Cloth simulation-based construction of pitfree canopy height models from airborne LiDAR data

Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

Quantification of occlusions influencing the tree stem curve retrieving from single-scan terrestrial laser scanning data

Background: The stem curve of standing trees is an essential parameter for accurate estimation of stem volume.This study aims to directly quantify the occlusions within the single-scan terrestrial laser scanning(TLS) data,evaluate its correlation with the accuracy of the retrieved stem curves, and subsequently, to assess the capacity of single-scan TLS to estimate stem curves.Methods: We proposed an index, occlusion rate, to quantify the occlusion level in TLS data. We then analyzed three influencing factors for the occlusion rate: the percentage of basal area near the scanning center, the scanning distance and the source of occlusions. Finally, we evaluated the effects of occlusions on stem curve estimates from single-scan TLS data.Results: The results showed that the correlations between the occlusion rate and the stem curve estimation accuracies were strong(r = 0.60–0.83), so was the correlations between the occlusion rate and its influencing factors(r = 0.84–0.99). It also showed that the occlusions from tree stems were the main factor of the low detection rate of stems, while the non-stem components mainly influenced the completeness of the retrieved stem curves.Conclusions: Our study demonstrates that the occlusions significantly affect the accuracy of stem curve retrieval from the single-scan TLS data in a typical-size(32 m × 32 m) forest plot. However, the single-scan mode has the capacity to accurately estimate the stem curve in a small forest plot(< 10 m × 10 m) or a plot with a lower occlusion rate, such as less than 35% in our tested datasets. The findings from this study are useful for guiding the practice of retrieving forest parameters using single-scan TLS data.

Mapping Spatially Seamless Fractional Vegetation Cover over China at a 30-m Resolution and Semimonthly Intervals in 2010-2020 Based on Google Earth Engine

Fractional vegetation cover(FVC)is a critical biophysical parameter that characterizes the status of terrestrial ecosystems.The spatial resolutions of most existing FVC products are still at the kilometer level.However,there is growing demand for FVC products with high spatial and temporal resolutions in remote sensing applications.This study developed an operational method to generate 30-m/15-day FVC products over China.Landsat datasets were employed to generate a continuous normalized difference vegetation index(NDVI)time series based on the Google Earth Engine platform from 2010 to 2020.The NDVI was transformed to FVC using an improved vegetation index(VI)-based mixture model,which quantitatively calculated the pixelwise coefficients to transform the NDVI to FVC.A comparison between the generated FVC,the Global LAnd Surface Satellite(GLASS)FVC,and a global FVC product(GEOV3 FVC)indicated consistent spatial patterns and temporal profiles,with a root mean square deviation(RMSD)value near 0.1 and an R^(2) value of approximately 0.8.Direct validation was conducted using ground measurements from croplands at the Huailai site and forests at the Saihanba site.Additionally,validation was performed with the FVC time series data observed at 151 plots in 22 small watersheds.The generated FVC showed a reasonable accuracy(RMSD values of less than 0.10 for the Huailai and Saihanba sites)and temporal trajectories that were similar to the field-measured FVC(RMSD values below 0.1 and R2 values of approximately 0.9 for most small watersheds).The proposed method outperformed the traditional VIbased mixture model and had the practicability and flexibility to generate the FVC at different resolutions and at a large scale.

A General and Effective Method for Wall and Protrusion Separation from Facade Point Clouds

As a critical prerequisite for semantic facade reconstruction,accurately separating wall and protrusion points from facade point clouds is required.The performance of traditional separation methods is severely limited by facade conditions,including wall shapes(e.g.,nonplanar walls),wall compositions(e.g.,walls composed of multiple noncoplanar point clusters),and protrusion structures(e.g.,protrusions without regularity,repetitive,or self-symmetric features).This study proposes a more widely applicable wall and protrusion separation method.The major principle underlying the proposed method is to transform the wall and protrusion separation problem as a ground filtering problem and to separate walls and protrusions using ground filtering methods,since the 2 problems can be solved using the same prior knowledge,that is,protrusions(nonground objects)protrude from walls(ground).After transformation problem,cloth simulation filter was used as an example to separate walls and protrusions in 8 facade point clouds with various characteristics.The proposed method was robust to the facade conditions,with a mean intersection over union of 90.7%,and had substantially higher accuracy compared with the traditional separation methods,including region growing-,random sample consensus-,multipass random sample consensus-based,and hybrid methods,with mean intersection over union values of 69.53%,49.52%,63.93%,and 47.07%,respectively.Besides,the proposed method was general,since existing ground filtering methods(including the maximum slope,progressive morphology,and progressive triangular irregular network densification filters)can also perform well.

Explicitly Reconstructing RAMI-V Scenes for Accurate 3-Dimensional Radiative Transfer Simulation Using the LESS Model

Radiative transfer(RT)simulation based on reconstructed 3-dimensional(3D)vegetation scenarios can promote the validation and development of various retrieval algorithms to monitor the growing states of vegetation in large-scale,multi-angular,and multi-sensor ways.The radiation transfer model intercomparison(RAMI)has made great contributions to providing abstract and actual 3D vegetation scenarios,and to the benchmarking of RT models under developed evaluation systems.To date,RAMI has been updated to the fifth phase(RAMI-V).In this study,we try to implement explicit conversion from all the RAMI-V scenes to generic structural models in the Wavefront OBJ format.These reconstructed scenes are applied in the LESS RT model to probe the ability of its RT solvers to simulate all sorts of remote sensing observations and radiative budget,including the bidirectional reflectance factor(BRF),albedo,fraction of photosynthetically active radiation absorbed by vegetation,and threshold hemispherical photograph(THP).BRF simulations fully explain angle effects as well as variation and robustness of the normalized difference vegetation index.Energy conservation is well validated between simulated absorption and albedo.The gap fraction derived from THP is analyzed in directional and total situations.In addition,this paper guides us how to simplify basic geometries and tune the illumination resolution(0.02 is optimal)to balance the simulation accuracy and efficiency.The generic structural models and reliable simulation results can be referenced by other RT models and retrieval algorithms.

A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous Pixels

The bidirectional reflectance distribution function(BRDF)of the land surface contains information relating to its physical structure and composition.Accurate BRDF modeling for heterogeneous pixels is important for global ecosystem monitoring and radiation balance studies.However,the original kerneldriven models,which many operational BRDF/Albedo algorithms have adopted,do not explicitly consider the heterogeneity within heterogeneous pixels,which may result in large fitting residuals.In this paper,we attempted to improve the fitting ability of the kernel-driven models over heterogeneous pixels by changing the inversion approach and proposed a dynamic weighted least squares(DWLS)inversion approach.The performance of DWLS and the traditional ordinary least squares(OLS)inversion approach were compared using simulated data.We also evaluated its ability to reconstruct multiangle satellite observations and provide accurate BRDF using unmanned aerial vehicle observations.The results show that the developed DWLS approach improves the accuracy of modeled BRDF of heterogeneous pixels.The DWLS approach applied to satellite observations shows better performance than the OLS method in study regions and exhibits smaller mean fitting residuals both in the red and near-infrared bands.The DWLS approach also shows higher BRDF modeling accuracy than the OLS approach with unmanned aerial vehicle observations.These results indicate that the DWLS inversion approach can be a better choice when kernel-driven models are used for heterogeneous pixels.

High Spatial Resolution and High Temporal Frequency(30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets:Method Development and Validation

High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.

Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method

Accurate and rapid estimation of canopy cover(CC)is crucial for many ecological and environmental models and for forest management.Unmanned aerial vehicle-light detecting and ranging(UAV-LiDAR)systems represent a promising tool for CC estimation due to their high mobility,low cost,and high point density.However,the CC values from UAV-LiDAR point clouds may be underestimated due to the presence of large quantities of within-crown gaps.To alleviate the negative effects of within-crown gaps,we proposed a pit-free CHM-based method for estimating CC,in which a cloth simulation method was used to fill the within-crown gaps.To evaluate the effect of CC values and withincrown gap proportions on the proposed method,the performance of the proposed method was tested on 18 samples with different CC values(40−70%)and 6 samples with different within-crown gap proportions(10−60%).The results showed that the CC accuracy of the proposed method was higher than that of the method without filling within-crown gaps(R^(2)=0.99 vs 0.98;RMSE=1.49%vs 2.2%).The proposed method was insensitive to within-crown gap proportions,although the CC accuracy decreased slightly with the increase in withincrown gap proportions.

Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies

Both leaf inclination angle distribution(LAD)and leaf area index(LAI)dominate optical remote sensing signals.The G-function,which is a function of LAD and remote sensing geometry,is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD.Large uncertainties are thus introduced.However,because numerous tiny leaves grow on conifers,it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval.In this study,we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval.Specifically,a Multi-Directional Imager(MDI)was developed to capture stereo images of the branches,and the needles were reconstructed.The accuracy of the inclination angles calculated from the reconstructed needles was high.Moreover,we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and threedimensional(3D)tree models.Results show that the constant G assumption introduces large errors in LAI retrieval,which could be as large as 53%in the zenithal viewing direction used by spaceborne LiDAR.As a result,accurate LAD estimation is recommended.In the absence of such data,our results show that a viewing zenith angle between 45 and 65 degrees is a good choice,at which the errors of LAI retrieval caused by the spherical assumption will be less than 10%for coniferous canopies.

Estimation of Larch Growth at the Stem,Crown,and Branch Levels Using Ground-Based LiDAR Point Cloud

Tree growth is an important indicator of forest health and can reflect changes in forest structure.Traditional tree growth estimates use easy-to-measure parameters,including tree height,diameter at breast height,and crown diameter,obtained via forest in situ measurements,which are labor intensive and time consuming.Some new technologies measure the diameter of trees at different positions to monitor the growth trend of trees,but it is difficult to take into account the growth changes at different tree levels.The combination of terrestrial laser scanning and quantitative structure modeling can accurately estimate tree structural parameters nondestructively and has the potential to estimate tree growth from different tree levels.In this context,this paper estimates tree growth from stem-,crown-,and branch-level attributes observed by terrestrial laser scanning.Specifically,tree height,diameter at breast height,stem volume,crown diameter,crown volume,and first-order branch volume were used to estimate the growth of 55-year-old larch trees in Saihanba of China,at the stem,crown,and branch levels.The experimental results showed that tree growth is mainly reflected in the growth of the crown,i.e.,the growth of branches.Compared to onedimensional parameter growth(tree height,diameter at breast height,or crown diameter),three-dimensional parameter growth(crown,stem,and first-order branch volumes)was more obvious,in which the absolute growth of the first-order branch volume is close to the stem volume.Thus,it is necessary to estimate tree growth at different levels for accurate forest inventory.

A Bibliometric Visualization Review of the MODIS LAI/FPAR Products from 1995 to 2020

The MODIS LAI/FPAR products have been widely used in various fields since their first public release in 2000.This review intends to summarize the history,development trends,scientific collaborations,disciplines involved,and research hotspots of these products.Its aim is to intrigue researchers and stimulate new research direction.Based on literature data from the Web of Science(WOS)and associated funding information,we conducted a bibliometric visualization review of the MODIS LAI/FPAR products from 1995 to 2020 using bibliometric and social network analysis(SNA)methods.We drew the following conclusions:(1)research based on the MODIS LAI/FPAR shows an upward trend with a multiyear average growth rate of 24.9%in the number of publications.(2)Researchers from China and the USA are the backbone of this research area,among which the Chinese Academy of Sciences(CAS)is the core research institution.(3)Research based on the MODIS LAI/FPAR covers a wide range of disciplines but mainly focus on environmental science and ecology.(4)Ecology,crop production estimation,algorithm improvement,and validation are the hotspots of these studies.(5)Broadening the research field,improving the algorithms,and overcoming existing difficulties in heterogeneous surface,scale effects,and complex terrains will be the trend of future research.Our work provides a clear view of the development of the MODIS LAI/FPAR products and valuable information for scholars to broaden their research fields.