Search

The remote sensing of sun-induced chlorophyll fluorescence (SIF) is an emerging technique with immense potential for terrestrial vegetation sciences. However, the interpretation of fluorescence data is often hindered by the complexity of observed land surfaces. Therefore, advanced remote sensing models, particularly physically based simulations, are critical to accurately interpret SIF data. In this work, we propose a three-dimensional (3D) radiative transfer model that employs the Monte Carlo ray-tracing technique to simulate the excitation and transport of SIF within plant canopies. This physically based approach can quantify the various radiative processes contributing to the observed SIF signal with high fidelity. The model's performance is rigorously evaluated by comparing the simulated SIF spectra and angular distributions to field measurements, as well as conducting systematic comparisons with an established radiative transfer model. The results demonstrate the proposed model's ability to reliably reproduce the key spectral and angular characteristics of SIF, with the coefficient of determination ( R 2 ) exceeding 0.98 and root mean square error (RMSE) being less than 0.08 mW m−2 sr−1 nm−1 for both the red and far-red fluorescence peaks. Furthermore, the model's versatile representation of canopy structures, enabled by the decoupling of radiation and geometry, is applied to study the impact of 3D structure on SIF patterns. This capability makes the proposed model a highly attractive tool for investigating SIF distributions in realistic, heterogeneous canopy environments. [ABSTRACT FROM AUTHOR]

Anomalies displaying significant deviations between terrain elevation products acquired from spaceborne full-waveform LiDAR and reference elevations are frequently observed in assessment studies. While the predominant focus is on "normal" data, recognizing anomalies within datasets obtained from the Geoscience Laser Altimeter System (GLAS) and the Global Ecosystem Dynamics Investigation (GEDI) is essential for a comprehensive understanding of widely used spaceborne full-waveform data, which not only facilitates optimal data utilization but also enhances the exploration of potential applications. Nevertheless, our comprehension of anomalies remains limited as they have received scant specific attention. Diverging from prevalent practices of directly eliminating outliers, we conducted a targeted exploration of anomalies in forested areas using both transmitted and return waveforms from the GLAS and the GEDI in conjunction with airborne LiDAR point cloud data. We unveiled that elevation anomalies stem not from the transmitted pulses or product algorithms, but rather from scattering sources. We further observed similarities between the GLAS and the GEDI despite their considerable disparities in sensor parameters, with the waveforms characterized by a low signal-to-noise ratio and a near exponential decay in return energy; specifically, return signals of anomalies originated from clouds rather than the land surface. This discovery underscores the potential of deriving cloud-top height from spaceborne full-waveform LiDAR missions, particularly the GEDI, suggesting promising prospects for applying GEDI data in atmospheric science—an area that has received scant attention thus far. To mitigate the impact of abnormal return waveforms on diverse land surface studies, we strongly recommend incorporating spaceborne LiDAR-offered terrain elevation in data filtering by establishing an elevation-difference threshold against a reference elevation. This is especially vital for studies concerning forest parameters due to potential cloud interference, yet a consensus has not been reached within the community. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m2. Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (Rmatch) of tree tops is up to 0.92, the coefficient of determination (R2) of tree height estimation is up to.94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping. [ABSTRACT FROM AUTHOR]

Based on the strict radiative transfer algorithm, a new method is developed to derive the precipitable water vapor (PWV) and aerosol optical depth (AOD) from the ground-based direct sun irradiance measurements. The attenuated direct irradiance from 300 to 1700 nm was measured by a pair of grating spectroradiometers, MS711 and MS712 produced by EKO Instruments, located at the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), Beijing (39.98 ∘ N, 116.38 ∘ E), from June 2020 to March 2021. Compared with regular sun photometers such as CE-318 and POM, EKO instruments can measure a wider range of continuous spectra, but their field of view (FOV) is also relatively large. In the PWV inversion of this work, a strong water vapor absorption band around 1370 nm is introduced to retrieve PWV in a relatively dry atmosphere. The circumsolar radiation (CSR) of the EKO instruments is corrected to reduce the influence of scattering from a relatively larger FOV on the AOD inversion. The PWV and AOD inversion results obtained by MS711 and MS712 are compared with the synchronous data of the CE-318 sun photometer. The two retrieval results are highly consistent. The correlation coefficient, mean bias, and standard deviation of PWVEKO and PWVCIMEL are 0.999, -0.027 cm (-2.42 %), and 0.054 cm (3.93 %), respectively, and the relative deviations of the differences between the two are slightly larger for drier air (PWV < 0.5 cm) and lower solar elevation angle. The correlation coefficients of AODEKO and AODCIMEL at 380, 440, 500, 675, 870, and 1020 nm are greater than 0.99, and the relative deviations vary between -6.59 % and 4.27 %. [ABSTRACT FROM AUTHOR]

Estimation of downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. Although various algorithms have been proposed, effective validation methods are absent for rugged terrains due to the lack of rigorous methodology and reliable field measurements. We propose a two-step validation method for rugged terrains based on computer simulations. The first step is to perform point-to-point validation at local scale. Time-series measurements were applied to evaluate a three-dimensional (3-D) radiative transfer model. The second step is to validate the DSR at pixel-scale. A semiempirical model was built up to interpolate and upscale the DSR. Key terrain parameters were weighted by empirical coefficients retrieved from ground-based observations. The optimum number and locations of ground stations were designed by the 3-D radiative transfer model and Monte Carlo method. Four ground stations were selected to upscale the ground-based observations. Additional three ground stations were set up to validate the interpolated results. The upscaled DSR was finally applied to validate the satellite products provided by MODIS and Himawari-8. The results showed that the modeled and observed DSR exhibited good consistency at point scale with correlation coefficients exceeding 0.995. The average error was around 20 W/m2 for the interpolated DSR and 10 W/m2 for the upscaled DSR in theory. The accuracies of the satellite products were acceptable at most times, with correlation coefficients exceeding 0.94. From an operational point of view, our method has an advantage of using small amount of ground stations to upscale DSR with relatively high accuracy over rugged terrains. [ABSTRACT FROM AUTHOR]

Terrestrial LiDAR (TLS) instruments have great capability to describe canopy structure with unprecedented levels of detail in plot-scale forest measurement. However, in most cases, only parts of the trees within the sample plot are scanned due to occlusion effects from the other trees in the direction of the laser beam. These obstructions cause incomplete observation and missing features, resulting in the underestimation of tree attributes. Typical data acquisition with multi-scan mode seeks to mitigate occlusion effect and then provide almost full coverage of plot. The density of scan stations is determined by plot size, the density of plants and the metrics to be measured. Currently, the determination of density and locations of multiple scans depends on surveyor's experience and is lack of scientific protocols, generally, causing data redundancy and low efficiency of measurement, e.g. time cost in moving devices and scan registration. In fact, the occlusion effect primarily comes from the unknown complex spatial pattern of trees in natural forest conditions. UAVs are able to map the spatial locations of trees and are expected to provide basic knowledge for the optimization of TLS sampling pattern in forest conditions. The focus of this study is on strategies to derive multi-scan locations over forest plots. A novel chain-mode optimization of TLS sampling pattern was designed to maximize visibility of trees with minimum scans, hence improving the data quality and the efficiency of measurement. We proposed three basic optimization rules: (1) scan candidates prefer to locate on those positions undergoing low occlusion effect; (2) cumulative angular sampling (CAS) of each tree scanned from several directions satisfies the requirement user defined; (3) scan locations are uniformly distributed. In the implementation, 2D visibility analysis is firstly conducted for all the trees, simplified as circles in a plot, based on tree location map derived from UAV images. This step aims to find locally optimal scan locations following the first rule. Secondly, the CAS of each tree is geometrically computed. This step is devoted to select those trees requiring more scans to meet user level following the second rule. Thirdly, the former two steps are consecutively repeated in consideration of the third rule, only for those selected trees, until the CAS of each tree meets the user's requirements. Through two simulated forest plots where trees respectively distributed follow a spatial random pattern and a row-spaced pattern, the proposed chain-mode scans yielded comparable data quality with regular scans whereas the efficiency is improved by up to 30%. It is expected that this chain-mode optimization method can be applied in more forest experiments, especially in long-term TLS measurements. [ABSTRACT FROM AUTHOR]