Consistent patterns of common species across tropical tree communities

Trees structure the Earth’s most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations1,2,3,4,5,6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth’s 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories7, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world’s most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees

An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest

Canopy leaf area index (LAI), defined as the single-sided leaf area per unit ground area, is a quantitative measure of canopy foliar area. LAI is a controlling biophysical property of vegetation function, and quantifying LAI is thus vital for understanding energy, carbon and water fluxes between the land surface and the atmosphere. LAI is routinely available from Earth Observation (EO) instruments such as MODIS. However EO-derived estimates of LAI require validation before they are utilised by the ecosystem modelling community. Previous validation work on the MODIS collection 4 (c4) product suggested considerable error especially in forested biomes, and as a result significant modification of the MODIS LAI algorithm has been made for the most recent collection 5 (c5). As a result of these changes the current MODIS LAI product has not been widely validated. We present a validation of the MODIS c5 LAI product over a 121 km(2) area of mixed coniferous forest in Oregon, USA, based on detailed ground measurements which we have upscaled using high resolution EO data. Our analysis suggests that c5 shows a much more realistic temporal LAI dynamic over c4 values for the site we examined. We find improved spatial consistency between the MODIS c5 LAI product and upscaled in situ measurements. However results also suggest that the c5 LAI product underestimates the upper range of upscaled in situ LAI measurements. (C) 2010 Elsevier Inc. All rights reserved

Rapid characterisation of forest structure from TLS and 3D modelling

Raumonen et al.[1] have developed a new method for reconstructing topologically consistent tree architecture from TLS point clouds. This method generates a cylinder model of tree structure using a stepwise approach. Disney et al.[2] validated this method with a detailed 3D tree model where structure is known a priori, establishing a reconstruction relative error of less than 2%. Here we apply the same method to data acquired from Eucalyptus racemosa woodland, Banksia ameula low open woodland and Eucalyptus spp. open forest using a RIEGL VZ-400 instrument. Individual 3D tree models reconstructed from TLS point clouds are used to drive Monte Carlo ray tracing simulations of TLS with the same characteristics as those collected in the field. 3D reconstruction was carried out on the simulated point clouds so that errors and uncertainty arising from instrument sampling and reconstruction could be assessed directly. We find that total volume could be recreated to within a 10.8% underestimate. The greatest constraint to this approach is the accuracy to which individual scans can be globally registered. Inducing a 1cm registration error lead to a 8.8% total volumetric overestimation across the data set

Sensitivity of direct canopy gap fraction retrieval from airborne waveform lidar to topography and survey characteristics

Recently, Armston et al. (2013) have demonstrated that a new, physically-based method for direct retrieval of canopy gap probability P from waveform lidar can improve the estimation of P over discrete return lidar data. The success of the approach was demonstrated in a savanna woodland environment in Australia. The huge advantage of this method is that it uses the data themselves to solve for the canopy contrast term i.e. the ratio of the reflectance from crown and ground, ρ/ρ. In this way the method avoids local calibration that is typically required to overcome differences in either ρ or ρ. To be more generally useful the method must be demonstrated on different sites and in the presence of slope and different sensor and survey configurations. If it is robust to these things, slope in particular, then we would suggest it is likely to be widely useful. Here, we test the robustness of the retrieval of P from waveform lidar using the Watershed Allied Telemetry Experimental Research dataset, over the Heihe River Basin region of China. The data contain significant canopy, terrain and survey variations, presenting a rather different set of conditions to those previously used. Results show that ρ/ρ is seen to be stable across all flights and for all levels of spatial aggregation. This strongly supports the robustness of the new P retrieval method, which assumes that this relationship is stable. A comparison between P estimated from hemiphotos and from the waveform lidar showed agreement with Pearson correlation coefficient R=0.91. The waveform lidar-derived estimates of P agreed to within 8% of values derived from hemiphotos, with a bias of 0.17%. The new waveform model was shown to be stable across different off-nadir scan angles and in the presence of slopes up to 26° with R≥0.85 in all cases. We also show that the waveform model can be used to calculate P using just the mean value of canopy returns, assuming that their distribution is unimodal. Lastly, we show that the method can also be applied to discrete return lidar data, albeit with slightly lower accuracy and higher bias, allowing P comparisons with previously-collected lidar datasets. Our results show the new method should be applicable for estimating P robustly across large areas, and from lidar data collected at different times and using different systems; an increasingly important requirement