Vertical Structure Parameters Research Articles

Three-dimensional forest structure features extraction based on TomoSAR

Abstract. Forest structure is an important indicator for evaluating forest ecosystem and an indispensable input parameter for estimating forest carbon sink. Synthetic Aperture Radar (SAR) Tomography (TomoSAR), as a novel microwave remote sensing technique with 3D imaging capability, has been demonstrated to be an valuable tool for accurate inversion of forest structure. At present, the forest vertical structure parameters inversion is almost all concentrated on the forest height estimation. However, the spatial structure of forests is complex, and it is not enough to only use the forest height to express the forest structure. This fails to identify tree canopy, branch layer and underlying vegetation layer, etc., resulting in the partial and inaccurate forest vertical structure information. To solve this problem, this paper constructs 3D structural indices from tomograms, namely Horizontal Structure Index (HS) and Vertical Structure Index (VS). The HS primarily describes canopy density, compensating for the lack of information on the horizontal dimension of forest structure provided by traditional TomoSAR techniques; while VS describes the complexity of canopy distribution in the altitude direction, further enriching information on forest structure vertically. In order to verify their feasibility and validity, HS and VS are extracted using six fully-polarized L-band SAR images covering the Krycklan watershed in Sweden. Moreover, the accuracy is verified by using external high-precision LiDAR data. The results indicate a strong correlation between the structural indices extracted from TomoSAR and those extracted from LiDAR.

Mapping the vertical forest structure in a large subtropical region using airborne LiDAR data

Vertical forest structure (VFS) refers to the vertical stratification or layering of forest communities in space, which is a fundamental characteristic of a plant community. It plays a vital role in forest vitality and facilitates various ecological activities and processes. The mapping of VFS is of significant value for both ecological and forestry purposes. In this paper, we presented a novel approach for the automated mapping of VFS in a large subtropical region based on discrete airborne LiDAR data. Firstly, the LiDAR point clouds of a stand (plot or grid cell) were segmented into 100 height bins from the top to the ground, and a height-frequency histogram was obtained by calculating the proportion of the number of returns in the bins to the total number of returns, which objectively represented the vertical distribution of canopy material. Secondly, a univariate ten-order polynomial was used to fit the height-frequency histogram, enabling the generation of a continuous vertical canopy profile (pseudo-waveform) of the stand. Thirdly, a comprehensive set of vertical structure parameters was defined and extracted based on the pseudo-waveforms, which effectively characterized the vertical profile layer and the canopy layer. Fourthly, to construct a comprehensive framework, 43 model profiles were summarized from the field plots, taking into account the number of effective peaks in the pseudo-waveforms and other vertical structure parameters. Finally, 43 classification rules were developed and 18 judgment criteria were established using the vertical structure parameters of the mode profiles. They classified vertical forest structures into 24 classes with explicit spatial definitions. The classification of 1147 field plots resulted in an overall accuracy of 94.7% and a kappa coefficient of 0.937. The VFS mapping over a large area demonstrated an effective execution rate of 99.8% for both rules and criteria. The proposed approach exhibits high accuracy and excellent generalization ability across different forest types, species, and study sites, highlighting its ecological and forestry significance. It represents a significant advance in the automated classification and mapping of VFS in large subtropical regions using airborne LiDAR data. However, the proposed approach needs to be validated in other vegetation zones to assess its generalizability and extend its applicability.

Carbon Storage Estimation of Quercus aquifolioides Based on GEDI Spaceborne LiDAR Data and Landsat 9 Images in Shangri-La

The assessment of forest carbon storage plays a crucial role in forest management and ecosystem exploration, enabling the evaluation of forest quality, resources, carbon cycle and management. The Global Ecosystem Dynamics Investigation (GEDI) satellite provides a means to accurately measure these various forest vertical structure parameters by penetrating the forest canopy. However, the distribution of the footprint along the orbit track is heterogeneous and discontinuous, preventing the acquisition of spatially distributed carbon storage formation at the county level. Consequently, this study integrated GEDI and Landsat 9 data to estimate Quercus aquifolioides carbon storage in Shangri-La. By applying the Kriging interpolation to previously pretreated footprints, surface information from the GEDI L2B footprints was obtained. At the same time, Landsat 9 vegetation indices and band reflectance were extracted to analyze the correlation with the carbon storage of Quercus aquifolioides samples. Then, three methods (support vector machine, bagging, and random forest) were used to create a carbon storage estimation model for Shangri-La. The research results showed that (1) among the models for the selection of GEDI footprint parameters based on semi-variance, the optimal model of the digital_elevation_model was the spherical model, while the best model of percentage tree cover from the MODIS data (modis_treecover) and the foliage height diversity index (fhd_normal) was the exponential model. (2) Analyzing the thirty-three extracted independent variable factors correlated with the carbon storage of Quercus aquifolioides showed that the top five variables with the highest correlation were digital_elevation_model, modis_treecover, fhd_normal, DEM, and band 1 (B1). (3) After variable selection, the R2 = 0.82 and RMSE = 11.92 t/hm2 values of the Quercus aquifolioides carbon storage estimation model established via random forest were obtained, and its evaluation precision was superior to that of the support vector machine method and bagging regression. The carbon storage of Quercus aquifolioides was primarily in the range of 8.22~94.63 t/hm2, and the mean value was 42.44 t/hm2, while the total carbon storage was about 5,374,137.62 t. The findings from this paper illustrated the feasibility of obtaining carbon storage data on a county scale by combining GEDI LiDAR data with Landsat 9 optical data. The results also suggested a new perspective for combining GEDI L2B data with other remote sensing images to estimate other forest structure parameters.

Comprehensive Decision Index of Logging (CDIL) and Visual Simulation Based on Horizontal and Vertical Structure Parameters

The comprehensive indexes approach based on stand structure parameters is mainly used to select trees for harvest. However, these indexes do not consider the comprehensive impact of horizontal and vertical structures, leading to an incomplete analysis of the forest structure and an inaccurate selection of trees for harvest. To solve this problem, we constructed a comprehensive decision index of logging (CDIL), integrating horizontal and vertical structure parameters which can identify harvest trees more scientifically. In this study, we took the Shanxia Forest Farm in the Jiangxi Province of China as the experimental area and used mixed broadleaf/conifer forests at different ages as our experimental sample. We selected eight horizontal and vertical spatial structure parameters to establish an efficient, objective, and accurate comprehensive decision index of logging. We combined 3D visualization technology to realize the dynamic visualization simulation of the index at different intensities of tending and felling management. The results indicated that the proposed CDIL-index could effectively optimize the forest spatial structure. From the perspective of stand structure adjustment, the optimal thinning intensity was 20%. The average CDIL in each plot decreased by more than 80% after logging, while the change range of each plot was between 30% and 70% after the F index was applied to implement tending and logging. The CDIL was 11.4% more accurate in selecting trees for harvesting than the F index. In this study, the main conclusion is that the CDIL would enable forest managers to more accurately choose trees for harvesting, leading to forest adjustment that would reduce the competition pressure among trees and improve the distribution and health of trees, possibly making the forest structure more stable.

A Photon Cloud Filtering Method in Forested Areas Considering the Density Difference Between Canopy Photons and Ground Photons

Photon cloud data filtering is crucial when obtaining forest vertical structure parameters from photon-counting LiDAR data. The proposed method, for the first time, takes into account the influence of the density difference between canopy photons and ground photons. A moving overlapping window approach is introduced to reduce the impact of an uneven background noise environment first. In each window, a modified elevation histogram statistics vector in the elevation direction is proposed to increase the density difference between signal and noise photons while also reducing the density difference between canopy and ground photons. The filtering results show that the average overall accuracy (OA) and standard deviation of the proposed method reach almost 0.99 and 0.01, respectively, which are much better results than those of the other existing filtering methods. Specifically, with the increase in the ratio of canopy photons to ground photons, the F-measure value of the proposed method reaches almost 0.99, and is also stable, which demonstrates that the proposed approach can almost completely eliminate the influence of the density difference between canopy photons and ground photons on the filtering results. In addition, the forest canopy heights obtained based on the proposed filtering method achieve the lowest root-mean-square error (RMSE) value of 3.18 m, compared to the other filtering methods. In summary, the proposed photon cloud data filtering method can retrieve reliable forest canopy height information from photon cloud data, and outperforms the other compared filtering methods in the given test site.

A Method for Forest Canopy Height Inversion Based on Machine Learning and Feature Mining Using UAVSAR

The mapping of tropical rainforest forest structure parameters plays an important role in biodiversity and carbon stock estimation. The current mechanism models based on PolInSAR for forest height inversion (e.g., the RVoG model) are physical process models, and realistic conditions for model parameterization are often difficult to establish for practical applications, resulting in large forest height estimation errors. As an alternative, machine learning approaches offer the benefit of model simplicity, but these tools provide limited capabilities for interpretation and generalization. To explore the forest height estimation method combining the mechanism model and the empirical model, we utilized UAVSAR multi-baseline PolInSAR L-band data from the AfriSAR project and propose a solution of a mechanism model combined with machine learning. In this paper, two mechanism models were used as controls, the RVoG three-phase method and the RVoG phase-coherence amplitude method. The vertical structure parameters of the forest obtained from the mechanism model were used as the independent variables of the machine learning model. Random forest (RF) and partial least squares (PLS) regression models were used to invert the forest canopy height. Results show that the inversion accuracy of the machine learning method, combined with the mechanism model, is significantly better than that of the single-mechanism model method. The most influential independent variables were penetration depth, volume coherence phase center height, coherence separation, and baseline selection. With the precondition that the cumulative contribution of the independent variables was greater than 90%, the number of independent variables in the two study areas was reduced from 19 to 4, and the accuracy of the RF-RVoG-DEP model was higher than that of the PLS-RVoG-DEP model. For the Lope test area, the R2 of the RVoG phase coherence amplitude method is 0.723, the RMSE is 8.583 m, and the model bias is −2.431 m; the R2 of the RVoG three-stage method is 0.775, the RMSE is 7.748, and the bias is 1.120 m, the R2 of the PLS-RVoG-DEP model is 0.850, the RMSE is 6.320 m, and the bias is 0.002 m; and the R2 of the RF-RVoG-DEP model is 0.900, the RMSE is 5.154 m, and the bias is −0.061 m. The results for the Pongara test area are consistent with the pattern for the Lope test area. The combined “fusion model” offers a substantial improvement in forest height estimation from the traditional mechanism modeling method.

Integrating spaceborne LiDAR and Sentinel-2 images to estimate forest aboveground biomass in Northern China

BackgroundFast and accurate forest aboveground biomass (AGB) estimation and mapping is the basic work of forest management and ecosystem dynamic investigation, which is of great significance to evaluate forest quality, resource assessment, and carbon cycle and management. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), as one of the latest launched spaceborne light detection and ranging (LiDAR) sensors, can penetrate the forest canopy and has the potential to obtain accurate forest vertical structure parameters on a large scale. However, the along-track segments of canopy height provided by ICESat-2 cannot be used to obtain comprehensive AGB spatial distribution. To make up for the deficiency of spaceborne LiDAR, the Sentinel-2 images provided by google earth engine (GEE) were used as the medium to integrate with ICESat-2 for continuous AGB mapping in our study. Ensemble learning can summarize the advantages of estimation models and achieve better estimation results. A stacking algorithm consisting of four non-parametric base models which are the backpropagation (BP) neural network, k-nearest neighbor (kNN), support vector machine (SVM), and random forest (RF) was proposed for AGB modeling and estimating in Saihanba forest farm, northern China.ResultsThe results show that stacking achieved the best AGB estimation accuracy among the models, with an R2 of 0.71 and a root mean square error (RMSE) of 45.67 Mg/ha. The stacking resulted in the lowest estimation error with the decreases of RMSE by 22.6%, 27.7%, 23.4%, and 19.0% compared with those from the BP, kNN, SVM, and RF, respectively.ConclusionCompared with using Sentinel-2 alone, the estimation errors of all models have been significantly reduced after adding the LiDAR variables of ICESat-2 in AGB estimation. The research demonstrated that ICESat-2 has the potential to improve the accuracy of AGB estimation and provides a reference for dynamic forest resources management and monitoring.

High-resolution temporal detection of cyanobacterial blooms in a deep and oligotrophic lake by high-frequency buoy data

Cyanobacterial blooms are increasing in magnitude, frequency, and duration worldwide. However, our knowledge of cyanobacterial blooms dynamics and driving mechanisms is still limited due to their high spatiotemporal variability. To determine the potential driving mechanisms of cyanobacterial blooms in oligotrophic lakes, we collected a high-frequency depth profile of chlorophyll fluorescence (ChlF) and synchronous water quality, hydrometeorological data in early spring 2016 in oligotrophic Lake Qiandaohu. The vertical distribution of ChlF exhibited two patterns, “aggregated” and “discrete”, using Morisita's index, and the aggregated ChlF presented subsurface chlorophyll maxima during the thermal stratification period. The ChlF concentration was positively correlated with water temperature and negatively correlated with turbidity. Significantly linear relationships were observed between ChlF vertical structure parameters (e.g., Morisita's index, subsurface chlorophyll maxima depth and thickness) and thermal stratification parameters (e.g., mixing layer depth and relative water column stability). After rainstorm floods, the ChlF pattern suddenly change from “aggregated” to “discrete” and a ChlF concentration <1 μg/L was observed for 7–11 days with a significant increase in the mixing depth layer and turbidity. The results suggest that cyanobacterial blooms are robustly associated with thermal stratification and rainstorm floods in the deep and oligotrophic lake. Thermal stratification boosts surface phytoplankton accumulation by increasing water temperature, enhancing light availability and restricting phytoplankton vertical distribution. Rainstorm floods interrupt the accumulation by disrupting thermal stratification and decreasing the available light. Furthermore, wind speed and air temperature both regulate the phytoplankton dynamics by affecting thermal stratification. Our research quantifies the cyanobacterial bloom dynamics and their relationship between environmental factors, improving our knowledge of the driving mechanisms of cyanobacterial bloom for the protection of drinking water safety and aquatic organism health in lakes.

Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry

Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research.

Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

A framework for estimating cloudy sky surface downward longwave radiation from the derived active and passive cloud property parameters

The cloud-base temperature (CBT) is one of the parameters that dominates the cloudy sky surface downward longwave radiation (SDLR). However, CBT is rarely available at regional and global scales, and its application in estimating cloud sky SDLR is limited. In this study, a framework to globally estimate cloud sky SDLR during both daytime and nighttime is proposed. This framework is composed of three parts. First, a global cloudy property database was constructed by combing the extracted cloud vertical structure (CVS) parameters from the active CloudSat data and cloud properties from passive MODIS data. Second, the empirical methods for estimating cloud thickness (CT) under ISCCP cloud classification system and MODIS cloud classification system were developed. Additionally, the coefficients of CERES CT estimate models were refitted using the constructed cloud property database. With the estimated CT and reanalysis data, calculating the CBT is straightforward. The accuracy of the estimated CT for ISCCP cloud type is compared with the existing studies that were conducted at local scales. Our CT estimate accuracy is comparable to that of the existing studies. According to the validation results at ARM NSA and SGP stations, the CT estimated by the developed CT model for MODIS cloud type is better than that estimated by the original CERES CT model. Finally, the cloudy sky SDLR values were derived by feeding the estimated CBT and other parameters to the single-layer cloud model (SLCM). When validated by the ground measured SDLR collected from the SURFRAD network, the bias and RMSE are 5.42 W∙m−2 and 30.3 W∙m−2, respectively. This accuracy is comparable to the evaluation results of the mainstream SDLR products (Gui et al. 2010), the new evaluation results of SLCMs (Yu et al. 2018), and the accuracy of a new cloudy sky SDLR estimate method (Wang et al. 2018). All the derived CBTs improve the SDLR estimate accuracy more than the SLCM that directly uses cloud-top temperature (CTT). We will collect more ground measurements and continue to validate the developed framework in the future.

Vertical size structure is associated with productivity and species diversity in a short‐stature grassland: Evidence for the importance of height variability within herbaceous communities

AbstractQuestionsLocal size structure is frequently measured in plant populations and forested systems due to its association with population‐ and community‐level processes. In contrast, size structure is rarely examined within herbaceous communities despite evidence for size‐dependent processes in these systems, suggesting an important functional vegetation property may be widely overlooked. Here, we test whether vertical size structure (a) varies within a short‐stature grassland, (b) varies along a productivity gradient, and (c) enhances the understanding of a foundational ecological pattern: the diversity–productivity relationship.LocationRough fescue grassland in Alberta, Canada.MethodsWe measured the heights of individuals within 32 plots and used average height, the Gini coefficient, and a two‐parameter Weibull distribution to estimate community‐level vertical size structure. Linear mixed models were used to test whether size structure parameters were significantly associated with productivity, species richness, and species evenness.ResultsVertical size structure varied significantly among local communities, such that some were dominated by short individuals while others were comprised of a more equal number of taller individuals. With increasing productivity, there was an increase in average plant height and a decrease in height inequality. Decreased height inequality was associated with reduced species richness with evidence that loss was not size‐dependent. The inclusion of vertical size structure parameters increased the explained variance of the diversity–productivity relationship by ~35% while also increasing its parsimony.ConclusionsEven within a short‐stature grassland, there is substantial functional variation in vertical size structure. Productivity is strongly associated with size structure patterns, and the inclusion of height inequalities greatly enhances productivity–diversity relationships, likely because they are the product of assembly mechanisms such as size‐asymmetric competition, assemblage‐level thinning, and niche complementarity. Overall, this demonstrates that vertical size structure has been a missing mechanism in most community assembly theories and models and should be included in the future.

Estimating the Aboveground Carbon Density of Coniferous Forests by Combining Airborne LiDAR and Allometry Models at Plot Level.

Forest carbon density is an important indicator for evaluating forest carbon sink capacities. Accurate carbon density estimation is the basis for studying the response mechanisms of forest ecosystems to global climate change. Airborne light detection and ranging (LiDAR) technology can acquire the vertical structure parameters of forests with a higher precision and penetration ability than traditional optical remote sensing. Combining top of canopy height model (TCH) and allometry models, this paper constructed two prediction models of aboveground carbon density (ACD) with 94 square plots in northwestern China: one model is plot-averaged height-based power model and the other is plot-averaged daisy-chain model. The correlation coefficients (R2) were 0.6725 and 0.6761, which are significantly higher than the correlation coefficients of the traditional percentile model (R2 = 0.5910). In addition, the correlation between TCH and ACD was significantly better than that between plot-averaged height (AvgH) and ACD, and Lorey’s height (LorH) had no significant correlation with ACD. We also found that plot-level basal area (BA) was a dominant factor in ACD prediction, with a correlation coefficient reaching 0.9182, but this subject requires field investigation. The two models proposed in this study provide a simple and easy approach for estimating ACD in coniferous forests, which can replace the traditional LiDAR percentile method completely.

Developing a two-step algorithm to estimate the leaf area index of forests with complex structures based on CHRIS/PROBA data

Vegetation is a key factor affecting soil erosion, which depends not only on the horizontal structure of vegetation, but also on its vertical structure. The leaf area index (LAI) of plants, as an important structural parameter of the terrestrial ecosystem, can reflect horizontal vegetation coverage, vertical structure of vegetation, litter thickness and underground biomass. All these are the principal aspects of vegetation affecting soil erosion. The multi-angle remote sensing data can provide the stereoscopic and directional information of the surface target, which is helpful to extract the vertical structure parameters of vegetation effectively. Therefore, in order to improve the estimation accuracy of soil erosion, a two-step method was developed in this study to estimate the forest LAI under different vertical structures using the PROSAIL model and multi-angle CHRIS/PROBA data based on the field data. A total of 70 quadrats (10 m × 10 m sized) were deployed in Mount Zijin with undisturbed natural vegetation, considering the vegetation types and accessibility in the middle of August 2017. The pixels were first classified into two categories depending on complexity of vertical structure; then, a specific algorithm was applied to the different classes. For Class 1, the single angle data of 55° had the highest accuracy, indicating that, for forests with a simple vertical structure, the single angle data was sufficient for LAI estimation. For Class 2, the multi-angle data combination of 0°, 55° and −55° had the highest accuracy, indicating that, for forests with complex vertical structure, the multi-angle data can improve the accuracy of LAI estimation. The mean absolute percentage error (MAPE) and the root mean square error (RMSE) were applied to evaluate the accuracy of the LAI estimation algorithm. The results indicated that the two-step method can significantly improve the LAI estimation accuracy of forests with complex vertical structure, with a decrease in MAPE and RMSE of 19.52% and 23.08%, respectively, compared to the traditional empirical algorithm. The developed LAI estimation algorithm can be widely applied to forests with different vertical structures. Lastly, the two-step method was applied to the CHRIS/PROBA image acquired on September 7th, 2015 for mapping the spatial distribution of forest LAI in the western part of Mount Zijin. The result showed that the northern part and the southern part had higher LAI than the middle part of Mount Zijin.

Predicting forest height using the GOST, Landsat 7 ETM+, and airborne LiDAR for sloping terrains in the Greater Khingan Mountains of China

Sloping terrain of forests is an overlooked factor in many models simulating the canopy bidirectional reflectance distribution function, which limits the estimation accuracy of forest vertical structure parameters (e.g., forest height). The primary objective of this study was to predict forest height on sloping terrain over large areas with the Geometric-Optical Model for Sloping Terrains (GOST) using airborne Light Detection and Ranging (LiDAR) data and Landsat 7 imagery in the western Greater Khingan Mountains of China. The Sequential Maximum Angle Convex Cone (SMACC) algorithm was used to generate image endmembers and corresponding abundances in Landsat imagery. Then, LiDAR-derived forest metrics, topographical factors and SMACC abundances were used to calibrate and validate the GOST, which aimed to accurately decompose the SMACC mixed forest pixels into sunlit crown, sunlit background and shade components. Finally, the forest height of the study area was retrieved based on a back-propagation neural network and a look-up table. Results showed good performance for coniferous forests on all slopes and at all aspects, with significant coefficients of determination above 0.70 and root mean square errors (RMSEs) between 0.50 m and 1.00 m based on ground observed validation data. Higher RMSEs were found in areas with forest heights below 5 m and above 17 m. For 90% of the forested area, the average RMSE was 3.58 m. Our study demonstrates the tremendous potential of the GOST for quantitative mapping of forest height on sloping terrains with multispectral and LiDAR inputs.

A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data

The random volume over ground (RVoG) model associates vegetation vertical structure parameters with multiple complex interferometric coherence observables. In this paper, on the basis of the RVoG model, a truncated singular value decomposition (TSVD)-based method is proposed for forest height inversion from single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. In addition, in order to improve the applicability of TSVD for this issue, a new truncation method is proposed for TSVD. Differing from the traditional three-stage method, the TSVD-based inversion method estimates the pure volume coherence directly from the complex interferometric coherence, and estimates the forest height from the estimated pure volume coherence with a least-squares method. As a result, the TSVD-based method can adjust the contributions of the polarizations in the estimation of the model parameters and avoid the null ground-to-volume ratio assumption. The simulated experiments undertaken in this study confirmed that the TSVD-based method performs better than the three-stage method in forest height inversion. The TSVD-based method was also applied to E-SAR P-band data acquired over the Krycklan Catchment, Sweden, which is covered with mixed pine forest. The results showed that the TSVD-based method improves the root-mean-square error by 48.6% when compared to the three-stage method, which further validates the performance of the TSVD-based method.

Forest Vertical Parameter Estimation Using PolInSAR Imagery Based on Radiometric Correction

This work introduces an innovative radiometric terrain correction algorithm using PolInSAR imagery for improving forest vertical structure parameter estimation. The variance of radar backscattering caused by terrain undulation has been considered in this research by exploiting an iteration optimization procedure to improve the backscattering estimation for a Synthetic Aperture Radar (SAR) image. To eliminate the variance of backscatter coefficients caused by the local incident angle, a radiometric normalization algorithm has been investigated to compensate the influence of terrain on backscattering values, which hinders forest vertical parameter estimation. In vertical parameter estimation, species diversity and the spatial distribution of different vegetation have been modeled. Then, a combination of Fisher’s Alpha-Diversity model parameter estimation and the three-stage inversion method was designed for the vertical structure parameter. To demonstrate the efficiency of the proposed method in forest height estimation, the classical phase difference and three-stage inversion approach have been performed for the purpose of comparison. The proposed algorithm is tested on ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 (Radio Direction and Range Satellite 2) data sets for the Great Xing’an Mountain area and BioSAR (Biomass Synthetic Aperture Radar) 2007 data sets for the Remningstorp area. Height estimation results have also been validated using in-situ measurements. Experiments indicate the proposed method has the ability to compensate the influence of terrain undulation and improving the accuracy of forest vertical structure parameter estimation.

POTENTIAL APPLICATION OF NOVEL HYPERSPECTRAL LIDAR FOR MONITORING CROPS NITROGEN STRESS

Abstract. Precision agriculture has always been the research hotspot around the world. And the optimization of nitrogen fertilization for crops is the core concerns. It is not only to improve the productivity of crops but also to avoid the environmental risks caused by over-fertilization. Therefore, accurate estimation of nitrogen status is crucial for determining an nitrogen recommendation. Remote sensing techniques have been widely used to monitor crops for years, and they could offer estimations for stress status diagnosis through obtaining vertical structure parameters and spectral reflectance properties of crops. As an active remote sensing technology, lidar is particularly attractive for 3-dimensional information at a high point density. It has unique edges in obtaining vertical structure parameters of crops. However, capability of spectral reflectance properties is what the current lidar technology lacks because of single wavelength detection. To solve this problem, the concept of novel hyperspectral lidar (HSL), which combines the advantages of hyperspectal reflectance with high 3-dimensional capability of lidar, was proposed in our study. The design of instrument was described in detail. A broadband laser pulse was emitted and reflectance spectrum with 32 channels could be detected. Furthermore, the experiment was carried out by the novel HSL system to testify the potential application for monitoring nitrogen stress. Rice under different levels of nitrogen fertilization in central China were selected as the object of study, and four levels of nitrogen fertilization (N1-N4) were divided. With the detection of novel lidar system, high precision structure parameters of crops could be provided. Meanwhile, spectral reflectance properties in 32 wavebands were also obtained. The high precision structure parameters could be used to evaluate the stress status of crops. And abundant spectral information in 32 wavebands could improve the capacity of lidar system significantly. The results demonstrate that it is more effective for HSL system to distinguish different levels of nitrogen fertilization. Overall, HSL allows for probing not only high precision structure parameters but also spectral reflectance properties of crops. Compared with other approaches, the novel HSL has the potential to provide more comprehensive information of crops which can be assessed remotely in the application of precision agriculture.

POTENTIAL APPLICATION OF NOVEL HYPERSPECTRAL LIDAR FOR MONITORING CROPS NITROGEN STRESS

Precision agriculture has always been the research hotspot around the world. And the optimization of nitrogen fertilization for crops is the core concerns. It is not only to improve the productivity of crops but also to avoid the environmental risks caused by over-fertilization. Therefore, accurate estimation of nitrogen status is crucial for determining an nitrogen recommendation. Remote sensing techniques have been widely used to monitor crops for years, and they could offer estimations for stress status diagnosis through obtaining vertical structure parameters and spectral reflectance properties of crops. As an active remote sensing technology, lidar is particularly attractive for 3-dimensional information at a high point density. It has unique edges in obtaining vertical structure parameters of crops. However, capability of spectral reflectance properties is what the current lidar technology lacks because of single wavelength detection. To solve this problem, the concept of novel hyperspectral lidar (HSL), which combines the advantages of hyperspectal reflectance with high 3-dimensional capability of lidar, was proposed in our study. The design of instrument was described in detail. A broadband laser pulse was emitted and reflectance spectrum with 32 channels could be detected. Furthermore, the experiment was carried out by the novel HSL system to testify the potential application for monitoring nitrogen stress. Rice under different levels of nitrogen fertilization in central China were selected as the object of study, and four levels of nitrogen fertilization (N1-N4) were divided. With the detection of novel lidar system, high precision structure parameters of crops could be provided. Meanwhile, spectral reflectance properties in 32 wavebands were also obtained. The high precision structure parameters could be used to evaluate the stress status of crops. And abundant spectral information in 32 wavebands could improve the capacity of lidar system significantly. The results demonstrate that it is more effective for HSL system to distinguish different levels of nitrogen fertilization. Overall, HSL allows for probing not only high precision structure parameters but also spectral reflectance properties of crops. Compared with other approaches, the novel HSL has the potential to provide more comprehensive information of crops which can be assessed remotely in the application of precision agriculture.

Dynamic and Thermodynamic Predictors of Vertical Structure in Radar-Observed Regional Precipitation

Abstract Radar-observed vertical structure of precipitation as defined by contoured frequency by altitude diagrams (CFADs) is related to dynamic and thermodynamic environmental parameters. CFADs from 559 storms occurring over the years 2004–11 in the vicinity of Locarno, Switzerland, combined with Interim ECMWF Re-Analysis (ERA-Interim) data show that the radar-observed vertical structure of precipitation correlates with synoptic pattern (as defined by 1000- and 500-hPa geopotential heights), integrated water vapor flux, atmospheric stability, and vertical profiles of temperature, moisture, and wind. Following the analysis of vertical structure and environmental parameters, a generalized linear model (GLM) is developed for radar-observed vertical structure as a function of data from ERA-Interim. The GLM provides expected values for the vertical extent and magnitude of radar reflectivity and predicts storm vertical structure type with 79% overall accuracy. The relationships found between environmental parameters and storm vertical structure underscore the importance of including both dynamic and thermodynamic variables when evaluating climate change effects on precipitation. In addition, the ability of the GLM to reproduce storm types shows the potential for using GLMs as a link between lower-resolution global model data and high-resolution precipitation observations.