LAND SURFACE CHARACTERIZATION USING LIDAR REMOTE SENSING

Ralph Dubayah Department of Geography, University of Maryland, College Park, MD 20742 Robert Knox Code 923, NASA Goddard Space Flight Center, Greenbelt, MD 20771 Michelle Hofton Department of Geography, University of Maryland, College Park, MD 20742 J. Bryan Blair Code 924, NASA Goddard Space Flight Center, Greenbelt, MD 20771 Jason Drake Department of Geography, University of Maryland, College Park, MD 20742

INTRODUCTION For the last twenty-five years remote sensing has promised to revolutionize land management by delivering spatial information critical for land surface characterization. Resources have poured into this task, through the launch of seven Landsat satellites, numerous airborne and space-based sensors using multiangle, multispectral and radar techniques, the creation of several global land cover data bases, and for numerous research studies attempting to demonstrate the efficacy of a predominantly remote sensing/GIS approach to land management.

These best efforts have led to great advances in our ability to monitor and model the land surface. However, one may argue there is distance yet to go as land managers and others have difficulty getting the information they require for effective resource management from remote sensing. For example, in managing forests for old growth and wildlife habitat, information such as canopy cover, life form, large tree density, tree size (height and crown diameter), biomass, crown volume, height to live crown and vertical foliar diversity, among others, are routinely needed. Current remote sensing techniques provide few of these at acceptable accuracies for land management.

The situation should improve with the advent of the EOS-era generation of sensors, and with the new algorithms and models that will be applied to data from them; yet a gap likely still may remain. The primary reason for this is that important characteristics of the land surface are tied to vertical structure, and it is precisely at this measurement of vertical structure that passive optical and radar remote sensing techniques have difficulty. In both cases, optical and radar, the vertical component is not directly measured, and as a result must be modeled (e.g., see the extensive literature on invertible canopy models such as Ni et al. (1999)).

Lidar remote sensing is a breakthrough technology for deriving forest canopy structural characteristics. While previous studies have shown that lidar observations of forest canopies can be used to derive many of these structural attributes, there is a lack of maturity in algorithms and models, mainly caused by the dearth of airborne and space-based data and coincident ground measurements with which to develop these. Because the technique is relatively new as applied to canopy measurement (despite early studies using small-footprint lidar as far back as Aldred and Bonner (1985)), there is a tremendous need for experiments that integrate field work, remote sensing and subsequent analyses for retrieving the full complement of structural measures critical for forestry applications. Furthermore, forest managers and planners lack experience using lidar remote sensing. As a result, the immense potential of lidar for canopy description is largely unnoticed and untapped.

Unlike visible, near-infrared and radar remote sensing techniques that require relatively sophisticated models to recover basic canopy structure, lidar remote sensing using waveforms provides a simple, direct measurement of vertical structure such that recovering height and vertical diversity is straightforward and immediate. Indeed, the return lidar waveform is a record of the vertical distribution of intercepted canopy elements (as limited by the impulse response of the lidar system). The direct measurement of vertical diversity is of special interest because it greatly assists in identifying and monitoring forest stands for habitat suitability.

With the planned launch of the Vegetation Canopy Lidar Mission (VCL), a joint effort between the University of Maryland and NASA, the first global data sets of canopy vertical and horizontal structure will become available, and with it the vast potential of using lidar, along with other measurements to be obtained in the EOS-era, for improved monitoring and modeling of the Earth's surface. This chapter explores the use of lidar for land surface characterization and is organized as follows. First we briefly present the basics of lidar and laser altimetry. We then describe the general types of lidar systems that are in use now as well as the planned VCL mission. Lastly we discuss how lidar may be used to recover specific elements of forest structure and show examples from some of our airborne lidar work.

BACKGROUND

Lidar Remote Sensing Lidar (light detection and ranging) is an active remote sensing technique, analogous to radar, but using laser light. With such systems, a pulse of laser energy is fired towards the

surface. This incident pulse of energy then interacts with the surface, reflecting off of branches, leaves and ground, and back to the instrument where it usually is collected by a telescope. The time of flight of the pulse, from initiation, to return to sensor, is measured and provides a distance or range from the instrument to the object. Hence the common use of the term "laser altimetry".1 Because the pulse emitted by the system may be extended in time and space (for example it may be gaussian in distribution), and interacts with a vertically extended object, such as canopy, the return pulse is extended as well. Most surface lidar systems record the travel time of the laser pulse from its point of origin to either the first return of energy, the last return of energy, or both, but usually not the entire record of returned energy, i.e. the returned signal is not fully digitized. For vegetation studies it is useful to pick a near-infrared wavelength to maximize return signal and minimize background noise (e.g., from the Sun).

Laser altimetry has been used for a variety of applications since the late 1960's including systems flown on the Apollo 15, 16 and 17 missions to the Moon to derive surface topography (Kaula et al., 1974). Since then, airborne systems have been used to map regional topography around the world for a variety of geophysical purposes including volcanic hazard assessment, ice sheet elevation change, coastal erosion, and tree height derivation (e.g., Garvin, 1996; Krabill et al.; 1995; Magnussen and Boudewyn, 1998). The majority of these studies however, used small-footprint, low altitude lidar systems that generally do not record the shape of the return signal. In the early 1990’s, NASA’s Goddard Space Flight Center began developing techniques and algorithms for measuring vertical vegetation structure and height using medium-large footprint lidar systems, incorporating the shape of the return laser pulse (Blair et al., 1994; Harding et al., 1994). These techniques were later validated by comparison to ground measurements (e.g., Means et al., 1999; Lefsky et al., 1999). The NASA systems have also been used to prototype techniques, methods, and technology later flown by spaceborne altimeter systems such as the Shuttle Laser Altimeter (SLA) (Garvin et al., 1998), which in 1996 and 1997 provided the first global-scale laser altimeter data set and vertical roughness estimates of sampled terrain. The next space-based missions will be VCL (in 2000) and ICESAT (in 2001).

Existing lidar systems for land surface characterization can be distinguished based on a few characteristics: (1) whether they record the range to the first return, last return, multiple returns or fully digitize the return signal; (2) whether they are small footprint (so-called pencil beam systems, with beam diameters typically on the order of a few centimeters) or medium-large footprint systems (tens of meters); and, (3) based on their sampling rate/scanning pattern. Most commercial systems are low-flying, small-footprint (5-30 cm diameter), high pulse rate systems (1000-10 000 Hz) recording the range to the highest (and sometimes lowest) reflecting surface within the footprint, and are not fully imaging, utilizing instead many laser returns in close proximity to each other to recreate a surface. Such systems, while capable of generating very precise (decimeter level e.g., see

1 The terms lidar and laser altimetry have generally come to mean the same thing though originally they were distinct, as laser altimetry is primarily focused on a range measurement whereas lidar is concerned with analyzing the time series of returned energy. There is a growing convention to label all laser observations of the Earth's surface as laser altimetry, and laser observations of the Earth's atmosphere as lidar. We use the terms interchangeably here.

Huising and Pereira (1998) for a summary) and densely-sampled images of overflown terrain, do not provide a truly 3-dimensional view of the Earth’s structure at any one location. With recent advances in data processing (e.g., Hofton et al., in press), and our increasing ability to interpret the shape of the return waveforms (e.g., Blair and Hofton, 1999), medium-large footprint systems now allow us to precisely measure the 3-dimensional structure of the Earth.

Small footprint sensors are not optimal for mapping forest structure for several reasons. First, because of their small beam size, mapping large areas is time consuming and expensive. Secondly, small diameter beams frequently miss the tops of trees. Hence, unless many shots are taken, the true canopy topography must be reconstructed statistically. Lastly, with systems that only record first and/or last returns, it is difficult to determine whether or not a particular shot has penetrated the canopy all the way to ground. If the canopy closure is very high it is quite possible that only 1 in a 10 000 returns may be from the underlying topography (Blair and Hofton, 1999). If this topography cannot be reconstructed, accurate height determination is impossible (as the height of the canopy is measured relative to the ground).

Medium to large footprint systems have two advantages that help avoid these problems. First, they enable a wide image swath, which is the only feasible way to cover large areas on the ground given the expense of flight times. Secondly, larger footprints avoid the biases that are inherent in small footprint height recovery, as the small footprints frequently miss the tops of trees (see Nelson et al., 1997) for how this can interact with crown geometry). Conversely, if the footprint is too large, biases from the blurring of ground and canopy can become large as well, again affecting height recovery (Blair et al., 1994).

Figure 1 shows the basis for a medium-large footprint lidar return. The return signal (referred to as a return waveform or sometimes arcanely as an "echo") is fully digitized. In this example the first return above a threshold is used to derive the range to the top of the canopy, and the midpoint of the last return is used to find the range to ground, the subtraction of which yields laser-derived canopy height. The return waveform gives a record of the vertical distribution of nadir-intercepted surfaces (i.e. leaves and branches). At any particular height, the amplitude of the return waveform measures the strength of the return. Thus, for surfaces with a similar ensemble of reflectances and geometry within a footprint (and under similar atmospheric conditions), a larger amplitude indicates more canopy material and a smaller amplitude less.

The Laser Vegetation Imaging Sensor The airborne instrument used in our research is the Laser Vegetation Imaging Sensor (LVIS - pronounced "elvis") (Blair et al., 1999). LVIS is a medium-altitude imaging laser altimeter, designed and developed at NASA's Goddard Space Flight Center. As far as we know it is the only imaging, large footprint, fully digitizing lidar instrument. Variable-sized footprints and a randomly positionable laser beam and 7 degree telescope field of view allow for a variety of operating modes. Footprint diameters can be varied from 1 to 70 m, and footprint spacing can be varied both along and across track. In VCL emulator mode, LVIS operated at an altitude of 8 km above the ground and produces eighty, 25 m

diameter footprints separated by 25 m along and 12.5 m across track. The shape of the output and return pulses are digitally recorded with 30 cm vertical resolution (i.e., waveform digitization) along with the travel time of the pulse from the laser to the intercepted surface. Ancillary information such as the pointing direction and position of the laser at the time of each pulse (provided using an inertial navigation system and GPS unit) are also recorded. The combination of these data post-flight enables the geolocation of the laser footprint on the ground within a global reference frame (usually to better than 1 m accuracy) (Hofton et al., in press).

The VCL Mission The Vegetation Canopy Lidar Mission (Dubayah et al., 1997) is the first selected mission of NASA’s new Earth System Science Pathfinder program. The principal goal of VCL is the characterization of the three-dimensional structure of the Earth; in particular canopy vertical and horizontal structure and land surface topography. Scheduled for launch in 2000, VCL is a 5-beam instrument with 25 m continuous along track resolution. The five beams are in a circular configuration, 8 km across, and each beam traces a separate ground track spaced ~2 km apart, eventually producing 2 km coverage between 67° North and South (Figure 2). VCL’s core measurement objectives are: (1) canopy top heights; (2) vertical distribution of intercepted surfaces; and, (3) ground surface topographic elevations. These measurements are used to derive a variety of gridded data products.

The VCL mission has implemented a calibration and validation program to develop and assess algorithms, for developing calibrations, and for validating final data products. A series of field campaigns have been planned and executed in various locations including the deciduous hardwood forests of the Eastern United States, coniferous and savanna areas in the Western United States, and the tropical wet forests of Central America (some examples of which are presented below).

Aside from canopy height and canopy vertical distribution, VCL will not produce any other forest structure data sets. These, such as tree density and biomass, hopefully will be derived from the basic data sets by the user community. Thus, another important goal of the VCL calibration/validation program is to insure that the data sets produced by VCL through the process of algorithm development, calibration and validation are appropriate for the downstream applications for which they are intended (e.g., deriving biomass).

Previous Lidar Studies of Forest Structure

In this section we briefly summarize some of the past work in using lidar for recovering forest structure. Other references are found in sections below that discuss recovery of specific structural elements. Measurements from small-footprint laser altimeter instruments have been useful in estimating tree heights (Nelson et al., 1997), percent canopy cover (Weltz et al., 1994), timber volume and in some cases forest aboveground biomass (ABGM) (Nelson et al., 1988; Naesset, 1997). However, these fine-resolution

sensors tend to yield ground return only in relatively open forest canopies (Weishampel et al., 1996). In more dense forests these sensors typically provide information pertaining only to canopy roughness and do not accurately measure canopy heights. As a result these small-footprint laser altimeter instruments tend to yield poor AGBM estimates in dense forest areas (Nelson et al., 1997).

Measurements from next-generation, medium-to large-footprint lidar incorporating information contained in the laser return waveform are used to derive canopy height and structure in a variety of canopy closure conditions (e.g., Blair et al., 1994; Harding et al., 1994). Because these next generation lidar instruments consistently recover ground returns, even under conditions of high canopy closure, they have been shown to recover forest canopy structure that is statistically indistinguishable from field measurements (Lefsky, 1997), and are able to accurately capture spatial patterns of canopy heights (Drake and Weishampel, in press). These instruments have also allowed for accurate estimation of AGBM in both the Pacific Northwest of the United States (Means et al., 1999) and in the coastal plain of eastern Maryland (Lefsky et al., 1999). In both cases, data from the lidar instruments were incorporated into allometric regression models along with ground-based measures (e.g., tree diameters) to derive stand-level AGBM estimates. These relationships were found to be significant even under dense canopy conditions. For example, Means et al. (1999) could predict total stand AGBM with r2 values of up to 0.96 using lidar-based AGBM estimation models through biomass levels of 1300 Mg/ha, far exceeding the normal saturation point of radar (≈150 Mg/ha, Waring et al., 1995).

FOREST STRUCTURE FROM LIDAR

The two basic measurements of canopy lidar are vegetation height, that is, the “top” of the canopy, however defined, relative to the ground below it, and the vertical distribution of intercepting surfaces within the canopy. Other attributes of forest structure are modeled or inferred from these direct measurements, or inferred from the horizontal structures revealed by lidar transects (such as from VCL), by lidar images (as with airborne systems such as LVIS), or by fusion with other imaging sensors (optical or radar) as summarized in Table 1. In this section we discuss how lidar may be used to recover forest structure, using examples from our work with LVIS.

Direct Retrievals

Vegetation height Height is the central measurement of lidar systems, and, as long as the ground can be determined, is a straightforward measurement (although more accurately canopy height, not tree size, is measured). As described above, with medium to large footprint systems, the first return above a threshold can be used to estimate the top of the canopy, and the midpoint of the last return represents the ground return (Figure 1). Canopy height is calculated by subtracting the elevations of the first and last returns. Medium to large footprints avoid some of the complexities seen with small-footprint laser systems (see Schreier et al., 1984; Nelson et al., 1997), but still require validation with ground-measured field data and may require a modest calibration to optimize consistency with

more traditional forestry estimates of average stand height. Vegetation height is a function of species composition, climate and site quality, and can be used for land cover classification or in conjunction with vegetation indices. When coupled with species composition and site quality information, height serves as an estimate of stand age or successional state. Height and areal height variance can also be used to determine aerodynamic roughness for climate models.

Figure 3 shows an image of tree heights from a VCL calibration/validation experiment conducted over the tropical deciduous forests of La Selva Biological Station in Costa Rica using LVIS. Because LVIS is fully imaging, continuous fields of heights can be made. Figure 4 shows how histograms of heights can be used to infer successional stage as well.

Vertical distribution of canopy material By recording the complete time-varying amplitude of the return signal of the laser pulse between the first and last returns (representing the canopy top and the ground), a waveform is captured that is related to canopy architecture. Specifically, a canopy lidar return records reflections from the nadir-projected vertical distribution of the surface area of canopy components, foliage, trunk, twigs, and branches (Figure 5). Like the simple height estimate, the vertical distribution of laser returns provides a new means to classify vegetation, and provides the basis for estimating other important canopy descriptors, such as canopy cover and crown volume. It can function also as a predictor of the successional state of a forest (for example, see Figure 9). As a stand ages and grows, the vertical distribution of canopy components changes relative to younger stands. Older stands characterized by canopy gaps and trees of multiple ages and sizes exhibit a more even distribution of canopy components. Figure 6 shows the three-dimensional representation of vertical canopy structure from La Selva. The return waveform data (Figure 7) of La Selva show a strikingly complex vertical structure with multiple layers.

Crown volume Lidar can provide this measurement, as it is basically canopy height times the spatial extent of the waveforms. If the sub-canopy air space (i.e., the lower edge of the canopy) is well defined in the waveform (with few understory/midstory trees) live crown volume can be calculated. Otherwise, the canopy volume reported may include midstory, understory, or shrub layers.

Subcanopy Topography Lidars that perform full return waveform digitization, such as VCL and LVIS, directly measure subcanopy (and bare earth) topography. Areas of extremely high canopy closure can present problems, even with these systems, if instrument sensitivity is not kept high. This is because the incoming laser pulse is greatly obscured by the canopy, and the resulting reflectance from the ground is weak; possibly too weak to be distinguishable from background system or solar noise (in the near-infrared). However, both VCL and LVIS have been designed such that this is not a problem except in areas of very highest canopy closure (> 99%), and even then adds minor error to elevation estimates compared to the best operational digital elevation model (DEM) accuracies (such as USGS 30 m

DEM products). The ability of LVIS for subcanopy topographic mapping is illustrated dramatically in Figure 8.

Modeling and Inference

Biomass Because biomass is largely (~50%) carbon, it serves as a useful predictor of the amount of carbon in terrestrial pools (Brown, 1997). The only way to actually measure AGBM is through destructive sampling of a particular tree or forest stand, where all of the living tissue is oven-dried and then weighed. As a result, AGBM is usually estimated through a relationship with more easily measurable biophysical parameters.

Taller trees contain more wood and usually support more foliage and roots than shorter trees of the same species and diameter. Because of the mechanical properties of trees, diameter typically increases as trees become taller as well, further increasing wood volume and mass. Thousands of studies over the past two centuries have modeled non-linear allometric relationships between living mass or wood volume and field-measured diameter and/or height of individual trees. Remotely-sensed measurements can also exploit these biological constraints to model biomass from height. Thus, in addition to providing a unique metric, remotely measured heights are highly correlated with AGBM (e.g., Maclean and Martin, 1984; Nelson et al., 1984, 1988, 1997; Nilsson, 1996; Naesset, 1997; Lefsky et al., 1999; Means et al., 1999). Tropical forests, because of their high biomass (Dixon et al., 1994), are among the most important areas for estimating above ground carbon, yet have been poorly estimated via observations obtained from passive optical (Sader et al., 1989; Kimes et al., 1998) or radar (Luckman et al., 1997; Rignot et al., 1997) remote sensing.

We tested the ability of medium to large-footprint lidar to recover biomass over such areas using LVIS data from La Selva (Drake et al., 1999). Plot-level biomass was estimated using ground-based measurements of stem diameters (above buttressing) and a wet tropical allometric equation (Brown, 1997). For these plots, the coincident lidar data were used to create a relationship between the lidar data and the ground-based biomass. Using metrics derived from waveforms, Drake et al. (1999) could predict more than 90% of the variability in AGBM. Other waveform metrics may prove even better. These results are extraordinary given the difficulty of other techniques and for the first time present a strategy for mapping biomass in tropical areas.

Vertical foliage diversity and multiple layers Lidar waveforms record the distribution of nadir-intercepted surfaces, including both leaves and branches. However, there are shadowing effects as upper surfaces obscure some of those lower in the canopy. Because of light obscuration through dense forest, shaded sparse lower layers may be close to the detection limits of sensors. Investigations have used extinction coefficients to adjust foliage profiles for shadowing (Lefsky et al., 1999), but architectural variation in canopy self-shadowing complicates the relationship between lidar returns and leaf area. Airborne lidar returns acquired over various regions show marked diversity in foliage height and multiple layers (as shown in Figure 7). The lack of a completely general quantitative relationship between lidar waveforms and foliar

profiles does not preclude developing empirical relationships useful in particular regions and stand types.

Height to live crown When the lower surface of tree crowns is particularly well-defined for a stand, this can be direct measurement. More commonly, it will need to be inferred from canopy height and canopy height variability. Even when the lower canopy edge is clear, as in the profile data shown in Figure 5, there is no direct means of measuring whether this boundary is formed by live or dead branches from lidar alone.

Large tree density Medium to large footprint lidar can provide information about the incidence of large trees, especially if they are a good fraction of the total cover (whereas small footprint lidar, if the sampling rate is sufficiently high, will provide this directly by mapping every crown). This is not a direct measurement because there are issues related to crown shape and the presence of multiple large trees within a footprint contributing to the return signal, as opposed to one large tree. For example, the percentage of waveforms that show large height variations (indicative of emergent trees) can be found for an area and used to infer density. Histograms for an area may show distinctive multiple modes, indicative of emergent trees (e.g., see Figure 4). In this case, the crown diameters of large trees are known to be a significant fraction of the lidar footprint diameter, and therefore large tree density is straightforward to infer. Other approaches may use statistical relationships between lidar data and structural characteristics related to large trees. Drake et al. (1999), using waveform metrics, were able to accurately predict both basal area and stem diameter, the predicted distributions of which may then be used to infer large tree density.

Fusion Some forest characteristics cannot be determined either directly, or with modeling and inference from lidar data alone. In these cases, the vertical component provided by lidar can be fused with information from passive optical, thermal and radar remote sensing. We believe the best way to recover the characteristics of canopy cover, leaf area index (LAI), life form diversity and large tree density may be through fusion. For example, roughness/texture measures derived from radar backscatter or shadow fraction in passive optical images could be combined with lidar height and topography data to distinguish patches with abundant large trees from those producing rough/highly shadowed images for other reasons. Existing canopy models should be able to use lidar data, along with multispectral data to make better predictions. A general canopy model that includes multispectral, radar and lidar is probably not too far off. A second aspect of fusion concerns spatial regionalization, i.e., using spatially continuous remotely sensed data to map sparse lidar measurements, such as will be returned from VCL over the landscape.

Canopy cover and LAI For medium-large footprint lidar, canopy cover can be determined provided that gaps are large enough to encompass an entire footprint. Any large-amplitude returns near the ground (either from bare soil or understory) indicate canopy openness. Lidar waveforms

should provide information that relates to LAI, however the waveform records not only interception by leaves but also branches. As with foliar profiles, estimates of total LAI will depend on architectural obscuration within the canopy (i.e., vertical correlation or decorrelation). Accurate retrieval of LAI will thus be dependent on known lifeform or species level allometric relationships. Furthermore, in the semi-open canopy of woodlands, the relative strength of the ground return within waveforms can provide estimates of canopy closure, if ratios of ground and canopy reflectances are obtained from optical remote sensing. This is one area where extension of existing remote sensing canopy models could be very valuable.

Physiognomic or life form diversity Landcover classification should be greatly enhanced by the combination of multispectral data or radar data with lidar data, and should provide greater resolution in classifications than either data type alone. However, this area of research is still in its infancy because of the lack of lidar data sets. Preliminary work at La Selva has demonstrated that by themselves, lidar waveforms can be used to distinguish among important landcover types. Figure 9 shows the variability of vertical forest structure in different land cover types from pasture to primary tropical rainforest. Although the multispectral responses and radar backscatter from secondary and primary tropical rainforest are similar, the vertical distribution of intercepted surfaces as detected by medium to large-footprint lidar instruments is clearly different. As a result, fusion between these instruments should help overcome the limitations of either sensor alone.

Spatial Regionalization We discussed above how fusion is important for derivation of forest structure not directly retrievable from lidar. Another important use of fusion is for spatial extrapolation or regionalization of forest structure to regions where no lidar data exist. This is of no small concern for applied uses of lidar data, as the only planned source of such data from space, VCL, is a sampling/transecting mission. We estimate that after a two year VCL mission, a 2 km x 2 km grid cell on the surface of the earth may have only a few hundred observations (these observations occurring along transects where footprints are contiguous. Discovering ways of using, say, Landsat TM or MODIS data for spatial extrapolation of forest structure as derived from locally sparse lidar data is critical. The goal is to map lidar data so that they may become another layer in a GIS/remote sensing approach to land management.

The basis for regionalization via fusion is the assumption that there are relationships among forest structures and other, spatially continuous imagery, such as TM, that are uncovered or extended using lidar data. One strategy is to develop better canopy models that use sparse lidar data as model initializations, and then use multispectral imagery to map the model-derived structures. More empirical approaches may uncover relationships directly, shown in the simulation below. Small footprint (10 cm) lidar data, also gridded to 1 m horizontal resolution, are available for La Selva. Shadows at the times of TM overpasses where simulated from the gridded data using a solar radiation model (Dubayah and Rich, 1995), shown in Figure

10. The proportion of shadow should be a function of forest structure, with taller trees casting more shadows (i.e., the texture of light and shadow reflects structure). The 1 m shadow map was aggregated to 30 m, simulating the texture that would be observed in a TM scene. Figure 11 shows the relationship between height classes and simulated seasonal reflectance. Figure 12 shows how well the simulated TM pixels could then predict height. Sparse lidar data could be used to first find the relationship between height and reflectance, and once known, the relationship applied to the TM scene to map heights.

THE PATH AHEAD Although great progress has been made in land surface characterization using remote sensing, some important descriptors of the landscape have been elusive. As we look back across the decades of land observation from space, there have been numerous paradigm shifts. The largest of these have been associated with the genesis of methods that presented a new way of looking at the Earth. The first was passive optical remote sensing. Its limits have continually pushed forward with new and increasingly complex research from early radiative transfer and canopy models, to bi-directional reflectance (BRDF) and "hot spot" work, to neural net classifiers. Other optical techniques, such as hyperspectral, have attempted to glean even more structural or compositional information from these wavelengths. Hyper resolution, small pixel (1 m) systems have been introduced to resolve through direct observation that which is not easily modeled or inferred using multispectral data at TM or SPOT resolutions. Simultaneously, radar studies have flourished and evolved in their own fashion, through SAR, to the exciting potential of interferometric methods.

With the launch of VCL and the development of commercial airborne systems, another chapter is beginning in land characterization, and another tool added to our observational capability. We have attempted here to illustrate some of the revolutionary potential of lidar remote sensing for recovering forest structure. As with all new technology, there will be significant challenges before lidar routinely is used for land management. For the first time, however, we will have available spatial data that has a vertical component. As the motto for VCL states: "The Earth as never seen before."

ACKNOWLEDGMENTS We thank D. Clark, N. Casey-McCabe, B. Peterson, S. Prince, L. Rocchio and J. Weishampel for useful inputs and the La Selva field crew including L.C. Ramos and W.C. Miranda. This work is supported by NASA contract NAS597160 to the University of Maryland for the Vegetation Canopy Lidar Mission.

REFERENCES

A. H. Aldred and G. M. Bonnor, Application of airborne lasers to forest surveys, Information Report PI-X-51, Petawana National Forestry Institute, Canadian Forestry Service, (1985). J. B. Blair and M. A. Hofton, Geophys. Res. Lett., 26, 2509-2512 (1999). J. B. Blair, D. B. Coyle, J. L. Bufton, and D. J. Harding, Proceedings of the International Geoscience and Remote Sensing Symposium, vol. II, (ESA Scientific and Technical Pub. Noordwijk, The Netherlands, 1994) pp. 938-941. J. B. Blair, D. L. Rabine and M. A. Hofton, ISPRS J. Photo. Rem. Sens., 54, 115-122 (1999). S. Brown. Estimating biomass and biomass change of tropical forests: A primer, UN-FAO Forestry Paper 134, (Rome, Italy, 1997). R. K. Dixon, S. Brown, R. A. Houghton, A. M. Solomon, M. C. Trexler, and J. Wisniewski, Science 263, 185-190 (1994). J. B. Drake, J. F. Weishampel, For. Eco. and Management, (In Press). J. Drake, B. Peterson, D. Clark, R. Knox, J. Blair, M. Hofton, N. Casey-McCabe, and R. Dubayah, presented at Ecological Society of America annual meeting, Spokane WA (1999). http://www.inform.umd.edu/geog/vcl/posters/lsagbm.html R. Dubayah and P. Rich, Int. J. Geographical Information Systems, 9, 405-419 (1995). R. Dubayah, J. B. Blair, J. L. Bufton, D. B. Clark, J. Ja Ja, R. Knox, S. B. Luthke, S. Prince, and J. Weishampel, Land Satellite Information in the Next Decade II: Sources and Applications, (American Society for Photogrammetry and Remote Sensing, 1997) pp.100-112. J. B. Garvin, J. Bufton, J. Blair, D. Harding, S. Luthcke, J. Frawley, and D. Rowlands, Phys. Chem. Earth, 23, 1053-1068, (1998). J. B. Garvin, Spec. Pub. Geol. Soc. Lond., 110, 137-153 (1996). D. H. Harding, J. B. Blair, J. B. Garvin, W. Lawrence, Proceedings of the International Geoscience and Remote Sensing Symposium, vol. II, (ESA Scientific and Technical Pub. Noordwijk, The Netherlands, 1994) pp.1250-1253 (1994). M. A. Hofton, J. B. Blair, J.-B. Minster, J. R. Ridgway, N. P. Williams, D. L. Rabine and J. L. Bufton, Int. J. Remote Sensing (In Press).

E. J. Huising, and L. M. Gomes-Pereira, ISPRS J. Photo. Rem. Sens., 53, 245-261, (1998). W. M. Kaula, G. Schubert, R. E. Longenfelter, W. L. Sjorgen, and W. R. Wollenhaupt, Geochim. Cosmochim. Acta, 38, 3049-3058 (1974). D. S. Kimes, R. F. Nelson, D. L. Skole and W. A. Salas, Rem. Sens. of Envir., 65, 112-120 (1998). W. B. Krabill, R. Thomas, K. Jezek, K. Kuivinen and S. Manizade, Geophys. Res. Lett., 22, 2341-2344, (1995). M. A. Lefsky, Ph.D. Dissertation, University of Virginia (1997). M. A. Lefsky, D. Harding, W. B. Cohen, G. Parker and H. H. Shugart, Rem. Sens. Envir., 67, 83-98 (1999). A. Luckman, J. Baker, T. M. Kuplich, C. D. F. Yanasse and A. C. Frery, Rem. Sens. Envir. 60, 1-13 (1997). G. A. Maclean and G. L. Martin, Can. J. For. Res., 14, 803-810, (1984). S. Magnussen and P. Boudewyn, Can. J. For. Res., 28, 1016-1031, (1998). J. E. Means, S. A. Acker, D. J. Harding, J. B. Blair, M. A. Lefsky, W. B. Cohen, M. E. Harmon, and W. A. McKee, Rem. Sens. Envir., 67, 298-308 (1999). E. Naesset, Rem. Sens. Envir., 61, 246-253 (1997). R. Nelson, W. Krabill and G. Maclean, Rem. Sens. Envir. 15 (1984). R. Nelson, W. Krabill and J. Tonelli, Rem. Sens. Envir. 24, 247-267 (1988). R. Nelson, R. Oderwald and T. G. Gregoire, Rem. Sens. Envir., 60, 311-326 (1997). W. Ni, X. Li, C. E. Woodcock, M. R. Caetano, and A. H. Strahler, IEEE Trans. Geo science Rem. Sens., 27, 1-6, (1999). M. Nilsson, Rem. Sens. Envir., 56, 1-7 (1996). E. Rignot, W. A. Salas and D. L. Skole, Rem. Sens. Envir., 59, 167-179 (1997). S. A. Sader, R. B. Waide, W. T. Lawrence and A. T. Joyce, Rem. Sens. Envir., 28, 143-156 (1989).

H. Schreier, J. Lougheed, J. R. Gibson and J. Russell. Photogram. Eng. and Rem. Sens. 50, 1591-1598 (1984). R. H. Waring, et al., Bioscience 45, 715-723 (1995). J. F. Weishampel, K. J. Ranson and D. J. Harding, Selbyana 17, 6-14 (1996). J. F. Weishampel, J. B. Blair, R. G. Knox, R. Dubayah, and D. B. Clark, Int. J. Remote Sensing (In Press). M. A. Weltz, J. C. Ritche and H. D. Fox, Water Resources Research 30, 1311-1319 (1994).

TABLES

Table 1. Potential of lidar for deriving forest structural attributes. Some characteristics are directly measurable with lidar, such as height. Others may be derived using lidar data with models, such as biomass, or may be inferred. Lastly, other attributes may be derived with lidar observations used in fusion with other remote sensing data, e.g., life form (i.e., vegetation type) may be more accurately found when the vertical component provided by lidar is used with traditional multispectral classifications.

Structural Attribute Lidar Derivation Canopy Height Direct retrieval Crown Volume Direct retrieval Vertical Distribution of Canopy Materials Direct retrieval Biomass Modeled Vertical Foliar Diversity and Layers Modeled, inferred Height to Live Crown Inferred Canopy Cover/LAI Fusion with other sensors Life Form Fusion with other sensors Large Tree Density Fusion with other sensors, inferred

FIGURE CAPTIONS Figure 1. Basics of lidar return waveforms. Incident gaussian-distributed pulses of laser energy reflect off various portions of the canopy, resulting in a return waveform or "echo" where the amplitude of the pulse is a function of the number of reflecting surfaces (leaves and branches) at that height. The entire waveform gives the vertical distribution of surfaces intercepted by the incident beam. Some of the incident light penetrates all the way through the canopy, even for canopies of very high canopy closure (e.g., greater than 99%), and produce the last large-amplitude Gaussian-shaped spike in the waveform known as the ground return. Lidars do not retrieve canopy height, but rather a target range determined by measuring the travel time of the pulse (accounting for the speed of light through the atmosphere). Canopy height is determined by subtracting the range to the ground from that to the first detectable return or some threshold above that return. Figure 2. The VCL mission concept. VCL is in a low Earth orbit approximately 400 km above the Earth at an orbital inclination of approximately 67° (so that land areas between 67° N & S are observed). The instrument consists of 5 near-infrared laser beams, each of which can pulse at up to 242 pulses per second, producing contiguous along track footprint spacing with 25 m diameter footprints. The across track beam spacing is 2 km. The sampling nature of VCL is illustrated above. The spacecraft produces random tracks on the Earth (at 67° inclination to the equator) and observations of the Earth's forests are accumulated. These will eventually be used to make gridded products at a resolution as fine as 2 km x 2 km by the end of its nominal 18 month mission. Figure 3. Image of canopy heights acquired over the tropical forests of the La Selva Biological Station in Costa Rica using the Laser Vegetation Imaging Sensor (LVIS). Note the canopy detail and emergent trees. Imaging lidar provides a practical means for mapping canopy heights over landscapes. The image is a mosaic of several aircraft overpasses. Figure 4. The distribution of LVIS-derived canopy heights for three different land cover regions in the La Selva. Establishing key differences between these characteristic distributions will aid in land cover classification as well as habitat assessment (e.g., the data in the >40m region of the primary forest histogram indicate the presence of emergent trees - the absence of data in this height region could indicate degradation through selective logging or other forestry practices). Figure 5. Along track forest structure from airborne lidar over a forest in Maryland, USA. The green color intensity indicates the amount of canopy material. Notice the ability of lidar to distinguish the canopy topography, the underlying canopy air space, as well as the sub-canopy topography. The VCL mission will produce data similar to this transect globally, though at coarser (25 m) horizontal along-track resolution.

Figure 6. Three-dimensional views through the tropical forest of La Selva as reconstructed using LVIS, after Weishampel et al., in press. Lidar can detect heights at which most of the canopy material occur (green colors) as well as emergent trees (shown as the blue colors on the top plane of bottom figure). Figure 7. Example LVIS waveforms from the Sequoia National Forest, California (top), and the La Selva Biological Station, Costa Rica (bottom), showing the remarkable diversity of vertical structures and multiple canopy layers in forests around the world. The waveforms record the vertical distribution of intercepted canopy surfaces (leaves and branches). Notice the weaker ground returns in the La Selva waveforms caused by high canopy closures and low lying vegetation: portions of the incident beam are reflected by the upper canopy layers leaving less available at the surface to be reflected by the ground. Figure 8. Subcanopy topography of Costa Rica. This is a shaded-relief representation of topography as acquired by LVIS using 25 m diameter footprints on the ground, with overlapping footprints spaced every 12 m. The area shown encompasses the deeply dissected volcanic slopes in the south down to lowland, riverine areas of La Selva to the north (top of page). The range of elevations is from 250 m (red colors) to 30 m (blue colors). The view here is perhaps the first ever glimpse of the complex drainage network below the dense tropical foliage. Figure 9. Example lidar waveforms (solid lines) and normalized cumulative waveforms (dashed lines) from abandoned pasture, agroforestry, secondary and primary tropical rainforest landcover types. Both the height of the canopy and the shapes of waveform structure can be used to help distinguish among forest successional types. The cumulative waveforms sum the return as a function of height. Figure 10. (Top) A first return, pencil beam lidar system was used to map the canopy topography of La Selva. These observations were gridded to 1 m spacing to create a digital elevation model, a small subset of which is shown in the top panel. Lighter colors correspond to higher elevations. Note that individual tree crowns can be discerned easily. The rectangular shapes are agroforestry plots of commercially valuable mono- and polycultures. The dark vertical band in the center of the image is a river. (Bottom) The elevation model was then used to simulate Landsat Thematic Mapper reflectances. A solar radiation model with shadows was used to generate the "shaded relief" image on the bottom panel (again at 1 m spacing) at the time of a TM overpass (all vegetation was assumed to have the same near-infrared reflectance). This image was then aggregated to 30 m to simulate the effects of solar angle and shadows on TM scale resolutions as a function of season. Areas with taller vegetation should have more shadows, and hence lower reflectances when aggregated. Figure 11. Relationship between height classes (shown on far right) and simulated TM reflectances. A TM scene was simulated using the canopy digital elevation model from

Figure 10 for each month shown. An average scaled reflectance for each height class was then found (TM resolution pixels with a greater percentage shadow have lower reflectance; a sunlit canopy surface directly facing the Sun will have a scaled reflectance of 1.0). The lines shown give a portion of the BRDF (bi-directional reflectance distribution function) for each height class. Note that height classes separate easily by reflectance. The seasonal change in reflectance is also greater for short portions of the canopy than tall ones. The relationships were developed for the entire La Selva area, and not just the subsets shown in Figure 10. Figure 12. A linear regression that uses simulated TM reflectances for January and March (see Figure 10) as independent variables, and lidar derived heights as the dependent variable predicts heights for La Selva. A regression using only the March simulation performed almost as well, explaining about 85% of the variability of that using two dates. Thus, by fusing lidar data with TM, it may be possible to map height structure across a landscape, using sparse lidar data to develop the predictive relationships.