Field Measurement Data Research Articles

Study on intelligent recognition of urban road subgrade defect based on deep learning.

China's operational highway subgrades exhibit a trend of diversifying types and an increasing number of defects, leading to more frequent urban road safety incidents. This paper starts from the non-destructive testing of urban road subgrade defects using geological radar, aiming to achieve intelligent identification of subgrade pathologies with geological radar. The GprMax forward simulation software is used to establish multi-layer composite structural models of the subgrade, studying the characteristics of geological radar images for different types of subgrade diseases. Based on the forward simulation images of geological radar for subgrade defects and field measurement data, a geological radar subgrade defect image database is established. The Faster R-CNN deep learning algorithm is applied to achieve target detection, recognition, and classification of subgrade defect images. By comparing the loss value, total number of identified regions, and recognition accuracy as metrics, the study compares four improved versions of the Faster R-CNN algorithm. The results indicate that the faster_rcnn_inception_v2 version is more suitable for the intelligent identification of non-destructive testing of urban road subgrade defects.

Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data

Abstract. Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot effectively be achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range light detection and ranging (lidar) data, which enables the measurement of individual tree heights in forest plots with high precision. Apart from lidar data, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and synthetic aperture radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining the basal area of individual tree within plots using uncrewed aerial vehicle (UAV) lidar data, this study calculated the weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at a large spatial scale, we also developed a machine-learning-based mixed-effects (MLME) model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 and 13.3 m with standard deviations of 2.9 and 3.3 m, respectively. The accuracy of mapped products was validated utilizing lidar and field measurement data. The correlation coefficient (r) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while the root mean square error (RMSE) ranged from 2.6 to 4.1 and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024).

Influence depth of highway subgrade under heavy vehicle loads based on a theoretical model

A three-dimensional dynamic model of vehicle-road-unsaturated subgrade coupling is established. The semi-analytical and numerical solutions of three-dimensional dynamic response of highway subgrade under vehicle load are presented for the first time. Then, the accuracy of the theoretical model is verified by the field measurement data. Finally, the spatial distribution of dynamic stress and depth of working zone under different influencing factors are studied, and the calculation model of depth of working zone of highway subgrade is proposed. The results show that the dynamic stresses, σx, σy, σz, and τxz increase significantly with the increase of vehicle axle load and decrease significantly with the increase of road thickness under the same subgrade depth. In addition, with the increase of axle load and the decrease of road thickness, the peak values of dynamic stress σx, σy, σz, and τxz decrease more obviously along the depth of roadbed. In the process of vehicle movement, the traditional subgrade working area can be divided into a sensitive area and an influence area of dynamic load. The depth of these two areas is positively correlated with the axle load of the vehicle and negatively correlated with the thickness of the road surface. The advantage of the model in this paper is that the semi-analytical solution of the total stress component of the soil element in the course of traffic load is given.

Characteristic Analysis of Vertical Tidal Profile Parameters at Tidal Current Energy Site

Many mathematical models have been proposed to estimate vertical tidal current profiles. However, as previous studies have shown that tidal current energy sites have different characteristics in their vertical tidal current profiles, it is necessary to estimate the profiles from field-measured data for practical purposes. In this study, we measured layered tidal currents over two months using an acoustic Doppler current profiler (ADCP) to analyze the characteristics of vertical tidal current profiles at the Jangjuk Strait, a candidate site for tidal current energy. As a result, the power law parameter α and bed roughness β were estimated as 4.51–12.41 and 0.38–0.42, respectively. Additionally, the maximum roughness length representing seabed roughness in the logarithmic profile was estimated as 0.221 m, and the estimated friction velocity was 0.038–0.194 m/s. Furthermore, a high correlation was observed between the depth-averaged tidal current velocity and friction velocities at all sites during flood and ebb tide conditions. A high correlation was also found between the bed roughness, roughness length, and power law exponent at relatively deeper sites. Tidal current energy sites display distinct characteristics compared to other sea areas. Therefore, it is essential to account for field conditions when conducting numerical modeling and design.

Investigation of morphodynamic response to the storm-induced currents and waves in the Bay of Bengal

Storm surges, driven by Tropical Cyclones (TCs), pose a threat to the coastal areas of the Bay of Bengal (BoB), causing sediment transport and beach erosion. This study introduces a coupled hydrodynamic (TELEMAC-2D) -wave (TOMAWAC) -morphodynamic (GAIA) model to investigate morphodynamic changes during storm surges. The cyclonic wind is crucial for precisely predicting storm surges, wind waves, and morphodynamic bed evolution. Thus, this study introduced a modified parametric wind model to enhance wind field representation near and far from the cyclone center. The simulated storm surges and wind waves were validated against in-situ observations for TC Hudhud (2014) and TC Varadah (2016), and the simulated morphodynamic bed evolution was validated with field-measured data collected for TC Nivar (2020). Further, this study evaluates the performance of different bed load transport formulations for predicting morphodynamic bed evolution during TC Nivar (2020) under storm-induced currents and waves. Results indicate that the Engelund-Hansen model is most effective for currents alone, while the Bijker model excels for combined currents and waves along the open coast of BoB. The results also indicate that the incorporation of the effect of waves along with currents has enhanced the predictive capability of the coupled model framework.

The Aerodynamic Assessment and Shot Point Position Accuracy of UAV Seismic Source

An unmanned aerial vehicle (UAV) seismic source, suitable for complex terrains due to its eco-friendly, cost-effective, and efficient nature, operates by hovering and releasing impact objects to generate seismic waves. The aerodynamic behavior of these impact objects is vital for shotpoint position accuracy and drag magnitude. Excessive drag will result in the loss of seismic source energy, whereas the deviations of the shotpoint position will disrupt the geometric structure of the observation system, significantly impacting the quality of seismic data acquisition. Through theoretical calculations, the influencing factors of air resistance and cross-wind deflection are analyzed. Air resistance increases with the increase in mass, cross-sectional area, speed, and drag coefficient. Increasing the mass and throwing height can increase the upper limit of the output, but after the air resistance and gravity are balanced, the output kinetic energy of the drone’s seismic source will no longer increase when the throwing height is increased. Using computational fluid dynamics simulations and field tests, this study examines the aerodynamics of four prevalent impact object designs. The results show that shuttle and spherical structures have the least drag during descent and are less influenced by crosswinds, thus being optimal for this application. Field measurement data are also presented for reference.

Mapping Leaf Mass Per Area and Equivalent Water Thickness from PRISMA and EnMAP

With the continued advancement of spaceborne hyperspectral sensors, hyperspectral remote sensing is evolving as an increasingly pivotal tool for high-precision global monitoring applications. Novel image spectroscopy data, e.g., the PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), can rapidly and non-invasively capture subtle spectral information of terrestrial vegetation, facilitating the precise retrieval of the required vegetation parameters. As critical vegetation traits, Leaf Mass per Area (LMA) and Equivalent Water Thickness (EWT) hold significant importance for comprehending ecosystem functionality and the physiological status of plants. To address the demand for high-precision vegetation parameter datasets, a hybrid modeling approach was proposed in this study, integrating the radiative transfer model PROSAIL and neural network models to retrieve LMA and EWT from PRISMA and EnMAP images. To achieve this objective, canopy reflectance was simulated via PROSAIL, and the optimal band combinations for LMA and EWT were selected as inputs to train neural networks. The evaluation of the hybrid inversion models over field measurements showed that the RMSE values for the LMA and EWT were 4.11 mg·cm−2 and 9.08 mg·cm−2, respectively. The hybrid models were applied to PRISMA and EnMAP images, resulting in LMA and EWT maps displaying adequate spatial consistency, along with cross-validation results showing high accuracy (RMSELMA = 5.78 mg·cm−2, RMSEEWT = 6.84 mg·cm−2). The results demonstrated the hybrid inversion model’s universality and applicability, enabling the retrieval of vegetation parameters from image spectroscopy data and offering a valuable contribution to hyperspectral remote sensing for vegetation monitoring, though the availability of field measurement data remained a significant challenge.

Adaptive Control for Hydronic Radiant Heating System Using Occupant Behaviors in Residential Building

This study proposes an occupant-centric control strategy for residential heating systems, aiming to enhance thermal comfort and reduce energy consumption. A sensor station utilizing a frequency-modulated continuous wave radar sensor was developed to detect occupancy and infer activities within residential spaces. By analyzing field measurement data, schedules for occupancy and activities were established. These schedules were then used to implement a variable control strategy for the hydronic radiant heating system, adjusting its operating characteristics based on the identified activities. The proposed control strategy, which includes resetting the indoor set temperature during unoccupied periods and adjusting it during sleep to account for changes in metabolic rate and clothing insulation, resulted in significant energy savings. Compared to continuous operation, the hydronic radiant heating system’s energy consumption was reduced by approximately 21% on peak load days and up to 34% over three winter months. This study demonstrates the potential of occupant-centric control for achieving substantial energy savings in residential buildings while maintaining occupant thermal comfort.

Research on the Green Construction Technology of Stilt Houses Based on the Climate Adaptation of Transitional Seasons

Stilt houses are extremely adaptable to terrain and climate. However, current indoor thermal environment research in transitional seasons is not prominent. Therefore, in this research, a typical stilt house in southwest China was chosen as the research object and analyzed by combining Climate Consultant climate analysis software and field measurement data. The results showed that the indoor thermal stability of a stilt house was excellent during the transition season, and the attic had the most obvious climate regulation, with the maximum temperature difference between indoors and outdoors being 9 °C when the outdoor temperature was the highest. The difference between the mean radiant temperature and the average air temperature was only 0.04 °C, and the radiant effect of the enclosure on the interior was small. The indoor relative humidity ranged from 63.2% to 85.1%, showing high relative humidity, but the fluctuation was relatively stable. Stilt floors did not play a significant role in climate regulation during the transition season, and the semi-open space structure was more prone to moisture accumulation when the outdoor humidity was high. Regarding practical application, the climate adaptation strategies of shading, cooling, and dehumidification were applied in the transition season, but dehumidification was ineffective.

Field versus laboratory measurements of PFAS sorption by soils and sediments

Sorption by soils and sediments is an important process that influences the distribution, transport, and fate of per and polyfluoroalkyl substances (PFAS) in the environment. Many laboratory studies have been conducted to quantify magnitudes of PFAS sorption as a function of the properties of the PFAS, solutions, and porous media. A critical question is how representative are laboratory-measured sorption magnitudes for field applications. To address this question, log Koc data were compiled from the literature for a range of PFAS of different chain lengths and functional groups. The aggregated field-based data consisted of two types, in-situ measurements comprising paired soil/sediment and water samples and ex-situ measurements obtained from laboratory desorption experiments employing field-contaminated media. These two sets of field-based measurements were compared to a comprehensive data set of standard laboratory batch-type measurements. The compiled data sets represent an extremely wide range of soil and sediment properties. One important novel outcome is the observation that the enhanced sorption of short-chain PFAS observed in several prior laboratory studies is also observed for the field data. This differential enhanced sorption should be accounted for in site investigations and modeling studies. Another relevant outcome is the observation of consistency between measurements obtained with standard batch adsorption experiments and those from desorption studies employing either field-contaminated or artificially-contaminated media. The mean log Koc values determined for the in-situ measured field data were generally larger than the mean laboratory-determined values, particularly for the short-chain PFAS. Notably, however, values for subsets of the laboratory data representing measurements for lower aqueous concentrations or for soils with low organic-carbon contents (<1 %) compared well to the in-situ field data. Measurements compiled for anionic-polyfluoroalkyl, neutral, and cationic PFAS compared well to those for PFCAs and PFSAs. Overall, the results indicate that the laboratory measurements of PFAS sorption were generally representative of the field-derived magnitudes when compared on a consistent basis.

Above ground biomass estimation in the upper Blue Nile basin forests, North-Western Ethiopia

Forest ecosystems play a decisive role in the global climatic condition, as well as, provides a wide range of societal benefits, including fuel-wood, tourism, and ecosystem services are considered as one of the major sources of livelihood for the local people in the upper Blue Nile Basin. Therefore, rapid and accurate estimation of forest biomass is crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans. Because, above-ground biomass (AGB) estimation is important in determining the management, environmental, and economic roles of forests in the Blue Nile basin. The study was aimed at estimating above-ground biomass in the Upper Blue Nile Basin forests by integrating field-measured data with predictors from Sentinel-2 image. The relationship between measured AGB and sentinel-2 derived vegetation indices and biophysical parameters showed a good correlation result (r value ranging from 0.67 to 0.74). A stepwise regression analysis was carried out in order to develop AGB estimation model by identifying the most important variable. The result demonstrated that, green normalized difference vegetation index, leaf area index, fraction of absorbed photosynthetic active radiation and fractional vegetation cover achieved good performance in predicting AGB with R2 value > 0.5. AGB was estimated with a coefficient of determination (R2) of 0.59 adjusted R2 of 0.618 and root mean square error of (RMSE) 38.36 t/ha in comparison to field observations. The maximum AGB value of 268.32 t/ha was estimated in the Alemsaga natural forest, which is a highly protected dense forest stand from any entrance and disturbance. Generally, integrating field data with optical remote sensing data provides more reliable result for AGB estimation. Moreover, it is also recommended to employ RADAR and LiDAR remote sensing data products together in order to attain more precise estimate results of AGB with great potential for forest resource monitoring and management.

Reinforcement learning-based assimilation of the WOFOST crop model

Crop model assimilation is a crucial technique for improving the accuracy and precision of crop models by integrating observational data and crop models. Although conventional data assimilation methods such as Kalman filtering and variational methods have been widely applied, these methods often face limitations in data quality, model bias, and high computational complexity. This study explored the potential of reinforcement learning (RL) for crop model assimilation, which has the advantage of not requiring large datasets. Based on the WOFOST crop model, two RL environments were constructed: a Daily-Data Driven approach and a Time-Series Driven approach. The Proximal Policy Optimization (PPO) algorithm was used to train these environments for 100,000 iterations. The assimilation results were compared with the commonly used SUBPLEX optimization algorithm using four-year field measurement data and a public dataset with added random errors. Our results demonstrate that the Time-Series Driven RL model achieved assimilation accuracy comparable to the SUBPLEX optimization algorithm, with an average MAE of 0.65 compared to 0.76 for SUBPLEX, and a slight decrease in RMSE, while significantly reducing the computational burden by 365 times. In a multi-year stability test, the Time-Series Driven RL model and SUBPLEX had similar assimilation performance. This study demonstrates the potential of RL for crop model assimilation, providing a novel approach to overcome the limitations of conventional assimilation algorithms. The findings suggest that RL-based crop model assimilation can improve model accuracy and efficiency, with potential for practical applications in precision agriculture.

Kurtosis Control of Amplitude-Modulated non-Gaussian Signals for Fatigue Test

The amplitude modulation method was used to generate non-Gaussian signals that acted as excitation for fatigue tests. The fatigue life of structures under non-Gaussian excitation has been proven to be closely related to the features of the amplitude modulation signal (AMS) and kurtosis of the structural response. In this study, the modelling of the AMS by Beta and Weibull distributions and the resulting kurtosis range problem is first reviewed. To solve this problem, a new method for creating an AMS based on a linear combination of Beta and Weibull distributions is proposed. To ensure that the high kurtosis of the amplitude-modulated non-Gaussian signal is correctly transferred to the structural response, the method is further developed to fulfil the specifications for the fatigue damage spectrum (FDS) by controlling the spectral content of the AMS. Herein, a Gaussian AMS with a low-pass cutoff frequency is first generated and then converted to a Weibull or Beta AMS based on the cumulative distribution function (CDF) transformation. The proposed method is verified using simulated and field-measured data. The results show that the full range of specified kurtosis is achieved with the new AMS modelling method. The high kurtosis of the non-Gaussian input signal can be transferred to the linear system response if the mean value of AMS during the period of the system impulse response is the same as AMS.

Analyzing the impact of classroom design factors on acoustic comfort in higher education hybrid learning environments

The vitality of hybrid learning classrooms has escalated due to enabling factors including advancements in technology, the demand for flexible education, and responses to global crises. However, the inadequate preparation for implementing hybrid classrooms raises concerns about students' ability to understand speech due to a lack of attention to the importance of acoustic quality. As a result, this study aims to investigate the impact of classroom design factors, such as ceiling height, occupancy, and spatial layout configuration, on hybrid classroom acoustic performance. The field measurement data acquired from multiple classrooms was utilized for analysis. The impact of design factors on acoustic parameters, which comprise reverberation time (RT), speech transmission index (STI), and speech clarity (C50), was analyzed through simulation using ODEON room acoustic software. The simulation was conducted based on two distinctive categorizations, which were determined by the size of the classrooms. The outcomes revealed that the design factors consisting of ceiling height, occupancy level, and spatial layout arrangements influenced the acoustic performance at various microphones' locations in hybrid learning classrooms. Although the analysis effectively upholds students' learning experience and performance, it is valuable for designers and educational institutions to establish adequate acoustic qualities for hybrid learning environments.

Transient temperature and pressure coupling model of cementing injection stage in ultra deep wells considering segmental rheological model, casing eccentricity and U-tube effect

The geological environments of ultra deep wells such as high temperature and high pressure (HTHP), narrow safety density window pose significant challenges to cementing operation. Considering segmental rheological model and density model, casing eccentricity, U-tube effect and viscous dissipation, this paper established a transient temperature and pressure coupling model during cementing injection stage of ultra deep wells. The segmental rheological model can describe the rheological properties of cementing fluids under HTHP much better. Considering casing eccentricity and viscous dissipation can accurately model the variation of temperature and pressure profiles. Taking U-tube effect into the model, the formation process of vacuum section at top of the casing can be simulated. The fully implicit finite-difference approach was used to solve the coupling model, and the calculation results were compared with field measurement data and simulation data to verify its accuracy. The calculation errors of the developed model are respectively 10% and 5% compared to field measurement data and Wellplan’s simulation data. Numerical simulations were conducted to reveal the cementing fluid distribution, transient temperature distribution and pressure distribution during the cementing injection stage. The simulation results show that the variety of cementing fluids with different density, rheological properties and thermophysical parameters complicates the change mechanism of temperature and pressure. The temperature and pressure distributions both present unsteady characteristics during cementing injection stage, which puts forward higher requirements of pressure control for narrow safety density window formation. The influence of temperature and pressure on rheology of cementing fluids cannot be ignored, and segmental rheological model should be considered for accurate pressure prediction. Also the casing eccentricity cannot be ignored for cementing pressure calculation in deviated or horizontal wells. Managed pressure cementing (MPC) has a greater advantage in dealing with narrow density window formation. The synergistic control of cementing fluid density and back pressure can keep the equivalent circulating density (ECD) within the safe density window. Through the application of the transient temperature and pressure coupling model, we can simulate the variations of key dynamic parameters during cementing injection stage and optimize the cementing parameters.

Numerical simulation of the vibration response of the permafrost subgrade under harmonic irregularity in the Qinghai-Tibet Railway

Abstract Harmonic excitation can significantly increase the dynamic response of wheel-rail interactions when a train passes. In this study, based on the train-track vertical coupled dynamics model (ZL-TNTLM) and ABAQUS, a unified train-track-subgrade coupled dynamics model is established. Field measurement data corroborated its accuracy and reliability. The analysis focused on the dynamic response of the permafrost subgrade of the Qinghai-Tibet Railway (QTR) to harmonic excitation, including the track-sleeper force and vertical dynamic stress. This study examined the influences of the wavelength, wave depth, and railhead depression on the dynamic response of a subgrade. The results indicated that single-harmonic excitation amplifies the track-sleeper forces and the vertical dynamic stress in the subgrade, particularly near the harmonic centre. Additionally, axle load variations and seasonal thawing broaden the dynamic stress range on the subgrade, with harmonic excitation exacerbating this impact, thereby increasing the risk to the stability of the ice-rich permafrost layer. A decrease in wavelength and increase in wave depth result in a greater number of affected sleepers, intensifying the vertical vibrations of the subgrade, especially in the medium- and high-frequency ranges of vertical dynamic stress. Notably, wavelengths between 1 and 2 m significantly exacerbate the adverse effects on the ice-rich permafrost layer. Shorter wavelengths also induce higher-frequency vibrations. These findings offer valuable insights for the design, operation, and maintenance of permafrost subgrade systems under irregular excitation.

Impact of upstream buildings on Wind-Driven Rain Loading: Refining Obstruction Factor in ISO semi-empirical model based on CFD

CFD is a valuable tool for assessing Wind-Driven Rain (WDR) loading, one of the most important environmental loads for façade design. The majority of previous studies on this topic have primarily concentrated on simple building configurations, i.e., stand-alone buildings. Hence, prior findings may not be applicable to consider the impact of upstream buildings in urban areas, which significantly alter wind flow field, consequently, change WDR loadings on downstream building facades compared to the stand-alone building. Part A: four different steady-state RANS models (i.e., standard k−ω, realizable k−ε, RNG k−ε, and standard k−ε) coupled with the Eulerian Multiphase (EM) technique (RANS-EM) are compared and implemented using OpenFOAM-7. These models are validated and verified based on wind-tunnel and field measurement data obtained from the literature for a six-story mid-rise residential building located in an urban area in Vancouver, Canada. The study considers 13 distinct rainfall events, for the test building with/without overhangs. All four RANS models are deemed suitable for modeling WDR in urban areas, while the steady-state standard k-ω RANS-EM approach without incorporating turbulent dispersion showing slightly better performance, thus utilized for the reminder of the study. Part B: a sensitivity analysis is presented on how the upstream buildings influence the WDR loading on a downstream building, denoted as Obstruction Factor. A comparison between the CFD and ISO semi-empirical model shows significant discrepancies, potentially reaching up to factors of 5. Thus, updated Obstruction Factors are suggested to enhance the ISO model for more accurate estimation of WDR loads.

Fatigue life assessment of thermite welded rails based on laboratory tests and field measurement data

This in-depth study assesses the fatigue life of thermite-welded 60E1 rails by combining fatigue experimental tests with field measurement data. Material characterization, conducted using X-ray Diffraction (XRD), X-ray Fluorescence (XRF), and Scanning Electron Microscopy (SEM), aimed to understand the fatigue behavior of these materials. The research was particularly focused on high-speed rail lines in South Korea, targeting areas with influence of track condition to collect strain data. By combining dynamic modulus calculations from high-speed tensile tests with the rain flow counting method for determining wheel loads, and employing the Linear Cumulative Damage method for estimating rail fatigue life, this study provides novel insights into the maintenance and durability of thermite welded rails. The integration of detailed laboratory experiments with field stress data offers a significant contribution to the predictive maintenance and lifecycle management of high-speed railway infrastructure.

Soil carbon dynamics in perennial biomass crops on marginally productive cropland in southern Canada

Predicting changes in soil organic carbon (SOC) in perennial biomass crops using process-based models provides a greater understanding of land management impacts on climate mitigation through long-term soil carbon sequestration. The objective of this study was to predict long-term SOC dynamics in different perennial biomass crops [miscanthus (Miscanthus giganteus L.), switchgrass (Panicum virgatum L.), willow (Salix miyabeana L.)] as compared to secondary regrowth vegetation (successional site) and a row crop system. The Century model accurately predicted SOC when simulated values were compared to measured field data. Average SOC stocks over the 162-year simulation period to 20 cm, were highest in miscanthus (8521 g C m−2), followed by the successional site (6877 g C m−2), switchgrass (6480 g C m−2), willow (5448 g C m−2) and lowest in the row crop system (3995 g C m−2). Higher SOC stocks in the miscanthus than the successional site indicates that, despite frequent biomass harvest, perennial biomass crops can accumulate higher carbon in soil than when a marginally productive cropland is left to undergo secondary regrowth. However, this depends on the crop species, since the miscanthus was the only biomass crop that reached pre-cultivation (1911) SOC stock of 8288 g C m−2. Moreover, the perennial biomass crops enhanced SOC in the slow fraction, whereas row crops depleted SOC in this fraction. This indicates the vital contribution of perennial biomass crops in long-term SOC sequestration and their role in climate change mitigation, especially when grown on marginally productive croplands.

Forest above-ground biomass estimation based on strongly collinear variables derived from airborne laser scanning data

Airborne laser scanning technique (ALS) is the most appealing remote sensing technique for the precise estimation of forest above-ground biomass (AGB). Significantly strong correlations (collinearity) among the independent variables derived from ALS data decrease the accuracy of developed AGB models. To address this issue, we propose a novel variable selection algorithm-an improved sure independence screening (SPV), which integrates the Pearson correlation coefficient, threshold μ, and variance inflation factor. We further compared the performance of SPV-based, stepwise feature selection (SFS)-based, and least absolute shrinkage and selection operator (LASSO)-based AGB models developed with different regression approaches that were sensitive to strong collinearity. Field-measured data and corresponding ALS data, acquired from 1002 sample plots distributed across four distinct forest types within the Guangxi Zhuang Autonomous Region in Southern China, were used to evaluate variable selection techniques and develop AGB models. Results indicated that ALS variables selected by SPV exhibited weaker collinearity compared to those selected by SFS and LASSO. SPV-based AGB models outperformed SFS-based AGB models with higher leave-one-out cross-validation adjusted R2 (LOOCV Radj2) by 0.1% − 27.8%. SPV-based AGB models outperformed LASSO-based AGB models with higher LOOCV Radj2 by 0.4% − 16.3%. Hence, for variable selection in constructing AGB models (linear regression model, log–log regression model, and generalized additive model (GAM)) based on strongly collinear ALS variables, SPV is most preferred, followed by SFS and LASSO. The smooth curves from our GAMs developed using SPV-selected variables revealed that five canopy height variables (whp40, hmean, hp25, hp70, hskew), one canopy density variable (whp85), three density-related variables (nVegPts, bcc, and mcc), and one vertical structural variable (LADmean) were positively correlated with AGB. The canopy height variables (whp40, hmean, hp25, and hp70) were identified as the most important variables in estimating AGB for four forest types. The canopy density variable wdp85 showed a strong effect on AGB of the coniferous forests, whereas it had almost no effect on the AGB of broadleaved forests. Overall, this manuscript proposes a novel variable selection algorithm named SPV, aimed at addressing collinearity of variables derived from ALS data, which has significant implications for the application of ALS in forest inventory and forest modeling.