Donghui Xie

An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation

Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from airborne LiDAR (light detection and ranging) data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high accuracy. In this paper, we present a new filtering method which only needs a few easy-to-set integer and Boolean parameters. Within the proposed approach, a LiDAR point cloud is inverted, and then a rigid cloth is used to cover the inverted surface. By analyzing the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface. Finally, the ground points can be extracted from the LiDAR point cloud by comparing the original LiDAR points and the generated surface. Benchmark datasets provided by ISPRS (International Society for Photogrammetry and Remote Sensing) working Group III/3 are used to validate the proposed filtering method, and the experimental results yield an average total error of 4.58%, which is comparable with most of the state-of-the-art filtering algorithms. The proposed easy-to-use filtering method may help the users without much experience to use LiDAR data and related technology in their own applications more easily.

A Framework for Consistent Estimation of Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation, and Surface Albedo from MODIS Time-Series Data

Currently available land-surface parameter products are generated using parameter-specific algorithms from various satellite data and contain several inconsistencies. This paper developed a new data assimilation framework for consistent estimation of multiple land-surface parameters from time-series MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. If the reflectance data showed snow-free areas, an ensemble Kalman filter (EnKF) technique was used to estimate leaf area index (LAI) for a two-layer canopy reflectance model (ACRM) by combining predictions from a phenology model and the MODIS surface reflectance data. The estimated LAI values were then input into the ACRM to calculate the surface albedo and the fraction of absorbed photosynthetically active radiation (FAPAR). For snow-covered areas, the surface albedo was calculated as the underlying vegetation canopy albedo plus the weighted distance between the underlying vegetation canopy albedo and the albedo over deep snow. The LAI/FAPAR and surface albedo values estimated using this framework were compared with MODIS collection 5 eight-day 1-km LAI/FAPAR products (MOD15A2) and 500-m surface albedo product (MCD43A3), and GEOV1 LAI/FAPAR products at 1/112° spatial resolution and a ten-day frequency, respectively, and validated by ground measurement data from several sites with different vegetation types. The results demonstrate that this new data assimilation framework can estimate temporally complete land-surface parameter profiles from MODIS time-series reflectance data even if some of the reflectance data are contaminated by residual cloud or are missing and that the retrieved LAI, FAPAR, and surface albedo values are physically consistent. The root mean square errors of the retrieved LAI, FAPAR, and surface albedo against ground measurements are 0.5791, 0.0453, and 0.0190, respectively.

Accuracy evaluation of the ground-based fractional vegetation cover measurement by using simulated images

Digital photography is now the most widely used method to obtain the Fractional Vegetation Cover (FVC) in field measurements. Its accuracy is affected by shooting conditions and classification methods of digital images. In this paper, we chose summer maize as the study plant, used computer simulation method to control the shooting conditions strictly and generate simulated scene. Then a physically based ray-tracing (PBRT) algorithm was used to render the scene to obtain simulated images under different shooting conditions. Supervised classification and CIE L*a*b* color space threshold method were used to extract FVC values from the simulated images. Comparing the extracted FVC values with the scenes true FVC value, we evaluated the FVC accuracy of different shooting conditions and classification methods. The results can act as a guidance of digital photography to obtain the FVC.

Scaling of FAPAR from the Field to the Satellite

The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical biophysical parameter in eco-environmental studies. Scaling of FAPAR from the field observation to the satellite pixel is essential for validating remote sensing FAPAR product and for further modeling applications. However, compared to spatial mismatches, few studies have considered temporal mismatches between in-situ and satellite observations in the scaling. This paper proposed a general methodology for scaling FAPAR from the field to the satellite pixel considering the temporal variation. Firstly, a temporal normalization method was proposed to normalize the in-situ data measured at different times to the time of satellite overpass. The method was derived from the integration of an atmospheric radiative transfer model (6S) and a FAPAR analytical model (FAPAR-P), which can characterize the diurnal variations of FAPAR comprehensively. Secondly, the logistic model, which derives smooth and consistent temporal profile for vegetation growth, was used to interpolate the in-situ data to match the dates of satellite acquisitions. Thirdly, fine-resolution FAPAR products at different dates were estimated from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data using the temporally corrected in-situ data. Finally, fine-resolution FAPAR were taken as reference datasets and aggregated to coarse resolution, which were further compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) FAPAR product. The methodology is validated for scaling FAPAR from the field to the satellite pixel temporally and spatially. The MODIS FAPAR manifested a good consistency with the aggregated FAPAR with R2 of 0.922 and the root mean squared error of 0.054.

Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs

Spatial and temporal data fusion approaches have been developed to fuse reflectance imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS), which have complementary spatial and temporal sampling characteristics. The approach relies on using Landsat and MODIS image pairs that are acquired on the same day to estimate Landsat-scale reflectance on other MODIS dates. Previous studies have revealed that the accuracy of data fusion results partially depends on the input image pair used. The selection of the optimal image pair to achieve better prediction of surface reflectance has not been fully evaluated. This paper assesses the impacts of Landsat-MODIS image pair selection on the accuracy of the predicted land surface reflectance using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) over different landscapes. MODIS images from the Aqua and Terra platforms were paired with images from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) to make different pair image combinations. The accuracy of the predicted surface reflectance at 30 m resolution was evaluated using the observed Landsat data in terms of mean absolute difference, root mean square error and correlation coefficient. Results show that the MODIS pair images with smaller view zenith angles produce better predictions. As expected, the image pair closer to the prediction date during a short prediction period produce better prediction results. For prediction dates distant from the pair date, the predictability depends on the temporal and spatial variability of land cover type and phenology. The prediction accuracy for forests is higher than for crops in our study areas. The Normalized Difference Vegetation Index (NDVI) for crops is overestimated during the non-growing season when using an input image pair from the growing season, while NDVI is slightly underestimated during the growing season when using an image pair from the non-growing season. Two automatic pair selection strategies are evaluated. Results show that the strategy of selecting the MODIS pair date image that most highly correlates with the MODIS image on the prediction date produces more accurate predictions than the nearest date strategy. This study demonstrates that data fusion results can be improved if appropriate image pairs are used.

Modified gap fraction model of individual trees for estimating leaf area using terrestrial laser scanner

Abstract. Terrestrial laser scanners (TLS) have demonstrated great potential in estimating structural attributes of forest canopy, such as leaf area index (LAI). However, the inversion accuracy of LAI is highly dependent on the measurement configuration of TLS and spatial characteristics of the scanned tree. Therefore, a modified gap fraction model integrating the path length distribution is developed to improve the accuracy of retrieved single-tree leaf area (LA) by considering the shape of a single-tree crown. The sensitivity of TLS measurement configurations on the accuracy of the retrieved LA is also discussed by using the modified gap fraction model based on several groups of simulated and field-measured point clouds. We conclude that (1) the modified gap fraction model has the potential to retrieve LA of an individual tree and (2) scanning distance has the enhanced impact on the accuracy of the retrieved LA than scanning step. A small scanning step for broadleaf trees reduces the scanning time, the storage volume, and postprocessing work in the condition of ensuring the accuracy of the retrieved LA. This work can benefit the design of an optimal survey configuration for the field campaign.

Research on PAR and FPAR of crop canopies based on RGM

PAR and FPAR are two important variables in agricultural field. Some researches show that many factors, such as LAI(leaf area index), LAD(leaf ange distribution) and the heterogeneity of vegetation will affect the distribution of PAR and FPAR. In order to understanding the exchange process of material and energy, Radiosity-Graphics combined Model (RGM) (Qin et al., 2000) is used to simulate the distribution of PAR and FPAR in canopy and some effect factors, such as the structure of canopy and sun zenith angle, can be analyze carefully. PAR and FPAR of a typical winter wheat canopy is simulated and the results are validated with the measured data. They agreed well. Next work is to simulate and analyze several factors of the distribution of PAR and FPAR, including sun incident angle, LAD, LAI, special for the heterogeneous canopies such as that crop with width and narrow ridges which can direct cropping patterns and remote sensing inversion.

Quantitative remote sensing research on the vegetation 3-D visual simulation based on object oriented technique

In the field of remote sensing, it is important to understand interaction between light and vegetation. The interrelation of them has been addressed in many works, and many different radiant models of vegetation have been proposed, such as: geometrical optical models, turbid medium models, hybrid models and computer simulation models. With developing of quantitative remote sensing research, computer simulation models, for example, Monte Carlo simulation model and Radiosity show their importance in analyzing the experimental data. In order to continue calculating the reflectivity from the vegetation by using a computer simulation model, it is essential to build the 3D structure of the vegetation. Therefore, many 3D structure data and optical parameters about the real winter wheat were measured firstly, i.e. height of stem, positions and sizes of the leaves, distributions on the field of wheat. Because these data are numerous and discrete, it is very difficult to simulate the virtual scene with them directly. To cope with it, we arranged all data and parameters in several layers based on the object oriented technique. Moreover, in order to simplify and deduce the structural variables that will be applied to build the 3D visual winter wheat model, we analyzed experimental data statistically in the process of realistic structural model. Several geometric and logical relations about structural variables were developed subsequently, and some variables varying with season were summarized to get the simple regulation with the purpose of simulating growing process of the winter wheat. The extended Lindenmayer system (L-system) method is then used to simulate the virtual scene of winter wheat by giving a few structural variables simplified before. Once the simulation is correct, scattering and reflectance from the 3D structural scene can be calculated using the Monte Carlo simulation model or Radiosity and so on. Our results show that (a) our lighting simulation system efficiently provides the required information at the desired level of accuracy, and (b) the plant growth model is extremely well calibrated against real plants. Furthermore, the method and the relations developed in this paper can be used in other subjects, such as computer graphics.

Estimation of Daily Average Downward Shortwave Radiation over Antarctica

Surface shortwave (SW) irradiation is the primary driving force of energy exchange in the atmosphere and land interface. The global climate is profoundly influenced by irradiation changes due to the special climatic condition in Antarctica. Remote-sensing retrieval can offer only the instantaneous values in an area, whilst daily cycle and average values are necessary for further studies and applications, including climate change, ecology, and land surface process. When considering the large values of and small diurnal changes of solar zenith angle and cloud coverage, we develop two methods for the temporal extension of remotely sensed downward SW irradiance over Antarctica. The first one is an improved sinusoidal method, and the second one is an interpolation method based on cloud fraction change. The instantaneous irradiance data and cloud products are used in both methods to extend the diurnal cycle, and obtain the daily average value. Data from South Pole and Georg von Neumayer stations are used to validate the estimated value. The coefficient of determination (R2) between the estimated daily averages and the measured values based on the first method is 0.93, and the root mean square error (RMSE) is 32.21 W/m2 (8.52%). As for the traditional sinusoidal method, the R2 and RMSE are 0.68 and 70.32 W/m2 (18.59%), respectively The R2 and RMSE of the second method are 0.96 and 25.27 W/m2 (6.98%), respectively. These values are better than those of the traditional linear interpolation (0.79 and 57.40 W/m2 (15.87%)).

Reconstruction of Single Tree with Leaves Based on Terrestrial LiDAR Point Cloud Data

Many studies have been focusing on reconstructing the branch skeleton of a three-dimensional (3D) tree structure that is based on photos or point clouds scanned by a terrestrial laser scanner (TLS), but leaves, as the important component of a tree, are often ignored or simplified because of their complexity. Therefore, we develop a voxel-based method to add leaves to a reconstructed 3D branches structure based on TLS point clouds. The location and size of each leaf depend on the spatial distribution and density of leaves points in the voxel. We reconstruct a small 3D scene with four realistic 3D trees and a virtual tree (including trunk, branches, and leaves), and validate the structure of each tree through the directional gap fractions calculated based on simulated point clouds of this reconstructed scene by the ray-tracing algorithm. The results show good coherence with those from measured point clouds data. The relative errors of the directional gap fractions are no more than 4.1%, though the method is limited by the effects of point occlusion. Therefore, this method is shown to give satisfactory consistency both visually and in the quantitative evaluation of the 3D structure.