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Imaging spectroscopy is a powerful tool for mapping chemical leaf traits at the canopy level. However, covariance with structural canopy properties is hampering the ability to predict leaf biochemical traits in structurally heterogeneous forests. Here, we used imaging spectroscopy data to map canopy level leaf nitrogen (N mass ) and phosphorus concentrations (P mass ) of a temperate mixed forest. By integrating predictor variables derived from airborne laser scanning (LiDAR), capturing the biophysical complexity of the canopy, we aimed at improving predictions of N mass and P mass . We used partial least squares regression (PLSR) models to link community weighted means of both leaf constituents with 245 hyperspectral bands (426–2425 nm) and 38 LiDAR-derived variables. LiDAR-derived variables improved the model's explained variances for N mass (R 2 cv 0.31 vs. 0.41, % RSME cv 3.3 vs. 3.0) and P mass (R 2 cv 0.45 vs. 0.63, % RSME cv 15.3 vs. 12.5). The predictive performances of N mass models using hyperspectral bands only, decreased with increasing structural heterogeneity included in the calibration dataset. To test the independent contribution of canopy structure we additionally fit the models using only LiDAR-derived variables as predictors. Resulting R 2 cv values ranged from 0.26 for N mass to 0.54 for P mass indicating considerable covariation between biochemical traits and forest structural properties. N mass was negatively related to the spatial heterogeneity of canopy density, whereas P mass was negatively related to stand height and to the total cover of tree canopies. In the specific setting of this study, the importance of structural variables can be attributed to the presence of two tree species, featuring structural and biochemical properties different from co-occurring species. Still, existing functional linkages between structure and biochemistry at the leaf and canopy level suggest that canopy structure, used as proxy, can in general support the mapping of leaf biochemistry over broad spatial extents. [ABSTRACT FROM AUTHOR]

Integration of remote sensing (RS) data in forest inventories for enhancing plot-based forest variable prediction is a widely researched topic. Geometric consistency between forest inventory plots and areas for extraction of RS-based predictive metrics is considered a crucial factor for accurate modelling of forest variables. Achieving geometric consistency is particularly difficult with regard to angle-count sampling (ACS) plots, which have neither distinct shape nor distinct extent. This initial study considers a new approach for integrating ACS and RS data, where the concept of ACS is transferred to RS-based metrics extraction. By using the relationship between tree height and diameter at breast height (DBH), pixels of a RS-based canopy height model are extracted if their value suggests a DBH that would lead to inclusion in an angle-count sample at the given distance to the plot centre. Different variations of this approach are tested by modelling timber volume in national forest inventory plots in Germany. The results are compared to those achieved using fixed-radius plots. A root mean square error of approximately 42% is achieved by both the new and fixed-radius approaches. Therefore, the new approach is not yet considered sufficient for overcoming all difficulties concerning the integration of ACS plot and RS data. However, possibilities for improvement are discussed and will be the subject of further research. [ABSTRACT FROM AUTHOR]

Background: Mapping tropical forest structure is a critical requirement for accurate estimation of emissions and removals from land use activities. With the availability of a wide range of remote sensing imagery of vegetation characteristics from space, development of finer resolution and more accurate maps has advanced in recent years. However, the mapping accuracy relies heavily on the quality of input layers, the algorithm chosen, and the size and quality of inventory samples for calibration and validation. Results: By using airborne lidar data as the 'truth' and focusing on the mean canopy height (MCH) as a key structural parameter, we test two commonly-used non-parametric techniques of maximum entropy (ME) and random forest (RF) for developing maps over a study site in Central Gabon. Results of mapping show that both approaches have improved accuracy with more input layers in mapping canopy height at 100 m (1-ha) pixels. The bias-corrected spatial models further improve estimates for small and large trees across the tails of height distributions with a trade-off in increasing overall mean squared error that can be readily compensated by increasing the sample size. Conclusions: A significant improvement in tropical forest mapping can be achieved by weighting the number of inventory samples against the choice of image layers and the non-parametric algorithms. Without future satellite observations with better sensitivity to forest biomass, the maps based on existing data will remain slightly biased towards the mean of the distribution and under and over estimating the upper and lower tails of the distribution. [ABSTRACT FROM AUTHOR]

The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) is a spaceborne LiDAR sensor. It is the first LiDAR instrument which can digitize the backscattered waveform and offer near global coverage. Among others, scientific objectives of the mission include precise measurement of vegetation canopy heights. Existing approaches of waveform processing for canopy height estimation suggest Gaussian decomposition of the waveform which has the limitation to properly characterize significant peaks and results in discrepant information. Moreover, in most cases, Digital Terrain Models (DTMs) are required for canopy height estimation. This paper presents a new automated method of GLAS waveform processing for extracting vegetation canopy height in the absence of a DTM. Canopy heights retrieved from GLAS waveforms were validated with field measured heights. The newly proposed method was able to explain 79% of variation in canopy heights with an RMSE of 3.18 m, in the study area. The unexplained variation in canopy heights retrieved from GLAS data can be due to errors introduced by footprint eccentricity, decay of energy between emitted and received signals, uncertainty in the field measurements and limited number of sampled footprints. Results achieved with the newly proposed method were encouraging and demonstrated its potential of processing full-waveform LiDAR data for estimating forest canopy height. The study also had implications on future full-waveform spaceborne missions and their utility in vegetation studies. [ABSTRACT FROM AUTHOR]