The application of aerial stereo imagery on the measurement of forest three-dimensional structures is growing in recent years due to the rapid development of unmanned aerial vehicle platforms and automatic processing algorithms of stereo images. Yet there is still no clear knowledge about how the description of forest three-dimensional structures is affected by settings of critical acquisition parameters of stereo images. This study systematically addressed the impacts of image resolutions and forward overlaps over a broad range by using LiDAR data as reference. The different combinations of image resolutions and forward overlaps used in this study are produced by image average downsampling and subsetting. Their performances were evaluated from four aspects, including computation loads, point densities, estimation of canopy height indices at forest stand level, and the vertical distribution of point clouds over forest stands and along forest transects with different levels of canopy closure. Results showed that the coupling between image resolutions and forward overlaps in the data processing should be given full consideration. Generally, finer image resolutions require higher forward overlaps; otherwise, much of the area could not be detected and sparse trees were easily missed. It was a better choice to degrade image resolutions while keeping forward overlap in data processing if more blank areas appeared or sparse trees could not be detected. The good match between image resolutions and forward overlaps could dramatically reduce the computation load while keeping the estimation accuracy.
Leaf area index (LAI) is an important forest structural parameter in the process of photosynthesis. Most studies have investigated the estimation of forest LAI using the spectral information of optical remote sensing images or the height information from LiDAR data. This paper explored the estimation of forest LAI using canopy cover and forest height information extracted from stereo imagery acquired by cameras onboard an unmanned aerial vehicle (UAV). UAV remote sensing has gradually become practical in recent years. Two alternative methods were proposed to extract forest height information. The height indices method extracted forest height indices within each forest plot based on the vertical histogram of the canopy height model derived from stereo imagery, while the segmentation method characterized forest plots using tree numbers and average tree heights based on the individual tree segmentation. The results showed that canopy cover and forest height are complementary in the estimation of forest LAI no matter what method was used. The combined use of canopy cover and forest height information extracted by the segmentation method had a better estimation accuracy of forest LAI with R2 = 0.833 and RMSE = 0.288. This paper demonstrated a new approach to predict forest LAI using UAV optical images.
Drone lidar has the potential to provide detailed measurements of vertical forest structure throughout large areas, but a systematic evaluation of unsampled forest structure in comparison to independent reference data has not been performed. Here, we used ray tracing on a high-resolution voxel grid to quantify sampling variation in a temperate mountain forest in the southwest Czech Republic. We decoupled the impact of pulse density and scan-angle range on the likelihood of generating a return using spatially and temporally coincident TLS data. We show three ways that a return can fail to be generated in the presence of vegetation: first, voxels could be searched without producing a return, even when vegetation is present; second, voxels could be shadowed (occluded) by other material in the beam path, preventing a pulse from searching a given voxel; and third, some voxels were unsearched because no pulse was fired in that direction. We found that all three types existed, and that the proportion of each of them varied with pulse density and scan-angle range throughout the canopy height profile. Across the entire data set, 98.1% of voxels known to contain vegetation from a combination of coincident drone lidar and TLS data were searched by high-density drone lidar, and 81.8% of voxels that were occupied by vegetation generated at least one return. By decoupling the impacts of pulse density and scan angle range, we found that sampling completeness was more sensitive to pulse density than to scan-angle range. There are important differences in the causes of sampling variation that change with pulse density, scan-angle range, and canopy height. Our findings demonstrate the value of ray tracing to quantifying sampling completeness in drone lidar.
Cleistogamy is a mating system in which plants produce some proportion of closed, autonomously self-pollinating flowers. Cleistogamous flowers differ from chasmogamous flowers, which are open flowers capable of outcrossing. Both dimorphic cleistogamy (cleistogamous and chasmogamous flowers produced on the same plant) and complete cleistogamy occur. Cleistogamy has been hypothesized to be a bet hedging strategy for reducing risk in the face of unpredictable pollinator availability. However, conflicting results across species and challenges connecting theory to data have prevented researchers from proving that cleistogamy is bet hedging. To test the bet hedging hypothesis, we investigated the distribution of over 400 cleistogamous species through biogeographical analyses. We find that cleistogamy is more prevalent in cooler, more variable environments. Additionally, we find that among cleistogamous species, complete cleistogamy is more likely to occur in warmer, more stable, tropical and subtropical environments. We hypothesize that the difference in distribution between complete and dimorphic cleistogamy may be driven by the opposing forces of selection to increase cleistogamy proportion and extinction risk, which we test using a heuristic Markov transition model. We conclude that the distribution of cleistogamy suggests that the strategy has evolved in variable environments, consistent with expectations for bet hedging.