Terrestrial laser scanning to estimate plot-level forest canopy fuel properties

This paper evaluates the potential of a terrestrial laser scanner (TLS) to characterize forest canopy fuel characteristics at plot level. Several canopy properties, namely canopy height, canopy cover, canopy base height and fuel strata gap were estimated. Different approaches were tested to avoid the effect of canopy shadowing on canopy height estimation caused by deployment of the TLS below the canopy. Estimation of canopy height using a grid approach provided a coefficient of determination of R² = 0.81 and an RMSE of 2.47 m. A similar RMSE was obtained using the 99th percentile of the height distribution of the highest points, representing the 1% of the data, although the coefficient of determination was lower (R² = 0.70). Canopy cover (CC) was estimated as a function of the occupied cells of a grid superimposed upon the TLS point clouds. It was found that CC estimates were dependent on the cell size selected, with 3 cm being the optimum resolution for this study. The effect of the zenith view angle on CC estimates was also analyzed. A simple method was developed to estimate canopy base height from the vegetation vertical profiles derived from an occupied/non-occupied voxels approach. Canopy base height was estimated with an RMSE of 3.09 m and an R² = 0.86. Terrestrial laser scanning also provides a unique opportunity to estimate the fuel strata gap (FSG), which has not been previously derived from remotely sensed data. The FSG was also derived from the vegetation vertical profile with an RMSE of 1.53 m and an R² = 0.87.     

Accuracy of tree diameter estimation from terrestrial laser scanning by circle-fitting methods

This study compares the accuracies of diameter at breast height (DBH) estimations by three initial (minimum bounding box, centroid, and maximum distance) and two refining (Monte Carlo and optimal circle) circle-fitting methods The circle-fitting algorithms were evaluated in multi-scan mode and a simulated single-scan mode on 157 European beech trees (Fagus sylvatica L.). DBH measured by a calliper was used as reference data. Most of the studied circle-fitting algorithms significantly underestimated the mean DBH in both scanning modes. Only the Monte Carlo method in the single-scan mode significantly overestimated the mean DBH. The centroid method proved to be the least suitable and showed significantly different results from the other circle-fitting methods in both scanning modes. In multi-scan mode, the accuracy of the minimum bounding box method was not significantly different from the accuracies of the refining methods The accuracy of the maximum distance method was significantly different from the accuracies of the refining methods in both scanning modes. The accuracy of the Monte Carlo method was significantly different from the accuracy of the optimal circle method in only single-scan mode. The optimal circle method proved to be the most accurate circle-fitting method for DBH estimation from point clouds in both scanning modes.     

Estimating aboveground and organ biomass of plant canopies across the entire season of rice growth with terrestrial laser scanning

Non-destructive and accurate estimation of crop biomass is crucial for the quantitative diagnosis of growth status and timely prediction of grain yield. As an active remote sensing technique, terrestrial laser scanning (TLS) has become increasingly available in crop monitoring for its advantages in recording structural properties. Some researchers have attempted to use TLS data in the estimation of crop aboveground biomass, but only for part of the growing season. Previous studies rarely investigated the estimation of biomass for individual organs, such as the panicles in rice canopies, which led to the poor understanding of TLS technology in monitoring biomass partitioning among organs. The objective of this study was to investigate the potential of TLS in estimating the biomass for individual organs and aboveground biomass of rice and to examine the feasibility of developing universal models for the entire growing season. The field plots experiments were conducted in 2017 and 2018 and involved different nitrogen (N) rates, planting techniques and rice varieties. Three regression approaches, stepwise multiple linear regression (SMLR), random forest regression (RF) and linear mixed-effects (LME) modeling, were evaluated in estimating biomass with extensive TLS and biomass data collected at multiple phenological stages of rice growth across the entire season. The models were calibrated with the 2017 dataset and validated independently with the 2018 dataset.The results demonstrated that growth stage in LME modeling was selected as the most significant random effect on rice growth among the three candidates, which were rice variety, growth stage and planting technique. The LME models grouped by growth stage exhibited higher validation accuracies for all biomass variables over the entire season to varying degrees than SMLR models and RF models. The most pronounced improvement with a LME model was obtained for panicle biomass, with an increase of 0.74 in R² (LME: R² = 0.90, SMLR: R² = 0.16) and a decrease of 1.15 t/ha in RMSE (LME: RMSE =0.79 t/ha, SMLR: RMSE =2.94 t/ha). Compared to SMLR and RF, LME modeling yielded similar estimation accuracies of aboveground biomass for pre-heading stages, but significantly higher accuracies for post-heading stages (LME: R² = 0.63, RMSE =2.27 t/ha; SMLR: R² = 0.42, RMSE =2.42 t/ha; RF: R² = 0.57, RMSE =2.80 t/ha). These findings implied that SMLR was only suitable for the estimation of biomass at pre-heading stages and LME modeling performed remarkably well across all growth stages, especially for post-heading. The results suggest coupling TLS with LME modeling is a promising approach to monitoring rice biomass at post-heading stages at high accuracy and to overcoming the saturation of canopy reflectance signals encountered in optical remote sensing. It also has great potential in the monitoring of other crops in cloud-cover conditions and the instantaneous prediction of grain yield any time before harvest.     

Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)

Surveying techniques such as terrestrial laser scanner have recently been used to measure surface changes via 3D point cloud (PC) comparison. Two types of approaches have been pursued: 3D tracking of homologous parts of the surface to compute a displacement field, and distance calculation between two point clouds when homologous parts cannot be defined. This study deals with the second approach, typical of natural surfaces altered by erosion, sedimentation or vegetation between surveys. Current comparison methods are based on a closest point distance or require at least one of the PC to be meshed with severe limitations when surfaces present roughness elements at all scales. To solve these issues, we introduce a new algorithm performing a direct comparison of point clouds in 3D. The method has two steps: (1) surface normal estimation and orientation in 3D at a scale consistent with the local surface roughness; (2) measurement of the mean surface change along the normal direction with explicit calculation of a local confidence interval. Comparison with existing methods demonstrates the higher accuracy of our approach, as well as an easier workflow due to the absence of surface meshing or Digital Elevation Model (DEM) generation. Application of the method in a rapidly eroding, meandering bedrock river (Rangitikei River canyon) illustrates its ability to handle 3D differences in complex situations (flat and vertical surfaces on the same scene), to reduce uncertainty related to point cloud roughness by local averaging and to generate 3D maps of uncertainty levels. We also demonstrate that for high precision survey scanners, the total error budget on change detection is dominated by the point clouds registration error and the surface roughness. Combined with mm-range local georeferencing of the point clouds, levels of detection down to 6 mm (defined at 95% confidence) can be routinely attained in situ over ranges of 50 m. We provide evidence for the self-affine behaviour of different surfaces. We show how this impacts the calculation of normal vectors and demonstrate the scaling behaviour of the level of change detection. The algorithm has been implemented in a freely available open source software package. It operates in complex 3D cases and can also be used as a simpler and more robust alternative to DEM differencing for the 2D cases.