Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR

Accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. Forest AGB estimation has been conducted with a variety of data sources and prediction methods, but many uncertainties still exist. In this study, six prediction methods, including Gaussian processes, stepwise linear regression, nonlinear regression using a logistic model, partial least squares regression, random forest, and support vector machines were used to estimate forest AGB in Jiangxi Province, China, by combining Geoscience Laser Altimeter System (GLAS) data, Moderate Resolution Imaging Spectroradiometer (MODIS) data, and field measurements. We compared the effect of three factors (prediction methods, sample sizes of field measurements, and cross-validation settings) on the predictive quality of the methods. The results showed that the prediction methods had the most considerable effect on the prediction quality. In most cases, random forest produced more accurate estimates than the other methods. The sample sizes had an obvious effect on accuracy, especially for the random forest model. The accuracy increased with increasing sample sizes. The random forest algorithm with a large number of field measurements, was the most precise (coefficient of determination (R2) = 0.73, root mean square error (RMSE) = 23.58 Mg/ha). Increasing the number of folds within the cross-validation settings improved the R2 values. However, no apparent change occurred in RMSE for different numbers of folds. Finally, the wall-to-wall forest AGB map over the study area was generated using the random forest model

Pine polyphenols from Pinus koraiensis prevent injuries induced by gamma radiation in mice

Pine polyphenols (PPs) are bioactive dietary constituents that enhance health and help prevent diseases through antioxidants. Antioxidants reduce the level of oxidative damages caused by ionizing radiation (IR). The main purpose of this paper is to study the protective effect of PPs on peripheral blood, liver and spleen injuries in mice induced by IR. ICR (Institute of Cancer Research) male mice were administered orally with PPs (200 mg/kg b.wt.) once daily for 14 consecutive days prior to 7 Gy γ-radiations. PPs showed strong antioxidant activities. PPs significantly increased white blood cells, red blood cells and platelets counts. PPs also significantly reduced lipid peroxidation and increased the activities of superoxide dismutase, catalase and glutathione peroxidases, and the level of glutathione. PPs reduced the spleen morphologic injury. In addition, PPs inhibited mitochondria-dependent apoptosis pathways in splenocytes induced by IR. These results indicate that PPs are radioprotective promising reagents

Regional-scale drought monitor using synthesized index based on remote sensing in northeast China

Drought has a significant impact on agricultural, ecological, and socioeconomic spheres. Although many drought indices have been proposed until now, the detection of droughts at regional scales still needs to be further studied. The Standardized Vegetation Index (SVI) that represents vegetation growing condition, the Standardized Water Index (SWI) that represents canopy water content, and the Evaporative Stress Index (ESI) that quantifies anomalies in the ratio of actual to potential evapotranspiration were calculated based on the Moderate-resolution Imaging Spectroradiometer (MODIS) data. A new remote sensing-based Vegetation Drought Monitor Synthesized Index (VDSI) was proposed by integrating the SVI, SWI, and ESI in the northeast China. When tested against the in situ Standardized Precipitation Evapotranspiration Index (SPEI), VDSI with proper weights of three variables outperformed individual remote sensing drought indices. The county-level yields of the main crops in the study area from 2001 to 2010 were also used to validate the VDSI. The correlation analysis between the yield data and the VDSI data during the crop growing season was performed, and its results showed that VDSI during the crop reproductive growth period was strongly correlated with the variation of crop yield. It was proved that this index is a potential indicator for assessment of the spatial pattern of drought severity in northeast China

Winter remote sensing images are more suitable for forest mapping in Jiangxi Province

ABSTRACTJiangxi Province boasts the second-highest forest coverage in China. Its forests play a crucial role in providing essential ecosystem services and maintaining the ecological health of the region. High-resolution and high-precision forest mapping are significant in the timely and accurate monitoring of dynamic forest changes to support sustainable forest management. This study used Sentinel-2 images from four seasons in the Google Earth Engine (GEE) platform to map forest distribution. Moreover, the classification results were compared and analyzed using different classification algorithms and feature-variable combinations. Based on the overall accuracy, the optimal image seasonality, feature combinations and classification algorithms were selected, and the forest maps of Jiangxi Province were mapped from 2019 to 2021. The accuracy evaluation showed that the winter image classification results had the highest accuracy (above 0.88). The red edge bands carried by Sentinel-2 could effectively improve the classification accuracy. The Random Forest classifier is the optimal classification algorithm for forest mapping in Jiangxi Province. The forest mapping obtained can be used for ecological health assessment and ecosystem function. The study provides a scientific basis for accurate and timely extraction of forest cover and can serve as a valuable resource for forest management planning and future research

Characteristic strength of soils underlying foundations considering the effect of spatial variability

In Eurocode EC7, a “characteristic” strength is used as a cautious estimate of the local average strength that governs the bearing capacity of the foundation. The objective of this paper is to examine the correlation between the local average strength and the bearing capacity of a stiff caisson foundation resting on spatially variable ground using random finite element analyses. The results show that using the local average strength over some assumed or postulated failure zones tends to overestimate the mean bearing capacity of the ground. This can be attributed to two possible reasons. Firstly, the postulated failure zone is unlikely to be fully reflective of the real failure zone in spatially variable ground. Secondly, the bearing capacity is more affected by the strength of the weak zones than that of the strong zones. Both of these factors lead to a lowering of the bearing capacity. A more indicative way of determining a characteristic strength that will give a better indication of the bearing capacity is also recommended, together with a strength reduction factor that accounts for the effect of spatial variability.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data

An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. In this study, six methods, including partial least squares regression, regression kriging, k-nearest neighbour, support vector machines, random forest and high accuracy surface modelling (HASM), were used to simulate forest AGB. Forest AGB was mapped by combining Geoscience Laser Altimeter System data, optical imagery and field inventory data. The Normalized Difference Vegetation Index (NDVI) and Wide Dynamic Range Vegetation Index (WDRVI0.2) of September and October, which had a stronger correlation with forest AGB than that of the peak growing season, were selected as predictor variables, along with tree cover percentage and three GLAS-derived parameters. The results of the different methods were evaluated. The HASM model had the best modelling accuracy (small MAE, RMSE, NRMSE, RMSV and NMSE and large R2). A forest AGB map of the study area was generated using the optimal model

A novel strategy based on dynamic surface-enhanced Raman scattering spectroscopy (D-SERS) for the discrimination and quantification of hydroxyl-sanshools in the pericarps of genus Zanthoxylum

The pericarps of genus Zanthoxylum (PZ) are used as a condiment in Chinese cuisine, while hydroxyl-sanshools (HS) are primary components evoking specific pungent sensations. This study developed a novel method for distinguishing the PZ species and detecting HS in PZ by combining dynamic surface-enhanced Raman scattering spectroscopy (D-SERS) with a homogeneous gold nanorods (AuNRs) substrate. Furthermore, principal compo-nent analysis (PCA) separated the D-SERS spectra data of 26 PZ samples into two groups. The concentration of hydroxyl-alpha-sanshool (alpha-SOH) in a range of 0.1-12 mg mL(-1) displayed an excellent linear relation with the peak intensity, while the detection limit at 0.03 mg mL(-1) the requirements for detecting HS in currently known PZ. Compared to high-performance liquid chromatography (HPLC) analysis, the established method demonstrated excellent accuracy in PZ samples. This study provided an alternative method based on D-SERS coupled with AuNRs for rapidly and accurately determinating HS in PZ samples