What is a Forest Ecologist Doing Working for NASA: The Use of NASA Technology and Models to Study Land Surface Abiotic and Biotic Processes
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Luvall, Jeffrey C.↗
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Mesoscopic modeling of the uniaxial compression and recovery of vertically aligned carbon nanotube forests
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Bernard K. Wittmaack↗
Detecting ecological signatures of long-term human activity across an elevational gradient in the Šumava Mountains, Central Europe
Central European mountains, including the Šumava Mountains located along the Czechia/Germany border, have a long and rich anthropogenic history. Yet, documenting prehistoric human impact in Central European mountain environments remains a challenge because of the need to disentangle climate and human-caused responses in terrestrial systems. Here, we present the first reconstructed water table depths (WTDs) from two sites, Pěkná and Blatenská slať, located in the Šumava Mountains. We compare these local WTD records with new and published pollen, non-pollen palynomorphs (NPPs), plant macrofossils, geochemistry and archeological records to investigate how changes in local hydrology and human activities impacted forest succession and fire activity throughout the Holocene across an elevational gradient. Using a generalized additive model, our results suggest that changes in forest succession and fire activity have been primarily caused by climate throughout the Holocene. However, humans have been utilizing mountain environments and their resources continuously since ∼4600 cal yr BP, thus playing a secondary role in modifying forest succession to increase resources beneficial to both humans and grazers. Over the last 1000 years, we provide evidence of directly observed human-caused modifications to the landscape. These results contribute to a growing body of literature illustrating human activities and landscape modifications in Central European mountains.
54 ENVIRONMENTAL SCIENCES↗
Microbial communities in the liver and brain are informative for postmortem submersion interval estimation in the late phase of decomposition: A study in mouse cadavers recovered from freshwater
Introduction Bodies recovered from water, especially in the late phase of decomposition, pose difficulties to the investigating authorities. Various methods have been proposed for postmortem submersion interval (PMSI) estimation and drowning identification, but some limitations remain. Many recent studies have proved the value of microbiota succession in viscera for postmortem interval estimation. Nevertheless, the visceral microbiota succession and its application for PMSI estimation and drowning identification require further investigation. Methods In the current study, mouse drowning and CO 2 asphyxia models were developed, and cadavers were immersed in freshwater for 0 to 14 days. Microbial communities in the liver and brain were characterized via 16S rDNA high-throughput sequencing. Results Only livers and brains collected from 5 to 14 days postmortem were qualified for sequencing. There was significant variation between microbiota from liver and brain. Differences in microbiota between the cadavers of mice that had drowned and those only subjected to postmortem submersion decreased over the PMSI. Significant successions in microbial communities were observed among the different subgroups within the late phase of the PMSI in livers and brains. Eighteen taxa in the liver which were mainly related to Clostridium_sensu_stricto and Aeromonas , and 26 taxa in the brain which were mainly belonged to Clostridium_sensu_stricto , Acetobacteroides , and Limnochorda , were selected as potential biomarkers for PMSI estimation based on a random forest algorithm. The PMSI estimation models established yielded accurate prediction results with mean absolute errors ± the standard error of 1.282 ± 0.189 d for the liver and 0.989 ± 0.237 d for the brain. Conclusions The present study provides novel information on visceral postmortem microbiota succession in corpses submerged in freshwater which sheds new light on PMSI estimation based on the liver and brain in forensic practice.
Wang, Linlin↗
Constraining long‐term model predictions for woody growth using tropical tree rings
The strength and persistence of the tropical carbon sink hinges on the long-term responses of woody growth to climatic variations and increasing CO 2 . However, the sensitivity of tropical woody growth to these environmental changes is poorly understood, leading to large uncertainties in growth predictions. Here, we used tree ring records from a Southeast Asian tropical forest to constrain ED2.2-hydro, a terrestrial biosphere model with explicit vegetation demography. Specifically, we assessed individual-level woody growth responses to historical climate variability and increases in atmospheric CO 2 (C a ). When forced with historical C a , ED2.2-hydro reproduced the magnitude of increases in intercellular CO 2 concentration (a major determinant of photosynthesis) estimated from tree ring carbon isotope records. In contrast, simulated growth trends were considerably larger than those obtained from tree rings, suggesting that woody biomass production efficiency (WBPE = woody biomass production:gross primary productivity) was overestimated by the model. The estimated WBPE decline under increasing C a based on model-data discrepancy was comparable to or stronger than (depending on tree species and size) the observed WBPE changes from a multi-year mature-forest CO 2 fertilization experiment. In addition, we found that ED2.2-hydro generally overestimated climatic sensitivity of woody growth, especially for late-successional plant functional types. The model-data discrepancy in growth sensitivity to climate was likely caused by underestimating WBPE in hot and dry years due to commonly used model assumptions on carbon use efficiency and allocation. To our knowledge, this is the first study to constrain model predictions of individual tree-level growth sensitivity to C a and climate against tropical tree-ring data. Our results suggest that improving model processes related to WBPE is crucial to obtain better predictions of tropical forest responses to droughts and increasing C a . Finally, more accurate parameterization of WBPE will likely reduce the stimulation of woody growth by C a rise predicted by biosphere models.
54 ENVIRONMENTAL SCIENCES↗
Carbon Consequences of Forest Disturbance and Recovery Across the Conterminous United States
Forests of North America are thought to constitute a significant long term sink for atmospheric carbon. The United States Forest Service Forest Inventory and Analysis (FIA) program has developed a large data base of stock changes derived from consecutive estimates of growing stock volume in the US. These data reveal a large and relatively stable increase in forest carbon stocks over the last two decades or more. The mechanisms underlying this national increase in forest stocks may include recovery of forests from past disturbances, net increases in forest area, and growth enhancement driven by climate or fertilization by CO2 and Nitrogen. Here we estimate the forest recovery component of the observed stock changes using FIA data on the age structure of US forests and carbon stocks as a function of age. The latter are used to parameterize forest disturbance and recovery processes in a carbon cycle model. We then apply resulting disturbance/recovery dynamics to landscapes and regions based on the forest age distributions. The analysis centers on 28 representative climate settings spread about forested regions of the conterminous US. We estimate carbon fluxes for each region and propagate uncertainties in calibration data through to the predicted fluxes. The largest recovery-driven carbon sinks are found in the South central, Pacific Northwest, and Pacific Southwest regions, with spatially averaged net ecosystem productivity (NEP) of about 100 g C / square m / a driven by forest age structure. Carbon sinks from recovery in the Northeast and Northern Lake States remain moderate to large owing to the legacy of historical clearing and relatively low modern disturbance rates from harvest and fire. At the continental scale, we find a conterminous U.S. forest NEP of only 0.16 Pg C/a from age structure in 2005, or only 0.047 Pg C/a of forest stock change after accounting for fire emissions and harvest transfers. Recent estimates of NEP derived from inventory stock change, harvest, and fire data show twice the NEP sink we derive from forest age distributions. We discuss possible reasons for the discrepancies including modeling errors and the possibility of climate and/or fertilization (CO2 or N) growth enhancements.
Williams, Christopher A.↗
Fire in the Brazilian Amazon: A Spatially Explicit Model for Policy Impact Analysis
This article implements a spatially explicit model to estimate the probability of forest and agricultural fires in the Brazilian Amazon. We innovate by using variables that reflect farmgate prices of beef and soy, and also provide a conceptual model of managed and unmanaged fires in order to simulate the impact of road paving, cattle exports, and conservation area designation on the occurrence of fire. Our analysis shows that fire is positively correlated with the price of beef and soy, and that the creation of new conservation units may offset the negative environmental impacts caused by the increasing number of fire events associated with early stages of frontier development.
Arima, Eugenio Y.↗
Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR
Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6–7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of -4.14 +/- 0.76 MgC/hay. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data.
Pinage, Ekena Rangel↗
Spatial Bayesian models project shifts in suitable habitat for Pacific Northwest tree species under climate change
Abstract We developed spatial Bayesian hierarchical models to assess potential climate change impacts on suitable habitat for five important tree species in the Pacific northwestern United States (California, Oregon, and Washington). Individual‐species models were fit with presence–absence data from forest inventory field plots and spatial relationships were specified through a conditional autoregressive model. This modeling approach allowed us to visualize uncertainty in response curves, map current and future prediction uncertainty, and provide interval estimates for change. Upward elevational or northward latitudinal shifts in climatically suitable habitat were projected for all species. Climate change impacts were the most damaging for noble fir ( Abies procera ), for which 79%–100% of the current range was projected to become climatically unsuitable by the 2080s. Although coastal Douglas‐fir ( Pseudotsuga menziesii var. menziesii ) has been projected by others to gain habitat in Canada, within our study area we projected a net loss of climatically suitable habitat (ca. 8000–31,400 km 2 ) under three of four future climate scenarios. A net loss in habitat was also projected for Oregon white oak ( Quercus garryana ) under three of four scenarios, with 40%–60% of the current range becoming unsuitable. Although there was no net loss of habitat for forest land blue oak under any scenario, other factors like competition may inhibit blue oak ( Quercus douglasii ) and white oak from occupying areas projected to increase in climatic suitability. Additionally, between 13% and 32% of blue oak's current range was projected to become unsuitable; some of these areas aligned with dieback following the 2012–2015 California drought, which our data set predates. Unlike the other four species, we projected a 17%–25% increase in climatically suitable habitat for California black oak ( Quercus kelloggii ), although 1%–20% of the current range was still projected to become unsuitable. Our findings indicate that, although some species will face more pressure in tracking climatically suitable habitat than others, climate change will impact the location of suitable habitat for many species.
Kralicek, Karin↗
Predicting the effectiveness of different mulching techniques in reducing post-fire runoff and erosion at plot scale with the RUSLE, MMF and PESERA models
Highlights: • RUSLE, MMF and PESERA, were able to predict the erosive response in burned areas and their mitigation. • These models can be calibrated at a small spatial scale and provide accurate results at greater spatial scales. • RUSLE model ideal for fast and simple risk assessment. • MMF or PESERA models could be the base of a post-fire management decision tool. Wildfires have become a recurrent threat for many Mediterranean forest ecosystems. The characteristics of the Mediterranean climate, with its warm and dry summers and mild and wet winters, make this a region prone to wildfire occurrence as well as to post-fire soil erosion. This threat is expected to be aggravated in the future due to climate change and land management practices and planning. The wide recognition of wildfires as a driver for runoff and erosion in burnt forest areas has created a strong demand for model-based tools for predicting the post-fire hydrological and erosion response and, in particular, for predicting the effectiveness of post-fire management operations to mitigate these responses. In this study, the effectiveness of two post-fire treatments (hydromulch and natural pine needle mulch) in reducing post-fire runoff and soil erosion was evaluated against control conditions (i.e. untreated conditions), at different spatial scales. The main objective of this study was to use field data to evaluate the ability of different erosion models: (i) empirical (RUSLE), (ii) semi-empirical (MMF), and (iii) physically-based (PESERA), to predict the hydrological and erosive response as well as the effectiveness of different mulching techniques in fire-affected areas. The results of this study showed that all three models were reasonably able to reproduce the hydrological and erosive processes occurring in burned forest areas. In addition, it was demonstrated that the models can be calibrated at a small spatial scale (0.5 m{sup 2}) but provide accurate results at greater spatial scales (10 m{sup 2}). From this work, the RUSLE model seems to be ideal for fast and simple applications (i.e. prioritization of areas-at-risk) mainly due to its simplicity and reduced data requirements. On the other hand, the more complex MMF and PESERA models would be valuable as a base of a possible tool for assessing the risk of water contamination in fire-affected water bodies and for testing different land management scenarios.
54 ENVIRONMENTAL SCIENCES↗
The application of forest classification from Landsat data as a basis for natural hydrocarbon emission estimation and photochemical oxidant model simulations in southeastern Virginia
The possible contribution by natural hydrocarbon emissions to the total ozone budget recorded in the Tidewater region of southeastern Virginia during the height of the summer period was examined. Natural sources investigated were limited to the primary HC emitters and most prevalent natural vegetation, the forests. Three types and their areal coverage were determined for Region VI of the Virginia State Air Pollution Control Board using remotely sensed data from Landsat, a NASA experimental earth resources satellite. Emission factors appropriate to the specific types (coniferous 0.24 x 10 to the 13th, mixed 0.63 x 10 to the 13th, deciduous 1.92 x 10 to the 13th, microgram/h), derived from contemporary procedures, were applied to produce an overall regional emission rate of 2.79 x 10 to the 13th microgram/h for natural non-methane hydrocarbons (NMHC). This rate was used with estimates of the anthropogenic NO(x) and NMHC loading, as input into a photochemical box model. Additional HC loading on the order of that estimated to be produced by the natural forest communities was required in order to reach certain measured summer peak ozone levels as the computer simulation was unable to account for the measured episodic levels on the basis of the anthropogenic inventory alone.
Salop, J.↗
Modeling of SAR returns from a red pine stand
Bright P-band radar returns from red pine forests have been observed on synthetic aperture radar (SAR) images in Bangor, Maine. A plot of red pine trees was selected for the characterization and modeling to understand the cause of the high P-band returns. The red pine stand under study consisted of mature trees. Diameter at breast height (DBH) measurements were made to determine stand density as a function of tree diameter. Soil moisture and bulk density measurements were taken along with ground rough surface profiles. Detailed biomass measurements of the needles, shoots, branches, and trunks were also taken. These site statistics have been used in a distorted Born approximation model of the forest. Computations indicate that the direct-reflected or the double-bounce contributions from the ground are responsible for the high observed P-band returns for HH polarization.
Lang, R. H.↗
Machine-Learning Prediction of CO Adsorption in Thiolated, Ag-Alloyed Au Nanoclusters
Here we propose a machine-learning model, based on the random-forest method, to predict CO adsorption in thiolate protected nanoclusters. Two phases of feature selection and training, based initially on the Au 25 nanocluster, are utilized in our model. One advantage to a machine-learning approach is that correlations in defined features disentangle relationships among the various structural parameters. For example, in Au 25 , we find that features based on the distribution of Ag atoms relative to the CO adsorption site are the most important in predicting adsorption energies. Our machine-learning model is easily extended to other Au-based nanoclusters, and we demonstrate predictions about CO adsorption on Ag-alloyed Au 36 and Au 133 nanoclusters.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The cross-correlation of galaxies in absorption with the Lyman α forest
ABSTRACT We present the first clustering measurement of Strong Blended Lyman α (SBLA) absorption systems by measuring their cross-correlation with the Lyman α forest. SBLAs are a new population of absorbers detected within the Lyman α forest. We find a bias of 2.329 ± 0.057, consistent with that of Damped Lyman α absorbers (DLAs). For DLAs, we recover a bias of 2.331 ± 0.057 larger than previously reported. We also find a redshift space distortion parameter β = 0.417 ± 0.010, also consistent with the recovered value for DLAs (β = 0.416 ± 0.010). This is consistent with SBLA and DLA systems tracing different portions of the circumgalactic medium of a broadly common population of galaxies. Given these common clustering properties, we combined them to perform a cross-correlation of galaxies in absorption with the Lyα forest. We find that the BAO scale uncertainty of this new measurement is 1.75 × that of Lyα autocorrelation and 1.6 × that of the quasar cross-correlation with the Lyα forest. We note that the current preferred metal contamination model for fitting the correlation functions with respect to the Lyα forest is not realistic enough for SBLA systems, likely due to their status as high redshift precision sites of high metal enrichment. Mock spectra including SBLA systems and their associated metal absorption are required to understand this sample fully. We conclude that SBLAs have the potential to complement the standard Lyα cosmological analyses in future surveys.
Astronomy & Astrophysics↗
Effect of Surface Heterogeneity on the Boundary-Layer Height: A Case Study at a Semi-Arid Forest
Here, we investigate the effects of an isolated meso-$\gamma$-scale surface heterogeneity for roughness and albedo on the atmospheric boundary-layer (ABL) height, with a case study at a semi-arid forest surrounded by sparse shrubland (forest area: 28 km 2 , forest length in the main wind direction: 7 km). Doppler lidar and ceilometer measurements at this semi-arid forest show an increase in the ABL height over the forest compared with the shrubland on four out of eight days. The differences in the ABL height between shrubland and forest are explained for all days with a model that assumes a linear growth of the internal boundary layer of the forest through the convective ABL upwind of the forest followed by a square-root growth into the stable free atmosphere. For the environmental conditions that existed during our measurements, the increase in ABL height due to large sensible heat fluxes from the forest (600 W~m -2 in summer) is subdued by stable stratification in the free atmosphere above the ABL, or reduced by high wind speeds in the mixed layer.
54 ENVIRONMENTAL SCIENCES↗
Predicting defects in laser powder bed fusion using in-situ thermal imaging data and machine learning
Variation in the local thermal history during the Laser Powder Bed Fusion (LPBF) process in Additive Manufacturing (AM) can cause micropore defects, which add to the uncertainty of the mechanical properties (e.g., fatigue life, tensile strength) of the built materials. In-situ sensing has been proposed for monitoring the AM process to minimize defects, but successful minimization requires establishing a quantitative relationship between the sensing data and the porosity, which is particularly challenging with a large number of variables (e.g., laser speed, power, scan path, powder property). Physics-based modeling can simulate such an in-situ sensing-porosity relationship, but it is computationally costly. In this work, we develop Machine Learning (ML) models that can use in-situ thermographic data to predict the micropore of LPBF stainless steel materials. This work considers two identified key features from the thermal histories: the time above the apparent melting threshold ($\tau$) and the maximum radiance ( T max ). These features are computed, stored for each voxel in the built material, and then used as inputs. The binary state of each voxel, either defective or normal, is the output. Different ML models are trained and tested for the binary classification task. In addition to using the thermal features of each voxel to predict its own state, the thermal features of neighboring voxels are also included as inputs. This is shown to improve the prediction accuracy, which is consistent with thermal transport physics around each voxel contributing to its final state. Among the models trained, the F1 scores on test sets reach above 0.96 for Random Forests. Feature importance analysis based on the ML models shows that T max is more important to the voxel state than $\tau$ . The analysis also finds that the thermal history of the voxels above the present voxel is more influential than those beneath it. Our study significantly extends the capability of using in-situ thermographic data to predict porosity in LPBF materials. Finally, since ML models are fast, they may play integral roles in the optimization and control of such AM technologies.
36 MATERIALS SCIENCE↗
Soil moisture drives microbial controls on carbon decomposition in two subtropical forests
Knowledge of microbial mechanisms is critical to understand Earth's biogeochemical cycle under climate and environmental changes. However, large uncertainties remain in model simulations and predictions due to the lack of explicit parameterization of microbial data and few applications beyond the laboratory. In addition, most experimental and modeling studies of warming-induced changes in soil carbon (C) focus on temperature sensitivity, neglecting concomitant effects of changes in soil moisture. Soil microbes are sensitive to moisture, and their responses can dramatically impact soil biogeochemical cycles. Here we represent microbial and enzymatic functions in response to changes in moisture in the Microbial-ENzyme Decomposition (MEND) model. Through modeling with long-term field observations from subtropical forests, we demonstrate that parameterization with microbial data in addition to respiration fluxes greatly increases confidence in model simulations. We further employ the calibrated model to simulate the responses of soil organic C (SOC) under multiple environmental change scenarios. The model shows significant increases in SOC in response to decreasing soil moisture and only minor changes in SOC in response to increasing soil temperature. Increasing litter inputs also cause a significant increase in SOC in the pine forest, whereas an insignificant negative effect is simulated in the broadleaf forest. We also demonstrate the co-metabolism mechanism for the priming effects, i.e., more labile inputs to soil could stimulate microbial and enzymatic growth and activity. Furthermore our study provides strong evidence of microbial control over soil C decomposition and suggests the future trajectory of soil C may be more responsive to changes in soil moisture than temperature, particularly in tropical and subtropical environments.
54 ENVIRONMENTAL SCIENCES↗
Hot, cold, or just right? An infrared biometric sensor to improve occupant comfort and reduce overcooling in buildings via closed-loop control
To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗