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Data from the Geoscience Laser Altimeter System (GLAS) aboard the Ice Cloud and land Elevation Satellite (ICESat) offer an unprecedented opportunity for canopy height retrieval at a regional to global scale. The data also provide useful information for forest stand level assessment at coincident locations. In this study height indices from light detection and ranging (LiDAR) waveforms were explored as a means of extracting canopy height; these were examined with reference to a mixed temperate forest in Gloucestershire, UK, containing planted stands with a mean age of 51 years and mean maximum height of 26.6 m. A method based on using a terrain index (TI; maximum minus minimum elevations from a 767 subset 10-m resolution digital terrain model (DTM)) to adjust the waveform extent (WE; signal begin minus signal end) produced an R 2 value of 0.89 when regressed against field measurements of maximum canopy height at footprint locations (field height50.91(WE2TI) + 4.86; root mean squared error (RMSE)52.99 m, coefficient significance p,0.001, intercept significance p.0.01). Multiple regression performed on both WE and TI with field measurements produced an R 2 of 0.90 and an RMSE of 2.86 m (field height51.0208WE20.7310TI; coefficient significance p,0.001, intercept not significant). Maximum canopy height estimates using an automated approach to ground return identification based on iterative fitting of Gaussian peaks (GP1_2 MAXAMP ) to the waveform explained 74% of variance when compared to field measurements (field height51.05(GP1_2 MAXAMP ); RMSE54.53 m, coefficient significance p,0.001, intercept not significant). The ability of satellite LiDAR to retrieve data for such a complex and diverse area further indicates the potential of this technique for both carbon accounting and forest management.

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We present new coarse resolution (0.5° &times; 0.5°) vegetation height and vegetation-cover fraction data sets between 60° S and 60° N for use in climate models and ecological models. The data sets are derived from 2003–2009 measurements collected by the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat), the only LiDAR instrument that provides close to global coverage. Initial vegetation height is calculated from GLAS data using a development of the model of Rosette et al. (2008) with with further calibration on desert sites. Filters are developed to identify and eliminate spurious observations in the GLAS data, e.g. data that are affected by clouds, atmosphere and terrain and as such result in erroneous estimates of vegetation height or vegetation cover. Filtered GLAS vegetation height estimates are aggregated in histograms from 0 to 70 m in 0.5 m intervals for each 0.5° &times; 0.5°. The GLAS vegetation height product is evaluated in four ways. Firstly, the Vegetation height data and data filters are evaluated using aircraft LiDAR measurements of the same for ten sites in the Americas, Europe, and Australia. Application of filters to the GLAS vegetation height estimates increases the correlation with aircraft data from <i>r</i> = 0.33 to <i>r</i> = 0.78, decreases the root-mean-square error by a factor 3 to about 6 m (RMSE) or 4.5 m (68% error distribution) and decreases the bias from 5.7 m to −1.3 m. Secondly, the global aggregated GLAS vegetation height product is tested for sensitivity towards the choice of data quality filters; areas with frequent cloud cover and areas with steep terrain are the most sensitive to the choice of thresholds for the filters. The changes in height estimates by applying different filters are, for the main part, smaller than the overall uncertainty of 4.5–6 m established from the site measurements. Thirdly, the GLAS global vegetation height product is compared with a global vegetation height product typically used in a climate model, a recent global tree height product, and a vegetation greenness product and is shown to produce realistic estimates of vegetation height. Finally, the GLAS bare soil cover fraction is compared globally with the MODIS bare soil fraction (<i>r</i> = 0.65) and with bare soil cover fraction estimates derived from AVHRR NDVI data (<i>r</i> = 0.67); the GLAS tree-cover fraction is compared with the MODIS tree-cover fraction (<i>r</i> = 0.79). The evaluation indicates that filters applied to the GLAS data are conservative and eliminate a large proportion of spurious data, while only in a minority of cases at the cost of removing reliable data as well. <br><br> The new GLAS vegetation height product appears more realistic than previous data sets used in climate models and ecological models and hence should significantly improve simulations that involve the land surface

We present a method and initial results for a model of the interaction of waveform lidar with a three-dimensional canopy representation. The model is developed from the FLIGHT radiative transfer model (North, 1996), based on Monte Carlo simulation of photon transport. Foliage is represented by structural properties of leaf area, leaf angle distribution (LAD), crown dimensions and fractional cover, and the optical properties of leaves, branch, shoot and ground components. Important characteristics of the model are that it can represent multiple scattering of light within the canopy and with the ground surface, simulate the return signal efficiently at multiple wavebands, and model the effects of topography. Spatial and temporal sampling characteristics of the lidar instrument are explicitly modelled. A sensitivity analysis gives expected effects of canopy parameters on the waveform, and indicates potential for retrieval of the canopy properties of fractional cover and leaf area, in addition to height.

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Commission VI, WG VI/4KEY WORDS: Stress detection, unmanned aerial vehicle, unmanned aerial system, UAV, UAS, camera calibration. ABSTRACT:Climate change has a major influence on forest health and growth, by indirectly affecting the distribution and abundance of forest pathogens, as well as the severity of tree diseases. Temperature rise and changes in precipitation may also allow the ranges of some species to expand, resulting in the introduction of non-native invasive species, which pose a significant risk to forests worldwide. The detection and robust monitoring of affected forest stands is therefore crucial for allowing management interventions to reduce the spread of infections. This paper investigates the use of a low-cost fixed-wing UAV-borne thermal system for monitoring disease-induced canopy temperature rise. Initially, camera calibration was performed revealing a significant overestimation (by over 1 K) of the temperature readings and a non-uniformity (exceeding 1 K) across the imagery. These effects have been minimised with a two-point calibration technique ensuring the offsets of mean image temperature readings from blackbody temperature did not exceed ± 0.23 K, whilst 95.4% of all the image pixels fell within ± 0.14 K (average) of mean temperature reading.The derived calibration parameters were applied to a test data set of UAV-borne imagery acquired over a Scots pine stand, representing a range of Red Band Needle Blight infection levels. At canopy level, the comparison of tree crown temperature recorded by a UAV-borne infrared camera suggests a small temperature increase related to disease progression (R = 0.527, p = 0.001); indicating that UAV-borne cameras might be able to detect sub-degree temperature differences induced by disease onset.

Airborne laser scanning (ALS) can be utilised to derive canopy height models (CHMs) for individual tree crown (ITC) delineation. In the case of forest areas subject to defoliation and dieback as a result of disease, increased irregularities across the canopy can add complications to the segmentation of ITCs. Research has yet to address this issue in order to suggest appropriate techniques to apply under conditions of forest stands that are infected by phytopathogens. This study aimed to find the best method of ITC delineation for larch canopies affected by defoliation as a result of a Phytophthora ramorum infection. Sample plots from two study sites in Wales, United Kingdom, were selected for ITC segmentation assessment across a range of infection levels and stand characteristics. The performance of two segmentation algorithms (marker-controlled watershed and region growing) were tested for a series of CHMs generated by a standard normalised digital surface model and a pit-free algorithm, across a range of spatial resolutions (0.15 m, 0.25 m and 0.5 m). The results show that the application of a pit-free CHM generation method produced improved segmentation accuracies in moderately and heavily infected larch forest, compared to the standard CHM. The success of ITC delineations was also influenced by CHM resolution. Across all plots the CHMs with a 0.25 m pixel size performed consistently well. However, lower and higher CHM resolutions also provided improved delineation accuracies in plots dominated by larger and smaller canopies respectively. The selected segmentation method also influenced the success of ITC delineations, with the marker-controlled watershed algorithm generating significantly more accurate results than the region growing algorithm (p < 0.10). The results demonstrate that ITCs in forest stands infected with Phytophthora ramorum can be successfully delineated from ALS when a pit-free algorithm is applied to CHM generation.