included. A pluri-disciplinary team categorized each transfer to the ED into 2 groups: appropriate or inappropriate. Direct medical and nonmedical costs were assessed from the French Health Insurance (FHI) perspective. Healthcare resources were retrospectively gathered from the FHI database and valued using the tariffs reimbursed by the FHI. Costs were recorded over a 6-month period before and after transfer to the ED. Other variables were used for analysis: sex, age, Charlson score, season, death and presence inside the NH of a coordinating physician or a geriatric nursing assistant. Results Among the 1037 patients initially included in the FINE study, 616 who were listed in the FHI database were included in this economic study. Among them, 132 (21.4%) had an inappropriate transfer to the ED. In the 6 months before ED transfer, total direct costs on average amounted to 8,145€ vs. 6,493€ in the inappropriate and appropriate transfer groups, respectively. In the 6 months after ED transfer, they amounted on average to 9,050€ vs. 12,094€. Conclusions Total costs on average are higher after transfer to the ED, but there is no significant increase in healthcare expenditure with inappropriate ED transfer. Support for NH staff and better pathways of care could be necessary to reduce healthcare expenditures in NH residents. Trial registration clinicaltrials.gov, NCT02677272.

Relevant studies in the fields of fire safety, occupational health and firefighter clothing were analyzed in this study to address the high injury rate among firefighters during fire rescue and the complexity of factors affecting skin burns. The findings indicate that the degree of skin burns in firefighters is primarily influenced by the heat source (heat flux, type), firefighter clothing (fabric, pattern) and the under-clothing air gap (thickness, characteristics). Since skin burns cannot be directly measured, internal skin heat transfer and burn prediction models are commonly employed to assess the impact of external factors on skin burns. These models can predict the safe working distance or time for firefighters. Investigating the influencing factors and prediction models of skin burns among firefighters holds significant value in enhancing operating procedures, optimizing firefighter clothing design and effectively preventing skin burns. [ABSTRACT FROM AUTHOR]

This study evaluates and contrasts changes in vegetation cover over three decades in two forest areas in the State of Chihuahua in northern Mexico with different management statuses: one with sustainable forest management and the other protected as a Flora and Fauna Protection Area. The hypothesis proposed that both areas would have maintained or increased their vegetation cover since 1995. Satellite images from the periods 1995–2008, 2008–2014, 2014–2022, and 1995–2022 were analyzed. The results showed that Ejido El Largo y Anexos significantly increased forest areas and reduced grasslands due to sustainable management practices, with a notable expansion of pine–oak and pine forests. In contrast, the Tutuaca Flora and Fauna Protection Area experienced a notable loss of oak and oak–pine forests, suggesting ineffectiveness in its conservation policies. However, there was less loss in Douglas Fir forests, indicating some effective protection efforts. The comparison reveals opposing dynamics: while Ejido El Largo y Anexos demonstrates success in sustainable management, the Tutuaca Flora and Fauna Protection Area faces conservation challenges. In conclusion, this study highlights the need for active management approaches to maintain ecosystem cover and functionality. [ABSTRACT FROM AUTHOR]

Simple Summary: Given its huge impact on global health, lung cancer remains a major diagnostic and therapeutic challenge. However, much has been achieved, and this review reports on recent advances, from the genetic understanding of lung cancer to personalized treatments and targeted therapies based on genetic landscape. Lung cancer, including both non-small cell lung cancer and small cell lung cancer, remains the leading cause of cancer-related mortality worldwide, representing 18% of the total cancer deaths in 2020. Many patients are identified already at an advanced stage with metastatic disease and have a worsening prognosis. Recent advances in the genetic understanding of lung cancer have opened new avenues for personalized treatments and targeted therapies. This review examines the latest discoveries in the genetics of lung cancer, discusses key biomarkers, and analyzes current clinical therapies based on this genetic information. It will conclude with a discussion of future prospects and potential research directions. [ABSTRACT FROM AUTHOR]

In Central Europe, the extent of bark beetle infestation in spruce stands due to prolonged high temperatures and drought has created large areas of dead trees, which are difficult to monitor by ground surveys. Remote sensing is the only possibility for the assessment of the extent of the dead tree areas. Several options exist for mapping individual dead trees, including different sources and different processing techniques. Satellite images, aerial images, and images from UAVs can be used as sources. Machine and deep learning techniques are included in the processing techniques, although models are often presented without proper realistic validation.This paper compares methods of monitoring dead tree areas using three data sources: multispectral aerial imagery, multispectral PlanetScope satellite imagery, and multispectral Sentinel-2 imagery, as well as two processing methods. The classification methods used are Random Forest (RF) and neural network (NN) in two modalities: pixel- and object-based. In total, 12 combinations are presented. The results were evaluated using two types of reference data: accuracy of model on validation data and accuracy on vector-format semi-automatic classification polygons created by a human evaluator, referred to as real Ground Truth. The aerial imagery was found to have the highest model accuracy, with the CNN model achieving up to 98% with object classification. A higher classification accuracy for satellite imagery was achieved by combining pixel classification and the RF model (87% accuracy for Sentinel-2). For PlanetScope Imagery, the best result was 89%, using a combination of CNN and object-based classifications. A comparison with the Ground Truth showed a decrease in the classification accuracy of the aerial imagery to 89% and the classification accuracy of the satellite imagery to around 70%. In conclusion, aerial imagery is the most effective tool for monitoring bark beetle calamity in terms of precision and accuracy, but satellite imagery has the advantage of fast availability and shorter data processing time, together with larger coverage areas. [ABSTRACT FROM AUTHOR]

In contrast to the well-investigated field of Synthetic Aperture Radar (SAR)-to-Optical translation, this study explores the lesser-investigated domain of Optical-to-SAR translation, which is a challenging field due to the ill-posed nature of this translation. The complexity arises as single optical data can have multiple SAR representations based on the SAR viewing geometry. To generate an SAR image with a specific viewing geometry, we propose a novel approach, which is termed SAR Temporal Shifting. Our model takes an optical image from the target timestamp and an SAR image from a different temporal point but with a consistent viewing geometry as the expected SAR image. Both of these inputs are complemented with a change map derived from optical images during the intervening period. This model then modifies the SAR data based on the changes observed in the optical data to generate the SAR data for the desired timestamp. Although similar strategies have been explored in the opposite SAR-to-Optical translation, our approach innovates by introducing new spatial evaluation metrics and cost functions. These metrics reveal that simply adding same-domain data as model input, without accounting for the distribution changes in the dataset, can result in model overfitting—even if traditional metrics suggest positive outcomes. To address this issue, we have introduced a change-weighted loss function that discourages the model from merely replicating input data by assigning greater cost to changes in the areas of interest. Our approach surpasses traditional translation methods by eliminating the Generative Adversarial Network's (GAN's) fiction phenomenon by learning to change the SAR data based on the optical data instead of solely relying on translation. Furthering the field, we have introduced a novel automated framework to build a despeckled multitemporal SAR–Optical dataset with consistent viewing geometry. We provide the code and the dataset used in our study. [ABSTRACT FROM AUTHOR]

Increasingly available spaceborne sensors provide unprecedented opportunities for large‐scale, timely and continuous tree species diversity (TSD) monitoring. However, given differences in spectral and spatial resolutions, the choice of sensor is not always straightforward. In this work, we investigated the effects of spatial and spectral resolutions for four spaceborne sensors (RapidEye, Landsat‐8, Sentinel‐2 and PlanetScope) on TSD mapping in an area of approximately 4000 km2 within the Black Forest, Germany. We employed a random forest (RF) regression model to predict Shannon–Wiener diversity based on seven types of spectral heterogeneity metrics (texture, coefficient of variation, Rao's Q, convex hull volume, spectral angle mapper, convex hull area and spectral species diversity) and a full survey dataset from 135 one‐ha sample plots. We compared the RF model's performance across sensors and spatial resolutions. Our results demonstrated that the Sentinel‐2‐based TSD model achieved the highest accuracy (mean R2: 0.477, mean root‐mean‐square error (RMSE): 0.274). The RapidEye‐based TSD model produced lower accuracy (mean R2: 0.346, mean RMSE: 0.303), but it was better than the PlanetScope‐ and Landsat‐based TSD models. The 10 m (for Sentinel‐2 and RapidEye) and 15 m (for PlanetScope) were the best spatial resolutions for predicting TSD. The NIR band was the most favourable spectral band for predicting TSD. Texture metrics and Rao's Q outperformed the other spectral heterogeneity metrics. Our results highlighted that spaceborne optical imagery (especially Sentinel‐2) can be successfully used for large‐scale TSD mapping but that the choice of sensors can significantly affect the resulting mapping accuracy in temperate montane forests. [ABSTRACT FROM AUTHOR]

Forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, multispectral satellite images and numerical map data. We evaluated k-nearest neighbors (k-NN) method-based predictions of forest variables for pixels in predicting tree lists of individual stands, including tree diameters at breast height and tree heights and then calculated stem volumes and tree species proportions. We compared alternative parameters (k-NN) using k of either 1 or 5 according to preliminary plot-level study and applying either measured trees (1-NN_trees) or mean stand characteristics (k-NN_stand). In the 1-NN_trees method, a tree list was generated based on the measured trees of the NFI plots, whereas in the 1-NN_stand and 5-NN_stand methods, a Weibull-based diameter distribution was recovered from the stand characteristics of the same inventory plots. In both methods, tree lists were predicted for each 16 m × 16 m pixel included in the stand compartment. Both methods performed well and resulted in 8–14% differences in the total volume compared with the field inventory of the 27 stands used for the evaluation. Moreover, the main tree species was correctly predicted for 74% of cases. The RMSE in total volume ranged from 25% (5-NN_stand) to 31% (1-NN_stand), while the smallest RMSEs in volume by tree species were 61% for broadleaves and 65% for pine and spruce using the 5-NN_stand. When comparing input data for a long-term growth simulation, the choice of the method was less influential as the effect of the error in the initial stand characteristics decreased over time during the simulation period. After 30-year simulation of the inventoried stands, the respective RMSEs were 9.4% for total volume and 39%, 50% and 59% for tree species, respectively. The satellite-based data with NFI plots were useful for predicting tree lists for pixels of a stand. However, the accuracy for operational forest management was still questionable. For a larger area's strategic information, the accuracy is considered adequate. [ABSTRACT FROM AUTHOR]

Accurate measurement and estimation of forest carbon sinks and fluxes are essential for developing effective national and global climate strategies aimed at reducing atmospheric carbon concentrations and mitigating climate change. Various errors arise during forest monitoring, especially measurement instability due to seasonal variations, which require to be adequately addressed in forest ecosystem research and applications. Seasonal fluctuations in temperature, precipitation, aerosols, and solar radiation can significantly impact the physical observations of mapping equipment or platforms, thereby reducing the data's accuracy. Here, we review the technologies and equipment used for monitoring forest carbon sinks and carbon fluxes across different remote sensing platforms, including ground-based, airborne, and spaceborne remote sensing. We further investigate the uncertainties introduced by seasonal variations to the observing equipment, compare the strengths and weaknesses of various monitoring technologies, and propose the corresponding solutions and recommendations. We aim to gain a comprehensive understanding of the impact of seasonal variations on the accuracy of forest map data, thereby improving the accuracy of forest carbon sinks and fluxes. [ABSTRACT FROM AUTHOR]

Mixed subtropical forests possess a high amount of carbon pool owing to their rich species diversity and carbon sequestration potential. The Dhaulasidh forest is located in Himachal Pradesh within the subtropical Himalayan region. This research aimed to identify: (1) Optimal satellite-derived Sentinel-2A indices for predicting biomass, (2) the best-fitting model for biomass estimation, and (3) changes in above-ground carbon stock due to biomass loss, using satellite remote sensing and quadrat-based approaches. Results indicated that Band 3 (Green), Band 5 (Red edge), the vegetation (VEG) index, and the Carotenoid reflectance index (CRI) were suitable for estimating above-ground biomass (AGB). Shannon and Simpson's diversity indices were calculated as 0.89 and 0.73, respectively. Significant contributors to AGB included Mallotus philippensis, Emblica officinalis, Cassia fistula, Acacia catechu, Ehretia laevis, Kydia calycina, and Lannea coromandelica. The AGB prediction model based on vegetation indices demonstrated a strong correlation between observed and predicted biomass (R²=0.65, p<0.001), with a mean absolute percentage error of 20% and root mean square error of 7.33 tonnes per pixel. The study predicted a total loss of 22,917.15 tonnes of CO2 in mixed subtropical forests, representing a 12.04% reduction in carbon stock within the study area. These findings offer critical baseline data for environmental management and carbon balance in the forest ecosystem, recommending that forest management practices after deforestation should be reviewed for remedial measures for any developmental activities. [ABSTRACT FROM AUTHOR]

The mango fruit plays a crucial role in providing essential nutrients to the human body and Pakistani mangoes are highly coveted worldwide. The escalating demand for agricultural products necessitates enhanced methods for monitoring and managing agricultural resources. Traditional field surveys are labour-intensive and time-consuming whereas remote sensing offers a comprehensive and efficient alternative. The field of remote sensing has witnessed substantial growth over time with satellite technology proving instrumental in monitoring crops on a large scale throughout their growth stages. In this study, we utilize novel data collected from a mango farm employing Landsat-8 satellite imagery and machine learning to detect mango orchards. We collected a total of 2,150 mango tree samples from a farm over six months in the province of Punjab, Pakistan. Then, we analyzed each sample using seven multispectral bands. The Landsat-8 framework provides high-resolution land surface imagery for detecting mango orchards. This research relies on independent data, offering an advantage for training more advanced machine learning models and yielding reliable findings with high accuracy. Our proposed optimized CART approach outperformed existing methods, achieving a remarkable 99% accuracy score while the k-Fold validation score also reached 99%. This research paves the way for advancements in agricultural remote sensing, offering potential benefits for crop management yield estimation and the broader field of precision agriculture. [ABSTRACT FROM AUTHOR]

Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. Based on Landsat 8, Sentinel-2A, and ALOS2 PALSAR data, this paper takes the artificial coniferous forests in the Saihanba Forest of Hebei Province as the object of study, fully explores and establishes remote sensing factors and information related to forest structure, gives full play to the advantages of spectral signals in detecting the horizontal structure and multi-dimensional synthetic aperture radar (SAR) data in detecting the vertical structure, and combines environmental factors to carry out multivariate synergistic methods of estimating the AGB. This paper uses three variable selection methods (Pearson correlation coefficient, random forest significance, and the least absolute shrinkage and selection operator (LASSO)) to establish the variable sets, combining them with three typical non-parametric models to estimate AGB, namely, random forest (RF), support vector regression (SVR), and artificial neural network (ANN), to analyze the effect of forest structure on biomass estimation, explore the suitable AGB of artificial coniferous forests estimation of machine learning models, and develop the method of quantifying saturation value of the combined variables. The results show that the horizontal structure is more capable of explaining the AGB compared to the vertical structure information, and that combining the multi-structure information can improve the model results and the saturation value to a great extent. In this study, different sets of variables can produce relatively superior results in different models. The variable set selected using LASSO gives the best results in the SVR model, with an R 2 values of 0.9998 and 0.8792 for the training and the test set, respectively, and the highest saturation value obtained is 185.73 t/ha, which is beyond the range of the measured data. The problem of saturation in biomass estimation in boreal medium- and high-density forests was overcome to a certain extent, and the AGB of the Saihanba area was better estimated. [ABSTRACT FROM AUTHOR]

Timber plantations across the world are suffering from the effects of increasingly frequent wildfires, which potentially degrade the wood of affected trees, depending on the exposure temperature and time. However, it is rather complicated to determine the exact temperature of the fire, or the temperature to which the wood was exposed. This study aimed to determine the exposure temperature of wood retrospectively through solid-state NMR analysis. Models were developed from softwood and hardwood samples exposed to defined temperatures, which successfully linked the NMR signal to the exposure temperature. Various fit equations were developed to link the half-width or peak area of the NMR signal to the exposure temperatures. Hard- and softwoods displayed noticeable differences: a linear function best described the half-width in the higher temperature region for Pine and Eucalyptus, whereas a parabolic function for the peak area of Eucalyptus yielded the best correlation to the entire temperature range. This non-destructive and direct method offers a valuable evaluation method to determine, if wood in burnt trees is degraded and can be processed. An informed choice can be made on the decision to use, or discard burnt wood. [ABSTRACT FROM AUTHOR]

The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management and tracking the carbon cycle of forest ecosystem. Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. Though many studies have demonstrated that a single machine learning model can produce highly accurate estimations of forest AGB in many situations, efforts are still required to explore the possible improvement in forest AGB estimation for a specific scenario under study. This study aims to investigate the performance of novel ensemble machine learning methods for forest AGB estimation and analyzes whether these methods are affected by forest types, independent variables, and spatial autocorrelation. Four well-known machine learning models (CatBoost, LightGBM, random forest (RF), and XGBoost) were compared for forest AGB estimation in the study using eight scenarios devised on the basis of two study regions, two variable types, and two validation strategies. Subsequently, a hybrid model combining the strengths of these individual models was proposed for forest AGB estimation. The findings indicated that no individual model outperforms the others in all scenarios. The RF model demonstrates superior performance in scenarios 5, 6, and 7, while the CatBoost model shows the best performance in the remaining scenarios. Moreover, the proposed hybrid model consistently has the best performance in all scenarios in spite of some uncertainties. The ensemble strategy developed in this study for the hybrid model substantially improves estimation accuracy and exhibits greater stability, effectively addressing the challenge of model selection encountered in the forest AGB forecasting process. [ABSTRACT FROM AUTHOR]

Background: Transfers of nursing home (NH) residents to the emergency department (ED) is frequent. Our main objective was to assess the cost of care pathways 6 months before and after the transfer to the emergency department among NH residents, according to the type of transfer (i.e. appropriate or inappropriate). Methods: This was a part of an observational, multicenter, case-control study: the Factors associated with INappropriate transfer to the Emergency department among nursing home residents (FINE) study. Sixteen public hospitals of the former Midi-Pyrénées region participated in recruitment, in 2016. During the inclusion period, all NH residents arriving at the ED were included. A pluri-disciplinary team categorized each transfer to the ED into 2 groups: appropriate or inappropriate. Direct medical and nonmedical costs were assessed from the French Health Insurance (FHI) perspective. Healthcare resources were retrospectively gathered from the FHI database and valued using the tariffs reimbursed by the FHI. Costs were recorded over a 6-month period before and after transfer to the ED. Other variables were used for analysis: sex, age, Charlson score, season, death and presence inside the NH of a coordinating physician or a geriatric nursing assistant. Results: Among the 1037 patients initially included in the FINE study, 616 who were listed in the FHI database were included in this economic study. Among them, 132 (21.4%) had an inappropriate transfer to the ED. In the 6 months before ED transfer, total direct costs on average amounted to 8,145€ vs. 6,493€ in the inappropriate and appropriate transfer groups, respectively. In the 6 months after ED transfer, they amounted on average to 9,050€ vs. 12,094€. Conclusions: Total costs on average are higher after transfer to the ED, but there is no significant increase in healthcare expenditure with inappropriate ED transfer. Support for NH staff and better pathways of care could be necessary to reduce healthcare expenditures in NH residents. Trial registration: clinicaltrials.gov, NCT02677272. [ABSTRACT FROM AUTHOR]

Purpose: Objective parameters for decision on adaptive radiotherapy depend on patient, tumor and treatment related factors. Present study reports geometric uncertainties occurring during high precision radiotherapy, beam fluence analysis and serial exit dose measurement as a patient-specific tool for adaptive radiotherapy. Materials and Methods: Serial exit dose fluence of 24 patients (at baseline and mid-treatment) undergoing IMRT/VMAT treatment were measured. Baseline and midtreatment exit dose evaluation was done using gafchromic films in predefined region of interest. Difference of volume of GTV at baseline (from simulation CT scan) and midtreatment CBCT scan was calculated (∆GTV). Results: Population based systematic errors (mm) were 4.15, 2.26, 0.88 and random errors (mm) were 2.56, 3.69, and 2.03 in mediolateral (ML), craniocaudal (CC) and anteroposterior (AP) directions respectively. Gamma pass rate reduced with incremental shift. For a 5 mm shift, maximum deviation was found in anteroposterior axis (22.16 ± 7.50) and lowest in mediolateral axis (12.85 ± 4.95). On serial measurement of exit dose fluence, tumor shrinkage significantly influenced gamma pass rate. The mean gamma pass rate was significantly different between groups with 50% shrinkage of tumor volume (86.36 vs 96.24, P = 0.008, on multivariate analysis P = 0.026). Conclusion: Rapid fall of gamma pass rate was observed for set up error of ≥3 mm. Serial measurement of exit dose fluence by radiochromic film is a feasible method of exit dose comparison in IMRT/VMAT, where EPID dosimetry is not available with linear accelerator configuration. Our study suggests that there is a significant difference between gamma pass rates of baseline and mid treatment exit dose fluence with greater than 50% tumor shrinkage. [ABSTRACT FROM AUTHOR]

Automatic and accurate detection of clouds and cloud shadows is a critical aspect of optical remote sensing image preprocessing. This paper provides a time series maximum and minimum mask method (TSMM) for cloud and cloud shadow detection. Firstly, the Cloud Score+S2_HARMONIZED (CS+S2) is employed as a preliminary mask for clouds and cloud shadows. Secondly, we calculate the ratio of the maximum and sub-maximum values of the blue band in the time series, as well as the ratio of the minimum and sub-minimum values of the near-infrared band in the time series, to eliminate noise from the time series data. Finally, the maximum value of the clear blue band and the minimum value of the near-infrared band after noise removal are employed for cloud and cloud shadow detection, respectively. A national and a global dataset were used to validate the TSMM, and it was quantitatively compared against five other advanced methods or products. When clouds and cloud shadows are detected simultaneously, in the S2ccs dataset, the overall accuracy (OA) reaches 0.93 and the F1 score reaches 0.85. Compared with the most advanced CS+S2, there are increases of 3% and 9%, respectively. In the CloudSEN12 dataset, compared with CS+S2, the producer's accuracy (PA) and F1 score show increases of 10% and 4%, respectively. Additionally, when applied to Landsat-8 images, TSMM outperforms Fmask, demonstrating its strong generalization capability. [ABSTRACT FROM AUTHOR]

Stabilizers play a crucial role in preventing undesirable decomposition and degradation reactions of propellants, thus ensuring their long-term performance and safe storage. This review highlights recent advancements in stabilizer selection and formulation techniques, aiming to enhance the stability of single base nitrocellulose (SBNC) propellants. It examines several types of stabilizers for SB-NC propellants, including their reaction mechanisms and effectiveness in preventing degradation reactions of the propellants, as well as the effects of their concentrations, particle sizes, and distributions on the propellants’ stability. Furthermore, it explores innovative approaches such as nano and green stabilizers with improved stability and compatibility. This review also provides insights into methods for evaluating the efficiency and propellant stability of the stabilizers, such as thermal analysis and accelerated aging tests. The findings of this review will assist in developing advanced propellant formulations that meet the growing demand for the applications of NC-based propellants. [ABSTRACT FROM AUTHOR]

A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates (winter and non-winter) of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers (such as global impervious surface and global tree cover) to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover masks, stepwise threshold adjustments were applied to reflectance, spectral index values, and Euclidean distance layers, with 62 combinations evaluated. Global (all seven scenes) and regional (arid, tropics, and temperate) adaptive thresholds were computed. An annual 95th and 5th percentile NDVI composite was used to provide temporal corrections to the decision functions, and these corrections were compared against the original model. The accuracy assessment found that the regional adaptive thresholds for both the two-date land cover and the temporally corrected land cover could accurately map land cover type within nine-class (68.4% vs. 73.1%), six-class (79.8% vs. 82.8%), and five-class (80.1% vs. 85.1%) schemes. Lastly, the five-class and six-class models were compared with a manually labeled deep learning model (Esri), where they performed with similar accuracies (five classes: Esri 80.0 ± 3.4%, region corrected 85.1 ± 2.9%). The results highlight not only performance in line with an intensive deep learning approach, but also that reasonably accurate models can be created without a full annual time series of imagery. [ABSTRACT FROM AUTHOR]

The uncontrolled spread of fire can have huge effects on ecosystems. In Ecuador, in 2022, wildfires caused a loss of 6566.66 hectares of vegetation cover. Ibarra is an Andean canton that has also been exposed to wildfires and their effects. The aim of this study was to map wildfire susceptibility in the Ibarra canton. Seven factors that directly affect these fires were examined: precipitation, temperature, water deficit, potential evapotranspiration, slope, proximity to roads, and land cover and land use. The variables were reclassified using Geographic Information Systems and a multicriteria analysis. The results showed that Ibarra has four susceptibility categories: very low, moderate, high, and very high. The more susceptible areas are those considered to have high and very high exposure, occupying 82% of the surface. Consequently, the most susceptible land covers are crops, pastures, shrub vegetation, and forests. [ABSTRACT FROM AUTHOR]