Terrestrial laser scanning and low magnetic field digitization yield similar architectural coarse root traits for 32-year-old Pinus ponderosa trees.

Background: Understanding how trees develop their root systems is crucial for the comprehension of how wildland and urban forest ecosystems plastically respond to disturbances such as harvest, fire, and climate change. The interplay between the endogenously determined root traits and the response to environmental stimuli results in tree adaptations to biotic and abiotic factors, influencing stability, carbon allocation, and nutrient uptake. Combining the three-dimensional structure of the root system, with root morphological trait information promotes a robust understanding of root function and adaptation plasticity.

Ecotourism and mangrove conservation in Southeast Asia: Current trends and perspectives.

Mangroves in Southeast Asia provide numerous supporting, provisioning, regulating, and cultural services that are crucial to the environment and local livelihoods since they support biodiversity conservation and climate change resilience. However, Southeast Asia mangroves face deforestation threats from the expansion of commercial aquaculture, agriculture, and urban development, along with climate change-related natural processes. Ecotourism has gained prominence as a financial incentive tool to support mangrove conservation and restoration.

Exploring UAS-lidar as a sampling tool for satellite-based AGB estimations in the Miombo woodland of Zambia.

To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage.

Bacterial Emission Factors: A Foundation for the Terrestrial-Atmospheric Modeling of Bacteria Aerosolized by Wildland Fires.

Wildland fire is a major global driver in the exchange of aerosols between terrestrial environments and the atmosphere. This exchange is commonly quantified using emission factors or the mass of a pollutant emitted per mass of fuel burned. However, emission factors for microbes aerosolized by fire have yet to be determined.

Use of Multi-Date and Multi-Spectral UAS Imagery to Classify Dominant Tree Species in the Wet Miombo Woodlands of Zambia.

Accurate maps of tree species distributions are necessary for the sustainable management of forests with desired ecological functions. However, image classification methods to produce species distribution maps for supporting sustainable forest management are still lacking in the Miombo woodland ecoregion. This study used multi-date multispectral Unmanned Aerial Systems (UAS) imagery collected at key phenological stages (leaf maturity, transition to senescence, and leaf flushing) to classify five dominant canopy species of the wet Miombo woodlands in the Copperbelt Province of Zambia.

Remodeling of colon plasma cell repertoire within ulcerative colitis patients.

Plasma cells (PCs) constitute a significant fraction of colonic mucosal cells and contribute to inflammatory infiltrates in ulcerative colitis (UC). While gut PCs secrete bacteria-targeting IgA antibodies, their role in UC pathogenesis is unknown. We performed single-cell V(D)J- and RNA-seq on sorted B cells from the colon of healthy individuals and patients with UC.

Multitemporal lidar captures heterogeneity in fuel loads and consumption on the Kaibab Plateau.

Background: Characterization of physical fuel distributions across heterogeneous landscapes is needed to understand fire behavior, account for smoke emissions, and manage for ecosystem resilience. Remote sensing measurements at various scales inform fuel maps for improved fire and smoke models. Airborne lidar that directly senses variation in vegetation height and density has proven to be especially useful for landscape-scale fuel load and consumption mapping.

B cell genomics behind cross-neutralization of SARS-CoV-2 variants and SARS-CoV.

Monoclonal antibodies (mAbs) are a focus in vaccine and therapeutic design to counteract severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants. Here, we combined B cell sorting with single-cell VDJ and RNA sequencing (RNA-seq) and mAb structures to characterize B cell responses against SARS-CoV-2. We show that the SARS-CoV-2-specific B cell repertoire consists of transcriptionally distinct B cell populations with cells producing potently neutralizing antibodies (nAbs) localized in two clusters that resemble memory and activated B cells.

Hundred year projected carbon loads and species compositions for four National Forests in the northwestern USA.

Background: Forests are an important component of the global carbon balance, and climate sensitive growth and yield models are an essential tool when predicting future forest conditions. In this study, we used the dynamic climate capability of the Forest Vegetation Simulator (FVS) to simulate future (100 year) forest conditions on four National Forests in the northwestern USA: Payette National Forest (NF), Ochoco NF, Gifford Pinchot NF, and Siuslaw NF. Using Forest Inventory and Analysis field plots, aboveground carbon estimates and species compositions were simulated with Climate-FVS for the period between 2016 and 2116 under a no climate change scenario and a future climate scenario.

Simulating Groundcover Community Assembly in a Frequently Burned Ecosystem Using a Simple Neutral Model.

Fire is a keystone process that drives patterns of biodiversity globally. In frequently burned fire-dependent ecosystems, surface fire regimes allow for the coexistence of high plant diversity at fine scales even where soils are uniform. The mechanisms on how fire impacts groundcover community dynamics are, however, poorly understood.

The Fire and Smoke Model Evaluation Experiment-A Plan for Integrated, Large Fire-Atmosphere Field Campaigns.

The Fire and Smoke Model Evaluation Experiment (FASMEE) is designed to collect integrated observations from large wildland fires and provide evaluation datasets for new models and operational systems. Wildland fire, smoke dispersion, and atmospheric chemistry models have become more sophisticated, and next-generation operational models will require evaluation datasets that are coordinated and comprehensive for their evaluation and advancement. Integrated measurements are required, including ground-based observations of fuels and fire behavior, estimates of fire-emitted heat and emissions fluxes, and observations of near-source micrometeorology, plume properties, smoke dispersion, and atmospheric chemistry.

Fire behavior and smoke modeling: Model improvement and measurement needs for next-generation smoke research and forecasting systems.

There is an urgent need for next-generation smoke research and forecasting (SRF) systems to meet the challenges of the growing air quality, health, and safety concerns associated with wildland fire emissions. This review paper presents simulations and experiments of hypothetical prescribed burns with a suite of selected fire behavior and smoke models and identifies major issues for model improvement and the most critical observational needs. The results are used to understand the new and improved capability required for the next-generation SRF systems and to support the design of the Fire and Smoke Model Evaluation Experiment (FASMEE) and other field campaigns.

Supervised spatial classification of multispectral LiDAR data in urban areas.

Multispectral LiDAR (light detection and ranging) data have been initially used for land cover classification. However, there are still high classification uncertainties, especially in urban areas, where objects are often mixed and confounded. This study investigated the efficiency of combining advanced statistical methods and LiDAR metrics derived from multispectral LiDAR data for improving land cover classification accuracy in urban areas.

Estimating Stand Height and Tree Density in Pinus taeda plantations using in-situ data, airborne LiDAR and k-Nearest Neighbor Imputation.

Accurate forest inventory is of great economic importance to optimize the entire supply chain management in pulp and paper companies. The aim of this study was to estimate stand dominate and mean heights (HD and HM) and tree density (TD) of Pinus taeda plantations located in South Brazil using in-situ measurements, airborne Light Detection and Ranging (LiDAR) data and the non- k-nearest neighbor (k-NN) imputation. Forest inventory attributes and LiDAR derived metrics were calculated at 53 regular sample plots and we used imputation models to retrieve the forest attributes at plot and landscape-levels.

Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data.

Basal area (BA) is a good predictor of timber stand volume and forest growth. This study developed predictive models using field and airborne LiDAR (Light Detection and Ranging) data for estimation of basal area in Pinus taeda plantation in south Brazil. In the field, BA was collected from conventional forest inventory plots.

Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data.

Background: LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data.

Disaggregating Tropical Disease Prevalence by Climatic and Vegetative Zones within Tropical West Africa.

Tropical infectious disease prevalence is dependent on many socio-cultural determinants. However, rainfall and temperature frequently underlie overall prevalence, particularly for vector-borne diseases. As a result these diseases have increased prevalence in tropical as compared to temperate regions.

The Science of Firescapes: Achieving Fire-Resilient Communities.

Wildland fire management has reached a crossroads. Current perspectives are not capable of answering interdisciplinary adaptation and mitigation challenges posed by increases in wildfire risk to human populations and the need to reintegrate fire as a vital landscape process. Fire science has been, and continues to be, performed in isolated "silos," including institutions (e.

Occupancy of red-naped sapsuckers in a coniferous forest: using LiDAR to understand effects of vegetation structure and disturbance.

Red-naped sapsuckers () are functionally important because they create sapwells and cavities that other species use for food and nesting. Red-naped sapsucker ecology within aspen () has been well studied, but relatively little is known about red-naped sapsuckers in conifer forests. We used light detection and ranging (LiDAR) data to examine occupancy patterns of red-naped sapsuckers in a conifer-dominated system.

Using climate-FVS to project landscape-level forest carbon stores for 100 years from field and LiDAR measures of initial conditions.

Background: Forest resources supply a wide range of environmental services like mitigation of increasing levels of atmospheric carbon dioxide (CO2). As climate is changing, forest managers have added pressure to obtain forest resources by following stand management alternatives that are biologically sustainable and economically profitable. The goal of this study is to project the effect of typical forest management actions on forest C levels, given a changing climate, in the Moscow Mountain area of north-central Idaho, USA.

Using satellite and airborne LiDAR to model woodpecker habitat occupancy at the landscape scale.

Incorporating vertical vegetation structure into models of animal distributions can improve understanding of the patterns and processes governing habitat selection. LiDAR can provide such structural information, but these data are typically collected via aircraft and thus are limited in spatial extent. Our objective was to explore the utility of satellite-based LiDAR data from the Geoscience Laser Altimeter System (GLAS) relative to airborne-based LiDAR to model the north Idaho breeding distribution of a forest-dependent ecosystem engineer, the Red-naped sapsucker (Sphyrapicus nuchalis).

Decay of aspen (Populus tremuloides Michx.) wood in moist and dry boreal, temperate, and tropical forest fragments.

In this study, we set up a wood decomposition experiment to i) quantify the percent of mass remaining, decay constant and performance strength of aspen stakes (Populus tremuloides) in dry and moist boreal (Alaska and Minnesota, USA), temperate (Washington and Idaho, USA), and tropical (Puerto Rico) forest types, and ii) determine the effects of fragmentation on wood decomposition rates as related to fragment size, forest age (and/or structure) and climate at the macro- and meso-scales. Fragment sizes represented the landscape variability within a climatic region. Overall, the mean small fragments area ranged from 10-14 ha, medium-sized fragments 33 to 60 ha, and large fragments 100-240 ha.