Remote Sensing for Sustainable Forest Management - Chapter

5 Forest Modeling and GIS … of the future developments in the handling of remote sensing data, none is likely to be more important than their integration with other data sources, to produce a comprehensive geographic information system. — J. R. G. Townshend, 1981 GEOGRAPHICAL INFORMATION SCIENCE Geographical information systems (GIS) are computer-based systems that are used to store and manipulate geographic information (Aronoff, 1989). Like remote sens-ing, GIS have emerged as a fully functional support for resource management following a series of intensive, synergistic, technologically driven activities over the last four decades. Developments have been built on the strengths of successive revolutions in computer technology and geography. GIS have their modern origins in the 1960s and 1970s, but conceptually can be traced much farther back to the earliest requirements to assess land capability using multiple criteria, and the need to perform map overlays. The potential contribution of GIS to sustainable forest management appears enormous; here is the ideal tool with which forest management issues can be addressed — simply, the relevant tasks are 1. To assemble a spatially referenced database across all relevant scales, and then 2. Put multiple analytical tools in the hands of the users so that the accu-mulated information can be made to provide answers that are needed. The simplicity of these statements and the general, casual, attitude toward geographical information and mapping sometimes found in forestry, are deceptive; GIS is no simple process! A great deal of complexity has become subsumed under the GIS label (Longley et al., 1999). GIS, like remote sensing, appears ill-defined and very broadly based. It is comprised of geographic objects (polygons, lines, points) and their attributes, with or even without reference to spatial components and complicated topology. Currently, it is defined more by what is done under the ©2001 CRC Press LLC banner of GIS than by any coherent definition of the field. GIS in forestry tends to be comprised of two major endeavors: 1. Geographic data management, including data collection, database devel-opment, and archiving, and 2. Geographic data analysis, including modeling and information extraction. In natural resources management, the time and effort devoted to the first task, geographical information management, is enormous. For those businesses and gov-ernments with substantial lands to manage, managing the vast array of spatially referenced information on those lands has emerged as an onerous responsibility, and can consume vast amounts of human and capital resources (Green, 1999). In recent years, many of the significant problems in this activity have been resolved — for example, database development, storage, output, and processing speed bottlenecks. Now, a trend to increasing emphasis on the latter set of tasks — that of geographic data analysis — is becoming apparent in the GIS research and applications literature. A prognosis on the final form of GIS and its contributions to sustainable forest management is premature and, because of the many known and unknown factors influencing the development and applications of GIS, would likely be unconvincing. The evolution of GIS is not yet complete (Longley et al., 1999). Instead, it is instructive to consider that during the last 10 years, a transition has taken place in GIS related to fundamental issues of geographic information, methods, and practical implementation of GIS in applications. The original concepts and tools of geograph-ical information systems continue to develop into a geographical information science (GIScience) (Goodchild, 1992). Comprised of concerns with the technical and sci-entific issues surrounding the use of geographical data in natural science and social science applications, GIScience appears well on the way to acceptance as a separate field with a unique focus and research agenda (Goodchild and Proctor, 1997). Practically speaking, GIScience appears to be rapidly replacing a GIS technological agenda with a mapping/functional analysis agenda. In the future, there will be increasing emphasis on using GIScience to satisfy user needs (Albrecht, 1998; Gibson, 1999) as the technological problems which have preoccupied GIS developers appear to be in recession — solved, for the most part, or at least understood. A new GIScience mandate: providing the scientific basis for increased use of the new tool of GIS in real-world applications. In forestry, GIScience geographic data analysis is already making a substantive contribution to sustainable forest management in at least three ways: 1. Integration of multiple data sources, including remote sensing data, 2. Provision of input to models and the appropriate environment to run, validate, and generate model output, and 3. Mapping and database development. The first two contributions focus on the role of remote sensing and models within the infrastructure provided by a forestry GIS (Landsberg and Coops, 1999). These ©2001 CRC Press LLC two components are a critical development to facilitate flexible and innovative operational, tactical, and strategic forest management planning. REMOTE SENSING AND GISCIENCE Is remote sensing actually a part of GIScience? Uncertainty over whether remote sensing and GIS are actually different aspects of the same science has been common (Estes, 1985), but a growing consensus is emerging. The relationship between remote sensing and GIS is so strong that some have suggested that the potential contribution of each cannot be realized without continued, and finally, complete integration of the two endeavors (Ehlers et al., 1993; Estes and Star, 1997). There may be some resistance to this idea as GIS and remote sensing evolved at different rates, and tended to remain separate (Aronoff, 1989). Each field is serviced by separate journals and societies, but there are many common points of contact including meetings in which the other technology is heavily featured. Perhaps only a change in attitude or perspective is needed to further the goals of integration (Edwards, 1993). As Good-child (1992: p. 35) has suggested, “Ultimately it matters little to which of the many pigeon holes we assign each topic … one person’s remote sensing may well be another’s geographical information science.” The reality today is that almost every usable remote sensing image and image product will reside and find application at some point in its lifetime in a GIS environment. Obviously, a key methodological focus in remote sensing has been the extraction of forestry information from imagery using tasks in the image processing system. Again stating the obvious, much of the information produced by the analysis of imagery is geographic information. Increasingly, that information must be man-aged, together with other forestry information, in the GIS. The image processing system can be seen as one part of a larger GIS; to users, this makes great sense, simplifying some of the data issues, and methodology within the technological approach (Landsberg and Gower, 1996; Treweek, 1999). In turn, the GIS can be seen as one part of the larger, emerging world of GIScience, encompassing all issues of spatial data analysis and mapping (Haines-Young et al., 1993; Atkinson and Tate, 1999; Longley et al., 1999). One task of the new GIScience paradigm is to enable smooth integration of all the assembled technologies in support of the disciplinary tasks set before it. A quick glance at the literature of the past few decades reveals a symbiosis which can be seen to exist from the earliest, tentative first steps in remote sensing and GIS. An early concern was to use the GIS to manage the raw images as a spatial archive (Tomlinson, 1972). A suite of tools and techniques to provide image display and data exchange was built into most early GIS. Practically speaking, modern GIS contain the descendants of these tools, sometimes in the form of still more powerful tasks (such as the creation of polygons from image classification output, polygon decomposition, cleaning, and dissolve). GIS users and developers have long under-stood that much of the data required as input to their emerging systems would be obtained by remote sensing (Burroughs, 1986; Aronoff, 1989). Updating a GIS with remote sensing information continues to be an important and complex application ©2001 CRC Press LLC area (Wulder, 1997; Smits and Annoni, 1999). It is now widely understood that GIS and remote sensing integration goes both ways. In the late 1970s and early 1980s, for example, remote sensing scientists began to recognize that many image analysis tasks could be improved with access to other digital spatial data. These data — DEMs, soils maps, ecological land classifications, geophysical surfaces, and others — were increasingly held within a supporting GIS or relational database/computer cartography environment. Landgrebe (1978b) listed five key limits on the extraction of useful information from remote sensing data: the four types of image resolution (spectral, spatial, radiometric, and temporal), and the quality of ancillary data. On this level alone it seems likely that the dependency between the science and technology of remote sensing and the science and technol-ogy of geographical information will continue to strengthen. This strength will be based on the fact that rarely will the analysis of remote sensing or GIS data alone provide an advantage over the analysis of both together; one obvious exception exists in areas where the existing remote sensing or GIS data are unsuitable or untrust-worthy for a given mapping application, perhaps derived through some now obvi-ously deficient but previously acceptable methodology. Using GIS data to generate or supplement training data for image classifiers is increasingly common, as are combinations of GIS and remote sensing data in a single classification process. The effect of using remote sensing data from different sensors, the effect of image spatial context, the effect of existing map data in remote sensing forest classification, are all more readily addressed within the GIS environ-ment (Solberg, 1999). Despite these developments, there still may be a strong tendency to consider GIS simply as a useful way to generate remote sensing output products — princi-pally, forestry maps. No doubt a primary focus in remote sensing and GIS integration will continue to be maps and time-series of maps to support forest monitoring. Obviously, one of the primary ways in which forest managers access and present data is through the use of maps. A completely seamless digital environment that results in good, understandable maps based on the unique benefits of digital data is predicted to follow the largely paper-oriented era just passing (Davis and Keller, 1997). Remote sensing and GIS are moving rapidly to quantitative digital maps which tie the tremendous, but finite, complexity of landscape models to the infinite complexity of reality. An issue is to maintain or increase user accessibility to the science behind the maps. The capability of the GIS to determine the underlying uncertainty in the remote sensing data structures and maps and to document error propagation in spatial data are critical components of the analysis of remote sensing imagery with other digital data (Joy et al., 1994; Zhu, 1997). The complementarity of GIS and remote sensing (Wilkinson, 1996) can lead to increased capability for many types of environmental modeling and analysis. Increased GIS and remote sensing integration gives rise to a new concern: GIS and image processing system interoperability (Limp, 1999). Available commercial image processing systems differ only slightly in their ability to link to GIS, to handle ancillary data, to be used with field data, and to assist with sampling problems. All of these tasks, long recognized as critical in forestry, need to be documented carefully in any application. All are supported to some degree by virtually all of the commer- ©2001 CRC Press LLC cially available remote sensing image analysis and GIS systems — separately. The key issue is how to move quickly between the two systems, taking advantage of functionality that might exist in one system, but not in the other. There is concern over reducing the amount of data conversion that must take place (Hohl, 1998). But even within the GIS community interoperability is a major issue — how to ensure different GIS can talk to each other, share data, repeat analyses, provide comparable output? “Interoperability between computing infrastructures needs — much like every information exchange — a set of common rules and concepts that define a common understanding of the information and operations available in every coop-erating system” (Vckovski, 1999: p. 31). For those relying heavily on the remote sensing information as a primary input to the GIS, or requiring GIS information to analyze imagery, what features are needed to make the interface smooth? A common language and an instruction set providing seamless transfer of data would be a premium advantage. The current marketplace appears to be responding to this issue. Vckovski (1998) has gone further; users need to be provided with an environment in which they use a virtual data set. The system would feature trans-parent data access, web-based interoperable tools, geolibraries of objects and tools, adaptive query processing, and quick datum and projection changes. The key new development is a set of interfaces which provide data access methods. The virtual data set is not a standardized structure of physical data format, but a set of interfaces facilitating the ability to exchange and integrate information that is meaningful. Against this measure, current interoperability among GIS and image processing systems, and between the two, is practically zero. But increasingly, GIS functionality and image processing functionality are interchangeable; some key examples now exist where a GIS system has been used to interpret or process imagery in ways that just a few short years ago seemed exclusively the domain of proprietary image processing systems (Verbyla and Chang, 1997). Unsupervised classification, supervised classification, accuracy assessment, filtering and enhancements, removing noise — typically these functions were the reason to have an image processing system; now, all can be completed within a single GIS package without reference to a separate image processing system. Since the GIS typically has a large mandate within a resource management organization (Worboys, 1995; Burroughs and McDonnell, 1998; Goodchild, 1999), larger by far than the mandate enjoyed by most remote sensing, this trend might lead one to conclude that a separate image analysis system may be redundant in some situations. Since the systems are developing so quickly, with new functionality emerging almost overnight, the emphasis shifts to the GIS/remote sensing field personnel. A new position — a spatial data analyst — sometimes assumes greater responsibility and importance within the organization. One of the most valuable skills of any spatial data analyst is the ability to get something done that seemingly was not possible with the existing system. However, the complexity of some of the operations in remote sensing and GIS can be underestimated. Frustration can occur when analysts use a remote sensing image analysis system as if it were a GIS, or a GIS as if it were an image analysis system beyond the fairly simple processing mentioned above (classification or image enhancement). Typically, a GIS will contain many hundreds ©2001 CRC Press LLC ... - tailieumienphi.vn