Leaf area index (LAI) is the total one-sided area of all the leaves above a given ground area divided by the ground area. For example, a corn field having a LAI of 4 means that above each square meter of ground, there are four square meters of corn leaves. The amount of leaves in the canopy is a factor in determining the amount of light intercepted by the canopy, which in turn controls photosynthetic rates. Leaves contain pores, called stomata, through which carbon dioxide and water pass between the plant and the atmosphere. So the leaf area also sets limits on transpiration and photosynthesis. For different vegetation types LAI can vary from less than 1 for deserts to over 6-8 for rain forests (Figure 4.01).
There are a variety of methods for measuring LAI. The most straightforward, usually used in herbaceous or grassy canopies, is to simply define an area on the ground, clip off all the leaves, and measure their area. Dividing the total area of all the leaves by the ground area gives LAI. This time-consuming method has the added disadvantage of destroying the plants being studied. Usually this method is done just on small samples of the total area of interest.
Another approach, generally used in forests, is to directly measure a series of individual trees spanning a range of sizes (done for one species at a time). Leaf area is directly measured for each individual tree (this usually means cutting down the tree), along with several other plant measurements such as trunk diameter, tree height, and depth of the crown. A mathematical relationship is then developed between the measurements of diameter, height and crown, and the leaf area for each species. This relationship is referred to as an allometric equation; each separate species has its own allometric relationship or equation. Once an allometric equation has been developed, LAI can be estimated elsewhere in a tree stand using just the simple measurements of height, diameter and crown depth.
A third method used to determine LAI is measuring the fraction of incoming light that passes through the plant canopy. This is done by making assumptions about how leaves are distributed in the canopy, then measuring the size and number of gaps between the leaves. These two pieces of information can then be used to calculate LAI. Canopy gaps can be measured using high contrast photographs looking up at the sky through a canopy, or by recording the intensity of light transmitted down through a canopy.
The advantages of these optical canopy gap measurement approaches is that they can be collected fairly quickly with minimal disturbance to the vegetation, allowing repeated observations over time. However, there are some uncertainties with these methods. The optical measurements do not distinguish between leaves and other materials in the canopy, such as branches or tree trunks, nor can they separate live and dead leaves. Also, if the assumptions used to describe leaf distributions are incorrect, the calculation of LAI will be in error.
The problem with all of these approaches for measuring LAI is that they are difficult and time consuming to perform over large areas. Remote sensing provides a means to make repeatable, consistent measurements over large areas that can be related to LAI. Generally, LAI has been estimated from remotely sensed data by developing relationships between ground-measured LAI and spectral vegetation indices (SVIs, discussed below in Section 5 of this chapter). Most SVIs are sensitive to the differences between visible and near-infrared reflectance, so they can be used to distinguish green leaves from many other materials in the landscape. However, many factors other than LAI can affect the value of SVIs, so their use has some limitations. Often SVIs reach a saturation value once the LAI gets above 3 or 4, meaning that canopies with LAIs of 4 and 8 can have almost identical SVI values.
Canopy cover or closure is a measure of the fraction of the landscape covered by vegetation. Canopy cover, like LAI (leaf area index), is an important factor determining the amount of light intercepted or absorbed by the canopy, and photosynthetic rates. Canopy cover also determines how much rainfall is intercepted by vegetation before hitting the ground, a property that affects evaporation and erosion rates and consequently is important in hydrological studies.
Canopy cover can be measured using the same methodologies used for the optical measurement of LAI as described in the previous section. The canopy coverage is simply 1 minus the total gap fraction. Part of the interest in this canopy descriptor is that it avoids the need to make assumptions about leaf spatial distribution required to calculate LAI from the gap fraction data.
A pretty good measurement of canopy cover can be made with a simple instrument called a canopy densiometer. The canopy densiometer consists of a mirror (often a curved mirror to give a wider view) with a grid marked on it. When the densiometer is held horizontally below the canopy, a reflection of the canopy can be seen in the mirror. Each grid point on the mirror that appears covered by canopy is counted. The final tally of canopy-covered grid points is then divided by the total number of grid points on the mirror to get the fractional canopy coverage.
The remote sensing of canopy cover follows the same approach as described above for measurements of LAI, with relationships developed between canopy cover and spectral vegetation indices (SVIs), and has similar limitations.
Biomass is the mass (or weight) of living matter per unit area of ground. It is expressed in units such as grams per square meter or kilograms per hectare. Between different vegetation types, biomass ranges from around 100 kg/ha for deserts to 500,000 kg/ha for tropical rain forests. In the study of carbon budgets, biomass is important because it directly represents the amount of carbon stored in living plants. Biomass in an ecosystem is also an important determinant of respiration, a process that can be thought of as the reverse of photosynthesis. During photosynthesis, plants take up carbon dioxide and release oxygen. But plants also respire, using oxygen and expelling carbon dioxide, just as animals do. Photosynthesis produces food, while plant respiration uses some of the food that is made. To study the movement of carbon through an ecosystem, rates of both photosynthesis and respiration need to be examined.
Biomass is measured in the field using some of the same techniques described above for LAI (leaf area index). In areas such as grasslands, all of the plants in a given area are clipped, then dried and weighed. For trees, allometric approaches are used, where individual plants of varying size are cut down and weighed. Allometric relationships are developed between the readily made measurements of tree height and trunk diameter, and the harder to measure quantity of biomass.
Biomass can be estimated from remote sensing data using the same approaches described above for canopy cover and LAI. For herbaceous or grassy canopies most of the biomass is in the leaves, so the determination of LAI using SVIs (spectral vegetation indices) also gives an approximation of biomass. For many vegetation types, however, most of the biomass is in the form of woody tree trunks and branches.
Indirect estimations are the usual alternative for assessing these quantities, using relationships that have been established between biomass and other variables such as canopy cover or the amount of shadowing in a scene. Because different tree shapes cast different amounts of shadows, the shapes of trees can be inferred from the fraction of shadows in an image. This can provide the ratio of crown height to crown width, information that can improve biomass estimations.
The relationships between biomass and remote sensing indicators differ somewhat according to the presence and relative amounts of different biomass components in the vegetation cover structure. Consequently, each distinctive vegetation canopy has correspondingly distinct relationships or equations that can be used to "calibrate" biomass from remote sensing data (Figure 4.02).
In the discussion of biomass above, as in most references to biomass, the biomass being referred to is that which is above ground. However, as anyone who has had to pull weeds from a garden or lawn can attest, there is a lot of plant matter below ground. Though below ground biomass is important (it stores carbon and respires) it is often neglected since it is difficult to measure. The only way to directly assess below ground biomass is to dig pits and measure the biomass contained in them. This requires careful cleaning of all the fine roots, removing clumped soil and stones. Because of this time-consuming work, below ground biomass has not been described accurately for many areas. There is no remotely sensed method of collecting this information over large regions. Generally, investigators use any available below ground information to develop relationships to the amount of measurable above ground biomass and assume that these relationships hold across the world.
2.3.1 Radar remote sensing detection of biomass -- Radar (RAdio Detection And Ranging) is a type of remote sensing system that can directly observe tree trunks and branches. It is referred to as an active system, since it provides its own illumination energy, compared to passive optical systems already mentioned that use sunlight for illumination. Because it provides its own illumination, radar has the advantage of being able to be used day or night. Another advantage of radar over optical systems is that the microwaves are not affected much by the atmosphere or clouds, so radar can "see" through cloudy and hazy conditions.
Radar is actually an active microwave system, using energy with wavelengths ranging from one millimeter up to a meter, compared to optical remote sensors that detect light having wavelengths around one thousandth of a millimeter. In a radar system, microwave pulses are transmitted to a target, and some of that energy is reflected back to a detector that measures the timing and intensity of the return signal. The time it takes a pulse to return provides the distance (or range) to the target. For applications such as air traffic control, range is the most important piece of information from radar. But in remote sensing of vegetation, the intensity of the return signal is the useful information.
The intensity of radar signals returned from ground surface features depends on a number of complex and varied factors, but the primary factors are geometric and electrical characteristics. Radar has a "side lighted" nature; rather than looking straight down at the ground, most radar systems send out and receive signals in a side direction.
Local terrain slope variations (surface roughness) result in varying incidence angles between the microwave pulses and surface features. Slopes or surfaces facing a radar sensor produce high returns, while slopes facing away from a radar system give weak or no returns.
Surface roughness as measured by radar depends on the wavelength of the system in use and the local incidence angle. Greatly simplified, surfaces will be considered rough, acting as diffuse reflectors (scattering energy in all directions) the greater the surface's height variations are relative to the wavelength of the incident microwaves; and smooth, acting as specular reflectors (reflecting light in a direction away from the sensor) the smaller the surface's height variations are relative to the wavelength of the incident microwaves. The shape and orientation of landscape features and objects also affect radar returns. Corner reflectors (such as on many manmade objects like buildings) give very strong (bright) radar returns. The adjacent smooth surfaces of corners cause a strong reflection back toward a sensor.
One measure of an object's electrical character is the complex dielectric constant, an indication of the reflectivity and conductivity of material. For the microwave spectral region, most materials have dielectric constants ranging from 3-8 when dry, while water has a dielectric constant of about 80. Consequently, the greater the water content of a soil or vegetation, the stronger or brighter the radar return.
Plants and tree canopies typically tend to be good reflectors of radar energy, having large surface areas, generally high moisture contents, varying dielectric constants, microrelief, and features with sizes on the same order as the lengths of microwaves (1 mm - 1 m). The more canopy components (leaves, stems, stalks, tree trunks, branches, limbs) and the higher the canopy density, the stronger the radar return signal, an indication of more biomass in an area. Generally, shorter wavelengths (several centimeters) are better for sensing smaller crop canopies and leaves, while longer wavelengths (tens of centimeters) are preferred for detecting branches and trunks. Vegetation with high moisture content will give stronger returns than dried out, stressed vegetation.
There are some limitations to use of radar for estimating biomass. Rough surfaces, such as bare plowed fields, can be strong scatterers but with low biomass. As biomass increases there reaches a point where additional biomass no longer increases the return signal. The return signal has reached its maximum level and is no longer sensitive to larger biomass amounts. This point occurs well before biomass values are close to their maximum. Biomass estimations can be affected by fluctuations in moisture content which can occur over very short time periods.
Knowledge of the height and architecture (structure) of vegetation is useful for several reasons. Height is often strongly correlated with other biophysical variables, such as biomass. Consequently, accurate height data can be used to derive other parameters. Variations in canopy height, or roughness, affect the amount of atmospheric turbulence above a canopy. The amount of turbulence in turn influences the transfer of heat, moisture, and gases between the plants and the atmosphere.
The height distribution of leaves in the canopy gives information about the number and position of canopy layers. Leaf position affects light interception and thus photosynthesis. Canopy height can also be used to classify vegetation types. Height can be measured directly for smaller plants. For taller plants such as trees, given that tree tops are not easily accessible, height can be measured trigonometrically by measuring the angle to the tree top observed from a known distance from the tree.
Because of the number of leaves in a canopy it is nearly an impossible task to measure all of their heights, so height measurements are only made for selected sample leaves. One way of sampling leaves is to choose points on the ground and measure the height of the leaves above each point. Even using selected sampling, a large number of measurements are required to get a good description of leaf height distribution.
Canopy heights are difficult to determine using passive optical remote sensing methods. If the image pixel (picture element -- see Chapter 5 Sections 2.2 and 3.2.2) size is small relative to the height of an object, the length of the object's shadow along with the sun angle can be used to calculate height. But for large area studies this approach is generally not feasible.
2.4.1 Lidar remote sensing detection of height and architecture -- Lidars (LIght Detection And Ranging) are active optical sensor systems that can measure canopy height and vertical layering to an accuracy of tens of centimeters. A lidar emits short duration pulses of laser light consisting of photons at a specific wavelength. When transmitted downward from an aircraft or spacecraft toward a vegetation canopy, some of the photons hit leaves and branches at different levels down through the canopy, while other photons pass through canopy gaps and reach the ground. As the photons encounter various surfaces, they are scattered (reflected) in different directions, and some of the photons are reflected back in the direction of the sensor. The lidar measures the various return times and intensities as photons are received back at the sensor. Return times are directly related to the distance between the sensor and the reflecting surface, and the signal intensity is related to the area and reflectance of the surface.
Lidar measurements of a vegetated site can detect the elevation of the outer canopy surface and, through canopy gaps, the elevation of other layers beneath the canopy top (e.g., mid- and understory) as well as of the ground surface. The more photons returned from a given height in a canopy indicates more foliage or woody surface area. The measurements are processed to provide a canopy height distribution and the topography of the underlying ground. A satellite carrying a sensor called the Vegetation Canopy Lidar (VCL) is currently being developed to use this approach to make global measurements of vegetation height and structure. Figure 4.03 is a schematic showing the information gathered from canopy lidar sampling.