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State of the Art: The literature indicates that polarization is a growing phenomenon that significantly impacts the corporate world, affecting employees, consumers, shareholders, and society in general, in an individualized manner. Furthermore, it demands studies and strategies on how companies could react. Estado da arte: a literatura indica que a polarizacao e um fenomeno crescente que impacta significativamente o mundo corporativo, e afeta funcionarios, consumidores, acionistas e a sociedade em geral, de forma individualizada; alem disso, demanda estudos e estrategias de como as empresas podem reagir.

Canopy gaps express the time-integrated effects of tree failure and mortality as well as regrowth and succession in tropical forests. Quantifying the size and spatial distribution of canopy gaps is requisite to modeling forest functional processes ranging from carbon fluxes to species interactions and biological diversity. Using high-resolution airborne Light Detection and Ranging (LiDAR), we mapped and analyzed 5,877,937 static canopy gaps throughout 125,581 ha of lowland Amazonian forest in Peru. Our LiDAR sampling covered a wide range of forest physiognomies across contrasting geologic and topographic conditions, and on depositional floodplain and erosional terra firme substrates. We used the scaling exponent of the Zeta distribution (λ) as a metric to quantify and compare the negative relationship between canopy gap frequency and size across sites. Despite variable canopy height and forest type, values of λ were highly conservative (λ mean  = 1.83, s  = 0.09), and little variation was observed regionally among geologic substrates and forest types, or at the landscape level comparing depositional-floodplain and erosional terra firme landscapes. λ-values less than 2.0 indicate that these forests are subjected to large gaps that reset carbon stocks when they occur. Consistency of λ-values strongly suggests similarity in the mechanisms of canopy failure across a diverse array of lowland forests in southwestern Amazonia.

Indoor environments significantly influence human interaction, collaboration, and well-being, yet evaluating how architectural designs actually perform in fostering social connections remains challenging. This study demonstrates the use of 11 static-mounted lidar sensors to detect serendipitous encounters—collisions—between people in a shared common space of a mixed academic–residential university building. A novel collision detection algorithm achieved 86.1% precision and detected 14,022 interactions over 115 days (67 million person-seconds) of an academic semester. While occupancy strongly predicted collision frequency overall (R2 ≥ 0.74), significant spatiotemporal variations revealed the complex relationship between co-presence and social interaction. Key findings include the following: (1) collision frequency peaked early in the semester then declined by ~25% by mid-semester; (2) temporal lags between occupancy and collision peaks of 2–3 h in the afternoon indicate that social interaction differs from physical presence; (3) collisions per occupancy peaked on the weekend, with Saturday showing 52% higher rates than the weekly average; and (4) collisions clustered at key transition zones (elevator areas, stair bases), with an additional “friction effect”, where proximity to seating increased interaction rates (>30%) compared to open corridors. This methodology establishes a scalable framework for post-occupancy evaluation, enabling evidence-based assessment of design effectiveness in fostering the spontaneous interactions essential for creativity, innovation, and place-making in built environments.

As a major part of the urban green space system, street trees play a corresponding role in adjusting the thermal comfort of the environment and alleviating heat island effects. The correlation between the morphological structure and microclimate factors in the lower canopy of street trees was studied, using data that were captured with vehicle-borne LiDAR to model the morphological structure and geometric canopy features of six key street tree species in the built-up area of Zhumadian City, Henan Province. The regulating ability and differences of canopy geometry on cooling, humidification, shading, and Physiologically Equivalent Temperature (PET) were studied. Research shows that: (1) Canopy Volume (CV), Canopy Area (CA), Canopy Diameter (CD), and Tree Height (TH) have a linear negative correlation with air temperature, relative humidity, and luminosity. TH had significant effects on the air temperature and relative humidity (R2 = 0.90, 0.96), and CV and CD had significant effects on luminosity (R2 = 0.70, 0.63). (2) The oval-shaped plant (Platanus acerifolia (Aiton) Willdenow) had a strong cooling and shading ability, with an average daily cooling of 2.3 °C and shading of 318 cd/m2. The spire-shaped plant (Cedrus deodara (Roxb.) G. Don) had a strong ability to humidify, with an average daily humidification of 4.5%. (3) The oval-shaped and spire-shaped plants had a strong regulation ability on PET, and the daily average regulation values were 40.5 °C and 40.9 °C, respectively. (4) The CD of the oval-shaped plant had a significant effect on PET (R2 = 0.49), and the TH of the spire-shaped plant had a significant effect on PET (R2 = 0.80), as well as a significantly higher CV and Leaf Area Index (LAI) than other street tree species. Therefore, selecting oval and spire canopy-shaped plants with a thick canopy, dense leaves, and high CD and TH values as street trees can provide significant advantages in cooling, humidifying, and shading, and can effectively adjust human comfort in the lower canopy understory. This study is the first to apply LiDAR technology to the regulation of urban microclimate. The research results provide a theoretical basis and quantitative reference for street tree design from the perspective of outdoor thermal comfort evaluation and play a guiding role in the application of LiDAR to urban forestry research.

Coastal storms have consequences for human lives and infrastructure but also create important early successional habitats for myriad species. For example, storm-induced overwash creates nesting habitat for shorebirds like piping plovers (Charadrius melodus). We examined how piping plover habitat extent and location changed on barrier islands in New York, New Jersey, and Virginia after Hurricane Sandy made landfall following the 2012 breeding season. We modeled nesting habitat using a nest presence/absence dataset that included characterizations of coastal morphology and vegetation. Using a Bayesian network, we predicted nesting habitat for each study site for the years 2010/2011, 2012, and 2014/2015 based on remotely sensed spatial datasets (e.g., lidar, orthophotos). We found that Hurricane Sandy increased piping plover habitat by 9 to 300% at 4 of 5 study sites but that one site saw a decrease in habitat by 27%. The amount, location, and longevity of new habitat appeared to be influenced by the level of human development at each site. At three of the five sites, the amount of habitat created and the time new habitat persisted were inversely related to the amount of development. Furthermore, the proportion of new habitat created in high-quality overwash was inversely related to the level of development on study areas, from 17% of all new habitat in overwash at one of the most densely developed sites to 80% of all new habitat at an undeveloped site. We also show that piping plovers exploited new habitat after the storm, with 14-57% of all nests located in newly created habitat in the 2013 breeding season. Our results quantify the importance of storms in creating and maintaining coastal habitats for beach-nesting species like piping plovers, and these results suggest a negative correlation between human development and beneficial ecological impacts of these natural disturbances.

The benefits and services of urban forests are becoming increasingly well documented, with carbon storage being the main focus of attention. Recent efforts in urban remote sensing have incorporated additional data such as LiDAR data but have been limited to sections of an urban area or only certain species. Existing models are not generalizable to remaining unmeasured urban trees. To make a generalizable individual urban tree model, we used metrics from NAIP aerial imagery and NOAA and USGS LiDAR data for 2013 and 2019, and two crown-level urban tree biomass models were developed. We ran a LASSO regression, which selected the best variables for the biomass model, followed by a 10-fold cross-validation. The 2013 model had an adjusted R2 value of 0.85 and an RMSE of 1797 kg, whereas the 2019 model had an adjusted R2 value of 0.87 and an RMSE of 1444 kg. The 2019 model was then applied to the rest of the unsampled trees to estimate the total biomass and total carbon stored for all the trees in the county. Recommendations include changes to ground inventory techniques to adapt to the current methods and limitations of remote sensing biomass estimation.

State of the Art: The literature indicates that polarization is a growing phenomenon that significantly impacts the corporate world, affecting employees, consumers, shareholders, and society in general, in an individualized manner. Furthermore, it demands studies and strategies on how companies could react. Estado da arte: a literatura indica que a polarizacao e um fenomeno crescente que impacta significativamente o mundo corporativo, e afeta funcionarios, consumidores, acionistas e a sociedade em geral, de forma individualizada; alem disso, demanda estudos e estrategias de como as empresas podem reagir.

Understanding crop types and their annual cycles is key to managing natural resources, especially when the pressures on these resources are attributable to climate change and social, environmental, and economic policies. In recent years, the space sector’s development, with programs such as Copernicus, has enabled a greater availability of satellite data. This study uses a multi-sensor approach to retrieve crop information by developing a Proof of Concept for the integration of high-resolution SAR imagery and optical data. The main goal is to develop a pre-automatized processing chain that explores the temporal dimension of different crop. Results are related to the advantage of using a multi-sensor approach to retrieve vegetation biomass and vertical structure for the identification of phenological stages and different crops. The novelty consists of investigating the multi-temporal pattern of radiometric indices and radar backscatter to detect the different phenological stages of each crop, identifying the Day of the Year (DoY) in which the classes showed greater separability. The current study could be considered a benchmark for the exploitation of future multi-sensor missions in downstream services for the agricultural sector, strengthening the evolution of Copernicus services.

Small landslides, though frequent, are often overlooked despite their significant potential impact on human-affected areas. This study presents an analysis of the Bella Orxeta landslide in Alicante, Spain, a rotational landslide event that occurred in March 2017 following intense and continued rainfall. Utilizing multitemporal datasets, including LiDAR from 2009 and 2016 and drone-based photogrammetry from 2021 and 2023, we generated high-resolution digital terrain models (DTMs) to assess morphological changes, estimate displaced volumes of approximately 3500 cubic meters, and monitor slope activity. Our analysis revealed substantial mass movement between 2016 and 2021, followed by relatively minor changes between 2021 and 2023, primarily related to fluvial erosion. This study demonstrates the effectiveness of UAV and DTM differencing techniques for landslide detection, volumetric analysis, and long-term monitoring in urbanized settings. Beyond its scientific contributions, the Bella Orxeta case offers pedagogical value across academic disciplines, supporting practical training in geomorphology, geotechnical assessment, GIS, and risk planning. It also highlights policy gaps in existing territorial risk plans, particularly regarding the integration of modern monitoring tools for small-scale but recurrent geohazards. Given climate change projections indicating more frequent high-intensity rainfall events in Mediterranean areas, the paper advocates for the systematic documentation of local landslide cases to improve hazard preparedness, urban resilience, and geoscience education