ALBERT

Description: The characterization of plant nutrients is important to understand the process of plant growth in natural ecosystems. This study attempted to evaluate the performances of univariate linear regression with various vegetation indices (VIs) and multivariate regression methods in estimating grass nutrients (i.e., nitrogen (N) and phosphorus (P)) with canopy hyperspectral reflectance. Synthetically considering predictive accuracy, simplicity, robustness and interpretation, the successive projections algorithm coupled with multiple linear regression (SPA-MLR) method was considered optimal for grass nutrient estimation at the canopy level, when compared with the performances of 12 statistical modeling methods, i.e., univariate linear regression with nine published VIs and three classical multivariate regression methods (stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and support vector regression (SVR)). The simple ratio index ( ,  is derivative reflectance) model had comparable performance to SPA-MLR model for P estimation. SPA-MLR provided comparable prediction accuracies with only three first derivative spectral bands for N (715, 731 and 2283 nm) and P (714, 729 and 1319 nm) estimations, compared with PLSR and SVR models, which used the full spectrum. Moreover, SPA-MLR provided robust prediction with the lowest bias values for N (−0.007%) and P (0.001%) estimations, and the fitting line between predicted and measured values was closer to the 1:1 line than the other models. Finally, most of the bands selected by SPA-MLR indirectly relate to foliar chlorophyll content, which suggests good physical interpretation.

Description: With the significant progress of urbanization, cities and towns are suffering from air pollution, heat island effects, and other environmental problems. Urban vegetation, especially trees, plays a significant role in solving these ecological problems. To maximize services provided by vegetation, urban tree species should be properly selected and optimally arranged. Therefore, accurate classification of tree species in urban environments has become a major issue. In this paper, we reviewed the potential of light detection and ranging (LiDAR) data to improve the accuracy of urban tree species classification. In detail, we reviewed the studies using LiDAR data in urban tree species mapping, especially studies where LiDAR data was fused with optical imagery, through classification accuracy comparison, general workflow extraction, and discussion and summarizing of the specific contribution of LiDAR. It is concluded that combining LiDAR data in urban tree species identification could achieve better classification accuracy than using either dataset individually, and that such improvements are mainly due to finer segmentation, shadowing effect reduction, and refinement of classification rules based on LiDAR. Furthermore, some suggestions are given to improve the classification accuracy on a finer and larger species level, while also aiming to maintain classification costs.

Description: In this study, a static tensile test of secondary co-cure reinforcement (SCR) of laminates revealed the damage and fracture locations in the respective structure. Test results indicated that adhesive debonding was the primary cause of structural failure. Finite element modeling (FEM) performed on the large opening laminate and strengthening structure consisted of simulations of the axial tension experiment, damage assessment, and the final load estimate. It was observed that the tensile strength of SCR was increased by 10.81% in comparison with the unrepaired structure. The results of FEM indicated that the initiation and propagation of damage, and final failure, were located in the layer of reinforcing section which was bonded to the adhesive layer, proving that the performance of the adhesive layer was the dominating factor with regard to the reinforced structure and that the thickness of the reinforcing section could be reduced to lessen the weight.

Description: Hyperspectral remote sensing serves as an effective tool for estimating foliar nitrogen using a variety of techniques. Vegetation indices (VIs) are a simple means of retrieving foliar nitrogen. Despite their popularity, few studies have been conducted to examine the utility of VIs for mapping canopy foliar nitrogen in a mixed forest context. In this study, we assessed the performance of 32 vegetation indices derived from HySpex airborne hyperspectral images for estimating canopy mass-based foliar nitrogen concentration (%N) in the Bavarian Forest National Park. The partial least squares regression (PLSR) was performed for comparison. These vegetation indices were classified into three categories that are mostly correlated to nitrogen, chlorophyll, and structural properties such as leaf area index (LAI). %N was destructively measured in 26 broadleaf, needle leaf, and mixed stand plots to represent the different species and canopy structure. The canopy foliar %N is defined as the plot-level mean foliar %N of all species weighted by species canopy foliar mass fraction. Our results showed that the variance of canopy foliar %N is mainly explained by functional type and species composition. The normalized difference nitrogen index (NDNI) produced the most accurate estimation of %N (R2CV = 0.79, RMSECV = 0.26). A comparable estimation of %N was obtained by the chlorophyll index Boochs2 (R2CV = 0.76, RMSECV = 0.27). In addition, the mean NIR reflectance (800–850 nm), representing canopy structural properties, also achieved a good accuracy in %N estimation (R2CV = 0.73, RMSECV = 0.30). The PLSR model provided a less accurate estimation of %N (R2CV = 0.69, RMSECV = 0.32). We argue that the good performance of all three categories of vegetation indices in %N estimation can be attributed to the synergy among plant traits (i.e., canopy structure, leaf chemical and optical properties) while these traits may converge across plant species for evolutionary reasons. Our findings demonstrated the feasibility of using hyperspectral vegetation indices to estimate %N in a mixed temperate forest which may relate to the effect of the physical basis of nitrogen absorption features on canopy reflectance, or the biological links between nitrogen, chlorophyll, and canopy structure.

Description: Due to the absence of evident absorption features and low concentrations, the copper (Cu) concentration in plant leaves has rarely been estimated from hyperspectral remote sensing data. The capability of remotely-sensed estimation of foliar Cu concentrations largely depends on its close relation to foliar chlorophyll concentration. To enhance the subtle spectral changes related to chlorophyll concentration under Cu stress, this study described a wavelet-based area parameter (SWT (605−720), the sum of reconstructed detail reflectance at fourth decomposition level over 605−720 nm using discrete wavelet transform) from the canopy hyperspectral reflectance (350−2500 nm, N = 71) of Carex (C. cinerascens). The results showed that Cu concentrations had negative and strong correlation with chlorophyll concentrations (r = －0.719, p 〈 0.001). Based on 1000 random dataset partitioning experiments, the 1000 linear calibration models provided a mean R2Val (determination coefficient of validation) value of 0.706 and an RPD (residual prediction deviation) value of 1.75 for Cu estimation. The bootstrapping and ANOVA test results showed that SWT (605−720) significantly (p 〈 0.05) outperformed published chlorophyll-related and wavelet-based spectral parameters. It was concluded here that the wavelet-based area parameter (i.e., SWT (605−720)) has potential ability to indirectly estimate Cu concentrations in Carex leaves through the strong correlation between Cu and chlorophyll. The method presented in this pilot study may be used to estimate the concentrations of other heavy metals. However, further research is needed to test its transferability and robustness for estimating Cu concentrations on other plant species in different biological and environmental conditions.

Description: Knowledge of both state (e.g., soil moisture) and flux (e.g., actual evapotranspiration (ETa) and groundwater recharge (GR)) hydrological variables across vadose zones is critical for understanding ecohydrological and land-surface processes. In this study, a one-dimensional process-based vadose zone model with generated soil hydraulic parameters was utilized to simulate soil moisture, ETa, and GR. Daily hydrometeorological data were obtained from different climate zones to drive the vadose zone model. On the basis of the field phenomenon of soil moisture temporal stability, reasonable soil moisture spatiotemporal structures were reproduced from the model. The modeling results further showed that the dependence of ETa and GR on soil hydraulic properties varied considerably with climatic conditions. In particular, the controls of soil hydraulic properties on ETa and GR greatly weakened at the site with an arid climate. In contrast, the distribution of mean relative difference (MRD) of soil moisture was still significantly correlated with soil hydraulic properties (most notably residual soil moisture content) under arid climatic conditions. As such, the correlations of MRD with ETa and GR differed across different climate regimes. In addition, the simulation results revealed that samples with average moisture conditions did not necessarily produce average values of ETa and GR (and vice versa), especially under wet climatic conditions. The loose connection between average state and flux hydrological variables across vadose zones is partly because of the high non-linearity of subsurface processes, which leads to the complex interactions of soil moisture, ETa, and GR with soil hydraulic properties. This study underscores the importance of using soil moisture information from multiple sites for inferring areal average values of ETa and GR, even with the knowledge of representative sites that can be used to monitor areal average moisture conditions.

Description: Femtosecond laser filament induced condensation and precipitation in a cloud chamber Scientific Reports, Published online: 5 May 2016; doi:10.1038/srep25417