DEVELOPMENT OF A METHODOLOGY FOR MONITORING CHANGES IN GHANAIAN FOREST RESERVES A Graduate Thesis Submitted In Partial Fulfilment of the Requirements For the Degree of Master of Science in Forestry Faculty of Forestry and the Forest Environment Lakehead University Ontario, Canada By Veronica Nana Ama Asa June 2000 ^318788 SD 644'€3 As I IktS'-tS wrirm Lakehead University OFFICE OF GRADUATE STUDIES AND RESEARCH Name of Student: Veronica Nana Ama Asare Title of Thesis: Development of a Methodology for Monitoring Changes In Ghanaian Forest Reserves Degree Awarded: M.Sc.F. This thesis has been prepared under my supervision and the Candidate has complied with =t)- /nO Date ABSTRACT Asare, V. N. A. 1999. Development of a Methodology for Monitoring Changes in the Ghanaian Forest Reserves. MSc. (Forestry) Thesis. Faculty of Forestry and the Forest Environment, Lakehead University, Thunder Bay, Ontario, Canada. 130 pp. Advisor: U.T. Runesson, PhD. Keywords: Ghana, remote sensing, geographic information system, global positioning system, change detection, forest reserves, deforestation, forest management, forest inventory The Ghanaian Forests are a significant component of the country’s development. Occasioned by the rapid population growth of the country, increasing phenomena such as shifting agriculture, logging, fuelwood harvesting and fire outbreaks have claimed over 70 % of the original forests. The reduction of forests has stimulated the development of management tools to control forest depletion. In order to focus the intervention of forest managers and environmental planners, the rate and impact of forest depletion must be monitored and well documented. Financial constraints and the lack of adequate maps have hindered the setting up of effective monitoring mechanisms. This study illustrated the feasibility for using Landsat data and GIS to map changes in the Ghanaian forest reserves. GIS was used to create the initial database for the study. Three image analysis change detection methods namely image algebra (image differencing), spectral temporal and spectral temporal principal component analysis were employed. The results of the analysis showed that spatial distributions of the changed areas produced by all three methods were similar, varying only in the extent. The remote sensing image analysis required the information stored in the GIS database for rectification and for the assessment of the classification procedure. A quantitative accuracy assessment was not possible for the procedures due to limited ground truthing. The use of GPS in field data collection was demonstrated by its use in delineating the boundary of a selected reserve. The GPS data was able to adequately display the reserve boundary, the spatial distribution of Taungya and farms along the boundary as well as relocated boundary pillars. All new layers of information generated from the research were displayed and stored in the GIS. Finally, the importance of the outlined procedures in the monitoring of Ghanaian forest and the limitations of the study were discussed. LIBRARY RIGHTS STATEMENT In presenting this thesis in partial fulfilment of the requirements for the degree of Master of Science in Forestry at Lakehead University at Thunder Bay, I agree that the University shall make it freely available for inspection. This university thesis is made available by my authority for the purposes of academic study and research and may not be copied or reproduced in whole or in part (except as permitted by the Copyright Laws) without my written authority. Signature_______________ Date iv A CAUTION TO THE READER This MSc.F. thesis has been through a semi-formal process of review and comment by at least two faculty members. It is made available for loan by the faculty for the purpose of advancing the practice of professional and scientific forestry. The reader should realize that opinions expressed in this document are the opinions and conclusions of the student and do not necessarily reflect the opinions of the supervisor, the faculty or the University. To the memory of my father: Mr. Ernest Edmund Asare. ACKNOWLEDGEMENTS My utmost thanks to the Almighty GOD for his enduring and divine guidance, protection and provision. I wish to express my profound gratitude to Dr. Ulf Runesson (Faculty of Forestry and the Forest Environment) my graduate advisor, my committee members, Dr. James Quashie-Sam (IRNR-UST, Ghana) and Mr. Francis Agurgo (Ghana Forest Service) and my external examiner Dr. Prah (Faculty of Geodetic Engineering UST, Ghana) for their guidance and review of this thesis. I am indebted to the Canadian International Development Agency (CIDA) for funding my graduate Study and this thesis. As well, the successful completion of this thesis is due to the contributions of the LU-CARIS computer facilities at Lakehead University. 1 also wish to extend my appreciation to Arnold Rudy and Bob Pickard for sharing their wealth of knowledge in GIS and Remote Sensing programs with me. I am also grateful to both the teaching and non-teaching staff of Lakehead University particularly, those from the Faculty of Forestry and the Forest Environment and the Office of Graduate Studies and Research for assisting me in one way or the other during the program. Further, my appreciation to all graduate students on the Ghana/CIDA program as well as my other course mates for their friendship. I acknowledge the contribution of the Ghana Forest Service (formerly the Forestry Department of Ghana) for providing satellite data and progress maps. My thanks are due to Mr. Abrokwa (Ghana Forest Service, Technical Assistant) and the Forest Guards assigned to the Tinte Bepo forest reserve for their assistance during field data collection. To Ms. Angie Albers (my Landlady) and my roommate Miss. Catherine Wakesho my heartfelt appreciation for giving me a home away from home. As well my ardent gratitude to the members of the Redwood Alliance Church particularly, Mrs. Miriam Lappala (who introduced me to the church), Ms. Ursula Danner, Mr. and Mrs. Reg and Marianne Jones for their prayer support and fellowship. Finally, my exceptional appreciation to my mother Mrs. Ernestina O. Asare and my siblings whose love, care, encouragement and support have been indispensable throughout my studies at Lakehead University. Veronica Nana Ama Asare. TABLE OF CONTENT ABSTRACT .............................................................................................................................U - ACKNOWLEDGEMENTS.......................................................................................................... VI TABLES ............................................................................................................................ IX. FIGURES .............................................................................................................................X-. 1.0 INTRODUCTION.................................................................................................................... 1.. 2.0 LITERATURE REVIEW..........................................................................................................S.. 2.1 FOREST RESERVATION.................................................................................................. .5.. 2.2 DEFORESTATION..............................................................................................................9.. 2.2.1 Causes o f Deforestation.............................................................................................1.1 2.2.1.1 Demand for Agricultural Land....................................................................................11 2.2.1.2 Fuelwood Harvesting.................................................................................................12 2.2.1.3. Logging/Timber Harvesting...................................................................................... 12 2.3.1.4. Forest Fires.............................................................................................................13 2.2.1.5 Mining and Quarrying for Minerals.......................................................................... 14 2.2.2 The Need to Control Deforestation............................................................................ 1.4 2.4 FOREST INVENTORY IN GHANA..................................................................................2.1 2.4.1 Stock Survey...............................................................................................................23 2. 4.2 Dynamic Survey........................................................................................................ 28 2.4.3 The National Inventory (Static inventory)..................................................................28 2. 5 REMOTE SENSING........................................................................................................ 3D 2.5.1 Satellite Remote Sensing........................................................................................... 34 2.5.1.1 Landsat.................................................................................................................... 35 2.5.1.2 SPOT................................ 38 2.5.1.3 Indian Remote Sensing............................................................................................ 41 2.6 GLOBAL POSITIONING SYSTEM..................................................................................43 2.7 GEOGRAPHIC INFORMATION SYSTEMS................................................................... .4.7 2.8 INTEGRATING REMOTE SENSING, GIS AND GPS.................................................... 49 2.9 DIGITAL CHANGE DETECTION.....................................................................................51 2.9.1 Pre-processing of Satellite Data................................................................................5.7 2.9.1.1 Radiometric Normalization of Multi-Date Images........................................................52 2.9.1.2 Geometric Correction (Image Rectification)............................................................... 53 2.9.2 Change Detection Algorithms.................................................................................... 55 2.9.3 Image Classification................................................................................................... 59 2.9.4 Accuracy Assessment................................................................................................ 6.0 3.0 STUDY AREA...................................................................................................................... .62 3.1 THE HIGH FOREST ZONE OF GHANA........................................................................ .62 3.1.1 Asukese Forest Reserve............................................................................................ 65 3.1.2 Bia Tano Forest Reserve........................................................................................... 67 3.1.3 Tinte Bepo Forest Reserve........................................................................................ 68 4.0 STUDY METHODOLOGY...................................................................................................22 4.1 DATASET......................................................................................................... .72 4.1.1 Landsat Data............................................................................................................ 73 4.1.2 GPS Field data collection (Field Survey)..............................................................7.4 4.2 DATA PROCESSING.....................................................................................................7.6 4.2.1 Arc/Info GIS Coverages.......................................................................................... 7.6 4.2.1.1 GPS Data Processing...............................................................................................78 4.2.2 Pre-processing of Landsat Data.............................................................................80 4.2.3 Change Detection Procedures................................................................................84 4.2.3.1 Image Classification................................................................................................. 85 5.0 RESULTS 87 5.1 ARC/INFO COVERAGES................................................................................................ .8.7 5.2 SATELLITE IMAGE PROCESSING................................................................................90 5.2.1 Image Pre-processing.............................................................................................9.0 5.2.2 Change Detection Procedures................................................................................91 6.0 DISCUSSION............................................. 1Q1 6.1 GEOGRAPHIC INFORMATION SYSTEM.................................................................... 102 6.2 GLOBAL POSITIONING SYSTEM................................................................................ .104 6.3 CHANGE DETECTION IMAGE ANALYSIS..................................................................106 6.4 STUDY LIMITATIONS.................................................................................................... .109 7.0 CONCLUSION AND RECOMMENDATIONS.............................................................. .1.11 7.1 CONCLUSION.................................................................................................................111 7.2 RECOMMENDATIONS...................................................................................................1.13 LITERATURE CITED.......................................................................................................... 115 APPENDICES .....................................................................................................................123 APPENDIX I COMPARTMENT INSPECTION FORM.................................................... 129 APPENDIX II SAMPLE STOCK SURVEY FIELD BOOK............................................... .130 APPENDIX III AML USED TO PROJECT TICS OF TOPOGRAPHIC MAPS FROM LATITUDE/LONGITUDE TO GHANA TRANSVERSE MERCATOR CORDINATE SYSTEM...............................................................................131 APPENDIX IV AN EXAMPLE OF RAW GPS DATA FILE............................................... .132 APPENDIX V AML USED TO PROJECT GPS DATA FROM LATITUDE/LONGITUDE TO GHANA TRANSVERSE MERCATOR CORDINATE SYSTEM................ .133 TABLES T a b le 2 .1 Sum m ary o f f o r e s t b e n e fits a t v a r io u s le v e ls . S o u rc e : S e g u ra e t a l . 1996 ....... 15 Ta b le 2. 2 La n d s a t M is s io n s .............................................................................................................................. 35 Ta b le 2. 3 C h arac ter ist ic s o f MSS d a ta . C o m p lie d from Ca m p b e ll 1996....................................37 Ta b le 2 .4 C h arac ter ist ic s of La n d s a t TM . C o m p lie d from Ca m p b e ll 1996................................37 Ta b le 2. 5 SPOT M is s io n s . C o m piled from Ca m p b e ll (1996) a n d SPOT Im a g e (1998)..........39 Ta b le 2 .6 C h ar ac ter ist ic s of S P O T im a g e s . C o m p lie d from Ca m p b e ll 1996.............................40 Ta b le 2 .7 IRS m issio n s C o m plied from C H A AR T (1998); FAS (1998) a n d TE LS A T (19 98 )....4 2 ix X FIGURES Figure 2 .1 A rrangement of stock Survey lines. Source : FD 1995.......................................................25 Figure 2. 2 Field A rrangement of the Survey T eam . Source : FD 1995.................................................27 Figure 2. 3 Typical Spectral reflectance of soil vegetation and w ater . So urce : Lillesan dan d Ke if e r 1994............................................................................................................................................31 F igure 2 .4 Remote S ensing Platforms . Source Barrett and Curtis 1992......................................... 33 F igure 3 .1 Ecological Zones of Ghana. Source : A tta-Q uayson , 1987................................................63 F igure 3. 2 Forest Types within the H igh Forest Z one .............................................................................. 64 Figure 3. 3 A sukese Forest Reserve ................................................................................................................65 F igure 3 .4 B ia Tano Forest Reserve ................................................................................................................68 Figure 3. 5 T inte Bepo Forest Reserve........................................................................................................... 69 F igure 5 .1 a Overlay of the boundaries of A sukese captured from Topographic and FD PROGRESS MAPS..................................................................................................................................... 88 Figure 5.1 b Overlay of the boundaries of B ia Tano captured from Topographic and FD PROGRESS MAPS..................................................................................................................................... 88 Figure 5 .1 c Overlay of the boundaries of T inte Bepo captured from the three data sources . ..................................................................................................................................................................89 Figure 5. 2 Rectified La n d s a tTM image of A sukese and Bia Ta n o .......................................................... 90 Figure 5. 3 Change C lasses expressed as percentage of total for Asukese Forest Reserve.. 92 Figure 5. 4 Change Classes expressed a s percentage of area for Bia Tano Forest Reserve... 93 F igure 5. 5 Change Classes expressed as percentage of area for T inte Bepo Forest Reserve .93 Figure 5. 6 Identification of T rends in Change Detection Methods......................................................95 Figure 5. 7 a Spectral Temporal Change map of A sukese ........................................................................ 95 Figure 5. 7 b Spectral Temporal PCA Change map of A sukese...............................................................96 F igure 5. 7 c Image D ifferencing Change map of A sukese Forest Re s e r v e ....................................... 96 Figure 5 .8 a Spectral T emporal Change map of B ia Tano Forest Reserve ........................................97 F igure 5. 8 b Spectral Temporal PCA Change map of Bia Tano Forest Reserve ..............................97 Figure 5.8 c Image D ifferencing Change map of B ia Tano Forest Reserve ......................................... 98 Figure 5 .9 a Spectral T emporal Change map of T inte Bepo Forest Reser ve ...................................98 Figure 5. 9 b Spectral Temporal PCA Change map of T inte Bepo Forest Reserve ......................... 99 F igu re 5. 9 c Image D iffe re n c in g Change map o f T in te Bepo F o re s t R eserve ................................... 99 1.0 INTRODUCTION Today's forestry is challenged as never before to meet diverse and conflicting demands on forestland and resources. Forests are a national heritage that must be protected in the interest of society; at the same time, they are a resource that must be utilized for the well being of that same society. In many instances, the latter function has dominated with little or no regard to the former. The result is a decline in forest resources. The Ghanaian Forests are a significant component of the country’s development. Besides ecological benefits, the forests contribute to the economic and social well being of the people. Ecologically, the forests protect the fragile tropical soils by preventing erosion and recycling nutrients. They also serve as a protective barrier against the dry northeast winds that blow over the country and maintains a suitable microclimate for agriculture particularly the production of the Ghana's most important crop; cocoa. Further, the forests protect watersheds and maintain biodiversity by providing a habitat for numerous plants and animals (Prah 1994). Economically the forest industry is the third foreign exchange earner after cocoa and minerals. For example in 1995 timber exports contributed 9 percent of the total external earnings of Ghana and 11 % of the Gross Domestic Products (GDP) (FAO 1997). The industry employs over 100 000 individuals, and provides a livelihood for well over 3 000 000 people. In recent times the forest sector serves as support for the growing tourist industry (WTO 1997). The social contribution of the 2 forests stems from the provision of the basic needs of the people in the form of non­ timber forest products (NTFP) and many other intangible benefits such as cultural symbols, ritual artifacts and sacred groves. Unfortunately, the exploitation of the numerous benefits from the forest has eroded the resource. Occasioned by the rapid population growth of the country, increasing phenomena such as shifting agriculture, logging, fuelwood harvesting mining and fire outbreaks have been claiming a great extent of the forests. It has been estimated that over 70 % of the original 8.22 million hectares of closed forest in Ghana have been destroyed (IIED 1992; Ntiamoa-Baidu 1992). The reduction of forests threatens ecological sustainability and socio-economic development. This realization coupled with increasing local and international outcry over environmental issues relating to forestry has stimulated the development of management tools to control forest depletion. In order to focus the intervention of forest managers and environmental planners, the rate and impact of forest depletion must be monitored and well documented. Such information is essential to support the implementation of appropriate policy responses to forest depletion. Further, monitoring improves the basis for understanding the mechanisms or events that create changes in the forest ecosystem. Previously, changes in the forest Ghanaian cover were not efficiently monitored and depletion rates were mere estimates or quotations from out-dated publications from 1960’s and 1970’s. However, the trend has changed and the issue of deforestation and forest management is receiving a great deal of attention. A major hindrance to setting effective monitoring mechanisms in place has been the prohibitive cost associated with conventional surveys. Financial constraints led to the abandoning of regular field surveys initiated in the sixties (Prah 1994). The need for a national inventory had long been expressed but was withheld due to similar constraints until the ODA provided financial assistance in 1985. From then until 1989 a total of 500,000 ha of reserves were sampled and inventoried using traditional survey methods (Francois 1989). Though valuable information relating to the status of Ghanaian forest was obtained the survey was time consuming and was not considered cost effective. The project was to continue to cover all forest reserves but once again it has been constrained (Flint and Hardcastle 1992). The challenges of the national inventory have urged the resource managers to explore new ways of handling large volumes of forest resource data. There is the need for development of a method that is efficient, cost effective and rapid for change detection, documentation and mapping of the forest environment. Much interest has been expressed in the possibility of using remote sensing and geographic information systems (GIS). In this regard various workshops and training sections have been organized. Remote sensing coupled with GIS play an important role in monitoring vast geographic areas. Remote sensing data are advantageous for characterizing land cover change because they are objective and spatially comprehensive. GIS is useful for describing, querying and displaying spatial patterns in vegetation cover. The integration of remote sensing image analysis and GIS to measure and monitor land cover change is therefore a logical and useful synthesis. Accordingly, this research targets the following objectives. 3 1. To evaluate the potential of image analysis with Landsat imagery to detect vegetation change in forest reserves over a period of time. 2. To map out the vegetation changes derived from Landsat image analysis in a GIS environment. 3. To propose an inventory system for on going monitoring of the Ghanaian forests. 4 5 2.0 LITERATURE REVIEW 2.1 FOREST RESERVATION Forestry in Ghana dates back to 1906 when the timber protection ordinance was passed to control the felling of "commercial tree species" (TEDB 1991; MLF 1994). Prior to this period, exploitation of the forest was largely restricted to subsistence agriculture. Only small portions of the High Forest Zone were cultivated at any given time. Farmland was allowed to revert to forest after two or three years of cropping. As such almost all the High Forest Zone was covered with mature forest. Products, such as Gum copal, rubber and kola, which constituted the first products to be traded in, were obtained primarily in the wild from the forests. The first export crop of significance to be cultivated in the zone was coffee. There was also an upsurge in the production of oil palm for export in the mid-nineteenth century (Hall and Swaine 1981). Yet all the foregoing, crops were insignificant compared to cocoa (Thebroma cacao) which was introduced in 1878. Upon its introduction, farmers in the eastern part of the zone eagerly adopted cocoa and by 1911 Ghana was the World's leading producer of cocoa; a position held until 1978. By 1900, the forests were rapidly being cleared for farming (especially cocoa farming). The alarming rate of forest destruction necessitated the formulation of legislation to reserve at least a portion of the forest (Hall and Swaine 1981). Accordingly, the Forestry Department was established in 1909. The main concern of the early foresters was the danger that the savanna would creep into the forest zone. This concern was based on the existence of little patches of savanna within the northern forest margin (Thompson 1910). A report by a British forester, H. N. Thompson (1910), stressed on the value of the forests as having significant influence on climatic conditions of the country and highlighted agricultural development as the main cause of deforestation. The need for governmental commitment and the enactment of forest legislation for the protection of the forests was also emphasized (Prah 1994). Consequently, a forest bill proposing the establishment of reserves was drafted in 1911. Opposition from local people who interpreted the move as a means for the Colonial Government to expropriate native land prevented the bill from becoming a law (Prah 1994). In Ghana, a system of multiple rights to land exists, whereby, land is owned by entire communities. Chiefs (stools) hold the land in trust, for the benefit of subjects. The stool could lease the land to farmers who paid taxes expressed as a share of produce from the land. Subjects of the stool had the right to hunt game, fish in rivers, pan rivers for gold and collect any other forest products not cultivated by the tenant (farmer) (Amanor 1996). It was the protection of these rights that prompted the local people's initial objection to forest reservation. However, between 1909 and 1914, the Forestry Department toured the forest zone, improving knowledge of the flora and locating areas for eventual reservation until the First World War caused the closure of the department. After the war, the local authorities were persuaded to pass bylaws to reserve part of their forests voluntarily. However, the formation of reserves under the native bylaws was made slowly and as at 1923 only 260 km2 had been reserved. Eventually, a forest 7 ordinance was passed in 1927 giving the government power to order reservation where local people continued to resist. A target of 15 500 km2 of reserves was set and by the beginning of the Second World War, in the face of much opposition, this target had virtually been achieved (Hall and Swaine 1981). The main values of the forest were defined as shelterbelts against the Savanna winds, protection of watersheds, control of local climate and landscape stability (Hawthorne and Abu-Juam 1995). These values are depicted in the orientation, distribution and some of the small sizes of the reserves (Danso 1996). Based on figures in the 1980's the proportion of each forest type that is reserved is Wet Evergreen 29 %, Moist Evergreen 31 %, Upland Evergreen 100 %, Moist Semi-deciduous 20 %, Dry Semi-deciduous 17 %, Southern Marginal 4 % and South-east Outlier 29 % (IUCN 1988). Reservation did not alter ownership of forestland except in isolated instances where ownership disputes forced Government to purchase rights. The 1927 ordinance allowed two options for management of the reserves. Management could be either by the owners under the direction of Forestry Department or by the Forestry Department (on the Government's behalf) for the benefit of the owners (FAO 1986). In practice, the latter option was peremptory. The Forestry Department allowed the land-owning communities access to forest produce for domestic purposes only. Entry for commercial purposes requires the consent of the department (IIED 1993). The boundaries of reserves were clearly demarcated (FAO 1985) and cocoa farms existing within the boundary at the time of demarcation, were allowed to persist (Hall and Swaine 1981). Such farms are called admitted farms. 8 Planned forest management within the forest reserves of Ghana became operational after the adoption of the first forest policy in 1948 (Prah 1994; MLNR 1996). The reserves were categorized into working circles (WC) over which different management objectives were pursued. The working circles are: • the productive WC which occupied 73 % of the total area reserved and is to be managed for the sustainable yield of timber; • the protection WC occupying 27 % of reserves and managed solely for environmental purposes and; • the research WC, which occupies an insignificant portion of reserves and managed for scientific research. Implementation of sound management was restricted by lack of inventory data. Up to 1956, most of the productive forest was covered by 2.5 - 5 percent stratified random enumeration surveys. The survey, like management at the time was commercially oriented (Prah 1994) concentrating on the volume of marketable timber. The composition of Ghana's tropical forests is typical of its kind, a heterogeneous mixture of numerous species. Unfortunately exploitation is heavily biased towards very few species (Brookman-Amissah 1981). The species were grouped into four classes depending on their economic value at the time. Various silvicultural systems, involving various regimes of canopy manipulations were tried to induce natural regeneration and enhance the growth of the preferred species. However, the silvicultural systems induced the regeneration of less desirable, light demanding and fast growing species (Brookman-Amissah 1981; FAO 1985; Prah 1994). The silvicultural systems practiced included Tropical Shelterwood System 9 (TSS), Enrichment planting, Modified Selection System (MSS) and Girth Limit System. Prah (1994) gives details of the various silvicultural systems. Concerns about the ability of the natural forest to meet the country’s growing demand of wood in the long term also led to the adoption of the Taungya System from Southeast Asia. The system was basically an agroforestry approach where food crops were cultivated in alleys of tree crops until canopy closure. Of the estimated 500 km2 of plantations that were established under the system only 105 km2 (33 %) were considered successful. Teak was the most successful species cultivated. Indigenous hardwoods species were widely planted but with very low success (Prah 1994). As a result of the successive failure of silvicultural systems, permanent sample plots were initiated throughout the forest zone in 1969 to obtain information about growth and silvicultural measures necessary for satisfactory growth of the forest (Prah 1994). Deforestation is the clearance and conversion of forestland to other usage (Whitmore and Sayer 1992). From pre-agriculture to the present, scientific evidence suggests that the world's forest area has declined by one-fifth, from about 5 to 4 billion hectares (WRI 1990). Historically, the forests have diminished as pressures have been placed on them by expanding human populations. The need for land for non-forest purposes and demands on the timber resource have combined to reduce the forest resource. A countervailing force in favour of the forest, however, has been the recognition of the importance of the forest for environmental protection. As a result, in some places, especially in the temperate regions, marginal agriculture has 2.2 DEFORESTATION ceased and the forest has been reintroduced, through both natural and artificial processes (Sedjo and Lyon 1990; WR11991). Forest management still poses acute policy issues, as industrialists, loggers, naturalists, hikers and hunters urge their conflicting interests, but in much of the temperate world, the forest area is stable (Repetto 1988; Sedjo and Lyon 1990). In contrast, in the tropical regions of the world, the forest area continues to diminish (Repetto 1988; Sedjo and Lyon 1990; Johnson 1991; WRI 1991). Despite increased technology to assess the rate of tropical deforestation, figures are extremely variable and much debated. The most cited figures are those of the UN Food and Agriculture Organization (FAO) that initially specified the rate of Tropical deforestation at 11 million hectares per year; 7.5 and 3.8 million hectares of closed and open forests respectively (FAO 1981). Recent remote sensing revealed that these figures were underestimated. Hence, in 1990 the FAO released a new estimate of 17 million hectares per year whilst the World Research Institute (WRI) estimated 20 million hectares. Large as they are, these figures denote only the areas converted to other land uses. However, tropical forests are also deteriorating in quality. Each year, over four million hectares of virgin forests are logged becoming secondary forests (Melillo et al. 1985). Meyers (1980) summed the issue as: $ * ( The tropical forests are undergoing conversion - including disruption, I degradation, impoverishment, depletion and outright destruction, i.e. forms of conversion that range from marginal modification to fundamental transformation. The situation is no different in the tropical forest of Ghana. Annual reports of the Forestry Department (FD) indicate that deforestation, which began about a century ago, has been accelerating. In Ghana deforestation is not efficiently 10 monitored and current rates are best estimates (MED 1992). Generally, it has been estimated that over 70 % of the original 8.22 million hectares of closed forest in Ghana have been destroyed (NED 1992; Ntiamoa-Baidu 1992). More specifically, between 1937/38 and 1980/81 the area of the high forest reduced by 64 %. In the 1980's the annual rate of deforestation was estimated at 2 % (World Bank 1988). According to current estimates for 1990 - 95 this rate has been reduced to 1.3 % (FAO 1997), but this should be viewed in the light that only thirty percent of the resource remains. 2.2.1 Causes of Deforestation Deforestation is prompted by population growth. In 1993 it was estimated that Ghana's 17 million population is rapidly increasing at a rate of 3.12. This rapid increase provides the impetus for the causes of deforestation, which include: increasing demand for agricultural land, fuelwood harvesting, logging, forest fires and mining. 2.2.1.1 Demand for Agricultural Land Agriculture is the pre-eminent economic activity in Ghana. It supports 70 % of the population, occupies 65 % of the available land base, and contributes more than 50 % of the national revenue (Allotey 1988). The method of cultivation is the shifting agriculture or bush fallow and involves slashing and burning of forestland. Under the system long fallow periods allow the restoration of soil and vegetation cover to develop. Nonetheless, increase in demand associated with population growth, does not permit the long fallow periods necessary for forests to regenerate. Increased cash cropping has compounded the demand for subsistent agriculture (MLNR 1996). 11 Further, abetted by government policy that allowed the conversion of unreserved forestland, shifting cultivation has left very little forests outside forest reserves (Prah 1994; TEDB 1991). According to estimates in the annual reports of the FD, the proportion of forest outside reserves declined from 20 % in 1955 to 5 % in 1972. The most devastating aspect of shifting cultivation is the use of fire. During the dry season, fires occur in both the savanna and forests, many due to uncontrolled farm fires. Presently the demand for agricultural land is leading to encroachments in the forest reserves (IUCN 1988). 2.2.1.2 Fuelwood Harvesting The prime source of energy in Ghana is from biomass, in the form of fuelwood and charcoal, which accounts for more than 80 % of the country's total energy consumption. In rural communities, where population is concentrated dependency on fuelwood exceeds 95 %. The average annual consumption is about 700,000 tones. However, most of this volume is derived from trees felled in the savanna zone and from logging residues (MLNR 1996). Actual contribution of the use of wood for fuel to deforestation has not been investigated. However, it is widely believed that the effect is concentrated in the transition and savanna zones and the impact on the high forest zone is minimal (MLNR 1996). 2.2.1.3. Logging/Timber Harvesting Excessive logging has been an important contributor to deforestation. Initially, timber was extracted from unreserved forests. Logging preceded the conversion of these forests to agricultural land. As the forests outside reserves kept diminishing the bulk of the timber supplies from the reserves has increased. Timber extracted 12 13 from the reserves has risen steadily from 13 % of total production in 1958, to 51 % in 1971, 58 % in 1973, 70 % in 1974, and 78 % in 1975 (Brookman-Ammisah 1981). Annual reports of the Forest Product Inspection Bureau (FPIB) indicate that these figures increased further to 82 % and 81 % in 1990 and 1991 respectively. After which period measures such as restriction of log export (MLNR 1996) and the withdrawal of degraded forest reserves from harvesting (Prah 1994) were taken to reverse the situation. However, these figures relate only to data officially recorded by the FD and FPIB. By its' nature, illegal timber harvesting is difficult to determine. It includes extraction by authorized concessionaires who fell trees beyond yield specified by the FD and other operators who harvest trees without felling permits (MLNR 1996). Besides, logging damages the residual forest. A survey indicated that 10 % of established trees were damaged at an extraction rate of two trees per hectare (IUCN 1988). Further, the loading area and roads suffer particularly from soil erosion and The increased battering of the forests would have had milder consequences had it not been for fires that run more readily through disturbed forest patches. It is well appreciated that fire cannot easily penetrate the intact closed forest due to the moisture retention capacity of the vegetation (Foggie 1962). Fires occur annually in the dry season usually from November to May. Although some fires start from natural causes many result from activities of farmers, hunters and palm wine tappers. These fires which were previously a mild, peripheral poor regeneration (Hawthorne and Abu-Juam 1995). 2.3.1.4. Forest Fires 14 and occasional threat have escalated due to the degraded nature of forests resulting from excessive logging (Hawthorne 1994; MLNR 1996). Fire has a negative effect on forest regeneration contributing significantly to deforestation (MLNR 1996). Hawthorne, (1994) discussed the influence of fire on forest regeneration in Ghana. Burnt forests are dominated by weeds such as Chromolaena odorata (acheampong) (Danso 1996) and are more prone to burn in future (IIED 1993; MLNR 1996). Records indicate that, only 20 % of the High Forest Zone is covered by forest that has not experienced fires regularly (MLNR 1996). Currently, fire is the greatest threat to the long-term survival of the semideciduous forest, which constitute about 50 % of the forest area in Ghana (Ghartey 1989; Hawthorne 1991; MLNR 1996). 2.2.1.5 Mining and Quarrying for Minerals Mining and quarrying for gold and diamonds, especially by the small-scale operators and large-scale mining for bauxite and manganese pose serious threats to forest in the High Forest Zone (MLNR 1996). Whilst underground gold mining uses substantial amounts of wood for pit props; surface mining operations remove forest biomass and soils (Hawthorne and Abu-Juam 1995). 2.2.2 The Need to Control Deforestation The impacts of forest depletion occur along multiple dimensions mirroring the many values of the forests. A well-managed forest is a constantly self-renewing resource producing many benefits (Poore and Sayer 1991). These benefits range from ecological through social to economic. Further, their influence extends from local to the global environment. Table 2.1 provides a summary of the benefits: 15 The harmful aspects of tropical deforestation have been documented at length (Barney 1980; Myers 1980, 1984; Kummer 1992). Negative effects of deforestation (like forest values) can be separated into three broad scales: macro (global), meso (national and regional), and micro (local) (Kummer 1992). Table 2 .1 Summary of forest benefits at various levels. Source: Segura e ta l. 1996. Benefits from the Forest Global National Local Maintenance of biological diversity X X X Climatic change X X Microclimate regulation X Maintenance of hydrological cycle X X X Soil and water quality conservation X X Wind and noise control X Wood products X Non-wood products X Natural scenery X X X Recreation and ecotourism X X Cultural and Religious services X X X The macro effect of reduced tropical genetic diversity due to deforestation is probably the most important and has received the most attention in the literature (Barney 1980; Myers 1980, 1984; Repetto 1992; WRI 1991). According to Myers (1984), the value of the tropical forests springs from their biological diversity. They constitute some of the world's oldest and richest ecosystems, containing more than fifty percent of all species of plants and animals on some six percent of the world's surface area (Poore and Sayer 1991; WR11991). Some of these species have made important contributions through their genetic resources to modern agriculture, medicine and industry. Genetic material from tropical forests have been used by plant breeders to produce disease and pest resistance in crops such as coffee, cocoa, bananas pineapples, maize and rice. Pyrethrins, rotenoids, and other insecticides have evolved in tropical plants and insect predators and parasites found in tropical forests control at least 250 different agricultural pests (Myers 1984). Tropical plants are dominant in 80 percent of the world's health care. They have been used in the manufacturing of drugs for malaria, leukaemia, amoebic dysentery, hypertension etc. Current uses represent a minor fraction of the potential benefits as only a tiny fraction of these species has been investigated (Repetto 1988). Wilson (1992) stated that Tropical forests are a potential source of new wealth and scientific knowledge with unused plant reservoirs for food crops, pharmaceuticals, fiber, petroleum substitutes, and other products. Given the rapid rate of deforestation, the loss of species diversity has been estimated as three extinctions per day. At this rate, both current and potential benefits are being lost. Cures to AIDS and cancer may be lost before they are discovered (Johnson 1991; WR11990). Genetic material vital to crop stability and food security is also disappearing. Besides the macroscale effect, reduced biological diversity due to deforestation has both mesoscale and microscale aftermath. The forest ecosystem provides a habitat for plants and animals that sustain local and indigenous population who rely greatly on this high level of diversity for minor forest products such as rattan, resins, gum, game, medicine and a vast array of naturally occurring foods (Johnson 1991; Kummer 1992). In Ghana, these minor products termed non­ timber forest products (NTFP) feature prominently in the lives of the rural communities. Besides obtaining their basic needs, the rural people are sustained by the trade in NTFP. Important among these commodities includes bush meat (game), mushrooms, chewing sticks, food wrappers, traditional medicine and building poles 16 17 (Danso 1996). In places where forest cover have dwindled and reserves have became severely degraded few if any of these benefits remain. Because rural peoples' exploitation of the forest resource is largely confined to NTFPs many equate their fate to the rehabilitation of these forests. This dependence is vividly expressed in the lament of an old woman: The reserves have changed significantly because of excessive logging and bush fires...much of the game have disappeared... there are fewer mushrooms, pestles, building poles and medicine and there is more sickness now (CFM 1995). Besides, export earnings from NTFPs are rising steadily and constitute a new area of employment for Ghanaians. Forest clearing also leads to marked changes in climatic conditions at all scales. These climatic changes are brought about through effects on components of radiation and water budgets (Mather and Sdasyuk 1991). Forests influence the composition and heat retaining capacity of the atmosphere and the heat and water exchange characteristics of the earth's surface. Dwindling forest cover therefore results in instability in hydrological regimes (POORE AND SAYER 1991). A major hypothesis links deforestation to increase surface reflectance (albedo) and hence to surface temperature changes that could influence precipitation (Myers 1988). In Ghana, there have been recording of more erratic rainfalls and droughts as the area under closed forest has diminished (TEDB 1991). The Ghanaian forests also serve as a protective barrier against the dry Northeast (Harmattan) winds that blow over the country between November and February maintaining a moist atmosphere for agriculture especially the production of cocoa the country’s most important commercial crop (Prah 1994). On the macroscale, tropical forest destruction is contributing to increased atmospheric carbon dioxide levels. Biomass of the earth's various ecosystems act as reservoirs for carbon. The earth’s forest stores 450 billion metric tons of carbon, which is 2 0 - 100 times, more carbon per unit area than croplands (WRI 1989). Not only the capacity to withhold carbon from the atmosphere is lost when forests are cleared; the stored carbon oxidizes and is released. For example the burning that follows most forest clearing in the tropics converts some of the stored carbon in vegetation into C 0 2. As well, the decay of the remaining vegetation and the decline in soil organic matter adds additional C 02 to the atmosphere contributing to global warming (Myers 1988). Deforestation is second only to the burning of fuels as a source of atmospheric carbon dioxide. Current estimates of such emissions range from 10 to 30 percent of the global annual carbon dioxide increase (Johnson 1991). At the mesoscale and microscale, other effects of deforestation which, have received attention are large-scale soil erosion, land degradation, and flooding (Kummer 1992). Forests protect watersheds and ensure adequate quality and flow of fresh water. The multi-storied structure of the tropical forests with its vast amount of foliage helps break the impact of tropical downpour on the soil. This allows rainfall upon reaching the ground to percolate steadily into the soil or run off into streams and rivers at a gradual rate (Myers 1985). Conversely, the removal of forest cover (especially on sloping land) leads to soil erosion, increased runoff, and sedimentation that may increase downstream flooding during the rainy season or decreased stream flow in the dry season (Barney 1980; Myers 1980). In addition, erosion leads to the removal of the thin upper layers of soil and reduces the organic matter content and the potential for regeneration (Korem 1985; Zaimeche 1994). 18 19 Moreover, many tropical rainforest ecosystems survive on poor soils by quickly recycling the nutrients leached from dead leaves, plants and other organic matter before they can accumulate and decay in the top layer of the soil as in most temperate forests. Other vegetation such as agricultural crops is unable to duplicate this rapid and complex recycling ability of the rainforest. Consequently, the removal of the rainforest is accompanied by soil degradation through erosion and laterization or other processes (WR11991). The issue of soil degradation is very important in Ghana. Ghanaian soils are susceptible to all forms of erosion. It has been observed that most soil nutrients are found in the topsoil (15 - 20 cm depth) and that the organic matter and plant nutrient content decreases sharply just below the topsoil. These soils are fragile and light textured and erode readily when devoid of vegetation. High incidence of erosion due to the removal of vegetation cover has been reported in some areas of Ghana (Asare 1992). In addition, on the mesoscale and microscale, sustainable economic opportunities from timber are lost as potentially productive forest is destroyed (Kummer 1991). Indeed the economic development of many nations has been closely linked to the forest through the timber industry. In many places forests are still valued in terms of usable timber (Johnson 1991). Similarly the timber industry in Ghana plays a significant role in the nation's economic development. It is the third most important foreign exchange earner after cocoa and minerals. In 1995 timber exports contributed 9 percent of the total external earnings of Ghana and 11 % of the Gross Domestic Products (GDP) (FAO 1997). The industry employs over 100 000 individuals, and provides a livelihood for over 3 000 000 people. In a country where 20 the level of unemployment is as high as 20 percent, the industry's ability to maintain such a level of employment is very significant (ACWP 1997). Yet still another microscale effect is the loss of fuelwood. In the dry tropics this may be the immediate harmful effect of deforestation as it is the largest form of forest drain in such areas (FAO 1987). In Africa, deforestation has already meant large increases in time spent on fuelwood gathering (particularly by women). In Ghana, the prime source of energy is fuelwood and charcoal and in rural communities, where population is concentrated dependency on fuelwood exceeds 95 % (MLNR 1996). Additional mesoscale and microscale effects include the loss of amenity and recreational resources as well as loss of cultural heritage. Forests enhance the scenic quality of the environment and provide opportunities for outdoor recreation for local residents and foreign visitors. As such, they serve as support for the development of tourism (Poore and Sayer 1991). In Ghana the tropical rainforests have been developed into nature parks for the ecology-minded tourist. Unique m I* ecosystems where colonies of monkeys live in symbiotic relationship with the local ^ human community as in the nature sanctuary at Buabeng-Fiema village in Brong Ahafo provides a great source of tourist attraction. Other rainforest related tourist attractions include the national parks at Kakum and the Ankasa Forest where the forests' nature trails provide a great way to view numerous birds and butterflies. Presently, tourism is the fastest growing sector of the Ghanaian economy. In the decade of 1985 -1995, earnings from tourism grew from US $ 20 million to US $ 237 million, representing 3.5 per cent of GDP (WTO 1997). Finally, Forests are part of the cultural heritage of the countries in which they occur. They contribute to the folklore and traditions of the people (Poore and Sayer 1991). In the stance of Ghana, the forests provide many intangible benefits such as cultural symbols, ritual artifacts and sacred groves. The sacred groves, which serve as burial grounds and sites for a variety of religious purposes often profoundly, influence local culture (Prah 1994). Evidently, forests have an undisputed and vital role in sustaining the natural and human environment (FAO 1985). Consequently its depletion has adverse effects on all aspects of human existence. The recognition of this fact has initiated mechanisms and management practices to promote the sustainable yield of forest values. 2.4 FOREST INVENTORY IN GHANA Forest inventory is a systematic procedure for collecting mensurational data on forest ecosystem, data processing and analysis, and summary presentation of the data by classes. In this sense, the forest inventory is defined as both the method of estimating the forest data and the estimates (the inventory) themselves (Cunia 1981). To manage a nation's forest resources adequately, the forest manager must know where the natural resource occurs, their condition and their rate of change over time due to growth, mortality, or drain by harvesting in order to balance the many demands on the resource for optimum utilization. In this scheme of best use, inventory data are needed at all levels of management. The specific needs of management dictate the type of inventory utilized. The inventory systems can be classified as operational (stand), management, or national (regional) (Cunia 1981; FAO 1981a). 21 Operational or stand inventory is intensive and primarily designed to estimate the current values of the forest biomass. While estimates of forest growth may sometimes be of interest, they are seldom of primary concern. The data obtained from such inventory are used for specific purposes or for short-term planning such as timber sales, harvesting operations, planning silvicultural treatments or assessing the damage caused by epidemic diseases or insect, fires or windstorms (Cunia 1981; FAO 1981a). The management inventory is designed to ensure a continuous flow of information about the general conditions of the forest resources (Cunia 1981). The inventory estimates apply to relatively large areas such as entire management units (Cunia 1981; FAO 1981a). The results are expressed as general statements about such factors as species composition, tree diameter distribution, average site quality and past trends in forest conditions. Management inventory data are primarily used for medium- and long-range planning. They are used to calculate the allowable cut; to schedule logging operations (time and space); to plan for production increases; to make stand projections; or, to identify areas of applied research. In addition, the inventory system provides a means to monitor past stand projections. It provides an indication as to whether the consequences of the management decisions are as predicted (Cunia 1981). The regional or national forest inventory is the most extensive, covering very large forest areas, such as an entire country or suitably defined geographic, economic, or political regions of the country. The main objectives are generally similar to those of the management inventory since estimates of both current values 22 and rates of change of forest resources are normally required. However, the resource data collected are much more general in nature and they are used to address issues of national concern such as defining a national forest policy, expressing this policy as a set of laws and national programs, and creating the necessary organizational structure to carry out these programs. In Ghana, the management of the permanent forest estates (forest reserves) has for a long time employed various field surveys to determine the volumes of timber as well as the growing stocks in individual reserves. The different types of inventory systems namely, operational, management and national are locally termed, stock survey, dynamic inventory and static inventory respectively. 2.4.1 Stock Survey The stock survey provides quantitative information, which is used to determine whether or not a compartment within a reserve can be harvested. The survey provides information for the calculation of yield and identification of specific trees to be removed during harvesting. The process of stock survey and yield allocation for a standard 128-hectare compartment is costly and time consuming and may take up to a year. In order to avoid the expense of time and money on a compartment that may not be eligible for harvesting a pre-survey compartment inspection is carried out prior to the stock survey (FD 1995). Compartments within forest reserves are demarcated based on written schedules. The initial step of planning a harvest is to produce a compartment map at a scale of 1:10 000 from the written schedules. The compartment map becomes the 23 basis for the logging plan and is updated with information from both the pre-survey compartment inspection and stock survey (FD 1995). The pre-survey compartment inspection is planned on the map. Access to the compartment is chosen and the areas to be inspected are sketched on the map before fieldwork begins. The inspection is conducted in three separate locations within the compartment covering one fifth of the total area of the compartment (FD 1995). During the inspection process, information regarding topography, stocking of Class I species, forest canopy and understorey conditions are recorded on the Compartment Inspection Form (appendix I). On the compartment map features to be considered during logging are marked. Such features include among others riparian areas, swamps, road and skid trail location and suitable sites for log yards (FD 1995). Once the compartment Inspection has been completed, a decision is made whether or not to proceed with the stock survey. If a substantial portion of the forest has been degraded or topography is such that much of the forest cannot be logged, a stock survey is generally not carried out. On the other hand, if the compartment is found to be suitable for harvesting the stock survey operation proceeds. The total cost of the survey is borne by the concession holders (FD 1995). A team of 15 specialists, whose competence in the survey procedure is tested periodically, conducts the stock survey. The team consisting of a Technical Officer (TO), two forest guards and twelve laborers is able to complete a survey of a standard compartment (128 hectares) within 20 days (FD 1995). The survey is planned on the compartment map. The longest compartment boundary is selected as a base line and labeled "A" and the opposite boundary labeled "B" on the map. Strip lines numbered sequentially are marked along the length of the compartment running from "A" to "B" at thirty-meter intervals. Every odd numbered strip line is labeled as a survey line and even numbered strips labeled flank lines. Wooden posts to be erected at the beginning and end of each strip line are marked and numbered sequentially with a suffix "A" or "B" depending on which boundary it is located. Figure 2.1 shows the layout of the stock survey lines. 25 Figure 2 .1 . Arrangement of stock Survey lines. Source: FD 1995. On the field the survey commences with re-demarcation of the compartment boundary. The boundary is cleared of all vegetation to a width of two meters; any missing compartment pillars are replaced. Demarcation of strip lines begins from a comer of the compartment along the "A" boundary. The lines are cut parallel on a continuously monitored compass bearing and are also cleared of vegetation to a width of two meters. The stock survey team is allocated tasks as follows: • The TO supervises all activities and records all the necessary information in the field book (appendix II). • The forest guards (technical assistants) act as "sweepers" moving between the flank and survey lines and assist the TO by checking the accuracy of tree identification and measurements. They also ensure that all tree measurements are recorded. • Two “tree spotters”, identify tree species, measure (non-buttress trees) and inscribe stock survey numbers on the trees. • Hypsometer or tangent stick man and assistant measure the diameter of buttress trees. • Two "chain men" measure the survey line. • Two laborers clean (weed) the survey lines. • Four laborers clean the boundary line. The team is arranged on the field as in figure 2.2. Two strips on either side of the survey line are enumerated at a time to make up an enumeration drift of sixty meters wide. Information on all FIP class 1 species with a diameter of fifty centimeters or more is recorded. For each tree the subsequent information are recorded. • The distance along the survey strip. • The perpendicular distance from the survey line. 26 27 A species code. The diameter at breast height (dbh). A serial number termed the stock survey number. CP Tree spotter 30 meters 30 meters (2B -> Tree spotter Chain man I Team leader Tree spotter Tree spotter Technical Assistants Tangent stick man and assistant I Chain man l Technical Assistants CP Compartment Boundary — Survey Line — Flank Line CP Compartment Pillar ( ^ ) Survey line Marker Figure 2. 2 Field Arrangement of the Survey Team. Source: FD 1995. Further, site information and forest condition scores are also recorded. The site information include, ground slope recorded at every sixty meters along the survey line, rivers and streams, roads and skid tracks, swamps, farms, rocky outcrops and evidence of damage caused by fires. A forest condition score based on a visual assessment of the area in the immediate vicinity of the observer (approximately twenty-meter radius) is also recorded at sixty-meter intervals along the survey strip (FD 1995). Upon completion of the stock survey a 10 % check survey is conducted (by a different team) to ascertain the accuracy of the stock survey. The check survey for a standard 128-hectare compartment consists of complete re-measurement of four enumeration drifts. It normally lasts two days. 2. 4.2 Dynamic Survey In order to monitor the growth and dynamics of the forest continuously, Permanent Sample Plots (PSPs) have been established in some of the productive forest reserves since the 1960s. These plots were covered by a 2.5 - 5 % stratified random enumeration every five years. The intent was to obtain data on growth and mortality of the forest as well as a means of monitoring the effects of harvesting on the sustainability of the forest (Prah 1994). Initially only few commercially desirable tree species were measured. However, to reflect the increased use of species and the dynamics of the forest in more detail this method of selecting trees was abandoned in 1985 and all trees greater than 10 cm dbh are measured (Blackett 1989). All the measured trees are marked and numbered to enable comparative measurements during subsequent inventories. Presently, a total of 600 PSPs has been established: ten in each Forest Management Unit (FMU) within the High Forest Zone (Prah 1994). Data from the dynamic surveys are used to compute growth models and improve the allocation of yield. 2.4.3 The National Inventory (Static inventory) The need for national growing stock estimates of the Ghanaian forests became paramount after the temporary shortening of the felling cycle in 1970's (Francois 1989). The ever-increasing pressure on the forest by the Timber industry 28 exacerbated this need and gave rise to the fear that demand was outstripping supply (Blackett 1989). Unfortunately, a national inventory could not be undertaken due to financial constraints until some assistance was forthcoming under an FAO/UNDP project in the Subri Forest Reserve area in 1980. Although this project inventoried just about 150 000 ha of forest area, the project initiated the formation of a forest inventory unit within the Forestry Department (Francois 1989). The national inventory itself commenced in 1985 with financial assistance from the Overseas Development Administration (Francois 1989). The national inventory, which was referred to as the Forest Inventory Project (FIP), was carried out in temporary sample plots obtained through a 0.25 % sample of reserves (Blackett 1989; Prah 1994). These reserves were selected by a stratified systematic sampling method. The stratification was based on the major ecological zones of the High Forest; the total area sampled being directly proportional to the reserved area within the zone (Blackett 1989). To achieve the 0.25 % sampling intensity one-hectare plots were laid out at intersections of a 2 x 2 kilometer grid. The grid was then randomly superimposed on maps of the forest reserves to select plot location. On the field, the plots were established using compass and chain survey from boundary pillars or other geographic features (Blackett 1989). All trees above 30-cm dbh in each sample plot were measured (Blackett 1989; Prah 1994). In all 1332 plots were enumerated covering an area of 546 600 hectares within 43 reserves (Blackett 1989). The FIP as it was conducted was time involving and costly. It took about 4480 man months to complete the fieldwork and the total costs were 1.1 million pounds sterling from the ODA and 59 million cedis from the Government of Ghana. The 29 project was to continue to cover the entire reserves but once again it has been constrained. The project revealed that though the reserve boundaries have stood to the onslaught of time the forest resource within them have significantly eroded. 2. 5 REMOTE SENSING Remote sensing is the art and science of obtaining information about an object, area or a phenomenon from a distance (Fischer etal. 1976; Aronoff 1989; Lillesand and Kiefer 1994). According to Aronoff (1989), the science of remote sensing provides the instrument and theory to understand how objects can be detected whilst the art of remote sensing is the development and use of analysis techniques to generate useful information. Remote sensors are made up of detectors that obtain information about natural phenomena by measuring electromagnetic radiation (EMR). The EMR covers a broad range of wavelength, travels at the speed of light (3 x 108 m s '1) and interacts with objects upon reaching them (Aronoff 1889; Jensen 1996; Wilkie and Finn 1996). Interaction of energy and matter is object specific and therefore variations in the amount and wavelength of detected EMR give objects or phenomena distinctive spectral signature and makes it possible to distinguish between different features (Aronoff 1889; Jensen 1996; Wilkie and Finn 1996). Figure 2.3 exemplifies the reflectance characteristics of green vegetation, soil, and water in the visible and near infrared wavelengths of the electromagnetic spectrum. Remotely sensed data are obtained using either passive or active remote sensing systems. Passive sensors record naturally occurring EMR (primarily solar radiation) that is reflected or emitted from terrain features (Jensen 1996; Wilkie and 30 Finn 1996). Two categories of passive sensors can be identified namely photographic and non-photographic. Photographic systems operate in the visible and near infrared portions of the spectrum (0.36|j.m - 0.9^m) whereas non-photographic sensors can operate from the range of X-ray to radio wavelengths (Barrett and Curtis 1992). 31 Wavelength (nm) Figure 2. 3 Typical Spectral reflectance of soil vegetation and water. Source: Lillesand and Keifer 1994. Active sensors on the other hand, bathe terrain with their own source of EMR and record the amount of radiation flux returning to the sensor system. Since such radiation are generated under relatively controlled conditions, much can be obtained about the way in which they are affected by features in the environment (Jensen 1996). Radio Direction and Ranging (Radar) is a common example of an active system exploiting EMR. A major prerequisite for understanding both the practical and conceptual aspects of remote sensing is the knowledge of image resolution. In broad terms image resolution is the ability of the remote sensing system to record and display fine detail (Campbell 1996). Specifically four types of resolution can be distinguished: spectral, spatial, radiometric and temporal. Spectral resolution refers to the dimension and number of wavelength intervals in the electromagnetic spectrum to which a sensor is sensitive (Simonett 1983; Jensen 1996; Wilkie and Finn 1996). Spatial resolution is the size of the ground patch resolved by the sensor described either as an angular or a distance measure (Erdas 1997). Radiometric resolution defines the sensitivity of the sensor to differences in the intensity of radiant flux reflected or emitted from the terrain, object or phenomenon of interest (Jensen 1996; Wilkie and Finn 1996). That is the ability of the sensor system to record different levels of brightness values (Campbell 1996). Usually, this is referred to as the number of bits the recorded energy is divided into (Erdas 1997). Finally, temporal resolution indicates how often a sensor obtains imagery of a particular area (Erdas 1997; Campbell 1996; Jensen 1996; Wilkie and Finn 1996). There are many Remote sensing data acquisition options available. These options can be generally classified based on three platforms: ground, airborne and spaceborne observation platforms. The figure below depicts Barrett and Curtis' (1992) classification of remote sensing platforms based on these categories. Each platform has its own particular advantages and disadvantages. Terrestrial and airborne platforms offer very high spatial resolution but provide localized and simultaneous coverage. Satellite (spaceborne) platforms on the other hand provide a more synoptic view of the landscape at a relatively coarse spatial resolution. As a result of the trade-off between spatial resolution and area coverage, selection of appropriate platform form depends on whether the question to be address is 32 33 localized and refined or more regionalized and coarse-grained (Wilkie and Finn, 1996). However, it is not uncommon for remote sensing applications to involve multi- platform operations (Barrett and Curtis, 1992). Remote Sensing Platforms Surface Airborne Space Ground Vehicles Mask & Towers Rockets Orbiting Deep Space Spacecraft probes Manned Unmanned (Satellites) Light aircraft Heavy Ballons including aircraft drones & helicopters. fer ! ■ Figure 2 .4 Remote Sensing Platforms. Source Barrett and Curtis 1992. In spite of the spatial trade-off, satellite platforms offer several distinct advantages over terrestrial and airborne platforms (Barrett and Curtis 1992; Erdas, 1997; Wilkie and Finn, 1996). These include but not exclusively: • Stability of platform; • Frequent and repeated coverage; • Consistency in the manner in which data are recorded; • Lower cost of obtaining and interpreting data for larger areas. The listed capabilities renders satellite remote sensing well suited to monitoring many of the world's broad scale environmental problems (Campbell 1996). 34 2.5.1 Satellite Remote Sensing Initial efforts aimed at imaging the earth surface from space were rather incidental outcome of the development of meteorological satellites. Beginning in 1960 with the series of Television and Infrared Observation Satellite (TIROS), early weather satellites return very coarse and virtually indistinct images of the earth's surface (Lillesand and Kiefer 1994; Campbell, 1996). As the sensors on board the meteorological satellites were refined the images become more distinct. The exciting future of remote sensing from space became even more apparent during the manned space programs of the 1960's: Mercury, Gemini, and Apollo. As earth resources imaging was not the primary goal of the of these endeavors hand held cameras were used and overall quality of the acquired images were poor. However, the ventures demonstrated that useful and sometimes unique earth resources data could be obtained from space (Lillesand and Kiefer 1994). According to Campbell (1996), these earlier systems provided both the design and operational experience necessary for the successful operation of current earth observation satellites. Presently, earth observation satellites consists of scanners with sensors made up of detectors calibrated to record reflected electromagnetic energy as brightness values within specific regions of the EMS (Jensen 1996). Two important satellites that have provided the majority of remotely sensed digital images in use today are the EOSAT’s Landsat and French SPOT satellites (Erdas 1997). The subsequent sections describe these satellites and the relatively new Indian Remote Sensing satellites, which offers very promising resolutions. 35 2.5.1.1 Landsat The glances of the earth resources provided by the early meteorological satellites and manned spacecraft missions provided the impetus for NASA with the cooperation of the US department of interior to initiate a study of the feasibility of a series of Earth Resources Technology Satellites (ERTS) in 1967. The program resulted in a planned, sequence of six satellites that were given before launched designations as ERTS A, B, C, D, E and F to become ERTS 1, 2, 3, 4, 5 and 6 after successful launch into prescribed orbits. ERTS A was launched in 1972, as the first satellite designed specifically for the acquisition of data of the earth resources. Prior to the launch of ERTS B in 1975, NASA renamed the ERTS program Landsat (Land Satellites) program (to distinguish it from the planned seasat oceanographic satellites program) (Lillesand and Keifer 1994). Table 2.2 highlights the characteristics of Landsat 1 through 6 missions. Table 2. 2 Landsat Missions. Satellite Launched Retired Sensors Orbit Landsat1 Landsat 2 Landsat 3 Landsat 4 Landsat 5 Landsat 6 23ra July 1972 22nd January 1975 5th March 1978 16th July 1982 1st March 1978 5th October 1993 6m January 1978 27th July 1983 7th September 1983 Operational Operational Failed upon launch MSS, RBV MSS, RBV MSS, RBV TM, MSS TM, MSS ETM 18 day/ 900 km 18 day/ 900 km 18 day/ 900 km 16 day/ 705 km 16 day/ 705 km 16 day/705 km The first generation of Landsat (Landsat 1, 2 and 3) were launched into a near circular, sun-synchronous, near-polar, orbit at a nominal altitude of 900 km. The orbit was selected so that satellite ground trace repeated its earth coverage at the same local time every 18 days. The satellites were also designed and operated to collect data over a 185-km swath. On board these satellites were two sensor systems: the Return Beam Vidicom (RBV) Camera and the Multispectral Scanner (MSS). The RBV was a Camera-like instrument designed to provide relative to the MSS high geometric accuracy but lower spectral and radiometric detail (Barrett and Curtis 1992; Lillesand and Keifer 1994; Campbell 1996). On Landsat 1 and 2 the RBV system consisted of three (television-like) cameras aimed to view the same ground area simultaneously; each sensing a different segment of the spectrum such that the images they acquire register to one another to form a three band multispectral representation. On Landsat 3 the spatial resolution of the RBV system was improved through the implementation of a two-camera broad band system (i.e. two panchromatic cameras) (Campbell 1996). However, due to technical malfunctioning of the RBV sensors, the MSS replaced it as the primary sensors on all three satellites (Barrett and Curtis 1992; Lillesand and Kiefer 1994; Campbell 1996). The MSS on board Landsat 1 and 2 was a four-channel system covering two bands in the visible and two in the near infrared wavelengths of the spectrum. In addition to these bands a thermal band was incorporated into the MSS onboard Landsat 3. Unfortunately, operating problems cause the thermal band to fail shortly after launch. Thus all three MSS systems effectively produced data in the same four bands. Table 2.3 details the characteristics of the MSS data. In general the data acquired by MSS, were found to be much better than anticipated and demonstrated the merits of satellite observation of the earth by proving its utility across a broad range of applications. Currently, the first three Landsat are no longer in service, nevertheless they have acquired a large library of images that are available as a worldwide baseline reference (Campbell 1996). 36 37 Table 2. 3 Characteristics of MSS data. Complied from Campbell 1996. Band Resolution Spectral Radiometric Spatial Temporal 1 0.5-0.6 pm 7 bits 79 m 18 day 2 0.6-0.7 pm 7 bits 79 m 18 day 3 0.7-0.8 pm 7 bits 79 m 18 day 4 0.8-1.1 pm 7bits 79 m 18 day The second generation of Landsats (Landsats 4 and 5) carried an identical MSS as well as the Thematic Mapper (TM) which is a more sophisticated version of the MSS on an improved platform launched into orbits with similar characteristics as its predecessor but at a nominal altitude of 705 km. The lowered nominal altitude allows for a temporal resolution of 16 days (Barrett and Curtis 1992; Lillesand and Kiefer 1994; Campbell 1996). The second sensor on board, the TM, is an advanced system incorporating radiometric and geometric improvements relative to MSS. The spectral improvements of the sensor include the acquisition of data in seven instead of four bands. Further, the wavelength range and the allocation of the TM bands have been chosen to improve the spectral differentiability of major earth features (Campbell 1996; Lillesand and Kiefer 1994). Table 2.4 lists the characteristics of TM data. Table 2 .4 Characteristics of Landsat TM. Complied from Campbell 1996. Band Resolution Spectral Radiometric Spatial Temporal 1 0.45-0.52 pm 8 bits 30 m 16 days 2 0.52-0.60 pm 8 bits 30 m 16 days 3 0.63-0.69 pm 8 bits 30 m 16 days 4 0.76-0.90 pm 8 bits 30 m 16 days 5 1.55-1.75 pm 8 bits 30 m 16 days 6 10.4-12.5 pm 8 bits 120 m 16 days 7 2.08-2.35 pm 8 bits 30 m 16 days The next in the series of Landsats (Landsat 6) was designed to occupy an identical orbit to Landsats 4 and 5. The sensor onboard, the Enhanced Thematic 38 Mapper (ETM) incorporated the same seven spectral bands with the same spatial resolution as the TM. The ETM's major improvement over the TM was the addition of a panchromatic band operating in the 0.50 (j.m - 0.9 (j.m range with a spatial resolution of 15 meters. Unfortunately, Landsat 6 with its ETM did not achieve orbit when launched on October 1993 (Lillesand and Kiefer 1994). Presently, the scheduled launch for Landsat 7 has been delayed due to necessary changes in the design of the electrical power supply of its main sensor (Isbell et al. 1998). This sensor, the Enhanced Thematic Mapper Plus (ETM+) is designed to response to improvements long requested by the data user community while maintaining the essential characteristics of Thematic Mapper type data. Similar to the ETM, the spectral bands present on the TM of Landsats 4 and 5 are part of the ETM+. Ground resolution remains unchanged at 30 meters, except for the thermal band that the resolution is increased from 120 meters to 60 meters. A panchromatic band with 15- meter resolution has also been added for rectification and image sharpening (Komar eta l. 1998). Spot - Le Systeme Pour I'Observation de la Terre was conceived and designed by the Centre National d'Etudes Spatiales (CNES) with the cooperation of other European organization. From its inception the SPOT system was designed as a commercially oriented program to provide high quality service and data for an operational user community. Initiated in 1977 the program began operation in 1986 with the launch of SPOT 1. This was followed by SPOT 2 in 1990 and SPOT 3 in 1993 (table 2.5). 2.5.1.2 SPOT 39 Table 2. 5 SPOT Missions. Compiled from Campbell (1996 ) and SPOT Image (1998 ) Satellite Launched Status Sensors Orbit SPOT 1 SPOT 2 SPOT 3 *SPOT 4 22™ February 1986 21st January 1990 25th September 1993 24th March 1998 Backup to SPOT 2 Primary Satellite Operational Operational HRV (2) HRV (2) H RV (2) HRVIR/ Vegetation Instrument 26 day/ 830 km 26 day/ 830 km 26 day/ 830 km 26 day/ 830 km ‘ Discussed subsequently. All three satellites have a circular, near polar sun-synchronous orbit at a nominal altitude of 832 km. The orbit pattern for SPOT repeats every 26 days (temporal resolution). Additionally, SPOT sensors have oblique or off-nadir viewing capability, which allows the acquisition of data for a given area at frequencies ranging from successive days, to a few weeks. This increases the potential for acquiring good quality images of areas where cloud cover is recurrent or problematic. Alternatively, the same area can be imaged from separate positions (different satellite passes) to acquire stereo coverage (Lillesand and Kiefer 1994; Campbell 1996). The SPOT sensors consist of two identical High Resolution Visible (HRV) imaging systems and auxiliary magnetic tape recorders. Each HRV has a ground swath of 60 km wide and can operate independently either in a panchromatic (PAN) or multispectral (XS) mode. In the PAN mode the HRV provides fine spatial detail but records a rather broad spectral region. On the contrary, in the XS mode the sensor records three bands of finer spectral resolution but coarse spatial resolution. The spectral and spatial image characteristics of SPOT PAN and XS are given in the subsequent table. It is possible to enhance the lower spatial detail of the XS images by superimposing them on the fine spatial detail of PAN images of the same area (Campbell 1996). On March 24th 1998, SPOT 4 was successfully launched with enhanced performance and capability compared to its predecessors. A principal feature of SPOT 4 is the High Resolution Visible and Infrared (HRVIR) sensor, which is a modification of the HRV, ported on SPOTs 1-3. HRVIR is similar to the HRV but possess an additional spectral band in the middle infrared (1.58 pm to 1.75 pm) that offers better vegetation discrimination. In its multispectral (XS) mode therefore the HRVIR acquires four bands of data (1,2,3, and middle infrared) at a 20-meter resolution. In the monospectral mode, the 10-meter resolution panchromatic band (0.51 pm to 0.73|jm) has been replaced with a band identical to band 2 (0.61 pm - 0.68pm). In other words, band 2 is operated in both a 10-meter and 20-meter resolution modes. This allows for onboard registration of all spectral bands (SPOT Image 1998). Table 2 .6 Characteristics of SPOT images. Complied from Campbell 1996. 40 Band Resolution Spectral Radiometric Spatial Temporal XS 1 0.50-0.59 pm 8 bits 20 m 16 days XS 2 0.61-0.68 pm 8 bits 20 m 16 days XS 3 0.79-0.89 pm 8 bits 20 m 16 days Pan 0.51-0.73 pm 8 bits 10 m 16 days In addition SPOT 4 carries an auxiliary instrument termed the Vegetation Instrument. This Vegetation instrument is a wide-angle (2000-km-wide swath) earth observation instrument offering a spatial resolution of 1 km (at Nadir) and high radiometric resolution. It uses identical spectral bands as the HRVIR instruments (plus an additional band known as BO (0.43-0.47 pm) for oceanographic applications). The ability of the high-resolution (HRVIR) and low-resolution (Vegetation) instruments to acquire imagery simultaneously, and their use of identical spectral bands offer unique advantages for easier interpretation at a variety of scales (SPOT Image 1998). 2.5.1.3 Indian Remote Sensing The evolution of the Indian space program is quite unique demonstrating how effectively a high-technology program can be conceived and implemented by a developing country. Utilizing the benefits of international cooperation effectively, India today has a viable, integrated, self-supporting space program. After carrying out a series of air-borne remote sensing experiments, India set up a Landsat data reception center in 1975 to learn the art of remote sensing data reception, analysis and utilization (NASA 1998). From this modest beginning and following the successful demonstration flights of two coarse-resolution remote sensing satellites (Bhaskara 1 and Bhaskara 2 launched in the 1979 and 1981), the Indian Space Research Organization (ISRO) initiated a series of high-resolution earth observation satellites - The Indian Remote Sensing Satellite (IRS) - in 1988 (Government of India 1989; Campbell 1996). Currently, seven satellites have been launched in the series, six of which have been successful. The launch dates, sensors, and operational status of the various satellites are indicated in table 2.7. IRS-1 A and IRS-1 B were launched into 22-day repeating orbits of 905-km mean altitude and 99 degrees inclination. Both satellites host a trio of Linear Imaging Self-Scanning (LISS) remote sensing instruments working in four spectral bands: 0.45pm - 0.52 pm 0.52pm - 0.59 pm, 0.62pm - 0.68 pm, and 0.77pm - 0.86 pm. LISS-1 images a swath of 148 km with a resolution of 72.5 meters the two identical LISS II instruments (LISS-IIA and LISS-IIB) exhibit a narrower field-of-view (74-km 41 42 swath) but are aligned to provide a composite 145-krn swath with a 3-km overlap and a resolution of 36.25 m (CHAART 1998). Table 2. 7 IRS missions Complied from CHAART (1998); FAS (1998) and TELSAT (1998). Satellites Launch Date Status Sensors Orbit IRS-1 A 1988 LISS I & II 22 days/905 km IRS-1 B 1991 Operational LISS I & II 22 days/905 km IRS-1 E 1993 Lost at launch LISS II IRS-P2 1994 Operational LISS II 24 days/817 km IRS-1 C 1995 Operational PAN, LISS III, WiFS 24 days/817 km IRS-P3 1996 Operational MOS-A, MOS-B, 5days/817 km MOS-C, WiFS IRS-1 D 1997 Operational PAN, LISS III, WiFS 24 days/817 km IRS-1 E, which was a modified IRS-1 A, equipped with LISS-I and a German Monocular Electro-Optical Stereo Scanner was lost when its launch vehicle failed to achieve orbit in 1993. Subsequently, IRS-P2 was launched into an 817-km, sun- synchronous orbit with a temporal resolution of 24 days. IRS-P2 carried the LISS-II system similar to that of IRS-1 A and IRS-1 B but with a ground resolution of 32 m X 37m. The total swath width imaged by IRS-P2 is 131 km (CHAART 1998). The Indian Remote Sensing began a new Era with the Launch of IRS-1 C in 1995. This satellite and its identical twin IRS-1 D launched in 1997 carry three different imaging sensors: A four channel LISS-III, a panchromatic scanner (PAN), and a two channel Wide Field Scanner (WiFS) (table 2.7). These satellites have a polar, circular, sun-synchronous 817-km orbit with a 24-day repeat cycle. In addition the PAN can be pointed for 5-day repeat off-nadir viewing. The LISS-III sensor provides multispectral data collected in four bands of the visible, near infrared and middle infrared regions. The spectral resolution and swath of the visible and NIR bands are 23.5 m and 141 km and that of the mid-IR region is 70.5 m and 148 km respectively. The Panchromatic sensor sacrifices swath width for higher resolution by providing data with a spatial resolution of 5.8 m at a ground swath of 70 km and a temporal resolution of 5 days. The 5.8-meter resolution can be resampled to 5 meters and is currently the best of any civilian remote sensing satellites. The third sensor - WiFS collects data in two spectral bands and has a ground swath of 810 km with a spatial resolution of 188.3 m (CHAART 1998). Between the launching dates of IRS-C and IRS-D an experimental IRS-P3 was launched in 1996. This satellite carries two different imaging sensors: Modular Optoelectronic Scanner (MOS) and Wide Field Scanner (WiFS). The satellite has a polar, circular, sun-synchronous 817-km orbit with a 5-day repeat cycle. The MOS has a swath width of 200 km and provides 18 spectral bands with a 500 m spatial resolution in the visible, NIR and MIR wavelengths. The WiFS on the other hand has a swath width of 770 km and provides data with 188m spatial detail in three bands namely red, NIR and MIR (CHAART 1998). 2.6 GLOBAL POSITIONING SYSTEM A relatively new technique of field data collection that is increasingly being employed in forestry is satellite navigation systems or Global positioning systems (GPS). GPS is a 24-hour, all weather satellite base radio navigation system developed by the United States Department of Defense (DoD). The system is composed of three segments: the space, control, and user segments. The space segment consists of a constellation of 24 orbiting satellites, in about 20 000 km orbits. The control segment is a network of five ground stations that operate and closely monitor the satellites’ orbits so that precise locations of the satellites are 43 known. The user segment consists of the GPS backpack or handheld receivers and a worldwide user community (Clarke 1997; Hoffmann-Wellenhof etal. 1994). Essentially, satellites and ground base receivers transmit similar coded radio signals such that the time delay between emission and reception can be used to compute the distance between the satellite and the receiver. Three or four range measurements can be used to establish a three-dimensional position. The accuracy of the GPS computed position ranges from “geodetic quality” (within centimeters) to “resource quality” (within meters) depending on the receiver quality and collection method (Jasumback 1992). Kruczynski and Jasumback (1993) classified GPS accuracy into four generic levels. Of these two are based on autonomous operation (the use of one receiver) whilst the other two are based on differential operation (the use of a second receiver at a known location). Level 1 accuracy, termed the standard position service (SPS) is achieved by autonomous operation of a GPS receiver and includes an error deliberately induced by the DoD. This error source is referred to as selective availability (SA) and it is regulated such that SPS yields a horizontal accuracy of 100 meters (2 dims). The SPS is provided at no cost to civilian and commercial users worldwide. The U.S. military, its allies and a select number of authorized users, use a decryption key to remove the SA errors and obtain a level of service termed the Precise Positioning Service (PPS) with a specified accuracy of 16 meters. In practice there are several additional sources of errors besides SA that affect the accuracy of a GPS derived position. These include satellite clock and ephemeris errors, errors due to poor satellite geometry, (Geometric Dilution of Precision (GDOP)), unmodeled ionospheric and tropospheric effects (atmospheric delays) and Multipath errors (the combination of direct and reflected signals) (NRC 1995; Kruczynski and Jasumback 1993). Users can overcome most of these errors with the exception of Multipath by the use of differential GPS (DGPS) techniques. DGPS is based on knowledge of a highly accurate geodetically surveyed location of a reference or base receiver. The reference receiver computes a correction factor by comparing its known location to the GPS derived position or observed code ranges. The correction factor is then applied to a roving or field receiver to obtain an improved position (NRC 1995; Kruczynski and Jasumback 1993; Hofmann-Wellenhof et al. 1994). This usually occurs as a post processing task using data recorded at a base station or can be performed in real time by the use of a radio link between the reference and the field receivers. The capability of DGPS is based on the fact that GPS satellites error sources are comparable over a region of 500 km and are therefore virtually eliminated by differential processing. However, multipath errors, since they are not common to both receivers (reference and field) cannot be removed by differential techniques and can only be reduced by a multipath antenna. Proper algorithms are important to an accurate, differential GPS solution. If the receiver uses the code that modulates the GPS carrier frequency, differential GPS can yield accuracy between 3 to 6 meters (Trimble 1999). This type of DGPS that is achieved from code-phase measurements can be referred to as level 3. In level 4, referred to as the carrier phase, the receiver actually measures the phase of the carrier signal. The carrier phase usually requires the use of dual frequency receivers. Typical accuracy for this level is in the order of a centimeter; but it requires mathematically intense 45 46 computations, and operations are sensitive to signal blockage and user motion (Kruczynski and Jasumback 1993; Hofmann-Wellenhof et al. 1994). Numerous applications of GPS for field activities in forestry have been documented (Kruczynski and Jasumback 1993; Lui and Brantigan 1995; D’Eon 1995; Gillis and Leckie 1996; Courteau 1996; Tortosa and Beach 1996). The impact of the technology is due to cost and time savings as well as accuracy improvements over traditional mapping and surveying methods. Another major advantage of GPS over these traditional methods is that its use does not require a line of sight between adjacent surveyed points. Further, the ability to walk or drive around collecting co­ ordinate information at sample points by GPS has obvious implications. Generally monitoring forest condition and inventories involve the use of permanent sample plots (PSP) and temporal sample plots (TSP). Work by D’Eon (1995) showed that GPS offers quick, accurate, precise and easy solution to the problem of reporting the location of these sample plots. Another study by Liu and Brantigan (1995) compared differential GPS for forest traverse surveys with the compass-and-chain surveys in eight forest stands and established that DGPS surveys of forest stand boundaries could meet or exceed accuracy standards and is more cost-effective than the traditional compass-and-chain traverse. In operational forest management Courteau (1996) indicated that among other things, GPS navigation means rapid and accurate cutblock boundary demarcation, monitoring of machinery, accurate location of sample plots and rapid harvest plan updates as every harvested tree can be georeferenced along with its diameter and species. In a field test in Zaire, Wilkie (1989), observed that GPS performed under demanding conditions and was able to obtain three-dimensional positions in inaccessible areas 47 often moderately enclosed by vegetation. He thus concluded that GPS technology is practical means of obtaining accurate geographic location data in inaccessible, poorly mapped regions of the world. GPS data have also proved effective in mapping forest fires (Tortosa and Beach 1996; Lawrence etal. 1995), surveying and updating forest road network (Gillis and Leckie 1996; Johansson and Gunnarsson 1998; Lawrence etal. 1995; Eggleston 1992), mapping clear cuts (Bergetron and Jasumback, 1990) and real-time monitoring of thinning performance (Thor et al. 1998). 2.7 GEOGRAPHIC INFORMATION SYSTEMS The scope of Geographic Information Systems (GIS) is extremely broad integrating many subject areas (DeMers 1997). Attempts to incorporate all these subject areas have resulted in numerous definitions of the term GIS each developed from a different perspective or disciplinary origin (Chrisman 1997). Cowen (1990) argues that GIS is best defined as a decision support system involving the integration of spatially referenced data in a problem solving environment. This definition well emphasis the ultimate application of GIS. However, most common definitions emphasize the main components as well as sub-functions of GIS (Clarke 1986; Rhind 1988; Dueker and Kjerne 1989; Aronoff 1989). Accordingly, the term GIS is applied to computer-assisted systems (hardware and software) for the capture, storage, retrieval, analysis, and display of geographically referenced data. Geographic data are commonly classified into three fundamental components of attribute, space and time (Chrisman 1997; Aronoff 1989). Attributes describe the properties of features and are maintained in a database management system (DBMS) while the spatial elements are described in one of two general types of spatial structure: vector and raster. Vector structures are those in which discrete elements, points, lines, and polygons, are represented digitally by a series of two- dimensional coordinates (x and y) which imply magnitude and direction (Smith and Maidment 1995). A raster or cell-based structure is represented by a geometric array of rectangular or square cells, each with an assigned value. The third fundamental component of geographic information time though often not explicitly stated is critical as features at specific locations are described as they existed at a point in time. Originally developed as a cartographic tool, GIS has evolved to be a powerful tool for spatial data management. GIS is characterized by the unique ability to overlay data layers and perform spatial queries to create new information, the results of which are automatically mapped and tabulated. With