Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers to extract insights from Multisource Remote Sensing. This study aims to use these technologies for mapping summer and winter Land Use/Land Cover features in Cuenca de la Laguna Merín, Uruguay, while comparing the performance of Random Forests, Support Vector Machines, and Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission imagery, Google Earth Engine, training and validation datasets and quoted classifiers. The methods involve creating a multisource database, conducting feature importance analysis, developing models, supervised classification and performing accuracy assessments. Results indicate a low significance of microwave inputs relative to optical features. Short-wave infrared bands and transformations such as the Normalised Vegetation Index, Land Surface Water Index and Enhanced Vegetation Index demonstrate the highest importance. Accuracy assessments indicate that performance in mapping various classes is optimal, particularly for rice paddies, which play a vital role in the country’s economy and highlight significant environmental concerns. However, challenges persist in reducing confusion between classes, particularly regarding natural vegetation features versus seasonally flooded vegetation, as well as post-agricultural fields/bare land and herbaceous areas. Random Forests and Gradient-Boosting Trees exhibited superior performance compared to Support Vector Machines. Future research should explore approaches such as Deep Learning and pixel-based and object-based classification integration to address the identified challenges. These initiatives should consider various data combinations, including additional indices and texture metrics derived from the Grey-Level Co-Occurrence Matrix.

Climate change is widely recognized as a significant contributor to both wildfire initiation and spread, conditions such as high temperatures and prolonged droughts facilitating the rapid ignition and propagation of fires. As a result, extreme weather events can trigger fires through lightning strikes with increases in frequency and severity. Despite this, we argue that it is important to distinguish and clarify the concepts of fire occurrence and fire spread, as these phenomena are not directly synonymous in the field of fire ecology. This review examined the published literature to determine if climate factors contribute to fire occurrence and/or spread, and evaluated how well the concepts are used when drawing connections between fire occurrence and fire spread related to climate variables. Using the PRISMA bibliographic analysis methodology, 70 scientific articles were analyzed, including reviews and research papers in the last 5 years. According to the analysis, most publications dealing with fire occurrence, fire spread, and climate change come from the northern hemisphere, specifically from the United States, China, Europe, and Oceania with South America appearing to be significantly underrepresented (less than 10% of published articles). Additionally, despite climatic variables being the most prevalent factors in predictive models, only 38% of the studies analyzed simultaneously integrated climatic, topographic, vegetational, and anthropogenic factors when assessing wildfires. Furthermore, of the 47 studies that explicitly addressed occurrence and spread, 66 percent used the term “occurrence” in line with its definition cited by the authors, that is, referring specifically to ignition. In contrast, 27 percent employed the term in a broader sense that did not explicitly denote the moment a fire starts, often incorporating aspects such as the predisposition of fuels to burn. The remaining 73 percent focused exclusively on “spread.” Hence, caution is advised when making generalizations as climate impact on wildfires can be overestimated in predictive models when conceptual ambiguity is present. Our results showed that, although climate change can amplify conditions for fire spread and contribute to the occurrence of fire, anthropogenic factors remain the most significant factor related to the onset of fires on a global scale, above climatic factors.

Geo Big Data and Sentinel-2 have been extensively employed to map rice paddies and general land use/land cover. Focusing on the environmental value and relevance of rice production in Cuenca de la Laguna Merín in Uruguay, the research aimed to 1) map rice paddies and other land use/land cover classes, 2) compare the capabilities of Random Forest and Support Vector Machine for classifying two different Sentinel-2 time series stacks, and 3) identify the most important features according to Random Forest. In addition to quoted imagery and classifiers, the materials include Google Earth Engine, GEEMAP, and Python's Scikit-learn GridSearchCV. The main methods comprised hyperparameter tuning, supervised classification, and accuracy assessment. Quoted assessment revealed that the four maps performed well. The feature importance analysis highlighted the Near-Infrared and Shortwave Infrared as the most relevant features. Future research should focus on integrating diverse data sources and comparing different time series than those employed here.