David L. B. Jupp

Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests

Airborne and ground-based lidars are useful tools to probe the structure of forest canopies. Such information is not readily available from other remote sensing methods but is essential for modern forest inventory in which growth models and ecological assessment are becoming increasingly important. This study was undertaken to investigate the capacity of current airborne and ground-based ranging systems to provide data from which useful forest inventory parameters can be derived. Additional data collected included standard forest inventory, hemispherical photography, and optical point-quadrat sampling. Four contrasting study sites were established within an existing study area in the Bago and Maragle State Forests, New South Wales, Australia. A simple and standard set of models was fitted to the data to establish consistency between methods and current practice. Methods to reduce the bias induced by interaction of the size of the airborne laser scanner (ALS) footprint and thresholding used in ranging systems are demonstrated by the use of first and last returns and the intensity of the returns. A measure analogous to predominant height was calculated from an average of a number of the highest ALS returns within an area. This estimate agreed with field measured predominant heights within the uncertainty of the measurements. Data from a ground-based scanning rangefinder system were used to model leaf area index (LAI). These LAI estimates coincided with those from hemispherical canopy photographs. The validation work presented in this paper justifies further development of the instrumentation and analyses to combine results from multi-angular systems with data from airborne systems to alleviate some of the problems associated with the vertical view. Current laser ranging systems can be used to derive canopy structural parameters such as height, cover, and foliage profile provided information based on multiple returns or the intensity of returns is used to minimize the bias induced by the size of the footprint and the detection threshold.

The use of variograms in remote sensing: I. Scene models and simulated images

Theoretical and empirical studies of variograms are presented. The sensitivity of variograms is studied through varyig parameters of scene models both in calculating explicit variograms and in simulating images. It is found that the heights of variograms are related to the proportion of an area covered by objects. It is shown that the range of influence of a variogram is related to the size of the objects in the scene and that the shape of the variogram becomes more rounded as the variance in the size distribution of objects increases. In the second part, empirically calculated variograms from real digital images are used to demonstrate these theoretical findings. These calculated variograms also show the periodicity in ground scenes and reveal anisotropy.

The use of variograms in remote sensing: II. Real digital images

Abstract An improved understanding of the nature and causes of spatial variation in images would provide a basis for the development of new image analysis techniques that use spatial data in more logical ways. Empirically calculated variograms from real digital images demonstrate many of the findings of previous theoretical research using a disc model of scenes and simulated images including: (1) the heights of variograms are related to the density, or proportion of an area covered by objects, (2) the range of influence of a variogram is related to the size of the objects in the scene, and (3) the shape of the variogram becomes more rounded as the variance in the distribution of the sizes of objects increases. In addition, empirically calculated variograms show the periodicity in ground scenes and reveal anisotropy. Two-dimensional variograms are more difficult to interpret with respect to shape, periodicity, and distance to the sill but are necessary for revealing anisotropy. Results are encouraging concerning future possibilities for directly recovering ground-scene parameters using variograms if an appropriate model of ground scenes is used.

The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review

Abstract This paper reviews how remote sensing is being used, and can be used, to assist in providing support for the decision-making process for the declaration of areas experiencing drought exceptional circumstances in Australia. To assess how remotely sensed data can be used, a review of current international uses of remotely sensed data was made and several topics requiring further research by remote sensing specialists identified. Consequently, the review focuses on current techniques used by Australian agencies, and also assesses current international methods, as well as proposing some possible new research directions that may use remote sensing for drought assessment and management. The primary scientific information that remote sensing can provide is the estimation of vegetation cover and condition, soil moisture, and the spatial limits to drought exceptional circumstances.

Autocorrelation and regularization in digital images. I. Basic theory

Spatial structure occurs in remotely sensed images when the imaged scenes contain discrete objects that are identifiable in that their spectral properties are more homogeneous within than between them and other scene elements. The spatial structure introduced is manifest in statistical measures such as the autocovariance function and variogram associated with the scene, and it is possible to formulate these measures explicitly for scenes composed of simple objects of regular shapes. Digital images result from sensing scenes by an instrument with an associated point spread function (PSF). Since there is averaging over the PSF, the effect, termed regularization, induced in the image data by the instrument will influence the observable autocovariance and variogram functions of the image data. It is shown how the autocovariance or variogram of an image is a composition of the underlying scene covariance convolved with an overlap function, which is itself a convolution of the PSF. The functional form of this relationship provides an analytic basis for scene inference and eventual inversion of scene model parameters from image data. >

Modeling lidar waveforms in heterogeneous and discrete canopies

This study explores the relationship between laser waveforms and canopy structure parameters and the effects of the spatial arrangement of canopy structure on this relationship through a geometric optical model. Studying laser waveforms for such plant canopies is needed for the advanced retrieval of three-dimensional (3D) canopy structure parameters from the vegetation canopy lidar (VCL) mission. For discontinuous plant canopies, a hybrid geometric optical and radiative transfer (GORT) model describing the effects of 3D canopy structure parameters of discrete canopies on the radiation environment has been modified for use with lidar. The GORT model is first used to describe the canopy lidar waveforms as a function of canopy structure parameters and then validated using scanning lidar imager of canopies by echo recovery (SLICER) data collected in conifer forests in central Canada during the boreal ecosystem-atmosphere study (BOREAS). Model simulations show that the clumping in natural vegetation, such as leaves clustering into tree crowns causes larger gap probability and smaller waveforms for discontinuous plant canopies than for horizontally homogeneous plant canopies. Ignoring the clumping effect can result in significantly lower values for the estimated foliage amount in the profile and in turn lower estimated biomass. Because of clumping, only the gap probability and apparent vertical projected foliage profile can be directly retrieved from the canopy lidar data. The retrieval is sensitive to the ratio of the volume backscattering coefficients of the vegetation and background, and this ratio depends on canopy architecture as well as foliage spectral characteristics.

Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna ® )

A prototype upward-scanning, under-canopy, near-infrared light detection and ranging (lidar) system, the Echidna® validation instrument (EVI), built by CSIRO Australia, retrieves forest stand structural parameters, including mean diameter at breast height (DBH), stand height, distance to tree, stem count density (stems/area), leaf-area index (LAI), and stand foliage profile (LAI with height) with very good accuracy in early trials. We validated retrievals with ground-truth data collected from two sites near Tumbarumba, New South Wales, Australia. In a ponderosa pine plantation, LAI values of 1.84 and 2.18 retrieved by two different methods using a single EVI scan bracketed a value of 1.98 estimated by allometric equations. In a natural, but managed, Eucalypus stand, eight scans provided mean LAI values of 2.28–2.47, depending on the method, which compare favorably with a value of 2.4 from hemispherical photography. The retrieved foliage profile clearly showed two canopy layers. A “find-trunks” algorithm processed the EVI scans at both sites to identify stems, determine their diameters, and measure their distances from the scan center. Distances were retrieved very accurately (r2 = 0.99). The accuracy of EVI diameter retrieval decreased somewhat with distance as a function of angular resolution of the instrument but remained unbiased. We estimated stand basal area, mean diameter, and stem count density using the Relaskop method of variable radius plot sampling and found agreement with manual Relaskop values within about 2% after correcting for the obscuring of far trunks by near trunks (occlusion). These early trials prove the potential of under-canopy, upward-scanning lidar to retrieve forest structural parameters quickly and accurately.

Autocorrelation and regularization in digital images. II. Simple image models

For pt.I see ibid., vol.26, no.4, p.463-73, July 1988. The variogram function used in geostatistical analysis is a useful statistic in the analysis of remotely sensed images. Using the results derived in Part I, the basic second-order, or covariance, properties of scenes modeled by simple disks of varying size and spacing after imaging into disk-shaped pixels are analyzed to explore the relationship between the image variograms and discrete object scene structure. The models provide insight into the nature of real images of the Earths surface and the tools for a complete analysis of the more complex case of three-dimensional illuminated discrete-object images. >

Modeling bidirectional reflectance of forests and woodlands using Boolean models and geometric optics.

Abstract Principles of geometric optics and Boolean models for random sets in a three-dimensional space provide the mathematical basis for a model of the bidirectional radiance or reflectance of a forest or woodland as remotely sensed by radiometric instruments. The model may be defined at two levels: whole-canopy and individual-leaf. At the whole-canopy level, the forest scene is treated as a collection of discrete canopy envelopes with simple geometric shapes that are arranged on a contrasting background. The scene includes four components: sunlit canopy, shadowed canopy, sunlit background, and shadowed background. The radiance or reflectance of the scene as a whole is modeled as the sun of the radiances or reflectances of the individual components as weighted by their areal proportions. The areal proportions of the components are determined by 1) principles of geometric optics as applied to the shapes of the canopy envelopes and 2) Boolean models for random set overlap. These yield the expected proportions of the components as a function of angles of irradiance and exitance. At the leaf level, the canopy envelope can be tretaed as containing an assemblage of leaves, and thus the radiance or reflectance is a function of the areal proportions of sunlit leaf, shadowed leaf, sunlit background, and shadowed background. Because the proportions of scene components are dependent upon the directions of irradiance and exitance, the model accounts for the “hotspot” that is well known in leaf and tree canopies. Because both whole-canopy and individual-leaf models are driven by the same principles of geometric optics and Boolean modeling, they may easily be combined together in a single, two-stage model. Moreover, through further application of the mathematics of random sets, the averaging and variance that occurs when a scene is imaged by a sensor with a finite field of view may be accommodated. In addition, the models are capable of inversion, yielding estimates of size, shape, and spacing of crowns and/or leaves from directional and spatial statistics of remotely sensed radiances.

Estimating evaporation from pasture using infrared thermometry: evaluation of a one-layer resistance model

A one-layer resistance model has been used with infrared thermometry to estimate sensible and latent heat flux in pastures near Goulburn, New South Wales. The model compares reasonably well with energy balance-Bowen ratio measurements. However, the relative error in the evaporation estimates becomes significant in very dry conditions and at low net radiation values. An aerodynamic surface temperature may be computed from independent sensible heat flux measurements, air temperature measurements and estimates of aerodynamic resistance. The differences between computed and observed surface temperatures show characteristic diurnal trends and vary between days of measurement. These differences may be caused by errors in measuring the sensible heat flux and the surface temperature and in estimating the aerodynamic resistance. The pasture data obtained in this study are used to assess such uncertainties. The major cause for differences between the computed and observed surface temperatures is considered to be the incompleteness of the vegetative cover during each of the experimental periods. The measured Ts values apply to both the vegetation and the soil surface, whereas the observed T0 values are based on a model which assumes complete cover and only considers foliage temperature. Finally, the two-layer model of Shuttleworth and Wallace is used to show that the relationship between the measured infrared surface temperature and the canopy air temperature depends on the aerodynamic resistances, the fractional vegetated area and the temperature differences between foliage, canopy air and soil surface.