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7 Forest Structure Estimation The … proper interpretation of remote sensor data requires a thorough understanding of the temporal and spatial characteristics inherent in the vegetation cover types present and of the related changes in spectral response. — R. M. Hoffer, 1978 INFORMATION ON FOREST STRUCTURE The spatial and statistical output from a classification procedure comprises one of the major information products on forest condition available by remote sensing; generally, a second set of forestry information products is obtained by continuous variable estimation procedures. Classification produces information on the features that are contained in the list of classes imposed on the image data; the result is typically a classification map. Continuous variable estimation produces information on features that vary continuously over the landscape depicted in imagery. The result may be a map or an image in which the tones correspond to the level or value of the feature of interest and vary over the extent of the map. The process can become more complex when continuously varying forest conditions are used in the process of classification. This is not usually a problem in conventional vegetation typing or species composition of stands; the map is derived via the usual logic of classification (Zsilinsky, 1964; Avery, 1968). But typing and compiling species composition are only two of the structural attributes of forest stands that are of interest, usually as part of a general forest inventory. Some of the other forest attributes of interest might include: 1. Forest crown closure, 2. Diameter at breast height (dbh), 3. Volume, 4. Height, 5. Stem density 6. Age, and 7. Stage of development. Some of these attributes can be considered as forest conditions in either discrete classes or as continuously varying attributes to be estimated at some level of precision. ©2001 CRC Press LLC As in species classifications, aerial photography has been instrumental in devel-oping maps of these forest structures almost exclusively through the photomorphic approach followed by field work, but also through direct image interpretations by manual means (e.g., height calculations by parallax or shadows, crown closure estimation using templates, etc.). Species composition has been classified using digital image classification techniques — with high spatial detail imagery — but generally without the level of acceptance accorded the aerial photographic approach, for a wide range of reasons, not the least of which is the difficulty in generating conventional maps with the digital methods (see Chapter 6). Digital classification has been used less frequently when the objective is to map other forest structures, because this type of mapping resembles more the estimation of a continuous variable rather than a discrete categorization. Classification of different density or height classes has been described (Franklin and McDermid, 1993), but applications of remote sensing aimed at these continuous aspects of the forest inventory have been driven largely by empirical or semiempirical model estimation. Unlike classification, which is typically driven by a statistical understanding of what the spectral response patterns mean, such models are based more on the relationships incorporated in a fundamental understanding of the physically based radiative transfer in forests. A plethora of such studies have been reported attempting to estimate individual forest parameters such as crown closure, basal area, or volume, as independent variables which can be predicted or estimated using a calibrated remote sensing image. The general approach is to: 1. Establish a number of field observation sites in a forest area, 2. Collect forest condition information at those sites, 3. Acquire imagery of the sites, 4. Locate the sites on the image, 5. Extract the remote sensing data from these sites, 6. Develop a model relating the field and spectral data, and finally, 7. Use the model to predict forest parameters for all forest pixels based on the spectral data. Typically, the objective is to predict the selected field variable through model analysis, with the available remote sensing data as the dependent variables. Then, the model is inverted to predict the independent variable (such as stand volume or density) over large areas of forest. In other words, the spatially explicit remote sensing data are considered the predictors of the locally known field parameters so that the remote sensing image can be used to map that parameter across the image landscape. The remote sensing data are inverted to provide predictions of the desired field variables. Intuitively this seems reasonable; users are aware of the fact that the remote sensing data are dependent on the field data, not the other way around. The common tool is model inversion; models developed through experimental or nor-mative designs are used to describe the relationships contained within a forest/remote sensing data set. The aim is to generate new insights which can guide the field scientists and help new applications become possible. ©2001 CRC Press LLC The physically based models are built mathematically on theoretical models that are typically designed to quantify advances in the ability to predict target and radiation interactions (Jupp and Walker, 1997). The model is driven by the principles of radiation physics to relate spectral properties to biophysical properties (Gerstl, 1990). The model is derived from current experimental understanding of radiation physics, geometry, and energy/chemical interactions. The role of such models in advancing the science of remote sensing cannot be overestimated; but typically, remote sensing data analysts and forestry users have little contact with these models. Their complex and demanding structure have meant that they will likely remain in the domain of the remote sensing instrumentation and radiation specialists (Silva, 1978; Woodham and Lee, 1985; Teillet et al., 1997) rather than the applications specialists (Landgrebe, 1978b; Strahler et al., 1986; Cohen et al., 1996b; Franklin and Woodcock, 1997). Empirical models might be constructed using the understanding derived from physically based models coupled with laboratory, field, and actual or simulated remote sensing data. Empirical remote sensing studies are plentiful — image clas-sifications, for example, are almost completely empirical. This is the probable way in which most users of remote sensing data will learn and apply their experiences. The empirical approach is a data-driven approach; learning proceeds from under-standing the data, data acquisition and the specific conditions under which models derived from those data were inverted. The form of the model can be inferred from physical considerations, while specific model parameters are estimated from empir-ical data. Unfortunately, purely empirical models have the disadvantage of being highly site specific (Waring and Running, 1998; Friedl et al., 2000). This modeling situation has given rise to an intermediate approach based on a set of semiempirical studies that are hybrids of the purely empirical and theoretical physical models. For example, a statistical (empirical) model of the relationships between reflectance and a canopy characteristics, such as leaf area index (LAI), may be augmented by a physical understanding of the processes involved; the effect of leaf angle, leaf distribution, and leaf shape might be modeled within the larger relationship between reflectance and leaf area well-established through vegetation indices such as the normalized-difference vegetation index (NDVI) (e.g., Chen and Cihlar, 1996; White et al., 1997). Canopy reflectance models based on geometric-optical modeling approximations of physical processes represent an example of an emerging semiempirical method in remote sensing; these models contain a mix of data-driven relationships and theoretical understanding to provide answers only available in more sophisticated or demanding experimental settings. Li and Strahler (1985) developed one of the first such models — the geometric-optical reflectance model, commonly referred to as the Li-Strahler model. Using the model in California, Woodcock et al. (1997) reported that the model appeared to confirm what had been learned in numerous empirical studies — namely, that canopy reflectance is dominated by canopy cover — and that the advantages of using a canopy reflectance model over an empirically derived relationship were marginal, or at least unclear. The application of forest reflectance modeling and coupling such models to physically based models that ©2001 CRC Press LLC incorporate growth and topography is in its infancy (Kimes et al., 1996; Gemmell, 2000). In particular, invertible canopy models are currently scarce and impractical for operational use due to their complexity and our still-evolving understanding; for example, Gemmell (2000) found that multiangle data were useful in improving the accuracy of forest characteristics derived by inversion, but that more extensive testing and validation over larger areas and different forest conditions was essential to better understand the limits of the methods. With a modest investment in training, such models could be used by applications specialists as well as the model developers. While specific results will vary, empirical methods used in one area to generate a relationship between spectral response and forest conditions generally can be applied, with few modifications, elsewhere. But when using some types of remote sensing data, such as Landsat TM data, and empirical models such as linear regres-sion techniques, other difficulties arise (Salvador and Pons, 1998a,b): 1. Typically low dynamic range of the data; generally, higher correlations can be obtained if log transformations are applied (Ripple et al., 1991; Baulies and Pons, 1995). For example, with respect to leaf biomass, after a certain density is reached, doubling that parameter will not affect the spectral response, but a log transform can help establish linear relationships; 2. Extensive atmospheric and geometric corrections are needed; 3. Difficulty in reducing sensitivity to extraneous factors (a standard feature of the normative remote sensing approach) (Gemmell, 2000); 4. Generally low spatial resolution relative to the objects under scrutiny — trees (Wynne et al., 2000), and; 5. Generally, small sample sizes often resulting in fewer degrees of freedom than required for extensive use. Perhaps the most important advantage of this approach is its accessibility. There are probably few users in forest management situations who are unable to find the resources to complete the simple normative design that is required to establish a purely empirical relationship between spectral response and, for example, canopy cover. All that is needed are the basic remote sensing infrastructure components, an airborne or satellite remote sensing image, and some field work. The methods are slightly more demanding than classifications, but probably not by much (Franklin, 1986; Walsh, 1987; Franklin and McDermid, 1993). While the exact form and nature of the empirical relationship will not remain stable as conditions change, it is also true that the relationship will rarely differ dramatically from what has already been reported or observed in an area. For example, the normal relationship between cover and red reflectance is expected to be expressed in a moderate negative correlation between the two variables because increasing cover (larger tree crowns, more leaves) results in more red light absorption (greater photosynthesis activity). Less red light will be detected by a sensor above the canopy. Perhaps the exact relationship is found to be an R value of –0.49. It is possible but not likely that the correlation between red reflectance and cover will be found to be +.49 in another, similar area. More likely, the new area will have a negative relationship of approximately the same magnitude. One interpretation of ©2001 CRC Press LLC TABLE 7.1 Relative Importance of Forest Variables in Explaining Airborne C-Band SAR Backscatter in 93 Alberta Boreal Forest Stands Covertype Hardwood Pine Spruce Rank-Order Variables Volume/ha, biomass/ha, mean age, pine cover Hardwood cover, pine cover, crown volume, crown closure No statistically significant relationships were found Source: Adapted from Ahern et al. (1996). this relationship might be that remote sensing images of a certain type of young stand are almost always brighter in the visible portion of the spectrum than older stands of that type. This relationship is as likely to be found in one location as in another. If the usual (or normative) relationship between cover and reflectance is one of decreasing reflectance with age for a given species, then this will be more or less likely to be true in New Brunswick as in Finland, Argentina, or Indonesia. The normal relationship must be established, tested, and understood in order for applications of the relationship to be developed. Similar logic and approaches have been reported using SAR imagery. In partic-ular, Ahern et al. (1996) exhaustively tested for relationships between SAR back-scatter and boreal forest stand structure measures, but none of the statistical relation-ships were strong enough to suggest that C-band backscatter might be capable of providing reliable estimates of stand structural parameters. Different species differed in the strength and significance of the relationships (Table 7.1). Wilson (1996), using a sample of stands from the same data set, took a different approach. First, multiple regression equations were developed that included SAR backscatter and texture measures to predict mean height of spruce and pine stands; standard errors were less than 15%. Then, the stands were grouped by forest inventory classes for height and crown closure. SAR data could provide discrimination of broad height and crown closure classes at reasonable accuracies (Table 7.2). So, despite low correlation between spectral response and a forest variable on a pixel-by-pixel basis, high levels of classification accuracy could still be generated over broader classes and areas. This is one approach to achieving a more successful (i.e., more useful) remote sensing estimation of a continuously varying forest condition; create logical classes and reduce the problem to a classification decision. After all, 42 to 57% classification accuracy of crown closure into four classes is not high; under most circumstances, however, this would be considered much better (more useful!) than nothing. The success of this empirical inversion idea has generated a vast literature comprised of specific studies and experiments. Many of these studies can be seen as contributing insights to satisfy the growing need to understand the appropriate role of remote sensing in providing information to sustainable forest management goals (Franklin and McDermid, 1993). A number of early empirical studies have served to demarcate the boundary for the use of airborne (Irons et al., 1987, 1991; ©2001 CRC Press LLC ... - tailieumienphi.vn
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