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. Using bacterial cell concentrations collected on unmanned aircraft systems over forest fires in Utah, USA, we determine bacterial emission factors (BEFs) for the first time. We estimate that 1.39 × 1010 and 7.68 × 1011 microbes are emitted for each Mg of biomass consumed in fires burning thinning residues and intact forests, respectively. These emissions exceed estimates of background bacterial emissions in other studies by 3-4 orders of magnitude. For the ∼2631 ha of similar forests in the Fishlake National Forest that burn each year on average, an estimated 1.35 × 1017 cells or 8.1 kg of bacterial biomass were emitted. BEFs were then used to parametrize a computationally scalable particle transport model that predicted over 99% of the emitted cells were transported beyond the 17.25 x 17.25 km model domain. BEFs can be used to expand understanding of global wildfire microbial emissions and their potential consequences to ecosystems, the atmosphere, and humans.

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. Through a systematic literature review approach, we examined the relationships between ecotourism and mangrove conservation in Southeast Asia based on scientific papers published from 2010 to 2022. Most of the studies were reported in Indonesia, Malaysia, Philippines, Thailand, and Vietnam, respectively, which were associated with the highest number of vibrant mangrove ecotourism sites and largest mangrove areas compared to the other countries of Southeast Asia. Mangrove-related ecotourism activities in the above countries mainly include boat tours, bird and wildlife watching, mangrove planting, kayaking, eating seafood, and snorkeling. The economic benefits, such as an increase in income associated with mangrove ecotourism, have stimulated infrastructural development in ecotourism destinations. Local communities benefited from increased access to social amenities such as clean water, electricity, transportation networks, schools, and health services that are intended to make destinations more attractive to tourists. Economic benefits from mangrove ecotourism motivated the implementation of several community-based mangrove conservation and restoration initiatives, which attracted international financial incentives and public-private partnerships. Since mangroves are mostly located on the land occupied by indigenous people and local communities, ensuring respect for their land rights and equity in economic benefit sharing may increase their intrinsic motivation and participation in mangrove restoration and conservation initiatives. Remote sensing tools for mangrove monitoring, evaluation, and reporting, and integrated education and awareness campaigns can ensure the long-term conservation of mangroves while sustaining ecotourism's economic infrastructure and social amenities benefits.

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. Object-based image analysis (OBIA) with a random forest algorithm was used on single date, multi-date, and multi-feature UAS imagery for classifying the dominant canopy tree species of the wet Miombo woodlands. It was found that classification accuracy varies both with dates and features used. For example, the August image yielded the best single date overall accuracy (OA, 80.12%, 0.68 kappa), compared to October (73.25% OA, 0.59 kappa) and May (76.64% OA, 0.63 kappa). The use of a three-date image combination improved the classification accuracy to 84.25% OA and 0.72 kappa. After adding spectral indices to multi-date image combination, the accuracy was further improved to 87.07% and 0.83 kappa. The results highlight the potential of using multispectral UAS imagery and phenology in mapping individual tree species in the Miombo ecoregion. It also provides guidance for future studies using multispectral UAS for sustainable management of Miombo tree species.

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. The best LiDAR-derived metrics to predict HD, HM and TD were H99TH, HSD, SKE and HMIN. The Imputation model using the selected metrics was more effective for retrieving height than tree density. The model coefficients of determination (adj.R2) and a root mean squared difference (RMSD) for HD, HM and TD were 0.90, 0.94, 0.38m and 6.99, 5.70, 12.92%, respectively. Our results show that LiDAR and k-NN imputation can be used to predict stand heights with high accuracy in Pinus taeda. However, furthers studies need to be realized to improve the accuracy prediction of TD and to evaluate and compare the cost of acquisition and processing of LiDAR data against the conventional inventory procedures.

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. Multiple linear regression models for predicting BA from LiDAR-derived metrics were developed and evaluated for predictive power and parsimony. The best model to predict BA from a family of six models was selected based on corrected Akaike Information Criterion (AICc) and assessed by the adjusted coefficient of determination (adj. R²) and root mean square error (RMSE). The best model revealed an adj. R²=0.93 and RMSE=7.74%. Leave one out cross-validation of the best regression model was also computed, and revealed an adj. R² and RMSE of 0.92 and 8.31%, respectively. This study showed that LiDAR-derived metrics can be used to predict BA in Pinus taeda plantations in south Brazil with high precision. We conclude that there is good potential to monitor growth in this type of plantations using airborne LiDAR. We hope that the promising results for BA modeling presented herein will stimulate to operate this technology in Brazil.

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.g., agencies versus universities), organizational structures (e.g., federal agency mandates versus local and state procedures for responding to fire), and research foci (e.g., physical science, natural science, and social science). These silos tend to promote research, management, and policy that focus only on targeted aspects of the "wicked" wildfire problem. In this article, we provide guiding principles to bridge diverse fire science efforts to advance an integrated agenda of wildfire research that can help overcome disciplinary silos and provide insight on how to build fire-resilient communities.

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. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-R2 = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics (R2 = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands.

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. Here we predicted field-observed fuel loads from airborne lidar and Landsat-derived fire history metrics with random forest (RF) modeling. RF models were then applied across multiple lidar acquisitions (years 2012, 2019, 2020) to create fuel maps across our study area on the Kaibab Plateau in northern Arizona, USA. We estimated consumption across the 2019 Castle and Ikes Fires by subtracting 2020 fuel load maps from 2019 fuel load maps and examined the relationship between mapped surface fuels and years since fire, as recorded in the Monitoring Trends in Burn Severity (MTBS) database. Results: R-squared correlations between predicted and ground-observed fuels were 50, 39, 59, and 48% for available canopy fuel, 1- to 1000-h fuels, litter and duff, and total surface fuel (sum of 1- to 1000-h, litter and duff fuels), respectively. Lidar metrics describing overstory distribution and density, understory density, Landsat fire history metrics, and elevation were important predictors. Mapped surface fuel loads were positively and nonlinearly related to time since fire, with asymptotes to stable fuel loads at 10-15 years post fire. Surface fuel consumption averaged 16.1 and 14.0 Mg ha- 1 for the Castle and Ikes Fires, respectively, and was positively correlated with the differenced Normalized Burn Ratio (dNBR). We estimated surface fuel consumption to be 125.3 ± 54.6 Gg for the Castle Fire and 27.6 ± 12.0 Gg for the portion of the Ikes Fire (42%) where pre- and post-fire airborne lidar were available. Conclusions: We demonstrated and reinforced that canopy and surface fuels can be predicted and mapped with moderate accuracy using airborne lidar data. Landsat-derived fire history helped account for spatial and temporal variation in surface fuel loads and allowed us to describe temporal trends in surface fuel loads. Our fuel load and consumption maps and methods have utility for land managers and researchers who need landscape-wide estimates of fuel loads and emissions. Fuel load maps based on active remote sensing can be used to inform fuel management decisions and assess fuel structure goals, thereby promoting ecosystem resilience. Multitemporal lidar-based consumption estimates can inform emissions estimates and provide independent validation of conventional fire emission inventories. Our methods also provide a remote sensing framework that could be applied in other areas where airborne lidar is available for quantifying relationships between fuels and time since fire across landscapes.

Antecedentes: La caracterización de la distribución física de los combustibles a través de paisajes heterogéneos es necesaria para entender el comportamiento del fuego, contabilizar las emisiones de humo, y manejar los ecosistemas para su resiliencia. Las mediciones mediante sensores remotos a varias escalas, aportan mapas para mejorar modelos de fuegos y dispersión de humos. Las mediciones con LIDAR aerotransportados que determinan directamente variaciones en altura y densidad de la vegetación, han probado ser especialmente útiles para el mapeo de la carga y el consumo de combustible a escala de paisaje. Predijimos la carga de combustibles en la planicie de Kaibab en el norte de Arizona, en los EEUU, estimamos el consumo a través de los incendios de Castle e Ikes de 2019, mediante la substracción de la carga de mapas de carga del 2020 menos los de 2019, y examinamos las relaciones entre el mapeo de los combustibles superficiales y años desde el fuego, registrados en la base de datos titulada Monitoreo de las Tendencias de la Severidad de los incendios (MTBS). Resultados: Las correlaciones de R2 entre valores de cargas predichos y aquellos de observaciones de campo fueron 50, 39, 59, y 48% para combustible disponible en el dosel, combustibles de 1 a 1000 h, mantillo y hojarasca por debajo del mantillo (duff), y combustible total superficial (la suma de combustibles de 1 a 1000 h y del mantillo y la hojarasca subyacente), respectivamente. Las medidas del LIDAR que describían la distribución del dosel y su densidad, la densidad del sotobosque, las medidas históricas de fuego provistas por el Landsat y la altura (elevación) fueron predictores importantes. Las cargas de combustibles mapeadas fueron positivamente y no linealmente relacionadas al tiempo desde el fuego, con asíntotas hacia cargas de combustible estables entre 10 y 15 años post fuego. El consumo de la carga de combustibles en superficie promedió 16,1 y 14,0 Mg por ha para los incendios de Castle e Ikes, respectivamente y fue positivamente correlacionada con la diferencia normalizada de la relación de quema (dNBR). Estimamos que el consumo del combustible superficial fue de 125,3 ± 54,6 Gg para el incendio de Castle y 27,6 ± 12,0 Gg para la porción del incendio de Ikes (42%), del cual los datos de LIDAR aerotransportados (pre y post fuego), estaban disponibles. Conclusiones: Demostramos y reforzamos que tanto el dosel como los combustibles superficiales pueden ser predichos y mapeados con una moderada precisión usando datos de LIDAR aerotransportados. Las medidas históricas de fuego provistas por el Landsat ayudaron a determinar la variación espacial y temporal de la carga de los combustibles superficiales y nos permitieron describir tendencias temporales en las cargas de combustible superficiales. Nuestros mapas y métodos de consumo y cargas de combustible son de utilidad para los gestores de recursos e investigadores que necesitan de estimaciones amplias de carga de combustible y emisiones a escala de paisaje. Los mapas de carga de combustibles basados en sensores remotos activos pueden ser usados para informar sobre decisiones de manejo de combustible y determinar metas de estructuras de cargas, promoviendo de esa manera la resiliencia del ecosistema. Las estimaciones de consumo basadas en LIDAR multitemporal pueden informar sobre estimaciones de emisiones y proveer de una validación de inventarios convencionales de emisiones por fuegos. Nuestros métodos también proveen de un marco conceptual de sensores remotos que pueden ser aplicados en otras áreas donde el LIDAR aerotransportado está disponible para cuantificar relaciones entre combustibles y tiempo desde el fuego en diferentes paisajes.

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. We included a sensitivity analysis that varied calculated disturbance probabilities and the dClim rule, which is one method used by Climate-FVS to introduce climate-related mortality. The dClim rule initiates mortality when the predicted climate change at a site is greater than the change in climate associated with a predetermined shift in elevation. RESULTS: Results of the simulations indicated the dClim rule influenced future carbon projections more than estimates of disturbance probability. Future aboveground carbon estimates increased and species composition remained stable under the no climate change scenario. The future climate scenario we tested resulted in less carbon at the end of the projections compared to the no climate change scenarios for all cases except when the dClim rule was disengaged on the Payette NF. Under the climate change scenario, species compositions shifted to climatically adapted species or early successional species. CONCLUSION: This research highlights the need to consider climate projections in long-term planning or future forest conditions may be unexpected. Forest managers and planners could perform similar simulations and use the results as a planning tool when analyzing climate change effects at the National Forest level.

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. Because fire can act as a stochastic agent of mortality, we hypothesized that a neutral mechanism might be responsible for maintaining plant diversity. We used the demographic parameters of the unified neutral theory of biodiversity (UNTB) as a foundation to model groundcover species richness, using a southeastern US pine woodland as an example. We followed the fate of over 7,000 individuals of 123 plant species for 4 years and two prescribed burns in frequently burned Pinus palustris sites in northwest FL, USA. Using these empirical data and UNTB-based assumptions, we developed two parsimonious autonomous agent models, which were distinct by spatially explicit and implicit local recruitment processes. Using a parameter sensitivity test, we examined how empirical estimates, input species frequency distributions, and community size affected output species richness. We found that dispersal limitation was the most influential parameter, followed by mortality and birth, and that these parameters varied based on scale of the frequency distributions. Overall, these nominal parameters were useful for simulating fine-scale groundcover communities, although further empirical analysis of richness patterns, particularly related to fine-scale burn severity, is needed. This modeling framework can be utilized to examine our premise that localized groundcover assemblages are neutral communities at high fire frequencies, as well as to examine the extent to which niche-based dynamics determine community dynamics when fire frequency is altered.

Forest structure and downed woody debris in boreal, temperate, and tropical forest fragments.

Forest fragmentation affects the heterogeneity of accumulated fuels by increasing the diversity of forest types and by increasing forest edges. This heterogeneity has implications in how we manage fuels, fire, and forests. Understanding the relative importance of fragmentation on woody biomass within a single climatic regime, and along climatic gradients, will improve our ability to manage forest fuels and predict fire behavior. In this study we assessed forest fuel characteristics in stands of differing moisture, i.e., dry and moist forests, structure, i.e., open canopy (typically younger) vs. closed canopy (typically older) stands, and size, i.e., small (10-14 ha), medium (33 to 60 ha), and large (100-240 ha) along a climatic gradient of boreal, temperate, and tropical forests. We measured duff, litter, fine and coarse woody debris, standing dead, and live biomass in a series of plots along a transect from outside the forest edge to the fragment interior. The goal was to determine how forest structure and fuel characteristics varied along this transect and whether this variation differed with temperature, moisture, structure, and fragment size. We found nonlinear relationships of coarse woody debris, fine woody debris, standing dead and live tree biomass with mean annual median temperature. Biomass for these variables was greatest in temperate sites. Forest floor fuels (duff and litter) had a linear relationship with temperature and biomass was greatest in boreal sites. In a five-way multivariate analysis of variance we found that temperature, moisture, and age/structure had significant effects on forest floor fuels, downed woody debris, and live tree biomass. Fragment size had an effect on forest floor fuels and live tree biomass. Distance from forest edge had significant effects for only a few subgroups sampled. With some exceptions edges were not distinguishable from interiors in terms of fuels.

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. We found that: i) aspen stakes decayed fastest in the tropical sites, and the slowest in the temperate forest fragments, ii) the percent of mass remaining was significantly greater in dry than in moist forests in boreal and temperate fragments, while the opposite was true for the tropical forest fragments, iii) no effect of fragment size on the percent of mass remaining of aspen stakes in the boreal sites, temperate dry, and tropical moist forests, and iv) no significant differences of aspen wood decay between forest edges and interior forest in boreal, temperate and tropical fragments. We conclude that: i) moisture condition is an important control over wood decomposition over broad climate gradients; and that such relationship can be non linear, and ii) the presence of a particular group of organism (termites) can significantly alter the decay rates of wood more than what might be predicted based on climatic factors alone. Biotic controls on wood decay might be more important predictors of wood decay in tropical regions, while abiotic constraints seems to be important determinants of decay in cold forested fragments.

Characterizing forest fragments in boreal, temperate, and tropical ecosystems.

An increased ability to analyze landscapes in a spatial manner through the use of remote sensing leads to improved capabilities for quantifying human-induced forest fragmentation. Developments of spatially explicit methods in landscape analyses are emerging. In this paper, the image delineation software program eCognition and the spatial pattern analysis program FRAGSTATS were used to quantify patterns of forest fragments on six landscapes across three different climatic regions characterized by different moisture regimes and different influences of human pressure. Our results support the idea that landscapes with higher road and population density are more fragmented; however, there are other, equally influential factors contributing to fragmentation, such as moisture regime, historic land use, and fire dynamics. Our method provided an objective means to characterize landscapes and assess patterns of forest fragments across different forested ecosystems by addressing the limitations of pixel-based classification and incorporating image objects.

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. Specific to tropical Africa, the tendency to incorrectly infer that tropical diseases are uniformly prevalent has been partially overcome with solid epidemiologic data. This finer resolution data is important in multiple contexts, including understanding risk, predictive value in disease diagnosis, and population immunity. We hypothesized that within the context of a tropical climate, vector-borne pathogen prevalence would significantly differ according to zonal differences in rainfall, temperature, relative humidity and vegetation condition. We then determined if these environmental data were predictive of pathogen prevalence. First we determined the prevalence of three major pathogens of cattle, Anaplasma marginale, Babesia bigemina and Theileria spp, in the three vegetation zones where cattle are predominantly raised in Ghana: Guinea savannah, semi-deciduous forest, and coastal savannah. The prevalence of A. marginale was 63%, 26% for Theileria spp and 2% for B. bigemina. A. marginale and Theileria spp. were significantly more prevalent in the coastal savannah as compared to either the Guinea savanna or the semi-deciduous forest, supporting acceptance of the first hypothesis. To test the predictive power of environmental variables, the data over a three year period were considered in best subsets multiple linear regression models predicting prevalence of each pathogen. Corrected Akaike Information Criteria (AICc) were assigned to the alternative models to compare their utility. Competitive models for each response were averaged using AICc weights. Rainfall was most predictive of pathogen prevalence, and EVI also contributed to A. marginale and B. bigemina prevalence. These findings support the utility of environmental data for understanding vector-borne disease epidemiology on a regional level within a tropical environment.

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

Red-naped sapsuckers (Sphyrapicus nuchalis) are functionally important because they create sapwells and cavities that other species use for food and nesting. Red-naped sapsucker ecology within aspen (Populus tremuloides) 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. We surveyed for sapsuckers at 162 sites in northern Idaho, USA, during 2009 and 2010. We used occupancy models and an information-theoretic approach to model sapsucker occupancy as a function of four LiDAR-based metrics that characterized vegetation structure and tree harvest, and one non-LiDAR metric that characterized distance to major roads. We evaluated model support across a range of territory sizes using Akaike's information criterion. Top model support was highest at the 4-ha extent, which suggested that 4 ha was the most relevant scale describing sapsucker occupancy. Sapsuckers were positively associated with variation of canopy height and harvested area, and negatively associated with shrub and large tree density. These results suggest that harvest regimes and structural diversity of vegetation at moderate extents (e.g., 4 ha) largely influence occurrence of red-naped sapsuckers in conifer forests. Given the current and projected declines of aspen populations, it will be increasingly important to assess habitat relationships, as well as demographic characteristics, of aspen-associated species such as red-naped sapsuckers within conifer-dominated systems to meet future management and conservation goals.

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. Harvest and prescribed fire management treatments followed by plantings of one of four regionally important commercial tree species were simulated, using the climate-sensitive version of the Forest Vegetation Simulator, to estimate the biomass of four different planted species and their C sequestration response to three climate change scenarios. RESULTS: Results show that anticipated climate change induces a substantial decrease in C sequestration potential regardless of which of the four tree species tested are planted. It was also found that Pinus monticola has the highest capacity to sequester C by 2110, followed by Pinus ponderosa, then Pseudotsuga menziesii, and lastly Larix occidentalis. CONCLUSIONS: Variability in the growth responses to climate change exhibited by the four planted species considered in this study points to the importance to forest managers of considering how well adapted seedlings may be to predicted climate change, before the seedlings are planted, and particularly if maximizing C sequestration is the management goal.

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). GLAS data occurred within ca. 64 m diameter ellipses spaced a minimum of 172 m apart, and all occupancy analyses were confined to this grain scale. Using a hierarchical approach, we modeled Red-naped sapsucker occupancy as a function of LiDAR metrics derived from both platforms. Occupancy models based on satellite data were weak, possibly because the data within the GLAS ellipse did not fully represent habitat characteristics important for this species. The most important structural variables influencing Red-naped Sapsucker breeding site selection based on airborne LiDAR data included foliage height diversity, the distance between major strata in the canopy vertical profile, and the vegetation density near the ground. These characteristics are consistent with the diversity of foraging activities exhibited by this species. To our knowledge, this study represents the first to examine the utility of satellite-based LiDAR to model animal distributions. The large area of each GLAS ellipse and the non-contiguous nature of GLAS data may pose significant challenges for wildlife distribution modeling; nevertheless these data can provide useful information on ecosystem vertical structure, particularly in areas of gentle terrain. Additional work is thus warranted to utilize LiDAR datasets collected from both airborne and past and future satellite platforms (e.g. GLAS, and the planned IceSAT2 mission) with the goal of improving wildlife modeling for more locations across the globe.

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

ABSTRACT 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. The best LiDAR-derived metrics to predict HD, HM and TD were H99TH, HSD, SKE and HMIN. The Imputation model using the selected metrics was more effective for retrieving height than tree density. The model coefficients of determination (adj.R2) and a root mean squared difference (RMSD) for HD, HM and TD were 0.90, 0.94, 0.38m and 6.99, 5.70, 12.92%, respectively. Our results show that LiDAR and k-NN imputation can be used to predict stand heights with high accuracy in Pinus taeda. However, furthers studies need to be realized to improve the accuracy prediction of TD and to evaluate and compare the cost of acquisition and processing of LiDAR data against the conventional inventory procedures.

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

ABSTRACT 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. Multiple linear regression models for predicting BA from LiDAR-derived metrics were developed and evaluated for predictive power and parsimony. The best model to predict BA from a family of six models was selected based on corrected Akaike Information Criterion (AICc) and assessed by the adjusted coefficient of determination (adj. R²) and root mean square error (RMSE). The best model revealed an adj. R²=0.93 and RMSE=7.74%. Leave one out cross-validation of the best regression model was also computed, and revealed an adj. R² and RMSE of 0.92 and 8.31%, respectively. This study showed that LiDAR-derived metrics can be used to predict BA in Pinus taeda plantations in south Brazil with high precision. We conclude that there is good potential to monitor growth in this type of plantations using airborne LiDAR. We hope that the promising results for BA modeling presented herein will stimulate to operate this technology in Brazil.