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Vegetation-Lidar Related Publications

Abstract. A method of predicting two forest stand structure attributes, basal area and aboveground biomass, from measurements of forest vertical structure was developed aan tested using field and remotely sensed canopy structure measurements. Coincidental estimates of the vertical distribution of canopy surface area (the canopy height profile), and field-measured stand structure attributes were acquired for two data sets. The chronosequence data set consists of 48 plots in stands distributed within 2.5 miles of Annapolis, MD, with canopy height profiles measured in the field using the optical-quadrat method. The stem-map data set consists of 75 subsetted from a single 32 ha stem-mapped stand, with measurements of their canopy height profiles made using the SLICER (Scanning Lidar Imager of Canopies by Echo Recovery) instrument, an airborne surface lidar system. Four height indices, maximum, median, mean, and quadratic mean canopy height (QMCH) were calculated from the canopy height profiles. Regressions between the indices and stand basal area and biomass were developed using the chronosequence data set. The regression equations developed from the chronosequence data set were then applied to height indices calculated from the remotely sensed canopy height profiles from the stem map data set, and the ability of the regression equations to predict the stem map plot’s stand structure attributes was then evaluated. The QMCH was found to explain the most variance in tile chronosequence data set’s stand structure attributes, and to most accurately predict the values of the same attributes in the stem map data set. For the chronosequence data set, the QMCH predicted 70% of variance in stand basal area, and 80% of variance in aboveground biomass, and remained nonasymptotic with basal areas up to 50 m² ha^-1 , and aboveground biomass values up to 450 Mg ha^-1 . When applied to the stem-map data set, the regression equations resulted in basal areas that were, on average, underestimated by 2.1 m² ha^-1, and biomass values were underestimated by 16 Mg ha^-1, and explained 37% and 33% of variance, respectively. Differences in the magnitude of the coefficients of determination were due to the wider range of stand conditions found in the chronosequence data set; the standard deviation of residual value were lower in that stem map data set than on the chronosequence data sets. Stepwise multiple regerssion was performed to predict the two stand structure attributes using the canopy height profile data directly as independent variables, but they did not improve the accuracy of the estimates over the height index approach.

Abstract. Scanning lidar remote sensing systems have recently become available for use in ecological applications. Unlike conventional microwave and optical sensors, lidar sensors directly measure the distribution of vegetation material along the vertical axis and can be used to provide three-dimensional, or volumetric, characterizations of vegetation structure. Ecological applications of scanning lidar have hitherto used one-dimensional indices to characterize canopy height. A novel three-dimensional analysis of lidar waveforms was developed to characterize the total volume and spatial organization of vegetation material and empty space within the forest canopy. These aspects of the physical structure of canopies have been infrequently measured, from either field or remote methods. We applied this analysis to 22 plots in Douglas fir/western hemlock stands on the west slope of the Cascades Range in Oregon. Each plot had coincident lidar data and field measurements of stand structure. We compared results from the novel analysis to two earlier methods of canopy description. Using the indices of canopy structure from all three methods of description as independent variables in a stepwise multiple regression, we were able to make nonasymptotic predictions of biomass and leaf area index (LAI) over a wide range, up to 1200 Mg ha21 of biomass and an LAI of 12, with 90% and 75% of variance explained, respectively. Furthermore, we were able to make accurate estimates of other stand structure attributes, including the mean and standard deviation of diameter at breast height, the number of stems greater than 100 cm in diameter, and independent estmates of the basal area of Douglas-fir and western hemlock. These measurements can be directly related to indices of forest stand structural complexity, such as those developed for old-growth forest characterization. Indices of canopy structure developed using the novel, three-dimensional analysis accounted for most of the variables used in predictive equations generated by the stepwise multiple regression.

Abstract. A scanning lidar, a relatively new type of sensor which explicitly measures canopy height, was used to measure structure of conifer forests in the Pacific Northwest. SLICER (Scanning Lidar Imager of Canopies by Echo Recovery), an airborne pulsed laser developed by NASA which scans a swath of five 1O-m diameter footprints along the aircraft's flightpath, captures the power of the reflected laser pulse as a function of height from the top of the canopy to the ground. Ground measurements of forest stand structure were collected on 26 plots with coincident SLICER data. Height, basal area, total biomass, and leaf biomass are estimated from field data could be predicted from SLICER-derived metrics with r² values of 0.95, 0.96, 0.96, and 0.84, respectively. These relationships were strong up to a height of 52 m, basal area of 132 m²/ha and total biomass of 1300 Mg/ha. In light of these strong relationships, large-footprint, airborne scanning lidar shows promise for characterizing stand structure for management and research purposes.

Abstract. Waveform-recording laser altimeter observations of vegetated landscapes provide a time-resolved measure of laser pulse backscatter energy from canopy surfaces and the underlying ground. Airborne laser 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 interesect, 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. The 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 2 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 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 assumptions of canopy uniformity, particularly regarding lack of clumping and vertically constant canopy reflectance, which bias the 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.

Abstract The vertical distribution of light transmittance was derived from field and laser altimeter observations taken in the same canopies of five forests of several ages (young to mature) and canopy types (eastern broadleaved and western tall conifer). Vertical transmittances were derived remotely from the Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) laser altimeter and in the field from measurements of Photosynthetically Active Radiation (P) made within the canopy using quantum sensors suspended from the gondola of a tower crane or atop small balloons. Derived numerical characteristics of mean transmittance profiles (the rate of attenuation, whole canopy transmittance, and the radiation-effective height) were similar for both methods across the sites. Measures of the variance and skewness of transmittance also showed similar patterns for corresponding heights between methods. The two methods exhibited greater correspondence in the eastern stands than in the western ones; differences in the interaction between canopy organization and the sensor characteristics between the stand types might explain this. The narrower, more isolated crowns of the western stands permit a deeper penetration into the canopy of nadir-directed laser light than of direct solar radiation from typical elevation angles. Transects of light transmittance in two stands demonstrate that the SLICER sensor can capture meaningful functional variation. Additionally, for one stand with numerous overlapping transects we constructed a three-dimensional view of the transmittance field. Using geostatistics, we demonstrated that the spatial covariance measured in the horizontal plane varied as a function of height. These results suggest a means to remotely assess an important functional characteristic of vegetation, providing a capacity for process-based ecological studies at large scales.

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