ALBERT

Description: This research evaluates the utility of several remote sensing data types for the purpose of mapping forest structure and related attributes at a regional scale. Several sensors were evaluated, including (i) single date Landsat Thematic Mapper (TM); (ii) multitemporal Landsat TM; (iii) Airborne Data Acquisition and Registration (ADAR), a sensor with high spatial resolution; (iv) Airborne Visible-Infrared Imaging Spectrometer (AVIRIS), a sensor with high spectral resolution; and (v) Scanning Lidar Imager Of Canopies By Echo Recovery (SLICER), a lidar sensor that directly measures the height and canopy structure of forest vegetation. To evaluate the ability of each of the sensors to predict stand structure attributes, we assembled a data set consisting of 92 field plots within the Willamette National Forest in the vicinity of the H.J. Andrews Experimental Forest. Stand structure attributes included age, basal area, aboveground biomass, mean diameter at breast height (DBH) of dominant and codominant stems, mean and standard deviation of the DBH of all stems, maximum height, and the density of stems with DBH greater than 100 cm. SLICER performed better than any other remote sensing system in its predictions of forest structural attributes. The performance of the imaging sensors (TM, multitemporal TM, ADAR, and AVIRIS) varied with respect to which forest structural variables were being examined. For one group of variables there was little difference in the ability of the these sensors to predict forest structural attributes. For the remaining variables, we found that multitemporal TM was as or more effective than either ADAR or AVIRIS. These results indicate that multitemporal TM should be investigated as an alternative to either hyperspectral or hyperspatial sensors, which are more expensive and more difficult to process than multitemporal Landsat TM.

Description: This study evaluates the relative ability of simple light detection and ranging (lidar) indices (i.e., mean and maximum heights) and statistically derived canonical correlation analysis (CCA) variables attained from discrete-return lidar to estimate forest structure and forest biomass variables for three temperate subalpine forest sites. Both lidar and CCA explanatory variables performed well with lidar models having slightly higher explained variance and lower root mean square error. Adjusted R2 values were 0.93 and 0.93 for mean height, 0.74 and 0.73 for leaf area index, and 0.93 and 0.85 for all carbon in live biomass for the lidar and CCA explanatory regression models, respectively. The CCA results indicate that the primary source of variability in canopy structure is related to forest height, biomass, and total leaf area, and the second most important source of variability is related to the amount of midstory foliage and tree density. When stand age is graphed as a function of individual plot scores for canonicals one and two, there is a clear relationship with stand age and the development of stand structure. Lidar-derived biomass and related estimates developed in this work will be used to parameterize decision-support tools for analysis of carbon cycle impacts as part of the North American Carbon Program.

Description: Spatially-explicit maps of aboveground biomass are essential for calculating the losses and gains in forest carbon at a regional to national level. The production of such maps across wide areas will become increasingly necessary as international efforts to protect primary forests, such as the REDD+ (Reducing Emissions from Deforestation and forest Degradation) mechanism, come into effect, alongside their use for management and research more generally. However, mapping biomass over high-biomass tropical forest is challenging as (1) direct regressions with optical and radar data saturate, (2) much of the tropics is persistently cloud-covered, reducing the availability of optical data, (3) many regions include steep topography, making the use of radar data complex, (4) while LiDAR data does not suffer from saturation, expensive aircraft-derived data are necessary for complete coverage. We present a solution to the problems, using a combination of terrain-corrected L-band radar data (ALOS PALSAR), spaceborne LiDAR data (ICESat GLAS) and ground-based data. We map Gabon's Lopé National Park (5000 km2) because it includes a range of vegetation types from savanna to closed-canopy tropical forest, is topographically complex, has no recent cloud-free high-resolution optical data, and the dense forest is above the saturation point for radar. Our 100 m resolution biomass map is derived from fusing spaceborne LiDAR (7142 ICESat GLAS footprints), 96 ground-based plots (average size 0.8 ha) and an unsupervised classification of terrain-corrected ALOS PALSAR radar data, from which we derive the aboveground biomass stocks of the park to be 78 Tg C (173 Mg C ha−1). This value is consistent with our field data average of 181 Mg C ha−1, from the field plots measured in 2009 covering a total of 78 ha, and which are independent as they were not used for the GLAS-biomass estimation. We estimate an uncertainty of ± 25 % on our carbon stock value for the park. This error term includes uncertainties resulting from the use of a generic tropical allometric equation, the use of GLAS data to estimate Lorey's height, and the necessity of separating the landscape into distinct classes. As there is currently no spaceborne LiDAR satellite in operation (GLAS data is available for 2003–2007 only), this methodology is not suitable for change-detection. This research underlines the need for new satellite LiDAR data to provide the potential for biomass-change estimates, although this need will not be met before 2015.

Description: Spatially-explicit maps of aboveground biomass are essential for calculating the losses and gains in forest carbon at a regional to national level. The production of such maps across wide areas will become increasingly necessary as international efforts to protect primary forests, such as the REDD+ (Reducing Emissions from Deforestation and forest Degradation) mechanism, come into effect, alongside their use for management and research more generally. However, mapping biomass over high-biomass tropical forest is challenging as (1) direct regressions with optical and radar data saturate, (2) much of the tropics is persistently cloud-covered, reducing the availability of optical data, (3) many regions include steep topography, making the use of radar data complex, (5) while LiDAR data does not suffer from saturation, expensive aircraft-derived data are necessary for complete coverage. We present a solution to the problems, using a combination of terrain-corrected L-band radar data (ALOS PALSAR), spaceborne LiDAR data (ICESat GLAS) and ground-based data. We map Gabon's Lopé National Park (5000 km2) because it includes a range of vegetation types from savanna to closed-canopy tropical forest, is topographically complex, has no recent contiguous cloud-free high-resolution optical data, and the dense forest is above the saturation point for radar. Our 100 m resolution biomass map is derived from fusing spaceborne LiDAR (7142 ICESat GLAS footprints), 96 ground-based plots (average size 0.8 ha) and an unsupervised classification of terrain-corrected ALOS PALSAR radar data, from which we derive the aboveground biomass stocks of the park to be 78 Tg C (173 Mg C ha−1). This value is consistent with our field data average of 181 Mg C ha−1, from the field plots measured in 2009 covering a total of 78 ha, and which are independent as they were not used for the GLAS-biomass estimation. We estimate an uncertainty of ±25% on our carbon stock value for the park. This error term includes uncertainties resulting from the use of a generic tropical allometric equation, the use of GLAS data to estimate Lorey's height, and the necessity of separating the landscape into distinct classes. As there is currently no spaceborne LiDAR satellite in operation (GLAS data is available for 2003–2009 only), this methodology is not suitable for change-detection. This research underlines the need for new satellite LiDAR data to provide the potential for biomass-change estimates, although this need will not be met before 2015.

Description: Lidar altimeter observations of vegetated landscapes provide a time-resolved measure of laser pulse backscatter energy from canopy surfaces and the underlying ground. Airborne lidar altimeter data was acquired using the Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) for a successional sequence of four, closed-canopy, deciduous forest stands in eastern Maryland. The four stands were selected so as to include a range of canopy structures of importance to forest ecosystem function, including variation in the height and roughness of the outer-most canopy surface and the vertical organization of canopy stories and gaps. The character of the SLICER backscatter signal is described and a method is developed that accounts for occlusion of the laser energy by canopy surfaces, transforming the backscatter signal to a canopy height profile (CHP) that quantitatively represents the relative vertical distribution of canopy surface area. The transformation applies an increased weighting to the backscatter amplitude as a function of closure through the canopy and assumes a horizontally random distribution of the canopy components. SLICER CHPs, averaged over areas of overlap where lidar ground tracks intersect, are shown to be highly reproducible. CHP transects across the four stands reveal spatial variations in vegetation, at the scale of the individual 10 m diameter laser footprints, within and between stands. Averaged SLICER CHPs are compared to analogous height profile results derived from ground-based sightings to plant intercepts measured on plots within the four stands. Tbe plots were located on the segments of the lidar ground tracks from which averaged SLICER CHPs were derived, and the ground observations were acquired within two weeks of the SLICER data acquisition to minimize temporal change. The differences in canopy structure between the four stands is similarly described by the SLICER and ground-based CHP results, however a Chi-square test of similarity documents differences that are statistically significant. The differences are discussed in terms of measurement properties that define the smoothness of the resulting CHPs and Lidar Altimeter Measurements of Canopy Structure - Harding et al. canopy properties that may vertically bias the CHP representations of canopy structure. The statistical differences are most likely due to the more noisy character of the ground-based CHPs, especially high in the canopy where ground-based sightings are rare resulting in an underestimate of canopy surface area and height, and to departures from the assumption of horizontal randomness which bias the CHPs toward the observer (upward for SLICER and downward for ground-based CHPs). The results demonstrate that the SLICER observations reliably provide a measure of canopy structure that reveals ecologically interesting structural variations such as those characterizing a successional sequence of closed-canopy, broadleaf forest stands.