Search Results (42)

Focusing on the Pārijāta Tree on the summit of Mount Sumeru, the centre of Asian cosmology, this study analyses its depictions and images. These include Chinese translations of Buddhist texts, the most notable depictions in the handscrolls from the Dūn Huáng and Harvard Art Museums, its representations in Japanese classical literature and early modern Japanese illustrations of Mount Sumeru. Finally, drawing from the discussions on trees in the Buddhist texts, A Forest of Pearls from the Dharma Garden [法苑珠林, Fǎ yuàn zhū lín], the study also addresses various issues surrounding tree felling, which are relevant to the current concerns of environmental protection. I argue that the Pārijāta Tree, positioned as the heavenly king of trees, holds significance as a core figure at the centre of the cosmos. The Pārijāta Tree can be said to serve as a metaphor for the supreme state pursued by Buddhist practitioners. Furthermore, this study suggests that issues related to Asian cosmology or worldviews should be pursued as important subjects in future research on environmental literature. Full article

This study investigated the prediction of unconfined compressive strength (UCS), a common measure of soil’s undrained shear strength, using fundamental soil characteristics. While traditional pavement subgrade design often relies on parameters like the resilient modulus and California bearing ratio (CBR), researchers are exploring the potential of incorporating more easily obtainable strength indicators, such as UCS. To evaluate the potential effectiveness of UCS for pavement engineering applications, a dataset of 152 laboratory-tested soil samples was compiled to develop predictive models. For each sample, geotechnical properties including the Atterberg limits, liquid limit (LL), plastic limit (PL), water content (WC), and bulk density (determined using the Harvard miniature compaction apparatus), alongside the UCS, were measured. This dataset served to train various models to estimate the UCS from basic soil parameters. The methods employed included multi-linear regression (MLR), multi-nonlinear regression (MNLR), and several machine learning techniques: backpropagation artificial neural networks (ANNs), gradient boosting (GB), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN). The aim was to establish a relationship between the dependent variable (UCS) and the independent basic geotechnical properties and to test the effectiveness of each ML algorithm in predicting UCS. The results indicate that the ANN-based model provided the most accurate predictions for UCS, achieving an R² of 0.83, a root-mean-squared error (RMSE) of 1.11, and a mean absolute relative error (MARE) of 0.42. The performance ranking of the other models, from best to worst, was RF, GB, SV, KNN, MLR, and MNLR. Full article

Forest canopy height (FCH) is a vital indicator for assessing forest health and ecosystem service capacity. Over the past two decades, full-waveform (FW) LiDAR has been widely employed for estimating forest biophysical variables due to its high precision in measuring vertical forest structures. However, the impact of terrain undulations on forest parameter estimation remains challenging. To address this issue, this study proposes a bisection approximation decomposition (BAD) method for processing GEDI L1B data and FCH estimation. The BAD method analyzes the energy composition of simplified echo signals and determines the fitting parameters by integrating overall signal energy, the differences in unresolved signals, and the similarity of inter-forest signal characteristics. FCH is subsequently estimated based on waveform peak positions. By dynamically adjusting segmentation points and Gaussian fitting parameters, the BAD method achieved precise separation of mixed canopy and ground signals, substantially enhancing the physical realism and applicability of decomposition results. The effectiveness and robustness of the BAD method for FCH estimation were evaluated using 2049 footprints across varying slope conditions in the Harvard Forest region of Petersham, Massachusetts. The results demonstrated that digital terrain models (DTMs) extracted using the GEDI data and the BAD method exhibited high consistency with the DTMs derived using airborne laser scanning (ALS) data (coefficient of determination R² > 0.99). Compared with traditional Gaussian decomposition (GD), wavelet decomposition (WD), and deconvolution decomposition (DD) methods, the BAD method showed significant advantages in FCH estimation, achieved the smallest relative root mean square error (rRMSE) of 17.19% and greatest mean estimation accuracy of 84.57%, and reduced the rRMSE by 10.74%, 21.49%, and 28.93% compared to GD, WD, and DD methods, respectively. Moreover, the BAD method exhibited a significantly stronger correlation with ALS-derived canopy height mode data than the relative height metrics from GEDI L2A products (r = 0.84, p < 0.01). The robustness and adaptability of the BAD method to complex terrain conditions provide great potential for forest parameters using GEDI data. Full article

The mechanisms underlying severe allergic asthma are complex and unknown, meaning it is a challenge to provide the most appropriate treatment. This study aimed to identify novel biomarkers for stratifying allergic asthmatic patients according to severity, and to uncover the biological mechanisms that lead to the development of the severe uncontrolled phenotype. By using miRNA PCR panels, we analyzed the expression of 752 miRNAs in serum samples from control subjects (n = 15) and mild (n = 11) and severe uncontrolled (n = 10) allergic asthmatic patients. We identified 40 differentially expressed miRNAs between severe uncontrolled and mild allergic asthmatic patients. Functional enrichment analysis revealed signatures related to inflammation, angiogenesis, lipid metabolism and mRNA regulation. A random forest classifier trained with DE miRNAs achieved a high accuracy of 97% for severe uncontrolled patient stratification. Validation of the identified biomarkers was performed on a subset of allergic asthmatic patients from the CAMP cohort at Brigham and Women’s Hospital, Harvard Medical School. Four of these miRNAs (hsa-miR-99b-5p, hsa-miR-451a, hsa-miR-326 and hsa-miR-505-3p) were validated, pointing towards their potential as biomarkers for stratifying allergic asthmatic patients by severity and providing insights into severe uncontrolled asthma molecular pathways. Full article

Cerebral strokes, the abrupt cessation of blood flow to the brain, lead to a cascade of events, resulting in cellular damage due to oxygen and nutrient deprivation. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. This research investigates the application of robust machine learning (ML) algorithms, including logistic regression (LR), random forest (RF), and K-nearest neighbor (KNN), to the prediction of cerebral strokes. Stroke data is collected from Harvard Dataverse Repository. The data includes—clinical, physiological, behavioral, demographic, and historical data. The Synthetic Minority Oversampling Technique (SMOTE), adaptive synthetic sampling (ADASYN), and the Random Oversampling Technique (ROSE) are used to address class imbalances to improve the accuracy of minority classes. To address the challenge of forecasting strokes from partial and imbalanced physiological data, this study introduces a novel hybrid ML approach by combining a machine learning method with an oversampling technique called ADASYN_RF. ADASYN is an oversampling technique used to resample the imbalanced dataset then RF is implemented on the resampled dataset. Also, other oversampling techniques and ML models are implemented to compare the results. Notably, the RF algorithm paired with ADASYN achieves an exceptional performance of 99% detection accuracy, exhibiting its dominance in stroke prediction. The proposed approach enables cost-effective, precise stroke prediction, providing a valuable tool for clinical diagnosis. Full article

Imaging spectroscopy (hyperspectral sensing) is a proven tool for mapping and monitoring the spatial distribution of vegetation species composition. However, there exists a gap when it comes to the availability of high-resolution spatial and spectral imagery for accurate tree species mapping, particularly in complex forest environments, despite the continuous advancements in operational remote sensing and field sensor technologies. Here, we aim to bridge this gap by enhancing our fundamental understanding of imaging spectrometers via complex simulated environments. We used DIRSIG, a physics-based, first-principles simulation approach to model canopy-level reflectance for 3D plant models and species-level leaf reflectance in a synthetic forest scene. We simulated a realistic scene, based on the same species composition, found at Harvard Forest, MA (USA). Our simulation approach allowed us to better understand the interplay between instrument parameters and landscape characteristics, and facilitated comprehensive traceability of error budgets. To enhance our understanding of the impact of sensor design on classification performance, we simulated image samples at different spatial, spectral, and scale resolutions (by modifying the pixel pitch and the total number of pixels in the sensor array, i.e., the focal plane dimension) of the imaging sensor and assessed the performance of a deep learning-based convolutional neural network (CNN) and a traditional machine learning classifier, support vector machines (SVMs), to classify vegetation species. Overall, across all resolutions and species mixtures, the highest classification accuracy varied widely from 50 to 84%, and the number of genus-level species classes identified ranged from 2 to 17, among 24 classes. Harnessing this simulation approach has provided us valuable insights into sensor configurations and the optimization of data collection methodologies to improve the interpretation of spectral signatures for accurate tree species mapping in forest scenes. Note that we used species classification as a proxy for a host of imaging spectroscopy applications. However, this approach can be extended to other ecological scenarios, such as in evaluating the changing ecosystem composition, detecting invasive species, or observing the effects of climate change on ecosystem diversity. Full article

Monitoring land surface phenology plays a fundamental role in quantifying the impact of climate change on terrestrial ecosystems. Shifts in land surface spring phenology have become a hot spot in the field of global climate change research. While numerous studies have used satellite data to capture the interannual variation of the start of the growing season (SOS), the understanding of spatiotemporal performances of SOS needs to be enhanced. In this study, we retrieved the annual SOS from the Moderate Resolution Imaging Spectroradiometer (MODIS) two-band enhanced vegetation index (EVI2) time series in the conterminous United States from 2001 to 2021, and explored the spatial and temporal patterns of SOS and its trend characteristics in different land cover types. The performance of the satellite-derived SOS was evaluated using the USA National Phenology Network (USA-NPN) and Harvard Forest data. The results revealed that SOS exhibited a significantly delayed trend of 1.537 days/degree (p < 0.01) with increasing latitude. The timing of the satellite-derived SOS was significantly and positively correlated with the in-situ data. Despite the fact that the overall trends were not significant from 2001 to 2021, the SOS and its interannual variability exhibited a wide range of variation across land cover types. The earliest SOS occurred in urban and built-up land areas, while the latest occurred in cropland areas. In addition, mixed trends in SOS were observed in sporadic areas of different land cover types. Our results found that (1) warming hiatus slows the advance of land surface spring phenology across the conterminous United States under climate change, and (2) large-scale land surface spring phenology trends extraction should consider the potential effects of different land cover types. To improve our understanding of climate change, we need to continuously monitor and analyze the dynamics of the land surface spring phenology. Full article

With the wide spread of massive open online courses ( MOOC ), millions of people have enrolled in many courses, but the dropout rate of most courses is more than 90%. Accurately predicting the dropout rate of MOOC is of great significance to prevent learners’ dropout behavior and reduce the dropout rate of students. Using the PH278x curriculum data on the Harvard X platform in spring 2013, and based on the statistical analysis of the factors that may affect learners’ final completion of the curriculum from two aspects: learners’ own characteristics and learners’ learning behavior, we established the MOOC dropout rate prediction models based on logical regression, K nearest neighbor and random forest, respectively. Experiments with five evaluation metrics (accuracy, precision, recall, F1 and AUC) show that the prediction model based on random forest has the highest accuracy, precision, F1 and AUC, which are 91.726%, 93.0923%, 95.4145%, 0.925341, respectively, its performance is better than that of the prediction model based on logical regression and that of the model based on K-nearest neighbor, whose values of these metrics are 91.395%, 92.8674%, 95.2337%, 0.912316 and 91.726%, 93.0923%, 95.4145% and 0.925341, respectively. As for recall metrics, the value of random forest is higher than that of KNN, but slightly lower than that of logistic regression, which are 0.992476, 0.977239 and 0.978555, respectively. Then, we conclude that random forests perform best in predicting the dropout rate of MOOC learners. This study can help education staff to know the trend of learners’ dropout behavior in advance, so as to put some measures to reduce the dropout rate before it occurs, thus improving the completion rate of the curriculum. Full article

Islands provide the opportunity to explore management regimes and research issues related to the isolation, uniqueness, and integrity of ecological systems. K’gari (Fraser Island) is an Australian World Heritage property listed based on its outstanding natural value, specifically, the unique wilderness characteristics and the diversity of ecosystem types. Our goal was to draw on an understanding of the natural and cultural environment of K’gari as a foundation on which to build a management model that includes First Nations Peoples in future management and research. Our research involved an analysis of papers in the peer-reviewed scientific literature, original reports, letters, and other manuscripts now housed in the K’gari Fraser Island Research Archive. The objectives of the research were: (1) to review key historical events that form the cultural, social, and environmental narrative; (2) review the major natural features of the island and threats; (3) identify the gaps in research; (4) analyse the management and conservation challenges associated with tourism, biosecurity threats, vegetation management practices, and climate change and discuss whether the requirements for sustaining island ecological integrity can be met in the future; and (5) identify commonalities and general management principles that may apply globally to other island systems and other World Heritage sites listed on the basis of their unique natural and cultural features. We found that the characteristics that contribute to island uniqueness are also constraints for research funding and publication; however, they are important themes that warrant more investment. Our review suggests that K’gari is a contested space between tourist visitation and associated environmental impacts, with an island that has rich First Nations history, extraordinary ecological diversity, and breathtaking aesthetic beauty. This juxtaposition is reflected in disparate views of custodianship and use, and the management strategies are needed to achieve multiple objectives in an environmentally sustainable way whilst creating cultural equity in modern times. We offer a foundation on which to build a co-management model that includes First Nations Peoples in governance, management, research, and monitoring. Full article

The accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels (1—Hopeless, 5—Good condition for long term) by two groups (Model-A with 16, and Model-B with 13 examiners) based on 17 clinical determining factors selected from the HSDM-CTPC. Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm. These three methods were evaluated against the gold standard data determined by consensus of three experienced prosthodontists, and their accuracy was analyzed. The decision tree classifier indicated the highest accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient boosting classifier and the random forest classifier was 0.6896, 0.6687, and 0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best accuracy among the three methods. The study contributes to the implementation of AI in the decision-making process of tooth prognosis in consideration of the treatment plan. Full article

Climate change is leading to increased drought intensity and fire frequency, creating early-successional landscapes with novel disturbance–recovery dynamics. In the Klamath Mountains of northwestern California and southwestern Oregon, early-successional interactions between nitrogen (N)-fixing shrubs (Ceanothus spp.) and long-lived conifers (Douglas-fir) are especially important determinants of forest development. We sampled post-fire vegetation and soil biogeochemistry in 57 plots along gradients of time since fire (7–28 years) and climatic water deficit (aridity). We found that Ceanothus biomass increased, and Douglas-fir biomass decreased with increasing aridity. High aridity and Ceanothus biomass interacted with lower soil C:N more than either factor alone. Ceanothus biomass was initially high after fire and declined with time, suggesting a large initial pulse of N-fixation that could enhance N availability for establishing Douglas-fir. We conclude that future increases in aridity and wildfire frequency will likely limit post-fire Douglas-fir establishment, though Ceanothus may ameliorate some of these impacts through benefits to microclimate and soils. Results from this study contribute to our understanding of the effects of climate change and wildfires on interspecific interactions and forest dynamics. Management seeking to accelerate forest recovery after high-severity fire should emphasize early-successional conifer establishment while maintaining N-fixing shrubs to enhance soil fertility. Full article

Vegetation indices are widely used to derive land surface phenology (LSP). However, due to inconsistent illumination geometries, reflectance varies with solar zenith angles (SZA), which in turn affects the vegetation indices, and thus the derived LSP. To examine the SZA effect on LSP, the MODIS bidirectional reflectance distribution function (BRDF) product and a BRDF model were employed to derive LSPs under several constant SZAs (i.e., 0°, 15°, 30°, 45°, and 60°) in the Harvard Forest, Massachusetts, USA. The LSPs derived under varying SZAs from the MODIS nadir BRDF-adjusted reflectance (NBAR) and MODIS vegetation index products were used as baselines. The results show that with increasing SZA, NDVI increases but EVI decreases. The magnitude of SZA-induced NDVI/EVI changes suggests that EVI is more sensitive to varying SZAs than NDVI. NDVI and EVI are comparable in deriving the start of season (SOS), but EVI is more accurate when deriving the end of season (EOS). Specifically, NDVI/EVI-derived SOSs are relatively close to those derived from ground measurements, with an absolute mean difference of 8.01 days for NDVI-derived SOSs and 9.07 days for EVI-derived SOSs over ten years. However, a considerable lag exists for EOSs derived from vegetation indices, especially from the NDVI time series, with an absolute mean difference of 14.67 days relative to that derived from ground measurements. The SOSs derived from NDVI time series are generally earlier, while those from EVI time series are delayed. In contrast, the EOSs derived from NDVI time series are delayed; those derived from the simulated EVI time series under a fixed illumination geometry are also delayed, but those derived from the products with varying illumination geometries (i.e., MODIS NBAR product and MODIS vegetation index product) are advanced. LSPs derived from varying illumination geometries could lead to a difference spanning from a few days to a month in this case study, which highlights the importance of normalizing the illumination geometry when deriving LSP from NDVI/EVI time series. Full article

Palm oil has been widely criticised for its high environmental impacts, leading to calls to replace it with alternative vegetable oils in food and cosmetic products. However, substituting palm oil would be environmentally beneficial only if the environmental footprint per litre oil were lower than those of alternative vegetable oils. Whether this is the case is not obvious, given the high oil yields of oil palm of up to 10 times those of alternative crops. Here, we combine global agricultural and environmental datasets to show that, among the world’s seven major vegetable oil crops (oil palm, soybean, rapeseed, sunflower, groundnut, coconut, olive), oil palm has the lowest average species richness and carbon footprint associated with an annual production of one litre of vegetable oil. For each crop, these yield-adjusted footprints differ substantially between major producer countries, which we find to be largely the result of differences in crop management. Closing agricultural yield gaps of oil crops through improved management practices would significantly reduce the environmental footprints per oil yield. This would minimise the need for further land conversion to oil cropland and indeed could increase production to such an extent that a significant area of oil croplands could be ecologically restored. Full article

Forest carbon sequestration offset protocols have been employed for more than 20 years with limited success in slowing deforestation and increasing forest carbon trading volume. Direct measurement of forest carbon flux improves quantification for trading but has not been applied to forest carbon research projects with more than 600 site installations worldwide. In this study, we apply carbon accounting methods, scaling hours to decades to 28-years of scientific CO₂ eddy covariance data for the Harvard Forest (US-Ha1), located in central Massachusetts, USA and establishing commercial carbon trading protocols and applications for similar sites. We illustrate and explain transactions of high-frequency direct measurement for CO₂ net ecosystem exchange (NEE, gC m⁻² year⁻¹) that track and monetize ecosystem carbon dynamics in contrast to approaches that rely on forest mensuration and growth models. NEE, based on eddy covariance methodology, quantifies loss of CO₂ by ecosystem respiration accounted for as an unavoidable debit to net carbon sequestration. Retrospective analysis of the US-Ha1 NEE times series including carbon pricing, interval analysis, and ton-year exit accounting and revenue scenarios inform entrepreneur, investor, and landowner forest carbon commercialization strategies. CO₂ efflux accounts for ~45% of the US-Ha1 NEE, an error of ~466% if excluded; however, the decades-old coupled human and natural system remains a financially viable net carbon sink. We introduce isoflux NEE for t¹³C¹⁶O₂ and t¹²C¹⁸O¹⁶O to directly partition and quantify daytime ecosystem respiration and photosynthesis, creating new soil carbon commerce applications and derivative products in contrast to undifferentiated bulk soil carbon pool approaches. Eddy covariance NEE methods harmonize and standardize carbon commerce across diverse forest applications including, a New England, USA regional eddy covariance network, the Paris Agreement, and related climate mitigation platforms. Full article

Forest insects and pathogens have significant impacts on U.S. forests, annually affecting an area nearly three times that of wildfires and timber harvesting combined. However, coupled with these direct effects of forest insects and pathogens are the indirect impacts through influencing forest management practices, such as harvesting. In an earlier study, we surveyed private woodland owners in the northeastern U.S. and 84% of respondents indicated they intended to harvest in at least one of the presented insect invasion scenarios. This harvest response to insects represents a potentially significant shift in the timing, extent, and species selection of harvesting. Here we used the results from the landowner survey, regional forest inventory data, and characteristics of the emerald ash borer (Species: Agrilus planipennis Fairmaire, 1888) invasion to examine the potential for a rapidly spreading invasive insect to alter harvest regimes and affect regional forest conditions. Our analysis suggests that 25% of the woodland parcels in the Connecticut River Watershed in New England may intend to harvest in response to emerald ash borer. If the emerald ash borer continues to spread at its current rate within the region, and therefore the associated management response occurs in the next decade, this could result in an increase in harvest frequencies, from 2.6% year⁻¹ (historically) to 3.7% year⁻¹ through to approximately 2030. If harvest intensities remain at levels found in remeasured Forest Inventory and Analysis plots, this insect-initiated harvesting would result in the removal of 12%–13% of the total aboveground biomass. Eighty-one percent of the removed biomass would be from species other than ash, creating a forest disturbance that is over twice the magnitude than that created by emerald ash borer alone, with the most valuable co-occurring species most vulnerable to biomass loss. Full article