Assessing the Impacts of Silvicultural Treatment Systems on Ecosystem Services: a Case of Carbon Sequestration and Biodiversity Conservation

Assessing the impacts of Silvicultural treatment systems

on Ecosystem services: a case of Carbon sequestration and Biodiversity

conservation

Evelyn Asante-Yeboah

March, 2010

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

By Evelyn Asante-Yeboah

Thesis submitted to the International Institute for Geo-Information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialization: Geo-information for Natural Resource Management

Thesis Assessment Board

Prof. Dr. Ir. Eric Smaling, Chairman, Degree Assessment Board, ITC (Chair)

Prof. A. A. Adimaddo, KNUST (Co-Chairman)

Dr. B. E. Prah, (External Examiner)

Prof. S. K Oppong, Internal Examiner, KNUST

Dr. E. M. Osei. Jnr, Internal Examiner, KNUST

Ir. Louise van Leeuwen, Course Co-ordinator, ITC

Supervisors: Ir. Louise van Leeuwen. (ITC), Prof. S.K. Oppong (KNUST)

KNUST

FACULTY OF GEO-INFORMATION SCIENCE AND EARTH OBSERVATION,OF THE UNIVERSITY OF TWENTE, ENSCHEDE, THE NETHERLANDS AND KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY, KUMASI, GHANA

Disclaimer

This document describes the work undertaken as part of a programme of study at the International Institute of Geo-Information Science and Earth Observation and the Kwame Nkrumah University of Science And Technology. All views and opinions expressed in this work remain the sole responsibility of the author, and do not necessary represent those of the two institutions.

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Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Abstract Forest management practices in Ghana for many years have been based on timber production for economic benefits. Silvicultural interventions mainly logging was the key management option in Ghana for favouring desired species for high-value end use. A shift in focus under the new Forest and Wildlife policy necessitated the need to manage forests for multiple benefits. Improved Silvicultural interventions mainly the Tropical shelter wood system, Post exploitation system and Selection system were tried experimentally in Bobiri Forest reserve in Ghana in the early 1960s. The intervention involved series of different operations geared towards the removal of undesired species to favour the growth of desired species. However, the current concerns of climate change and its associated effects have necessitated the need to manage forests to make them more contemporary to current situation. In this study an attempt is made to assess the impacts of these Silvicultural treatments on carbon sequestration and biodiversity conservation 45 years after interventions. The study was conducted in Bobiri Forest reserve in Ghana. The overall objective is ‘to contribute to the adoption of forest management strategies appropriate for multiple benefits with a major focus on sustaining environmental services such as carbon sequestration and biodiversity conservation in the natural tropical forests, by evaluating the performance of three silvicultural treated systems’. 96 sample plots were located in the three silvicultural treated systems and the untreated system (control). Aboveground biomass was estimated from a three nested circular plot (with horizontal radii of 12.62m, 8m and 4m) using simple random sampling. Standard tropical allometric equations were used to derive carbon stocks from field data collected. Pixel based approach (classified image) was used for the biomass/carbon mapping. Species diversity was estimated using Shannon-Weiner index. Spectral signatures were extracted from Aster image to examine the relationship between Aster data with forest parameters. Carbon density in the total silvicultural treated systems amounted to 530.67±67Mg. ha-1 (± S.E). The density of carbon in each silvicultural treated system ranged from a high of 586.86±18Mg.ha-1 in Tropical Shelterwood system (TSS) to a low of 457.82±19 in Post Exploitation System (PES). The untreated system (control) sequestered 275.68±Mg.ha-1 of carbon. A significant difference was observed in the carbon densities and species diversity of the silvicultural treatment systems. All relationships between Aster data with forest parameters were weak and not significant. It is therefore evident that the intervention has resulted in a forest with high carbon sequestration potential with diverse structural and species composition for ecosystem functioning. However, the method of intervention mainly poison (sodium arsenide) of trees in TSS and PES is considered environmentally unacceptable. Hence, the adoption of a selection system in which intensity of harvesting can be controlled will provide the multiple benefits expected of the forest.

Keywords: Forest management, silvicultural systems, carbon sequestration, species diversity vegetation indices.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Acknowledgement

I sincerely express my profound gratitude to the Almighty God for his blessings; guidance and gift of life granted me throughout this study.

I wish to express my appreciation to Tropenbos International and ITC capacity fund for sponsoring this course. To my supervisors, Louise van Leeuwen and Prof. Samuel Kingsley Oppong, I say a big thank you for your constructive criticism and helpful advices. To Michael Weir, and all staff of ITC, NRM department, and all KNUST GIS NATUREM staff, am most grateful for your contributions.

Special thanks go to Francis Kwabena Dwomoh of Forestry Research Institute of Ghana. I am most grateful for suggesting this topic, guiding me through the research work, and for the patience you had with me through out this work. I wouldn’t have come this far without your immense contribution. I owe you this work. I also say a big thank you to Patrick Vanlaake for his time and help during the proposal stage and to David Rossiter for his encouraging words.

Many thanks also go to the staff and workers of Bobiri Forest reserve and to all colleagues and friends who provided support in one way or the other.

Finally to the Asante-Yeboah family, I owe you many thanks for the support granted me in times of difficulties and the words of encouragement offered throughout my studies.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Dedication

I happily dedicate this piece of work to the entire Asante-Yeboah family for their prayers, support and encouragement throughout my studies. Your toil has brought me this far!!!

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Table of Contents Abstract ...... iv Acknowledgement ...... v Dedication ...... vi Table of Contents ...... vii List of Figures ...... x List of Tables ...... xi List of Acronyms ...... xii 1.0 INTRODUCTION ...... 1 1.1 Background ...... 1 1.2 Forest management practices in Ghana ...... 1 1.3 Silvicultural systems in Ghana ...... 2 1.3.1 Tropical Shelterwood System (TSS) ...... 2 1.3.2 Post Exploitation System (PES) ...... 2 1.3.3 The (Girth Limit) Selection System (SS) ...... 3 1.4 Forest management practices in the 21st century ...... 4 1.5 Biomass estimation and carbon sequestration ...... 4 1.6 Biodiversity and the functions of ecosystem ...... 5 1.7 Problem statement and Justification ...... 6 1.8 Research objectives ...... 6 1.8.1 Overall objectives ...... 6 1.8.2 Specific objectives ...... 7 1.9 Research Questions ...... 7 1.10 Hypothesis...... 7 1.11 Research approach...... 7 2.0 MATERIALS AND METHODS ...... 10 2.1 Study area ...... 10 2.1.1 Location, vegetation characteristics and compartment designation ...... 10 2.1.2 Criteria for selection of study area ...... 12 2.2 Remotely Sensed Data ...... 12 2.3 Software ...... 13 2.4 Methodology ...... 14 2.4.1 Flowcharts ...... 14

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Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

2.4.2 Image pre-processing ...... 16 2.4.3 Above ground biomass and carbon stocks inventory ...... 16 2.4.4 Species diversity estimation...... 18 2.4.5 Vegetation Indices ...... 18 2.4.6 Sampling design ...... 19 2.4.7 Estimation of biomass per tree ...... 22 2.4.8 Total above ground carbon stocks ...... 23 2.4.9 Species diversity index ...... 23 2.4.10 Computation of variables and statistical analysis ...... 24 2.3.10 Relationship between vegetation indices and ASTER bands with LAI and carbon stocks ...... 24 2.4.11. Mapping of aboveground carbon stocks ...... 25 3.0 RESULTS...... 26 3.1 Aboveground biomass and carbon stocks in the silvicultural treatment systems ...... 26 3.1.1 Total treatment area ...... 26 3.1.2 Classified image of the three Silvicultural treated systems ...... 27 3.1.3 Relationship between biomass and canopy openness ...... 27 3.1.4 Aboveground biomass distribution in the silvicultural treatment systems ...... 28 3.1.5 Spatial distribution of above ground biomass in the three silvicultural treated systems ...... 29 3.1.6 Mean carbon densities in the Silvicultural treatment systems ...... 30 3.1.7 Spatial distribution of aboveground carbon densities in the silvicultural treated systems ...... 31 3.1.8 Distribution density of DBH size classes in the silvicultural treatment systems ...... 33 3.1.9 Percentage contribution of DBH size classes to mean carbon densities in the ...... 33 Silvicultural treatment systems ...... 33 3.2 Species diversity ...... 34 3.2.1 Tree species composition in the Silvicultural treatment systems ...... 34 3.2.2 Tree Stand form and density in the silvicultural treatment systems ...... 36 3.2.3 Tree diversity in the silvicultural treatment systems ...... 36 3.3 Relationship between LAI and Mean carbon stocks with ASTER bands ...... 38 and selected Vegetation indices ...... 38 3.3.1 Correlation between Forest parameters and Aster data ...... 38 3.3.2 Correlation analysis between LAI with ASTER bands and selected vegetation indices ...... 39 4.0 DISCUSSIONS ...... 42 4.1 Aboveground biomass in the Silvicultural treatment systems ...... 42 4.2 Sequestered carbon stocks in the silvicultural treatment systems ...... 43

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Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

4.3 Species diversity in the silvicultural treatment systems ...... 44 4.4 Stand structure under the silvicultural treatment systems ...... 45 4.5 Relationship between Forest stand parameters and vegetation indices ...... 46 4.6 Relationship between forest stand parameters and spectral reflectance bands ...... 47 5.0 CONCLUSION ...... 50 6.0 RECOMMENDATION ...... 53 6.1 Carbon stocks ...... 53 6.2 Species diversity ...... 53 6.3 Vegetation indices ...... 53 7.0 REFERENCES ...... 55 8.0 APPENDICES ...... 61

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

List of Figures Figure 1: Conceptual diagram of the study ...... 9 Figure 2: Map of study area (top left: Map of Ghana showing regional capitals. Top right: ...... 11 Figure 3: Aster image of silvicultural treated systems with boundary of treated ...... 12 Figure 4: Methodological flowchart of research work ...... 15 Figure 5: Layout of the three circular nested plots ...... 20 Figure 6: Sketch of the layout of a G. I. S. P plot (a) and layout of 500m2 sample plots within ...... 22 Figure 7: Area in hectares of the various silvicultural treatment systems ...... 26 Figure 8: Supervised classification of the silvicultural treated systems ...... 27 Figure 9: Correlation between canopy openness and aboveground biomass (Mg/ha) ...... 28 Figure 10: Aboveground biomass densities in the Silvicultural treatment systems ...... 29 Figure 11: Spatial Distribution of Aboveground biomass in the three silvicultural treated systems ...... 30 Figure 12: Mean carbon densities (Mg.ha-1) in the silvicultural treatment systems ...... 31 Figure 13: Spatial distribution of Carbon density (Mg.ha-1) in the three silvicultural treated systems...... 32 Figure 14: Contribution of dbh size classes to sequestered carbon in each silvicultural treatment system ...... 34 Figure 15: Species star rating of the 70 species identified ...... 35 Figure 16: Distribution of commercial and pole size trees (a) and mean tree size (b) in the silvicultural treatment systems ...... 36 Figure 17: Scatter plots of LAI with reflectance bands and vegetation indices ...... 40

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

List of Tables Table 1: Comparison of intensity of operations in the silvicultural treated systems ...... 3 Table 2: Characteristics of ASTER VNIR and SWIR subsystems (adopted from Yamaguchi, et ...... 13 Table 3: Vegetation indices used in the study ...... 19 Table 4: Variables used in Shannon diversity index, and equitability ...... 24 Table 5: Pairwise comparison test of Mean carbon density in the silvicultural treatment systems ...... 32 Table 6: Tree stocking density of DBH size classes in the Silvicultural treatment systems ...... 33 Table 7: Treatment type and its respective number of species in each star rating category ...... 36 Table 8: Species diversity and richness in the silvicultural treatment systems ...... 37 Table 9: Pairwise comparison test of species diversity in the silvicultural treatment systems ...... 37 Table 10: Summary of impacts of Silvicultural treatment on carbon sequestration and ...... 37 Table 11: Correlation of the LAI and Mean C against the ASTER reflectance bands and ...... 39 Table 12: Linear regression model of LAI, ASTER bands and SVIs ...... 41 Table 13: Linear regression model of Mean sequestered carbon, ASTER bands and SVIs ...... 41

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

List of Acronyms

AGB Above Ground Biomass C Carbon CDM Clean Development Mechanism DBH Diameter a Breast Height FAO Food and Agriculture Organization Gg Giga grams GHG Green House Gases GIS Geographic Information S ystems GISP Girth Increment Selection Plot GLA Gap Light Analyzer GPS Global Positioning System Ha Hectare IUCN International Union for the Conservation of Nature LAI Leaf Area Index Mg Mega grams NDVI Normalized Differential Vegetation Index NDVIc Corrected Normalized Differential Vegetation Index PES Post Exploitation System RMSE Root mean square error RSR Reduced Simple ratio SR Simple Ratio SS Selection System SWIR Short Wave Infra-Red VI Spectral Vegetation Indices VNIR Visible and Near Infra-Red TSS Tropical Shelter wood system UNCED United Nations Conference on Environment and Development WGS World Geodetic System

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Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

1.0 INTRODUCTION

1.1 Background Natural ecosystems including the plants and animals within them provide humans with services that are very difficult to duplicate. It is often impossible to place an accurate monetary value on services provided by ecosystem, because the benefits they provide maybe indirect and thus difficult to measure (Notman, et al., 2006). Ecosystem services are often undervalued because of frequent lack of knowledge regarding the role they play.. The most widely known service of the forest ecosystem has been the provision of goods such as timber which can be quantified and valued. Yet, many services offered free of charge and difficult to quantify and value such as carbon sequestration, protection of water resources, contributing to climate stability, generating and maintaining biodiversity, are less known benefits of the ecosystem (IUCN, 2008). The ability of forest ecosystems to provide these services currently and in the future is determined widely by changes in socio-economic characteristics, forest management objectives, atmospheric composition and climate amongst other things (KNAW Research Information, 2009). Forest management practices have long aimed at timber production but in recent years the increasing effects of climate change has necessitated the need to adapt forest management practices in mitigating climate change and sustaining the resources by conserving biodiversity.

Tropical forests upon studies have been found to play essential role in the global carbon cycle thereby serving as a major reservoir of global terrestrial carbon (Chambers, et al.,

2007). Forests are both a source of carbon dioxide (CO2) when they are destroyed or degraded and a sink when conserved, managed, or planted sustainably. The recognition of this role led to forestry activities’ inclusion in the Kyoto protocol (Brown, 2002). Biodiversity maintenance is now also a key management objective and a requisite for sustainable forestry. Biodiversity and their services provided in the ecosystem are underpinned by supporting and regulatory services (eg pollination, climate regulation and primary productivity). This makes the value of biodiversity less visible to raise global concerns. However, the extent of the service provided depends not only on the area available and tree species but also the management system that is applied and whether it is based on sound scientific principles.

1.2 Forest management practices in Ghana The forest management practice in Ghana aims at achieving sustainable forest management by the year 2020 (Kufuor, 2000). This objective was to be achieved mainly through logging interventions principally to control the method and intensity of harvesting. However, this control system has been inadequate resulting in

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

unsustainable exploitation (Foli, et al., 2004). This was revealed through the decline in production of favoured species. It therefore became necessary to explore areas of silvicultural interventions that will promote social and economic benefits without the negative environmental impacts associated with poorly controlled logging operations (Parren & Graaf, 1995)

1.3 Silvicultural systems in Ghana Silviculture as defined by Franklin, et al., (1997) is “the art and science of manipulating forest stands to achieve human objectives, including the production of various goods and services.” Silvicultural systems involve procedures that are aimed at meeting the owner’s objectives and mostly depend on the interactions of timber types, forest conditions, and species present.

Attempts at developing silvicultural treatment systems for Ghana’s tropical forests date back to the late 1940s, this lead to the establishment of experimental plots in the Bobiri Forest Reserve(Mooney, 1963). Three main silvicultural treated systems, each entailing a series of forest operations, were experimented. These were the Tropical Shelter wood System (TSS), Post Exploitation System (PES) and the (Girth limit) Selection System (GLS or SS) (Alder, 1993; Mooney, 1963; Osafo, 1970; Parren & Graaf, 1995).The principal aim was to simplify the structure and composition of the forest through these interventions in order to make management easier (Mooney, 1963; Osafo, 1970).

1.3.1 Tropical Shelterwood System (TSS) The system was aimed at obtaining an even-aged high forest by natural regeneration (Ghana Forestry Dept, 1958). The system was developed in Malaysia in the early 1930’s (Osafo, 1970), and was modified and adopted in Nigeria between 1944 and 1946 (Oguntala, 1997; Parren & Graaf, 1995). Ghana begun to try it on an experimental scale in Bobiri Forest Reserve in 1947/48 and later expanded to cover other forest types (Osafo, 1970)

The TSS is an intensive natural regeneration system by which the young crop is established under the Shelter (overheads and the lateral) of the old one. It comprises a series of operations designed to open the canopy from below to induce regeneration, and stimulate the growth and development of existing young stands. The openings involved the cutting of lianas from the ground level as high as could be reached to free crowns of crop trees and reduce latter felling damages. In their operations small unwanted under storey trees were manually removed by cutting and larger unwanted trees in the lower and middle canopies poisoned with sodium arsenide

1.3.2 Post Exploitation System (PES) The PES is another system concentrated on natural regeneration. Like the TSS it also aims at obtaining an even-age high forest of valuable species. It was attempted briefly on experimental basis in Ghana between 1948 and 1960. The PES was tried mainly for

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

economic reasons to generate immediate income for operations, since TSS requires expenditure before income can be obtained. Treatments consisted of exploitation in the first year, followed by climber cutting, canopy opening by poisoning and cleaning as done in TSS (Osafo, 1968). Analysis of the results of treatment tried in the country showed that established regeneration consisted largely of the general utility species (Class II species) and not Class I species (Appendix F) whose regeneration and establishment was an objective of the system (Foli, 2004a; Foli, et al., 2004) . The poor regeneration of the Class I species was due to the fact that most of the mother trees were removed during exploitation (Britwum, 1976). The PES was abandoned early. Both the TSS and PES were tried on an experimental scale between 1948 and 1960 at the Bobiri, Asenanyo and Bia Research centres (Mooney, 1963)

1.3.3 The (Girth Limit) Selection System (SS) The system involves removal of the oldest and largest trees with some predetermined diameter limit being taken as a definition of the smallest trees (Foli, 2004b)

The Modified Selection System practiced in Ghana dated from 1956 and lasted until 1971 when it was suspended. This system was combined with the stock survey of all high valuable economic trees species (class I and II species) over 70 DBH and above after which improvement thinning of smaller trees was undertaken in the midst of a 15- year selective cycle, which was later extended to 25 years (Baidoe, 1970). The aim of the improvement thinning was to increase the survival rate and development of the class I and II species in the smaller girth class by reducing competition from climbers and “non valuable” trees. After mapping, all uneconomic trees and the immature ones suppressing the desired species were removed. Indeed a class I tree to be favoured or desired was freed from climbers and all under storey trees were cut back within a radius of 3.7m (Mooney, 1963; Osafo, 1970)

Table 1: Comparison of intensity of operations in the silvicultural treated systems Treatment system Activity Intensity of treatment Tropical shelter wood Weeding and tending Moderate system treatment done 10yrs prior to harvesting in 1970s

Selection system Removal of undesirable Minimal species to favour desirable species (class I and II species).

Post exploitation system Weeding and tending Severe treatment done 10yrs after harvesting in 1960s

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Since forest management in Ghana is tending increasingly towards multiple use system, an understanding of which management system favours the desired species and which also contributes to the global carbon cycle is essential for local silvicultural planning and management in Ghana. This is so because of the increase concerns on climate change effects and the need for a stable forest ecosystem to mitigate such effects (Willis, 2007).

1.4 Forest management practices in the 21st century As the forestry profession has grown, an understanding of the term "forest management" has broadened to span wider environmental and ecosystem issues, such as conservation of biological diversity, social and economic matters, carbon sequestration and more generally, the concept of sustainability (Schmid, et al., 2006; Willis, et al., 2007). Forest management in developing countries has received more attention since the United Nations Conference on Environment and Development (UNCED) in Rio in 1992. Forest management practices have been included in the Kyoto protocol as an activity for gaining carbon credits (Kim Phat, et al., 2004). In view of this, there have been some mechanisms to address how timber production, carbon sequestration and biodiversity can be controlled through management practices. This initiative is as a result of current and changing climate which has brought challenges for forest management in the 21st century.

1.5 Biomass estimation and carbon sequestration Carbon sequestration by tropical forest is important in the global carbon cycle because it removes carbon dioxide from the atmosphere and reduces the green house gases (GHGs) thereby minimizing global warming. Forest trees and all plants in general take up carbon dioxide from the atmosphere and through the process of photosynthesis convert it into starch and sugar for build-up. This makes forests an important natural ‘break’ on climate change (Gibbs, et al., 2007). The ability to measure accurately and precisely the amount of carbon sequestered in forests has increasingly gained attention due to the recognition of the role of forests in mitigating carbon dioxide emissions (Brown, 2002; Brown & Lugo, 1992). Biomass, often defined as ‘the total mass of living matter within a given unit of environmental area’ has been an important element in the carbon cycle, more specifically, carbon sequestration; it helps in quantifying pools and fluxes of GHG from terrestrial biosphere to the atmosphere associated with land-use and land cover changes (Cairns, et al., 2003). Apart from estimating biomass for carbon stocks accounting and monitoring (Brown, 2002), it has also been a usual practice in quantifying fuel and wood stock and helps in allocating harvestable amount of wood (Dias, et al., 2006) for forest management purposes. This has made biomass estimation projects an important component for national planning as well as scientific studies (Hall, et al., 2006; Zheng, et al., 2004; Zianis & Mencuccini, 2004). Since the inception of the Kyoto Protocol and the adoption of the United Nations Framework on Convention on Climate Change (UNFCCC), the estimation of the amount of carbon stored in biomass

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

and the changes there-in through the use of land, land-use change and forestry (LULUCF) activities has been a topic of significant debate. The ability of trees to sequester carbon has received renewed interest, since carbon sequestration projects in developing nations can receive investments from companies and governments that are wishing to offset their emissions of greenhouse gases through the Kyoto Protocol’s Clean Development Mechanism (CDM) (Fearnside, 1999). The type of forest management practice initiated to offset carbon sinks and source by human intervention are accounted for under the article 3.4 of the Kyoto protocol (UNCED, 1993).

1.6 Biodiversity and the functions of ecosystem Natural Forests are one of the largest repositories of biodiversity in the world (IUCN, 2008). According to some estimates they contain about 60-90% of all terrestrial species found on the planet. Some of these species could have some widespread economic or medicinal uses that still remain hidden, for example a cure for AIDS (IUCN, 2008). The conservation of these valuable genetic resources for future options that are yet undiscovered is thus a valuable service that forests provide to us and to future generations. Concerns are growing about the consequences of biodiversity loss for ecosystem functioning and for the provision of ecosystem services. Biodiversity is the term used to describe the diverse species in the ecosystem. It is a contracted version of biological diversity. The convention of biological diversity defines biodiversity as “the variability among living organisms from all sources including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are a part; this includes diversity within species, between species, and of ecosystems.”. The role and function of biodiversity in ecosystem is complex. Therefore services provided by the ecosystem is influence by the characteristics of the species present and their functional traits (Hooper, et al., 2005; Reich, et al., 2004). Ideally species diversity tends to make ecosystem more resistant and resilient to disturbance because of the characteristics that are associated with species which tend to enable the ecosystem adjust to environmental change (Hooper, et al., 2005; Reusch, et al., 2005). This implies that ecosystem can continue to function and provide critical service such as water purification; climate regulation etc if these species are maintained. Scheffer, et al., ( 2001), remarked that ecosystem with low resilience, when subject to shocks or disturbance, may reach a threshold at which unexpected change can occur.

Therefore assessing the current carbon stock and species diversity of an existing silvicultural system will aid in addressing the challenge of adopting a management practice which focuses on carbon sequestration, biodiversity conservation as well as timber production.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

1.7 Problem statement and Justification Historically, forest management has mostly considered biological issues with a strong focus upon Silviculture for production of wood. With present concerns, forest management is geared towards multiple end uses (Kufuor, 2000). These new forest requirements present new challenges to forest managers, who are faced with more complex and difficult tasks in the design and implementation of appropriate forest management plans (Winjum, et al., 1993). There is therefore an urgent need to increase the knowledge on how forest management can maintain and enhance different environmental services. Such knowledge will enable a change from traditional management of providing valuable wood products to incorporating the sustainability and restoration of ecosystem services. More specifically, the adoption of appropriate forest management systems can reduce tropical carbon emissions, increase/sustain biodiversity and enhance other ecosystem services as well.

Forestry practice in Ghana with the major focus on timber production led to the establishment of the silvicultural treatment systems mentioned above. At that time, the focus of forest management was on the production of timber of valuable species. Notwithstanding the challenge posed by managing forest for multiple end uses, there has been no study to review the silvicultural systems with the view of making them more adaptive to contemporary demands such as ecosystem services. Such a study is more important at a time when the threat of climate change poses many challenges for forest managers, and hence the increasing calls for adaptive forest management. This study therefore constitutes an attempt at assessing the management of natural tropical forests in Ghana with the aim of making them more responsive to current forest management challenges.

Moreover, remote sensing methods of developing models and algorithms for estimating carbon stocks from satellite data in tropical forest is unsuccessful due to poor relationships observed (Benefor, 2008). This failure could be attributed to the fact that vegetation indices measured at canopy level do not relate perfectly to DBH measurement below canopy which is often used for carbon estimations. An attempt is made in this study to establish a relationship of vegetation indices with Leaf Area Index (LAI).

1.8 Research objectives 1.8.1 Overall objectives This research aims to contribute to the adoption of forest management strategies appropriate for multiple benefits with a major focus on sustaining environmental services such as carbon sequestration and biodiversity conservation in natural tropical forests by evaluating the performance of three silvicultural treated systems.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

1.8.2 Specific objectives  To estimate and map aboveground biomass and carbon stocks in the silvicultural treatment systems.  To determine tree species diversity and assess the impact of the treatments on species diversity  To evaluate the performance of the silvicultural treatment systems on their suitability for conserving biodiversity and sequestering carbon.  To assess the relationship between selected vegetation indices and reflectance bands derived from Aster data with carbon stocks and LAI.

1.9 Research Questions 1. What is the concentration of aboveground biomass/carbon densities in the silvicultural treatment systems 2. How is the above ground biomass/carbon stocks distributed in the silvicultural treated systems? 3. Is the concentration of carbon stocks within the silvicultural treatment systems significant? 4. Which of the silvicultural treatment systems foster the most diverse of tree species? 5. Is the effect of silvicultural treatment carried out on biodiversity significant? 6. Which of the silvicultural treatment systems is most suitable for biodiversity conservation and carbon sequestration 7. How strong is the relationship between selected vegetation indices, and reflectance bands with aboveground biomass/carbon stocks and LAI?

1.10 Hypothesis 1. Carbon densities in the silvicultural treated systems are higher than carbon densities in the untreated system (control) 2. There is significant difference in species diversity in the silvicultural treatment systems 3. There exist a strong relationship between selected vegetation indices and reflectance bands with LAI and aboveground carbon densities.

1.11 Research approach The study followed the approach shown in the conceptual diagram in Figure 1. Relevant literature was be reviewed on the importance of tropical forests in sequestering carbon, and conserving biodiversity, the challenges faced with current forest management practices in implementing management objectives that will address the issue of environmental services as well as production services and Remote Sensing methods that can be used to estimate and map carbon densities and derivation of vegetation

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

indices. Existing methods of estimating biomass in a non destructive manner was used to estimate the amount of carbon stored in the each of the silvicultural treatment systems.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Literature review/desktop studies Current Forest Remote sensing Management techniques of Practices Silvicultural estimating carbon systems densities and VIs

Post Carbon Shelterwood Exploitation sequestration System Selection system system and Biodiversity conservation

Conceptualizing research, Definig objectives, research questions and hypothsis

Image Pre- processing Selection of study area

Vegetation field point collection Data/Logistic Satellite requirements images, Topo maps Biomass, carbon stock, and Data species diversity processing inventory and analysis

Results and Discussion

Conclusion and Recommendation

Figure 1: Conceptual diagram of the study

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

2.0 MATERIALS AND METHODS

2.1 Study area 2.1.1 Location, vegetation characteristics and compartment designation Bobiri Forest Reserve (BFR), the study area is located approximately 30km east of Kumasi, Ghana. The Reserve lies between latitude 60 40’ and 60 45’ North of the Equator and longitudes 10 15’ and 10 22’ West of the Greenwich (Figure 2). The topography is gently undulating between 183m and 248m above sea level. The direction of the slope is in the north-west to south-east, which is the general flow of all streams in the reserve. It covers an area of 54.6km2 (21.1sq. miles) and falls under the Juaso Forest Reserve which is within the Ejisu-Juaben district (637.2 km2) of Ashanti Region. Bobiri Forest Reserve is within the tropical moist semi-deciduous Forest Zone (Hall & Swaine, 1981). It experiences an annual precipitation of between 1200 and 1750mm. In the months of December and March, the area experiences its main dry season with a shorter dry season in August. Rainfall is bimodal. This is experienced from March to July and from September to November. The reserve prior to logging was diverse in species composition with Celtis species and Triplochiton scleroxylon species dominating. The forest reserve has been the experimental site for the three management systems: Post Exploitation System (PES), the Selection System (SS) and the Tropical Shelterwood System (TSS)

The reserve was created in 1939 when it was still an unexploited primary forest (Ghana Forestry Department, 1958). It has 73 compartments which are divided into 4; production, butterfly sanctuary, strict nature reserve and research compartments. The Reserve hosts the Bobiri Forest Arboretum with about 100 indigenous species on 1.7 hectare of land, the Bobiri Butterfly Sanctuary with about 340 butterfly species identified and the Bobiri Guest House. The Bobiri Forest reserve is under two separate managements: the production block is managed by the Forest Services Division (FSD) and the research, strict nature reserve and butterfly sanctuary blocks managed by the Forestry Research Institute of Ghana (FORIG).

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Figure 2: Map of study area (top left: Map of Ghana showing regional capitals. Top right: ASTER image 2007 showing boundary of silvicultural treatment systems. Bottom: compartment map of Bobiri Forest reserve showing compartments in the treated systems)

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Figure 3: Aster image of silvicultural treated systems with boundary of treated compartments.

2.1.2 Criteria for selection of study area Silvicultural systems date back in 1940s. Several plots were laid in Ghana, Cameroon, Cote d’lvoire, Liberia and Nigeria. In Ghana, Bobiri Forest is the active forest that has not been logged since the silvicultural interventions. The silvicultural treatment systems were conducted in the research compartments mainly in compartment 1,2,3,4,5,6,7 and 72. Some plots were laid in Compartment 19b which is the strict nature reserve to serve as control. It should therefore be noted that the use of Silvicultural treatment systems in this study refers to the treated system (TSS, PES, and SS) and the untreated system (control). Since the untreated system was carried out in the nature reserve, the use of nature reserve in this study means untreated system which is serving as the control. Hence, the names are referred to as such.

2.2 Remotely Sensed Data Aster image taken on 11th July 2007 was used for the study. The choice of the image was based on suitability and availability for the study area. Aster is a high spatial resolution multi-spectral imager onboard NASA’s Terra spacecraft which was launched in 1998 (Yamaguchi, et al., 1998). The surface reflectance product has nine spectral bands. Three

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

bands are located in the visible near-infrared (VNIR) and six in the short wave infra-red (SWIR) regions. The spatial resolutions for these two bands are 15m and 30m respectively (Table 2). The five bands located in the thermal infra-red region were not used in this study due to low spatial resolution. There was a two year lag between the image acquisition and field data collection; however, there is an assumption that the effect on the data analysis is negligible since no known disturbance has occurred in the area in the two-year period.

Compartment map of Bobiri Forest reserve showing compartments with treatments was used. The compartment map was scanned and geo-reference and was superimposed on the Aster image. A subset of the silvicultural treated system was created (Figure 3). This formed the image for the study. The subset created was loaded onto an iPAQ for the field data collection exercise.

The subset created from the Aster image of 11th July, 2007 was used in producing the aboveground biomass and carbon map. Vegetation indices and reflectance values were derived from the image.

A Topographic map with river and road maps (12th July, 2005) were used for geo- referencing of the compartment map.

Table 2: Characteristics of ASTER VNIR and SWIR subsystems (adopted from Yamaguchi, et al., 1998)

Subsystem Band number Spectral range(um) Spatial resolution(m) VNIR 1 0.52-0.60 15 2 0.63-0.69 3 0.76-0.69 SWIR 4 1.60-1.70 30 5 2.145-2.185 6 2.185-2.225 7 2.235-2.285 8 2.295-2.365 9 2.360-2.430

2.3 Software The following softwares were used for the study: Erdas imagine 9.2, ArcGIS 9.2, MS Excel 2007, ENVI 4.3, SAS system version 8.0, Gap light analyzer (GLA) version 2.0. Image processing was done with Arc Map 9.2 and Erdas Imagine 9.2. Vegetation indices were extracted from the Aster image using ENVI 4.3. Computation of all variables was done in MS Excel 2007, and all statistical analysis done using SAS system version 8.0 and MS Excel

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

2007. The GLA version 2.0 was used to analyze the digital hemispherical (fisheye) canopy photographs.

Pieces of Equipment used:

Fish eye camera (Canon digital camera with fish eye lens) was used for taking hemispherical photographs. Hemispherical canopy photography is one indirect optical technique that has been widely used in studies of canopy structure and forest light transmission. GPS and iPAQ were used for navigation and location of sample plots and recording of plot coordinates. A Diameter tape was used for dbh measurement and a surveyors tape (30m) was used for ground distance measurement.

2.4 Methodology 2.4.1 Flowcharts The methodological flow chart is indicated in Figure 4. It consists of an overlay of compartment map on rectified Aster image 2007 to produce an image of the silvicultural treated systems. Field data collection on dbh, species name and canopy photographs were used to estimate biomass and carbon densities, species diversity and LAI respectively. Vegetation indices and reflectance bands were extracted from the remotely sensed data (ASTER 2007) and a relationship established between vegetation indices and reflectance bands with LAI and carbon stocks. Finally, evaluation of the silvicultural treatment systems was done based on the statistical tests for species diversity and carbon densities.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Geo- ASTER Image of Field referenced image study data compartment inventory 2007 area Abovegrou map of Bobiri nd biomass forest Tree inventory reserve species Extraction of Forest Image diversity vegetation indices Canopy preprocess and reflectance Estimation of photographs ing bands biomass using Allometric Diversity equations estimation using Selection Photograph Shannon index of treated equation compartm Spectral analysis using ents VIs and Abovegrou Gap light analyzer reflectan nd Rectifie ce bands biomass d image Diversity index Leaf per treated Area AGB and untreated Index conversion to system (RQ4) carbon Overlay in Correlation GIS and and sub-setting regression statistics Carbon in Erdas analysis stocks Statistical (R1) analysis (ANOVA) test Effect of treatments on carbon stocks conversion (RQ 3)

Relationship Statistical between effects of carbon stocks Sequestered species & LAI with VIs carbon map (RQ5) & reflectance of study bands (RQ7) area (RQ 2)

Figure 4: Methodological flowchart of research work

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

2.4.2 Image pre-processing Level 1B of Aster image taken on 11th July 2007 was imported using ERDAS Imagine 9.2 This was done because the geographic co-ordinates for each ASTER scene are automatically stored in various places in the HDF and *met files which needs to be converted to *img files. The three VNIR bands and the six SWIR bands were imported separately and re-sampled using layer stack in ERDAS imagine 9.2

By default, the image was set in the WGS 84 Ellipsoid or Geographic Coordinate system. This default projection system was adopted and used because currently, Ghana is using the WGS 84 projection system.

Radiometric corrections were not performed on the image. This is because the image might have been taken on a single date with clear skies and low cloud cover which did not make atmospheric correction and haze correction in ATCOR possible.

Geo-referencing of the compartment map was done using tie points of the image, ground control points collected from the topographic map, rivers and road maps, and points from the field.

The ASTER image and Compartment map of Bobiri Forest reserve were further geo- referenced using image to map registration. This is because when overlaid, some overlaps occurred. A second order polynomial was used in the registration. A root mean square (RMSE) of 0.35 was obtained. This positional error is quite good since its well below 0.5 of a pixel. This image was used for data collection and production of maps.

2.4.3 Above ground biomass and carbon stocks inventory The importance of estimating carbon stocks in a non destructive manner has been emphasized by Sierra, et al., (2007) instead of the most accurate destructive method (de Gier, 2003). Many studies on biomass estimation have focused on above ground forest biomass (Brown, 1997; de Gier, 2003; Losi, et al., 2003) because majority of the total accumulated biomass in the forest ecosystem is stored above ground (Brown, 2002; Zheng, et al., 2004). Assessing biomass in forest systems is important for determining production purposes and for environmental management that is in determining the density of wood available for sustainable purposes is mostly achieved through biomass assessment. One of the advantages of biomass assessment is in monitoring carbon sequestration, in knowing how much carbon is lost or accumulated over time (Harmon, 2001; Zheng, et al., 2004).

FAO, (2005) defines biomass as “the organic material both above and below the ground, and both living and dead eg. trees, crops, grasses, tree litter, roots etc.” the main components of carbon pools in any forest ecosystem are mainly aboveground biomass (AGB), belowground biomass (BBG), dead wood, litter and soil organic matter (FAO, 2005;

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

IPCC, 2003,2006). With ease in logistical problems in field measurement, majority of biomass assessment focuses on AGB which accounts for the greatest fraction of total living biomass in a forest (Brown, 1997).

2.4.3.1 Methods for mapping aboveground biomass and carbon stocks The conventional methods of estimating biomass on small scale often results in good results, however, on a large scale, such methods cannot provide the spatial distribution of biomass. Notwithstanding this challenging issue, carbon sequestration by forests requires biomass assessment over large area. Remote sensing methods have been one key technique extensively used for mapping and monitoring of vegetation (Boyd, et al., 2002; Brown, 2002; Lu, et al., 2004). Many studies have employed the use of Remote Sensing (RS) techniques for assessing biomass on large scales (Dong, et al., 2003; Foody, et al., 2003; Foody, et al., 2001; Heiskanen, 2006; Lu, et al., 2004; Steininger, 2000; Zheng, et al., 2004). It presents a feasible way of acquiring forest stand parameter information at a reasonable cost with an acceptable accuracy due to its data advantages; these include repeated data collection, multi-spectral and multi- temporal images, fast digital processing of large quantities of data and compatibility with Geographic Information System (GIS) (Lu, 2006). There has been some observed high correlations between spectral data derived from remotely sensed data and vegetation parameters which make RS data a primary source for large scale AGB mapping. AGB as stated in Lu, (2006), can be estimated from remote sensing in using different approaches such as multiple regression analysis, K-nearest neighbor and neural network.

Typically, the estimation of forest variables using RS data specifically optical remote sensing data has been based on empirical relationships formulated between the forest variables measured in the field and satellite data, often expressed in the form of spectral vegetation indices (SVI). However, these empirical relationships are often affected by various factors including canopy closure, understory vegetation and background reflectance (Spanner, et al., 1990) cited in (Heiskanen, 2006). A set of vegetation indices have been developed to account for the background reflectance in soils (soil adjusted VI). Also some studies have stated the inclusion of the short wave infra-red (SWIR) into SVIs to unify different cover types and reduce the background effects (Brown, et al., 2000; Nemani, et al., 1993). Therefore a good understanding of the relationships between biomass and RS spectral data is a prerequisite for developing appropriate biomass estimation models (Steininger, 2000).

In this study, only above ground live tree biomass was considered. This included all above ground live tree biomass ≥5cm DBH. This dbh value was adopted from Brown, (2002) who found appreciable amount of aboveground biomass from ≥5cm dbh the 10cm dbh. Again Sierra, et al., (2007), used this dbh value in a secondary forest and ≥10dbh in a primary forest. This suggests that 5cm dbh value in secondary forest contributes to carbon stock

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

accounting. Grasses, herbs, shrubs and litter condition found in each plot were remarked and used in the discussion when necessary.

2.4.4 Species diversity estimation The density of the species in terms of number of individuals per unit area has been the most common measure of species richness (Montagnini & Jordan, 2005). Tree species diversity in tropical forest has been the focus for estimating species diversity simply because they are easier to see and count (Montagnini & Jordan, 2005). Species richness is an indicator of the relative wealth of species in a community. In spite of the many methods proposed for calculating species richness, direct count of species in samples is considered the most effective richness measure (Huang, et al., 2003). Direct species count, though lacks theoretical elegance, provides one of the simplest, most practical and most objective measures of species diversity (Bunge & Fitzpatrick, 1993) with a key factor of equal sample size. In this study tree species ≥5cm were used in the Shannon’s index for estimation of species diversity.

2.4.5 Vegetation Indices Vegetation index is a single number that quantifies vegetation biomass and/or plant vigour for each pixel in a remotely sensed image (Glenn, et al., 2008). The index is computed using several reflectance bands that are sensitive to plant biomass and vigour. Vegetation indices (VIs) are among the oldest tools in remote sensing studies. It generally depends on forest biomass and the surface area of the green vegetation (Weier & Herring, 2009). Numerous studies have shown that satellite-derived VIs are optical measures of canopy "greenness", a composite property of leaf chlorophyll content, leaf area, canopy cover and structure. In general, SVIs attempt to enhance the spectral contribution of vegetation while minimising that of the background, more specifically, when combination of red and near infrared bands are used (Heiskanen, 2006). ASTER image was employed in the VIs extraction because of its high spatial resolution in the visible to near infrared bands and the short wave infra red bands (Yamaguchi, et al., 1998). Table 3 shows the vegetation indices used in this study.

One of the most common vegetation index used in many applications of forest biophysical parameters is the Normalized Difference Vegetation Index denoted as NDVI. NDVI compares the reflectance values of the red and near-infrared regions in the electromagnetic spectrum (Weier &Herring, 2009). The NDVI value, which ranges from 0 to 1.0 for each pixel in an image in vegetation, helps identifying areas of varying levels of plant biomass/vigour. Higher values indicate high biomass/high vigour. Foody, et al., (2003) tested several VIs and found that NDVI was never among the vegetation indices defined in terms of strength of correlation with biomass of sample plots. However, the shortwave infra-red modification to NDVI denoted as NDVIc was found to correlate well with biomass. Zheng, et al., (2004) used five VIs and found NDVIc to best predict AGB. Moreover, NDVIc

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

helps in accounting for under storey effects and is useful in secondary forests (Zheng, et al., 2004). The Simple Ratio (SR) vegetation index calculates the ratio between the near infrared band and red bands (Jordan, 1969). An advantage of using the SR ratio is that, it minimizes shadow effect especially in areas of high mountains (Boschetti, et al., 2007). Heiskanen, (2006) and Lu, et al., (2004) confirm that SR significantly correlates with AGB. The shortwave modification to simple ratio called the Reduce Simple Ratio (RSR) has also been reported in studying the relationship of VIs with biomass. Brown, et al., (2000), remarked that, RSR reduces the effect of background reflectance’s and its sensitive to change in LAI. These vegetation indices were used in the study and represented in Table 3. In the forested environment, bare soil is rarely visible and the definition of soil line is difficult and discontinuous (Heiskanen, 2006). Hence the inclusion of soil adjusted vegetation indices in this study is of no use.

Vegetation has a high near-infrared reflectance, due to scattering by leaf mesophyll cells and has a low red reflectance, due to absorption in chlorophyll pigments. The value of the NDVI for vegetation will hence tend towards one. By contrast, clouds, water and snow have a larger red reflectance than near-infrared reflectance and these features thus yield negative NDVI values. Rock and bare soils have similar reflectances in the two bands and results in the values of NDVI near zero.

Table 3: Vegetation indices used in the study Indices Equation Equation no. NDVI NIR-RED/NIR+Red 1 NDVIc NIR-Red/NIR+Red*(1-(SWIR-SWIRmin/SWIRmax- 2 SWIRmin)) SR NIR/Red 3 RSR NIR/Red*(1-(SWIR-SWIRmin/SWIRmax-SWIRmin)) 4

2.4.6 Sampling design

2.4.6.1 Aboveground Biomass and carbon inventory Multistage sampling was used in the study. The area of interest within Bobiri Forest makes this a sampling design suitable. The Silvicultural treatment systems are located within the research compartments and nature reserve, using two-stage sampling the compartments served as primary units and the treated and untreated systems as secondary units. Two- stage sampling has the advantage of providing good estimates of timber inventories and runs at a very low intensity sample in all stands (Shiver & Borders, 1996). Sample Plots

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

were then selected randomly in each Silvicultural treatment system. For purposes of validation and comparison, some sample plots were laid in the nature reserve/untreated system as control.

A reconnaissance survey was carried out to acquire information on the total area in hectares of each Silvicultural treatment system. The number of plots to be sampled were then determined. A precision level within 10% of the true mean of biomass, at the 95% confidence level was set (Pearson, et al., 2005).

Circular plots of size 500m2(12.62m on flat terrain) (Pearson, et al., 2005) was employed in the data collection during field work. The circular plot was chosen because it is easy to delineate in the field and the number of border line trees was easier to exclude in measurements than any other shape.

Within each circular plot, three nested circular plot (Brown, 1997; IPCC, 2003; Pearson, et al., 2005) design of size 12.6m (lager plots), 8m (medium), and 4m (small plots) were used (Figure 5). This is to ensure highly variable representation of live-woody plants of different ages and also reduce double counting. Houghton, (1997) recommended the use of nested circular plots for carbon inventory in tropical ecological zones where tree age variation is highly dominant.

Large plot Radius 12.62m, medium plot Trees >50cm DBH Radius 8m Trees 16-49cm DBH small plot radius 4m ≥5cm-15cmdbh

Figure 5: Layout of the three circular nested plots

In the silvicultural treatment systems each treated compartment had a number of Permanent Sample Plots (PSP) in which treatment was carried out. The PSPs are referred to as Girth Increment Selection Plots (G.I.S.P). The plots are made up of 10000m2 (1ha) each distributed within the Silvicultural treatment systems. The PSP are further divided into 25plots (quadrants). These quadrants each measuring 20mx20m (0.4ha) are

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

numbered back and forth along the principal axis following the procedures laid down by Baidoe, (1970) to facilitate data collection and analysis.

The geo-referenced compartment map was overlaid on the image and the coordinates of each G.I.S.P plot noted. Random selection of ten (10) G.I.S.P plots was first made on the geometrically corrected Aster image for each of the silvicultural treated systems. Later six (6) of the ten (10) plots were selected based on accurate location inside a G.I.S.P plot. These G.I.S.P plots were positioned in the field with the use of an iPAQ with in-built GPS and a Garmin GPS. The GPS and the iPAQ were set in WGS 84 projection to locate the boundaries/pillars of each G.I.S.P plot randomly selected. In the field, four sample plots were selected randomly from each of the Six (6) G.I.S.P plot yielding a total of 24 sample plots per treated system. The slope of each plot was then recorded with the use of a sunto clinometer. This was to ensure the recommended horizontal 500m2 plot for the measurement of AGB and carbon stock inventory (Pearson, et al., 2005). Only two G.I.S.P plots were established in the nature reserve (untreated system), so random generation of sample plots in ArcGIS was not necessary.

The procedure laid down in Pearson, et al., (2005) in calculating the number of plots from a preliminary data using the mean and standard deviation was not employed in this study. This is because the number of sample plots calculated using this approach gave sample plot numbers that were not enough for statistical analysis. Since statistical analysis was a major component in this study, a minimum of 24 plots in each treated and untreated system was set up. Ideally, 30 plots has been the recommended minimum number of sample plots for statistical analysis (Michael Wier, personal communication: August 21, 2009). The 24plots was set due to time constraint and inadequate logistical resources. However, the total numbers of sample plots (96) were good enough to be used for statistical analysis (Michael Wier, personal communication: October 5, 2009).

Using a 30m tape, 10cm was measured from one end of a selected G.I.S.P quadrant (sample plot) and 12.62cm measured into the plot to locate the centre of the plot. At the centre, the three nested circular plot was laid. The diameter at breast height (DBH) was measured at a height of 1.3m. All trees within each circular plot were enumerated based on the respective circular nest employed, however when two 500m2 circular plots within a 0.4ha plots intercept, the affected trees were counted twice. When the circular plot falls outside the G.I.S.P treated plot (1ha), affected trees were excluded from enumeration (Figure 6). This is because such trees were not included in the treatment but in reality such trees seldom occur, sometimes not all. The coordinates of the centre plot were noted using the iPaq.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

a. Tree s in thi s region were exclu ded b.

20m

10000

Trees in this region were counted twice

Figure 6: Sketch of the layout of a G. I. S. P plot (a) and layout of 500m2 sample plots within two adjacent sub quadrants (b)

2.4.6.2 Species diversity inventory A 500m2 circular plot was used in the species diversity inventory. In the circular plot, all live trees ≥5cm were recorded with their local names and dbh values. Since the same 500m2 plot was used for both species diversity and biomass inventory, dbh of aboveground live trees between 5-15cm found within 8m and 12.6m radii and 16-49cm found within 12.6m radius were recorded under species diversity estimation and not under biomass estimation.

2.4.7 Estimation of biomass per tree Currently, there is no local biomass equation developed for Ghana, therefore the above ground tree biomass (Y in kg) for each measured tree was estimated using the FAO recommended allometric equation for broadleaf forest in moist tropical African zones, (1500-4000m). This equation is updated (Pearson, et al., 2005) (Equation 1) and was developed by Brown, (1997).

Aboveground tree biomass (kg/tree) Y =exp(-2.289+2.649*lnDBH-0.021*ln(DBH)2) 1.

Where DBH is the measured tree DBH in cm, therefore In (DBH) is the natural logarithm of DBH

Equation 1: Allometric equation used for estimating aboveground tree biomass

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

2.4.8 Total above ground carbon stocks The sample plot tree biomass values were converted to tree carbon stock (kg.ha-1C) by multiplication with the carbon fraction of biomass. A biomass to carbon conversion factor of 0.5 (Brown, 1997; IPCC, 2003) was used. The carbon analysis was done per plot. Extrapolation from sub plot level to hectare level was done for all sub concentric circles to produce carbon stock estimates. Extrapolation occurs by calculating the proportion of a hectare (10000m2) that is occupied in a given sample plot. Expansion factors for subplot with radius 4m, 8m and 12.62m were calculated and each subplot biomass scaled down to per hectare. Finally the carbon density for each silvicultural treatment system was obtained by summing up all carbon densities for each sampled plot location. The total carbon per treatment system was then converted to tons per hectare (tons/ha or Mg/ha). Finally, where necessary, further conversion to Giga grams (Gg) was done.

The mean carbon density for each silvicultural treatment system was determined by averaging the carbon densities of all sampled plots. Product of the mean carbon density and total size of each Silvicultural treatment system therefore yielded the total sequestered aboveground biomass of that particular Silvicultural treatments system.

2.4.9 Species diversity index Shannon index also known as Shannon-Wiener index (Equation 2) was used in estimating the diversity of the species for each silvicultural treatment system. Shannon index estimates the diversity of the species in an ecosystem by taking into consideration the abundance and evenness of the species observed. Unlike Simpson’s index which favours dominant species Magurran, (2004), Shannon index does not favour dominant species or rare species in its calculation.

Where pi is the proportion of individuals from the ith species, In(pi) is the natural log. of pi

Equation 2: Shannon-Weiner Diversity Index for estimating species diversity

Species were categorised into the various star ratings (Appendix E) to enable the reader be aware of the commercial values of these species in Ghana. Species richness was calculated through direct count of species in the sample plot and equitability was calculated using

Shannon’s equitability index (Eh). This is calculated by dividing H by Hmax. Equitability assumes a value between 0 and 1 with 1 being complete evenness. Table 4 defines the variables used in the diversity and equitability index.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Shannon’s equitability (Eh) = H/Hmax

Where Hmax is lnS

Table 4: Variables used in Shannon diversity index, and equitability Variable Meaning H Shannon’s diversity index S Total number of species in the community (richness) Pi Proportion of S made up of the ith species Eh Equitability (eveness)

2.4.10 Computation of variables and statistical analysis The raw data was organised in MS Excel 2007. These data were used to compute the various estimates (biomass and carbon, species diversity and LAI). A parametric test was performed because the biomass and diversity means were normally distributed (Appendix F). The General Linear Model (GLM) procedure in SAS version 8,(1999) was used for the ANOVA test. The use of GLM in SAS (1999) has been recommended by Littell, et al., (1998) as a procedure for correctly computing test for hypothesis in ANOVA. A one-way ANOVA was used for the mean comparison of carbon and species diversity. Multiple comparison tests using Tukey was done for pairwise significant difference. Tukey test is used with means of equal sample sizes (Bluman, 2004)

2.3.10 Relationship between vegetation indices and ASTER bands with LAI and carbon stocks ENVI 4.3 (Environment for Visualizing Images) software was used in the extraction of vegetation indices. Spectral signatures of each plot from the ASTER 2007 image were extracted to examine the relationship between mean sequestered carbon and LAI with the ASTER data. The vegetation indices: NDVI, NDVIc, RSR and SR were calculated based on the respective formulae as independent variables (Table 3). The reflectance values for the respective bands were also used. Thirty plots were sampled with subsequent thirty field points on the image. Canopy photographs were taken with the fish eye camera. LAI and percentage openness were deduced from the canopy photographs. ASTER band 2 was used as red band, band 3 as near-infrared band and band 4 as a shortwave-infrared band in the equations. Vegetation indices with soil component were not used in this study because bare soil background was not prominent in the forest environment of the study area.

Correlation and Regression analysis were carried out in Excel 2007 to explore the relationship between ASTER data (reflectance bands and vegetation indices) with the

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

forest parameters (carbon stocks and LAI). These relationships were represented in scatter plots. First, correlation between LAI and carbon (biomass) was established. Each VI and the reflectance bands were then correlated with LAI and mean carbon stocks. Models on the above relationships were developed using stepwise regression models.

2.4.11. Mapping of aboveground carbon stocks A pixel based approach using a classified image was used for the carbon mapping. An unsupervised classification was run on the ASTER (11th July, 2007) image in Erdas imagine version 9.2. The image was first classified into 30 classes based on the site reflectance values. The resulting classes were then merged into three classes based on percentage canopy openness (high, medium, low). A supervised classification was then carried out based on canopy openness. For validation purposes of the classified image and accuracy assessment, the percentage canopy openness data obtained from the hemispherical image analysis, were used together with the 30 field point data. A correlation analysis was established between percentage canopy openness and aboveground biomass.

In ArcGIS version 9.2, the classified image was reclassified using the spatial analyst tool. Each canopy openness class was assigned its respective carbon values. This was deduced from the correlation analysis performed.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

3.0 RESULTS

3.1 Aboveground biomass and carbon stocks in the silvicultural treatment systems 3.1.1 Total treatment area The total area of the silvicultural treated systems and untreated system currently is 1225.86 ha. The area consist of Tropical shelterwood system (TSS; 47%), Post exploitation system (PES; 39%), Selection system (SS; 10%) nature reserve (4%) (Figure 7). Two compartments were initially set up under the selection system but one is currently being logged.

600

500

400

300

Area/ha 200

100

0 TSS SS PES NR

Silvicultral treatment systems

Figure 7: Area in hectares of the various silvicultural treatment systems

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

3.1.2 Classified image of the three Silvicultural treated systems The overall classification accuracy was 76.67% with kappa statistics of 0.6447. The classified map was grouped into three classes based on canopy openness of the forest (Figure 8). These resulted in high, medium and low canopy classes. The percentage of openness ranged from 6-11%. Canopy openness was high in PES and low in TSS

Figure 8: Supervised classification of the silvicultural treated systems

3.1.3 Relationship between biomass and canopy openness The relationship between canopy openness was poor using all the 30 data. The relationship was improved when outliers were removed (Figure 9). The negative relationship depicts that aboveground biomass is inversely proportional to canopy openness (r=0.89).

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

r=-0.89143

700.0

600.0

500.0

400.0

300.0

200.0

100.0

0.0

Mean Mean biomass(Mg/ha) 0.0 5.0 10.0 Canopy openess(%)

Figure 9: Correlation between canopy openness and aboveground biomass (Mg/ha)

3.1.4 Aboveground biomass distribution in the silvicultural treatment systems An estimated amount of 1270.70Gg of dry aboveground biomass with mean of 933.84±138Mg.ha-1 (±S.E) was recorded in the silvicultural treatment systems. With respect to distribution status, Tropical shelterwood system accumulated an amount of 671.67Gg of AGB contributing to 53% of the total AGB estimated. With an amount of 130.53Gg of AGB, the selection system contributed 10% of the total AGB estimated. The Post exploitation system (437.03Gg of biomass) accounted for 34% of the total AGB estimated. The nature reserve which served as the control had an estimated amount of 31.46Gg of AGB (3%). However, mean AGB of the silvicultural treatment systems are higher in TSS and SS than in PES with NR being the least (Figure 10).

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

1200.00

1000.00

800.00

600.00

400.00

200.00 Mean Mean biomass(Mg/ha) 0.00 TSS SS PES Nature reserve

Silvicultural treatment systems

Figure 10: Aboveground biomass densities in the Silvicultural treatment systems

3.1.5 Spatial distribution of above ground biomass in the three silvicultural treated systems Due to the negative relationship observed in the correlation analysis (Figure 9), the treated system with the highest biomass density was assigned to the canopy class with low canopy openness. TSS was estimated with the highest biomass density and PES with the lowest biomass density. Spatial representation of aboveground biomass is high in TSS and low in PES in the silvicultural treated systems (Figure 11).

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Figure 11: Spatial Distribution of Aboveground biomass in the three silvicultural treated systems

3.1.6 Mean carbon densities in the Silvicultural treatment systems Since carbon is directly measured from AGB estimation, distribution of mean carbon densities for the silvicultural treatment systems followed a pattern similar to the AGB densities (Figure 12). Total sequestered carbon in the silvicultural treatment systems amounted to 635.35Gg with density of 466.92±69Mg.ha-1. Total sequestered carbon in the three silvicultural treated systems amounted to 619.62Gg with density of 530.67±38Mg.ha-1 (±S.E). With similar sequence in biomass, TSS sequestered 335.84Gg, SS sequestered 65.27Gg, PES sequestered 218.51Gg of carbon and the nature reserve sequestered 15.73Gg of carbon. The mean carbon densities in each silvicultural treatment system are represented in Figure 12.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

600.00

500.00

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200.00 Mean Mean C (Mg/ha)

100.00

0.00 TSS SS PES NR

Silvicultural treatment systems

Figure 12: Mean carbon densities (Mg.ha-1) in the silvicultural treatment systems

3.1.7 Spatial distribution of aboveground carbon densities in the silvicultural treated systems The spatial distribution of aboveground carbon stocks depicts similar trends in aboveground biomass. Similarly, TSS with the highest carbon density was assigned to the low canopy openness and PES, to the high canopy openness. Spatial distribution of Carbon stocks were high in TSS and low in PES (Figure 13)

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Figure 13: Spatial distribution of Carbon density (Mg.ha-1) in the three silvicultural treated systems

A significant difference was observed between the carbon means, (p <0.05; Appendix G). This indicates that there exists significant difference in the carbon means. Pair wise significant test showed significant difference in PES and NR (Table 5).

Table 5: Pairwise comparison test of Mean carbon density in the silvicultural treatment systems Treatment Systems Carbon density (MC/ha) 95% confidence interval TSS 586.86 A 551.82-621.90 SS 547.33 A 512.29-582.37 PES 449.48 B 414.43-484.52 NR 275.68 C 240.64-310.73 Standard error of mean carbon= 17.6 Means with the same letter (superscript) are not significantly different at 95% confidence limit

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

3.1.8 Distribution density of DBH size classes in the silvicultural treatment systems The total trees enumerated in the three silvicultural treatment systems amounted to 2951. Trees in the nature reserve amounted to 647. The DBH classes in the 5-15cm class were the highest recorded in all three treated systems as well as the nature reserve. Tropical shelterwood system had the highest number of trees in the 16-49cm and 50+ cm classes, selection system had the least number of trees in the 16-49cm DBH class and post exploitation system had the least number of trees in the 50+cm class. Nature reserve was the least in stocking density in all three dbh size classes. A total of five (5) trees were recorded in a DBH class above 148cm but these trees were not used in the analysis. The tree stocking density of each dbh size class in the Silvicultural treatment systems is represented in Table 6.

Table 6: Tree stocking density of DBH size classes in the Silvicultural treatment systems Silvicultural Systems 5-15. 16-49 50+

SS 4775 469 40

TSS 4377 945 60

PES 3780 796 30

NR 2984 547 20

3.1.9 Percentage contribution of DBH size classes to mean carbon densities in the Silvicultural treatment systems Sequestered carbon is high with large dbh class than with small dbh class (Figure 14). Tree density in 16-49 dbh class sequestered the highest carbon in all silvicultural treatment systems. The highest tree density was recorded in the 5-15cm dbh class however, this class accounted for the least carbon in all four silvicultural treatment systems.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Selection system Tropical shelterwood system

5-15. 50+ 5-15. 19% 50+ 36% 18% 33%

16-49 16-49 45% 49%

Post exploitation system Nature reserve

5-15. 5-15. 50+ 50+ 18% 20% 25% 30%

16-49 16-49 57% 50%

Figure 14: Contribution of dbh size classes to sequestered carbon in each silvicultural treatment system

3.2 Species diversity 3.2.1 Tree species composition in the Silvicultural treatment systems A total of 4295 trees enumerated in the silvicultural treatment systems belonged to 25 families and 55 genera. The individual composition of trees sampled with scientific, local and family names as well as the number of trees are represented per treatment system in Appendix A, B, C and D . The percentage contribution of trees enumerated was higher in each of the three silvicultural treatment systems than in the nature reserve with TSS (26.4%), PES (28.4%), SS (24.8%) and NR (20.4%). Funtumia elastica, Pterygota

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

macrocarpa, Sterculia tragacantha, Celtis zenkeri and Rinorea obligifelia were the commonly encountered species in the three silvicultural treated systems (Appendix A, B, C) whilst Baphia nitida, Hymenostegia afzelii and Nesogordonia papaverifera were the most encountered species in the nature reserve (Appendix D)

A total of 70 different species were identified in the 96 sampled plots. Species richness was low in TSS (57) and high in SS (64) (Table 8). All species identified in TSS were also identified in PES, SS and NR (Appendix A, B, C, D). In addition, some species were only identified in one or two treatment systems: Milicia excelsa, Omphalocarpum elatum, Uapaca guineensis, and Tetrapluera tetraptera were found only in the Selection System. Cleistopholos patens and Rauvolfia vomitoria were also found only in SS and PES. Cola nitida and Terminalia superba were found only in SS and NR. Daneilla ogea and Dialium aubrevilla found only in NR.

Star rating category of the 70 identified species is represented in percentage in Figure 15. 14(20%) species identified are categorized as red star species, 17 (24%) are pink star species and 9 (13%) are scarlet star species. Majority (40%) of the species identified (28) belonged to the green star species. However, Mammea Africana and Xylia evansii, where the only species identified as blue star (3%).

blue 3% red scarlet 20% 13%

pink green 24% 40%

Figure 15: Species star rating of the 70 species identified

The Selection system contains most of the scarlet and red star species. Nature reserve was least in number of scarlet species. However, the figures observed in species composition are very similar in all the silvicultural treatment systems (Table 7).

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Table 7: Treatment type and its respective number of species in each star rating category Treatment scarlet red Pink green blue Total type TSS 8 10 11 26 2 57 PES 8 10 14 27 2 61 SS 9 12 13 28 2 64 NR 7 10 14 28 2 61 total 243

3.2.2 Tree Stand form and density in the silvicultural treatment systems The stocking of pole size (5-30cm dbh) is higher in PES and TSS than in SS and NR (Figure 16a), subsequently commercial trees (30+dhb) are higher in TSS and PES than in SS and NR. However, mean tree size is greater in TSS and SS than in PES and NR (Figure 16b)

1000 Commercial Poles 18.5 900 18 800 700 17.5 600 17 500 16.5 400 Stocking/ha 300 size tree Mean 16 200 15.5 100 15 0 TSS PES SS NR TSS PES SS NR Silvicultural Treatment systems silvicultural treatment systems a b.

Figure 16: Distribution of commercial and pole size trees (a) and mean tree size (b) in the silvicultural treatment systems

3.2.3 Tree diversity in the silvicultural treatment systems The Shannon index derived for the selection system, tropical shelter wood system, post exploitation system and nature reserve are represented in Table 8. Tropical shelter wood

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

had the least species diversity explained by the lowest equitability and the least species richness. The selection system had the highest species diversity. The indices when subjected to analysis of variance test, revealed a significant difference (p<0.05; Appendix I).

Table 8: Species diversity and richness in the silvicultural treatment systems Treatment Total number of Species richness Shannon index Species trees per treatment equitability (evenness)

TSS 1135 57 2.44 0.60 PES 1218 61 2.67 0.64 SS 1064 64 2.69 0.64 NR 878 61 2.67 0.64

Pair wise comparison test resulted in a significant difference in TSS (Table 9). Thus TSS is significantly different in terms of species diversity from the other silvicultural treatment systems. Although a significant difference occurred, species diversity was similar in the nature reserve compared to SS and PES.

Table 9: Pairwise comparison test of species diversity in the silvicultural treatment systems

Treatment Systems Shannon Diversity index 95% Confidence interval SS 2.69 A 2.57-2.81 PES 2.68 A 2.55-2.80 NR 2.66 A 2.54-2.78 TSS 2.44 B 2.31-2.56 Standard error of diversity = 0.06 Means with the same letter are not significant at 95% confident limit

Table 10 summarises the silvicultural treatments and its impacts on species diversity and carbon sequestration. Whiles TSS resulted in high amount of sequestered carbon, it was the least in species diversity. Selection system was the highest in species diversity, also carbon sequestered was high. The nature reserve was least in sequestering carbon but has maintained most of the timber species.

Table 10: Summary of impacts of Silvicultural treatment on carbon sequestration and

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

species diversity Silvicultural Treatment carried Mean Carbon value Species diversity system out index Tropical shelter Tending, weeding 586.86±18 2.44 wood system (TSS) and poisoning of trees 10yrs before harvesting Post exploitation Tending, weeding 457.82±19 2.67 system (PES) and poisoning of trees 10yrs after harvesting Selection system Selective harvesting 547.32±17 2.69 (SS) of undesired species Nature reserve No treatment 275.68±19 2.67 (NR)

3.3 Relationship between LAI and Mean carbon stocks with ASTER bands and selected Vegetation indices

3.3.1 Correlation between Forest parameters and Aster data There was weak correlation between the forest parameters with Aster data (Table 11). howerver, LAI corelated negetively with ASTER reflectance bands and positively with Vegetation indices. Mean C did otherwise. Generally the corelations were better (though weak) between the ASTER data with Mean carbon than with LAI (Table 11). Band 2 coresponding to red band showed the a good correlation with Mean carbon (r=0.297) and band 4 corresponding with shortwave infrared band showed a good correlation with LAI (r=-0.313). The weakest correlation was observed in Band 3 with LAI and Mean C (Table 11). There was no considerable difference in correlation with the logarithmic transformation of the reflectance bands (Table 11). Moreover, the removal of outliers did not improve the relationship so are not shown in the results.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Table 11: Correlation of the LAI and Mean C against the ASTER reflectance bands and

spectral vegetation indices

LAI Mean carbon band 2 -0.101 0.297 band 3 -0.002 0.026 band 4 -0.313 0.239 Log(band 2) -0.101 0.284 Log(band 3) -0.001 0.025 Log(band 4) -0.315 0.237 NDVI 0.116 -0.135 SR 0.061 -0.126 RSR 0.061 -0.126 NDVIc 0.116 -0.135 correlation are not significant at 95% confident level

3.3.2 Correlation analysis between LAI with ASTER bands and selected vegetation indices LAI is ploted against reflectance bands 2, 3 and 4 and the selected vegetaion indices (Figure 17). The scatter plots of reflectance bands and vegetation indices versus Mean carbon are very similar to that of LAI so that of Mean carbon are not shown in the results. All the relationships observed were weak and directionless.

scatter plot of LAI and RED scatter plot of LAI and NIR band 4.2 4.2 4 4 3.8 3.8 3.6 LAI 3.6 LAI 3.4 3.4 3.2 3.2 3 3 76 78 80 82 80 85 90

RED NIR

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

scatter plot of LAI and SWIR scatter plot of LAI and NDVI band 4.2 4.2 4 4 3.8 3.8 3.6

3.6 LAI LAI 3.4 3.4 3.2 3.2 3 3 46 48 50 52 54 0.00 0.02 0.04 0.06

SWIR NDVI

scatter plot of LAI and scatter plot of LAI and SR NDVIc 4.2 4.2 4 4 3.8 3.8 3.6 3.6 LAI LAI 3.4 3.4 3.2 3.2 3 3 0.00 0.02 0.04 0.06 1.00 1.05 1.10 1.15

NDVIc SR

scatter plot of LAI and RSR 4.2 4 3.8

LAI 3.6 3.4 3.2 3 1.00 1.05 1.10 1.15

RSR

Figure 17: Scatter plots of LAI with reflectance bands and vegetation indices

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

The coefficients of determination resulting from the linear regression models were weak and varied from 0.000 to 0.097 for LAI (Table 12) and from 0.001 to 0.088 for Mean carbon (Table 13). The relationship observed between LAI with band 4 (R2=0.097) and mean carbon with band 2 (R2=0.088) were fairly higher than the rest of the relationships. Generally, variation indicated by the R2 values which is explained by the linear regression models for the independent variables were very weak. None of the models was reported due to very weak relationships.

Table 12: Linear regression model of LAI, ASTER bands and SVIs

Independent variable R R2 p value band 2(red) -0.101 0.010 0.974* band 3 (NIR) -0.002 0.000 0.976* band 4 (SWIR) -0.313 0.097 0.150* NDVI 0.116 0.014 0.419* SR 0.061 0.004 0.567* RSR 0.061 0.004 0.567* NDVIc 0.116 0.014 0.419* *Regression not significant at 95% confident level

Table 13: Linear regression model of Mean sequestered carbon, ASTER bands and SVIs

Independent variable R R2 p value band 2(red) 0.297 0.088 0.381* band 3 (NIR) 0.026 0.001 0.387* band 4 (SWIR) 0.239 0.057 0.132* NDVI -0.135 0.016 0.916* SR -0.125 0.018 0.435* RSR -0.125 0.016 0.435* NDVIc -0.135 0.018 0.916* *Regression model not significant at 95% confident level

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

4.0 DISCUSSIONS

4.1 Aboveground biomass in the Silvicultural treatment systems The principal aim of the silvicultural practices in Ghana mainly the Tropical Shelter wood System (TSS), Post Exploitation System (TSS) and the Selection System (SS) was to make management easier by simplifying the structure and composition of the forest (Mooney, 1963; Osafo, 1970). The interventions involved opening of canopy through the removal of undesirable species by poisoning prior to harvesting as done in TSS or after harvesting as done in PES. Under the SS, selective harvesting was done without any further intervention. These interventions were to favour the growth of desirable species and eliminate as much as possible the growth of undesirable species (Foli & Pinard, 2005).

However, detailed quantification of biomass in tropical forest ecosystem is of importance due to the role of forest in the global climate regulation. While the three silvicultural treated systems were managed for economic reasons, the realization of climate change effects requires the restoration of forest ecosystem to continually sequester carbon from the atmosphere. Despite this recognition, information on tropical forest biomass stocks are largely lacking (Sierra, et al., 2007). The overall mean of 1061±34Mg.ha-1 (± S.E) in the three silvicultural treated systems seems higher than values reported for tropical forest biomass (Brown & Lugo, 1992; Nascimento & Laurance, 2002; Sierra, et al., 2007; Woomer, et al., 2004). The research compartment of the Bobiri Forest reserve has a unique attribute which is characterised by high tree density with majority of the trees attaining canopy heights. This is justifiable due to no commercial harvesting activities over the past six (6) decades. According to Parren, (1999), the intervention carried out in Ghana (Bobiri Forest reserve) resulted in the best managed forest in West Africa in terms of environmental benefits. This may account for the high biomass values observed. The biomass equation used excludes trees with dbh above 148cm, hence the exclusion of the five (5) trees

Intervention carried out in TSS mainly opening of canopy from below may have resulted in less felling damages during harvesting. According to Parren, (2003), climbers present in a forest distorts the direction of felled tress which results in destroying of young seedlings, saplings and immature trees. The mean AGB in TSS amounted to 1173.72±38 Mg.ha-1. This high amount may suggest stands with good stem form. Moreover, Foli, et al., (2003) reported on excellent stem forms in TSS as opposed to PES and SS though more number of tree were enumerated in the latter than in the former. However, the use of poison in

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

eliminating undesirable trees is destructive to other ecosystem life forms and therefore unacceptable.

The presence of climbers and lianas is likely to cause felling damages in PES during harvesting. These damaged trees may have been removed during silvicultural operations 10 years after harvesting. Stands with poor stem forms were observed in PES yielding a low biomass value of 915.65±37 Mg.ha-1. In agreement with Foli, et al., (2003), the highest proportions of poor stand forms were observed in PES. However, for environmental quality and perpetual flow of ecosystem service, PES treated forest; with the use of poison is unacceptable since poisoning destroys other life forms necessary for ecosystem functioning.

Selective harvesting as practiced in selection system resulted in stands of good stem. This may result from the removal of uneconomic species which were suppressing the growth of desired species. The aim of the improvement thinning carried out during the stock survey to some extent has been achieved. Unlike in PES, harvesting in the selection system may be less destructive due to the improvement thinning in removing climbers. The biomass value observed, 1094.66±33 Mg.ha-1 , agrees with Foli, et al., (2003) who reported on stands with good stem form in SS. Moreover, Karani, (1970), reported the selection system of Ghana to be the highly successful treatment in West Africa. There is no doubt Ghana eventually opted for the selection system. However, in meeting current demands, managing forest with selective harvesting stands a better chance of contributing to ecosystem functioning and it’s considered environmentally acceptable.

The operations carried out in the three treatment systems significantly enhanced the biomass values compared to the untreated system (nature reserve). This may be due to the disturbance introduced into the system. According to Franklin, et al., (2002) and Whitmore, (1989), disturbance in forest brings about the forest cycles which influences the structure and composition of the forest. The low biomass value recorded in the untreated system agrees with Foli & Pinard, (2005) who found poor stem forms in the untreated forest (nature reserve) as compared to good and excellent stem forms in the three silvicultural treated systems. Since silvicultural treated systems significantly influenced biomass level, the method of intervention is as important as, in contributing to ecosystem services.

4.2 Sequestered carbon stocks in the silvicultural treatment systems Global terrestrial aboveground carbon stocks play an important role in global carbon cycle (Houghton, 2005). The overall aboveground carbon densities recorded in this study are fairly higher than values recorded in the tropical regions (Brown, 2002; Sierra, et al., 2007) and therefore supports the statement by Houghton, (2005) stated above. As reported in

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

literature, (Chambers, et al., 2007; Gibbs, et al., 2007) the level of carbon recorded in this study agrees with the fact that tropical forests sequesters more carbon making them an important sink and a natural “break” on climate change.

However, carbon stocks estimated in this study are higher than estimates reported by Benefoh, (2008) who studied carbon stocks in different land cover types. This difference can be explained by two factors: sampling in both productive (logged) and research (unlogged) sites as opposed to intensive sampling in only the research (unlogged) site as done in this study. Moreover, the objective of the treatments mainly was to maximise wood in the treated systems, therefore, the high biomass and carbon values observed. Secondly, slight difference in allometric equations (original and updated) used may have contributed to the difference observed.

Comparatively, carbon estimates are higher than estimates recoded by Dwomoh, (2009) who worked in the transitional zone of Ghana. An explanation may be due to the structural differences between the moist semi deciduous forest and the Dry forest of Ghana which holds different density of trees. Moreover, the fires that occurred in 1980s and the different allometric equations used in these two forests may have played a significant role in the difference in carbon values observed.

4.3 Species diversity in the silvicultural treatment systems Species diversity has been reported in making ecosystems more resistant and resilient to disturbance (Hooper, et al., 2005; Reusch, et al., 2005; Tilman, et al., 2006). According to Hooper, et al., (2005) and Reusch, et al., (2005), this is because species are more likely to be present in an area with characteristics that will enable the ecosystem to adjust to environmental change. This suggests that ecosystems can continue to function and provide essential goods and services perpetually when its diverse species compositions are maintained. According to Reich, et al., (2004), the services provided by ecosystems are largely influenced by the characteristics of species present and functional traits, there fore the loss of biodiversity will decline the resilience of the forest system. Scheffer, et al., ( 2001), remarked that, ecosystem with low resilience, when subject to shocks or disturbance may reach a threshold at which sudden change can occur. however, it has been reported in literature that ecosystem with diverse traits can compensate for the loss of other species after ecosystem disturbance has occurred (Hamilton, 2005). Moreover the continued loss of biodiversity may compromise our ability to tackle the increasing effects of climate change and ecosystem services now and in the future since Bunker, et al., (2005) has remarked that the sequestration potential of tropical forest in the future to be highly influenced by the future species composition. Notwithstanding this, , the silvicultural

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

interventions carried out were to exclude the establishment of certain species to favour the growth of only desire species (Baidoe, 1970; Mooney, 1963; Oguntala, 1997). This was geared towards maintaining few species for economic benefits. However, with the realization of the important roles of biodiversity in the ecosystem services requires the establishment and maintenance of diverse species (Tilman, et al., 2006). TSS resulted in low species diversity and was significantly different in the silvicultural treatment systems. This is justifiable due to the aims of the treatment carried out in TSS and also agrees with study done by Foli, et al., (2003). However, the decrease in species diversity may render such forest less functional in ecosystem services. On the other hand, Species diversity observed in PES is similar to the untreated system. This results was also observed and reported by Foli, et al., (2003) that, the high species diversity observed in PES is due to poor species forms resulting from the recruitment and re-establishment of species. At that time favouring of few economic species was the objective however, with current demands of forest biodiversity in ecosystem services, PES treated forest may be promising in rendering ecosystem services. Notwithstanding this, the method of intervention in PES and TSS, mainly poisoning of species with sodium arsenide is considered environmentally unfriendly and unacceptable. This is because; the use of poison can cause considerable damage to other many life forms in the forest ecosystem. These damages, eventually will limit the diverse function of the ecosystem. However, Selective harvesting has a better chance of sustaining the ecosystem in rendering important services. This is realized through the high diverse species in the silvicultural treatment systems. Moreover, the method of intervention is acceptable environmentally since it excluded the use of poison.

Additionally, some species have been reported to better respond to ecosystem stress than others. The presence of less effective species such as the increase abundance of lianas will result in high competition with tree species with promising life forms in ecosystem functioning (Willis, et al., 2007). On the other hand, Lianas are a major source of NTFPs for small scale entrepreneurs. This means that their removal can serve as raw materials to feed other small industries and that the method of removal is equally important. Foli, et al., (2003) reported on the selection system practised in Bobiri to be less effective compared with selection system practiced in Goaso with climber cutting and cleaning intervention. This justifies that liana communities can potentially reduce the functions of managed forest in ecosystem functioning and their removal will not go waste since it will serve as raw material for other small industries.

4.4 Stand structure under the silvicultural treatment systems The diverse plants and animal species encountered in stable ecosystems are largely due to the structural diversity of the forest ecosystem in supporting the lives of wild animals

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

(Sullivan, et al., 2009). As structural diversity decline, many species will go extinct due to habitat loss (Sullivan, et al., 2009). Natural forests are categorised into three levels; the understory, canopy and emergent and these structural and compositional heterogeneity contribute to high biological diversity (Rouvinen & Kuuluvainen, 2005). In this study, stand density (stocking/ha) was higher in the three silvicultural treated systems relative to the nature reserve (Figure 17). This suggests that disturbance in natural forests stimulate the growth of other species resulting in various structural development (Franklin, et al., 2002). This results also agrees with Foli & Pinard, (2005) who recorded high stand density in the three silvicultural treated systems than in the untreated system. However, a substantial effect of the intervention was on the density of pole-sized trees (Figure 17). This is because the intervention aimed at natural regeneration of species in the case of TSS and PES. The number of commercial trees in the silvicultural treated systems suggests that, even under intense intervention, the treated forest has mimicked the structure of a natural unlogged forest. According to Tews, et al., (2004), most plant habitat communities determine the physical structure of the environment and therefore have a considerable influence on the distribution and interactions of animal species. Therefore the diverse vertical stand structure in forest ecosystem is equally unique and plays essential roles in ecosystem functions. The high density of pole stocking in PES and TSS agrees with Foli, et al., (2003) since regeneration was a major aim of these interventions. Although, less intensive intervention as done in selection system did not encourage as much regeneration as did the TSS and PES, in meeting current demands, selection system may be better in supporting life forms and other environmental services due to the method of intervention used. PES and TSS are considered less effective and unacceptable with the use of poison even though Huang, et al., (2003)has reported on more diverse species in the understorey than in the canopy and emergent level of a natural forest.

Notwithstanding this, density of good stem form as observed in TSS and SS may present substantial amounts in biomass estimates. Biomass estimation is important for carbon stock accounting and monitoring(Brown, et al., 2000) and for allocating harvestable wood forest management purposes(Dias, et al., 2006) and scientific studies(Hall, et al., 2006). The management of forests not just for timber production but towards carbon sequestration is very essential due to the adverse effects of climate change and the role of natural tropical forests as natural ‘break’ to climate change effects (Gibbs, et al., 2007).

4.5 Relationship between Forest stand parameters and vegetation indices The increased attention to measuring selected properties of tropical forest stand parameters using remotely sensed data is largely due to the important roles of moist tropical forests in global warming, biodiversity conservation and ecosystems functions (Foody, et al., 2003; Lu, 2001; Lu, et al., 2002; Steininger, 2000). However, Lu, (2006), describes the difficulty involved in acquiring information about specific biophysical

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

parameter in a given area. He attributed this difficulty to the different characteristics in spectral wavelengths and wavelength combination especially in moist tropical forest. The complex forest stand structure and abundant vegetation in moist tropical forest makes this statement true (Lu, 2006)

Moreover, due to the complex biophysical environments and difficulty in field data collection, less attention has been devoted to moist tropical regions in measuring forest stand parameters from remotely sensed data. Most previous research has focused on boreal and temperate forest (Brown, et al., 2000; Eklundh, et al., 2003; Heiskanen, 2006; Muukkonen & Heiskanen, 2007) which is relatively simple in stand structure and tree species.

Benefoh, (2008), reported of poor relationship between selected vegetation indices (independent variables) and mean sequestered carbon (dependent variables). Similar poor relationship was observed in this study. Moreover, relationship between LAI and spectral vegetation indices did not agree with Heiskanen, (2006) who reported good relationship of LAI with vegetation indices. This result was not anticipated. A good relationship was expected with the removal of outliers but the opposite was observed. This may be due to the complex structure of tropical moist forest which results in saturation effect of vegetation indices

Notwithstanding this, NDVI and SR are two common VIs that have been widely reported in literature to correlate strongly with forest parameters (Heiskanen, 2006; Jordan, 1969; Lu, et al., 2004; Zheng, et al., 2004). This is due to their ability to measure photosynthetic activities in woody vegetations. Studies also have found that the SWIR modifications of the SR and NDVI denoted as RSR and NDVIc respectively improved the correlation with LAI (Brown, et al., 2000; Chen, et al., 2002; Steininger, 2000; Zheng, et al., 2004). None of these arguments was true in this study, a reason more likely to be attributed to the complex stand structure and vegetation species in moist tropical forest which causes saturation effect.

4.6 Relationship between forest stand parameters and spectral reflectance bands Establishing correct relationships between forest parameters and spectral reflectance is essential in understanding how image signatures relate to forest stand characteristics (Lu, 2006; Lu, et al., 2004). Several studies have reported on significant relationship between forest parameters (LAI and biomass) with spectral reflectance bands (Heiskanen, 2006; Lu, et al., 2004; Steininger, 2000; Tucker & Sellers, 1986). Lu, et al., (2004) and Heiskanen (2006) reported a significant relationship between red band and forest parameters.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

According to them, the chlorophyll pigment in the green leaves helps in absorbing radiation in the red wavelength and thus red reflectance is inversely related to the quantity of chlorophyll present in the canopy making them best predictors of carbon and LAI. Tucker and Sellers (1986) as cited in Heiskanen, (2006) also reported that, in the SWIR wavelength the reflectance decreases with increasing absorption due to water in the canopies. This usually results in negative relationships with LAI (Tucker & Sellers, 1986) in wetter areas. According to Lu, et al., (2004), relationship between stand parameters with NIR infrared band may be negative or positive depending on stand structure at a given time .The increased canopy shadowing effect of larger stands decreases understorey brightness due to increase density in biomass. This, according to Lu, et al., (2004), accounts for the positive or negative relationships.

Notwithstanding these arguments, all relationships between forest parameters and spectral reflectance bands were very weak and not significant in this study a reason more likely to be attributed to saturation of the red and NIR bands in the tropical moist forest. Heiskanen, (2006), reported of significant relationship with the log transform of the reflectance bands, but this was not seen in this study. However, a statement made by Tucker and Sellers, (1986) on the relationship between LAI with SWIR band was observed in this study; thus a negative relationship of LAI with SWIR band due to water absorption in the canopies. Moreover, results observed in this study are similar to results reported by Benefoh, (2008). In addition, no significant relationship was observed with the inclusion of LAI in this study. This may confirm with Lu, et al., (2004)who reported on the complex structure and vegetation species associated with moist tropical forest due to saturation effect.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

5.0 CONCLUSION

In an attempt to evaluate and compare the effectiveness of silvicultural systems as a management option for timber production in Ghana; in making them more contemporary to current demands of carbon sequestration and biodiversity conservation for perpetual use, the following conclusions are made based on the objectives and research questions. However it should be mentioned and noted that since there are insufficient data for comparison on the basis of same treatment types in different locations in Ghana, conclusion drawn are solely as a result of the state of the forest in Bobiri Forest reserve due to the silvicultural interventions carried out and not necessarily as a result of the intended output of a particular silvicultural treatment.

Objective 1: To estimate and map aboveground biomass and carbon stocks in the Silvicultural treatment systems.

What is the concentration of aboveground biomass in silvicultural treatment systems?

 Tropical shelter wood system received silvicultural intervention 10yrs before harvesting was done o This resulted in excellent stand form due to low felling damages during harvesting o The mean aboveground biomass in TSS amount to 1173.72 ±36Mg/ha and total of 671.67Gg  Post exploitation system received silvicultural intervention 10yrs after harvesting was done. o This resulted in stands of poor forms due to felling damages. The application of Silvicultural intervention resulted in the removal of such trees. o The mean aboveground biomass in PES amount to 915.65±37Mg/ha and total of 437.03Gg  Selection system: selective harvesting without any further intervention retained species with good stem forms. o Selection system holds AGB stock of 1094.66 ±36Mg/ha and total of 130.53Gg  Subsequently, the total aboveground biomass in the silvicultural treatment system amounted to 1270.70Gg with mean biomass of 933.84±138Mg/ha. The individual

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

amounts of AGB in the silvicultural treated systems were far higher than values observed in the untreated system.

What is the concentration of aboveground carbon stocks in the silvicultural treatment systems?

 TSS sequestered 335.83Gg amount of carbon, SS sequestered 65.26Gg and PES sequestered 218.51Gg. Carbon sequestration was higher in the three silvicultural treated systems than in the untreated system which sequestered 15.30Gg amount of carbon.  The total sequestered Carbon in the three silvicultural treated system amounted to 619.91Gg with mean of 530.67±38Mg.ha-1

How is the above ground carbon/biomass densities distributed in the three Silvicultural systems?

 Spatial distribution of ABG carbon stocks ranges from a high of 586.86 ±36 Mg.ha-1 in TSS to a low of 475.82±38 Mg.ha-1 in PES. This distribution is based on canopy openness in the treated systems.

Is the concentration of carbon stocks among the silvicultural treatment systems significant?

 Carbon stocks in TSS and SS were not significantly different. However a significant difference was observed between PES and the other treated systems (TSS and SS). Further significant difference was observed between the three silvicultural treated systems and untreated system (nature reserve).  This significant result gives enough evidence to accept hypothesis 1 and conclude that carbon means are higher in the three silvicultural systems than in untreated system (NR) (control)

Objective 2: To estimate tree species diversity and assess the impact of the treatments on species diversity.

Which of the Silvicultural treatment systems foster the most diverse of tree species?

 Selection system had the most diverse species (species richness). However, species diversity index were similar in the Silvicultural treatment systems.

Is the effect of the silvicultural treatments on biodiversity significant?

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

 A significant difference was observed among the silvicultural treatment systems with TSS being significantly different in terms of species diversity.  This significant difference gives enough evidence to accept hypothesis 2 and conclude that the effects of the silvicultural treatments on species diversity is significant.

Objective 3: To evaluate the performance of silvicultural treatment systems on their suitability for conserving biodiversity and sequestering carbon.

Which of the silvicultural treatment systems is most suitable for biodiversity conservation and carbon sequestration?

 The system carried out in Ghana after 45 yrs has assumed the structure of a natural forest. This resulted in high amount in sequestering carbon. In general, the systems adopted were suitable in sequestering carbon as compared to the untreated system  In terms of species diversity, the system presented a structural diversity which can support other life forms as opposed to the even age stand forest which was the objectives of the intervention  However, in the light of current environmental policies, the adoption of an improved selection harvesting system (SS) in which intensity of intervention can be controlled to stimulate the required regeneration of desired species is necessary. This is because even though TSS and PES better enhanced regeneration status, the method used for removing undesirable species notably poisoning of trees using arsenite is not environmentally friendly and unacceptable.

Objective 4: To assess the relationship between selected vegetation indices and reflectance bands derived from Aster data and forest parameters

How strong is the relationship between Aster data with forest stand parameters?

 Weak relationship was observed between selected vegetation indices and above ground carbon stocks. Inclusion of LAI and aster bands did not result in any improvement relationship in the study area.  Due to the P (probability) values which were not significant, I failed to accept hypothesis 3 and conclude that relationship between forest parameters with Aster data in moist tropical forest is very poor.  However, although no significant models were developed to use VIs to accurately estimated sequestered Carbon, this results provides basis for further investigation into the complex structure and composition of tropical forests using remote sensing and GIS techniques for carbon accounting and monitoring

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

6.0 RECOMMENDATION

6.1 Carbon stocks Since the lack of country specific equation for calculating biomass and adjusted Carbon, introduce certain uncertainties in the estimation of AGB and carbon, immediate research is necessary in developing specific site and species biomass equation for carbon accounting and monitoring more importantly with the current attention on climate change and willingness to preserve forest under the REDD initiative.

Moreover, a comprehensive carbon study including below ground carbon stock and carbon stored in necromas should be studied to further conclude which silvicultural treatment sequesters the most carbon

The adoption of an improved selective harvesting system is very much important. This will ensure the continuity of forests in providing ecosystem services as well as wood to feed the local industries. Enrichment planting can be used to fill the lost trees in natural forest after selective harvesting has been done. Alternatively, plantation establishment (even age) can address the economic reasons of forest management whiles natural forest are preserved for its perpetuity in ecosystem functioning.

6.2 Species diversity A more comprehensive study including liana composition and identification, which species of liana should be retained as NTFPs to serve as raw materials for small scale enterprises is very much important. This will present a better picture of which liana communities should be retained during selective harvesting.

Moreover, enrichment planting of lost species will help replace other desirable/important species which provide habitat for the animal communities is essential in maintaining the structural diversity of the forest for ecosystem services.

6.3 Vegetation indices A reason as to why weak relationship exit between vegetation indices and sequestered Carbon in tropical moist forest is very complex. However, research on developing models and algorithms which are appropriate for predicting sequestered carbon based on RS data in moist tropical forests is very much important, therefore future studies should incorporate the use of alternative imaging techniques such as RADAR, LiDAR and hyper-

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

spectral imagery (Quickbird and IKONOS) in the studying of relationship between VI and sequestered carbon in moist tropical forest.

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

7.0 REFERENCES Alder, D. (1993). Growth and yield in Bobiri forest reserve. Consultancy Report for the UK Overseas Development Administration. Consultancy Report, 14. Baidoe, J. (1970). The selection system as practiced in Ghana. Commonwealth Forestry Review; 49 (2), No. 140: 159-165. Benefor, D. T. (2008). Assessing the effects of land-use/land cover change on ecosystem services in the Ejusu JuabenDdistrict, Ghana: a case of carbon sequestration. MSc Thesis, ITC, Enschede, The Netherlands. Bluman, A. G. (2004). Elementary statistics: a step by step approach (5th edition ed.). McGraw Hill, New York, U.S.A. Boschetti, M., Bocchi, S., & Brivio, P. (2007). Assessment of pasture production in the Italian Alps using spectrometric and remote sensing information. Agriculture, Ecosystems and Environment, 118(1-4), 267-272. Boyd, D., Foody, G., & Ripple, W. (2002). Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing. Applied Geography, 22(4), 375-392. Britwum, S. (1976). Natural and artificial regeneration practices in the high forest of Ghana. Ghana Forestry Journal, 2: 45-49. Brown, L., Chen, J., Leblanc, S., & Cihlar, J. (2000). A Shortwave Infrared Modification to the Simple Ratio for LAI Retrieval in Boreal Forests:: An Image and Model Analysis. Remote Sensing of Environment, 71(1), 16-25. Brown, S. (1997). Estimating Biomass and Biomass Change of Tropical Forests: A primer (FAO Forestry Paper–134). Food and Agriculture Organization, Rome, Italy. Brown, S. (2002). Measuring carbon in forests: current status and future challenges. Environmental Pollution, 116(3), 363-372. Brown, S. (2002). Measuring, monitoring, and verification of carbon benefits for forest-based projects. Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 360(1797), 1669- 1683. Brown, S., & Lugo, A. E. (1992). Aboveground biomass estimates for tropical moist forests of the Brazilian Amazon. Interciencia. Caracas, 17(1), 8-18. Bunge, J., & Fitzpatrick, M. (1993). Estimating the number of species: a review. Journal of the American Statistical Association, 88(421), 364-373. Bunker, D., DeClerck, F., Bradford, J., Colwell, R., Perfecto, I., Phillips, O., Sankaran, M., & Naeem, S. (2005). Species loss and aboveground carbon storage in a tropical forest. Science, 310(5750), 1029. Cairns, M., Olmsted, I., Granados, J., & Argaez, J. (2003). Composition and aboveground tree biomass of a dry semi-evergreen forest on Mexico’s Yucatan Peninsula. Forest Ecology and Management, 186(1-3), 125-132. Chambers, J., Asner, G., Morton, D., Anderson, L., Saatchi, S., Espírito-Santo, F., Palace, M., & Souza, C. (2007). Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. Trends in Ecology & Evolution, 22(8), 414-423. Chen, J., Pavlic, G., Brown, L., Cihlar, J., Leblanc, S., White, H., Hall, R., Peddle, D., King, D., & Trofymow, J. (2002). Derivation and validation of Canada-wide coarse-resolution leaf area index maps using

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Franklin, J. F., Berg, D. R., Dale A, T., & John C, T. (1997). Alternative silvicultural approaches to timber harvesting: variable retention harvest systems. In:Kathryn A. Kohm, Jerry F. Franklin (Eds.), Creating a forestry for the 21st century: The science of ecosystem management 113-136. Ghana Forestry Department (1958). Bobiri Forest Reserve Working Plan for the period 1st April, 1955- 30th June, 1965. . Ghartey, K. K. F. (1989). Results of the Inventory. In: Wong, J.L.G. (ed). Ghana Forest Inventory Project Seminar Proccedings (Accra 1989). Kumasi, Ghana, Forst Inventory Project: 32-46. Gibbs, H., Brown, S., Niles, J., & Foley, J. (2007). Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters, 2, 045023. Glenn, E., Huete, A., Nagler, P., & Nelson, S. (2008). Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors, 8, 2136-2160. Hall, J., & Swaine, M. (1981). Distribution and ecology of vascular plants in a tropical rain forest. Forest vegetation in Ghana. Journal of Ecology, W. Junk, The Hague, 383pp

Hall, R., Skakun, R., Arsenault, E., & Case, B. (2006). Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume. Forest Ecology and Management, 225(1-3), 378-390. Hamilton, A. J. (2005). Species diversity or biodiversity? Journal of Environmental Management, 75(1), 89-92. Harmon, M. (2001). Carbon sequestration in forests: addressing the scale question. Journal of Forestry, 99(4), 24-29. Heiskanen, J. (2006). Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data. International Journal of Remote Sensing, 27(5-6), 1135-1158. Hooper, D., Chapin Iii, F., Ewel, J., Hector, A., Inchausti, P., Lavorel, S., Lawton, J., Lodge, D., Loreau, M., & Naeem, S. (2005). Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecological monographs, 75(1), 3-35. Houghton, J. (1997). Greenhouse Gas Inventory: Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Intergovernmental Panel on Climate Change. Houghton, R. (2005). Aboveground forest biomass and the global carbon balance. Global Change Biology, 11(6), 945-958. Huang, W., Pohjonen, V., Johansson, S., Nashanda, M., Katigula, M. I. L., & Luukkanen, O. (2003). Species diversity, forest structure and species composition in Tanzanian tropical forests. Forest Ecology and Management, 173(1-3), 11-24. IPCC (2003). Good Practice Guidance for Land Use, Land-Use Change and Forestry. Retrieved 10-09-09, from http://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf_contents.html IPCC (2006). 2008 IPCC Guidelines for national Greenhouse Inventories (Volume 4, AFOLU). National Greenhouse Gas Inventories Program: Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T. and Tanabe, K. (eds): Published; IGES, Japan. IUCN (2008). World Conservation Union (Content Partner); Stephen C. Nodvin (Topic Editor). 2008. "Forest environmental services." In: Encyclopedia of Earth. Eds. Cutler J. Cleveland (Washington, D.C.: Environmental Information Coalition, National Council for Science and the Environment). [First published in the Encyclopedia of Earth December 23, 2006; Last revised August 21, 2008; Retrieved December 7, 2009]. <>

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Karani, P. k. (1970). Silvicultural systems as practised in Ghana and species trial in Nigeria. Uganda Forest Department Technical Note no. 163.20pp. Kim Phat, N., Knorr, W., & Kim, S. (2004). Appropriate measures for conservation of terrestrial carbon stocks--Analysis of trends of forest management in Southeast Asia. Forest Ecology and Management, 191(1-3), 283-299. KNAW Research Information (2009). Project: Modelling and comparative analysis of silvicultural systems in view of the changing role of forestry in society (part of EU-SilviStrat: Silvicultural Strategies to Climate Chnage in Management of European Forests) Retrieved 27-07-09, from http://www.onderzoekinformatie.nl/en/oi/nod/onderzoek/OND1282052/ Kufuor, K. (2000). Forest Management in Ghana: Towards a Sustainable Approach. Journal of African Law, 44(1), 52-64. Littell, R., Henry, P., & Ammerman, C. (1998). Statistical analysis of repeated measures data using SAS procedures. Journal of Animal Science, 76(4), 1216. Losi, C., Siccama, T., Condit, R., & Morales, J. (2003). Analysis of alternative methods for estimating carbon stock in young tropical plantations. Forest Ecology and Management, 184(1-3), 355-368. Lu, D. (2001). Estimation of forest stand parameters and application in classification and change detection of forest cover types in the Brazilian Amazon basin. Ph.D Dissertation, Indiana State University. Lu, D. (2006). The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing, 27(7), 1297-1328. Lu, D., Mausel, P., Brond zio, E., & Moran, E. (2004). Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198(1-3), 149-167. Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2002). Above-ground biomass estimation of successional and mature forests using TM images in the Amazon Basin. In: D. Richardson, P. van Oosterom (Eds.), Adances in Spatial Data Handling. pp. 183-196. Magurran, A. E. (2004). Measuring biological diversity. African journal of aquatic science, 29(2), 285-286. Montagnini, F., & Jordan, C. (2005). Tropical forest ecology: the basis for conservation and management: Springer Verlag. Mooney, J. W. C. (1963). Silviculture in Ghana. Commonwealth Forestry Review 42, 112:159-163. Muukkonen, P., & Heiskanen, J. (2007). Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: A possibility to verify carbon inventories. Remote Sensing of Environment, 107(4), 617-624. Nascimento, H., & Laurance, W. (2002). Total aboveground biomass in central Amazonian rainforests: a landscape-scale study. Forest Ecology and Management, 168(1-3), 311-321. Nemani, R., Pierce, L., Running, S., & Band, L. (1993). Forest ecosystem processes at the watershed scale: sensitivity of remotely-sensed Leaf Area Index estimates. International journal of remote sensing(Print), 14(13), 2519-2534. Notman, E., Langner, L., Crow, T., Mercer, E., Calizon, T., Haines, T., Greene, J., Browne, T., Bergstrom, J., & Loomis, J. (2006). State of Knowledge: Ecosystem Services from Forests. Unpublished white paper, October. Washington, DC: USDA Forest Service. Accessed July, 16, 2007. Oguntala, A. B. (1997). Ecological basis of High Forest treatment in West Africa. Ghana J. of Forestry, 4, 41-50. Osafo, E. (1968). The application of the Tropical Shelterwood System (TSS) in coupe 2 of the Bobiri Research Centre. FPRI Technical Note, No. 3, 10 pp. Osafo, E. (1970). The development of silvicultural techniques applied to natural forests in Ghana. FPRI Technical Note No. 13, 18pp.

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Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

8.0 APPENDICES

Appendix A: Sub groupings of species in each treatment type

Scientific species local name Family star rating Count Albizia adianthifolia Panpanaaa Leguminosaea green 2 Albizia ferruginea Awianfosamena Leguminosae red 1 Albizia zygia Okoro Leguminosae pink 4 Alstonea boonei Nyamedua Apocynaceae green 4 Amphimas pterocarpoides Yaya Leguminosea red 8 Antiaris toxicana Kyenkyen Moraceae red 6 Baphia nitida Odwen Leguminosae green 58 Blighia sapida Akyen Sapindaceae green 9 blighia unijugata Akyenbire Sapindaceae green 6 Carapa procera Kwakuobisi Meliaceae green 10 Ceiba pentandra Oyina Bombacaceae red 5 Celtis adolfi-friderici Esa kosua Ulmaceae pink 6 Celtis mildbraedii Esa Ulmaceae pink 1 Celtis philippensis Esa fufuo Ulmaceae green 19 Celtis zenkeri Esa kokoo Ulmaceae pink 92 Chrysophyllum albidum Akasa Sapotaceae green 1 Cola gigantea Waterpuo Malvaceae green 27 Copaifera salikounda Ntedua Leguminosea pink 1 Corynanthe pachyceras Panprama Rubiaceae green 4 Entandrophragma angolense Edinam Meliaceae scarlet 66 Entandrophragma condollei Cedar kokote Meliaceae scarlet 5 Entandrophragma cylidricum Penkwa Meliaceae scarlet 2 Ficus exasperata Nyankyerene Nyankyerene green 5 Funtumia elastica Funtum Apocynaceae green 129 Guarea cedrata Kwabohuro Meliaceae red 51 Hannoa klaineana Hotrohotro Simaroubaceae green 7 Heritiera utilis Nyankomaa Malvaceae red 8 Hymenostegia afzelii Takrowa Leguminosae green 17 Trichilia prieuriana Kakaadikro Apocynaceae green 13 Khaya ivorensis Mahogany Meliaceae scarlet 10 Lannea welwitschii Kumnini Anarcadiaceae green 1 Macaranga barteri Opam Euphoebiaceae green 1 Mammea africana Bonpagya Guttiferae blue 1 Mansonia altissima Oprono Malvaceae red 14 Mareya micrantha Dubrafo Euphorbiaceae green 1 Morinda lucida Konkroma Rubiaceae green 1

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Myrianthus libericus Nyankomaanini Cecropiaceae green 2 Napoleonaea vogelii Obuaa Lecythidaceae green 2 Nesogordonia papaverifera Danta Malvaceae pink 36 Newbouldia laevis Sisimasa Bignoniaceae green 2 Petersianthus macrocarpus Esia Lecythidaceae pink 2 Piptandeniastrum africanum Dahoma Leguminosae scarlet 4 Pouteria aningeri Asanfena Sapotaceae scarlet 3 Pterygota macrocapa Kyereye Malvaceae scarlet 136 Pycanthus angolensis Otie Myristcaceae red 9 Ricinodendron heudelotii Owama Euphorbiaceae pink 3 Rinorea oblongifelia Mpawuo Violaceae green 58 Sterculia oblonga Ohaa Malvaceae pink 11 Sterculia rhinopetala Wawabima Malvaceae pink 158 Sterculia tragacantha Sofo Malvaceae green 39 Strombosia glaucescens Afena Olaccaceae pink 15 Terminalia ivoernsis Emire Combretaceae red 3 Terminalia superba Ofram Combretaceae red 10 Trichilia monadelpha Tenuro Meliaceae green 3 Triplochiton scleronxylon Wawa Malvaceae scarlet 20 Xylia evansii Samantawa Leguminosae blue 7 Zanthoxylum gilletii Ekuo Rutaceae green 1 Fima Fima Fima 5 Kanweno Kanweno Kanweno 10

Total 1135

Appendix B: species sub grouping in the Selection system

Star Scientific name Local name Family rating count Albizia adianthifolia Panpanaa Leguminosae green 2 Albizia zygia Okoro Leguminosae pink 4 Alstonia boonei Nyamedua Apocynaceae green 3 Antiaris toxicaria Kyenkyen Moraceae red 12 Antrocaryon micraster Aprokuma Anacardiaceae pink 3 Baphia nitida Odwen Leguminosea green 39 Blighia sapida Akyen Sapindaceae green 21 Blighia unijugata Akyenbire Sapindaceae green 2 Bussea occidentalis Kotoprepre Leguminosae green 3 Carapa procera Kwakuobisi Meliaceae green 17 Ceiba pentandra Oyina Bombacaceae red 1 Celtis adolfi-friderici Esa kosua Ulmaceae pink 9 Celtis philippensis Esa fofoo Ulmaceae pink 17

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Celtis zenkeri Esa kokoo Ulmaceae pink 102 Cleistopholos patens Ngonenkyene Annonaceae green 1 Cola gigantea Otaapuu Malvaceae green 11 Cola nitida Cola Malvaceae pink 3 Copaifera salikounda Ntedua Leguminosea red 2 Corynanthe pachyceras Panprama Rubiaceae green 12 Cylicodiscus gabunensis Deyan Leguminosae pink 2 Distemonanthus benthamianus Bonsamdua Leguminosae red 1 Drypetes aubrevillei Opahanini Euphorbiaceae green 1 Entandrophragma angolense Edinam Meliaceae scarlet 4 Entandrophragma condollei cedar kokote Meliaceae scarlet 2 Entandrophragma cylindricum Penkwa Meliaceae scarlet 8 Ficus exasperata Nyankerene Moraceae green 5 Funtumia elastica Funtum Apocynaceae green 101 Guarea cedrata Kwabohuro Meliaceae red 30 hannoa klaineana Hotrohoro Simaroubaceae green 6 Heritiera utilis Nyankoma Malvaceae red 11 Hymenostegia afzelii Takrowa Leguminosae green 47 Trichilia prieuriana Kaakaadikro Apocynaceae green 45 Rauvelfia vomitaria Kakapenpen Apocynaceae green 5 lannea welwitschii Kumnini Anarcadiaceae green 1 Kyaha ivorensis Mahogany Meliaceae scarlet 3 Macaranga barteri Opam Euphoebiaceae green 3 Mammea africana Ponpagya Guttiferae blue 1 Mansonia altissima Mansonia Malvaceae red 1 mareya micrantha Dubrafo Euphorbiaceae green 2 Milicia excelsa Odum Moraceae scarlet 1 Musanga cecropioides Odoma Cecropiaceae green 2 Myrianthus arboreus Nyankomaabire Malvaceae green 7 Myrianthus libericus Nyankomanini Cecropiaceae green 8 Napoleonaea vogelii Obuaa Lecythidaceae green 15 Nesogordonia papaverifera Danta Malvaceae pink 48 Omphalocarpum elatum Esonodokono Sapotaceae green 2 Petersianthus macrocarpus Esia Lecythidaceae pink 5 Piptadeniastrum africanum Dahoma Leguminosae scarlet 6 Pouteria spp. Asanfena Sapotaceae scarlet 21 Pterygota macrocarpa Kyereye Malvaceae scarlet 31 Pycnanthus angolensis Otie Myristcaceae red 11 Ricinodendron heudelotii Owama Euphorbiaceae pink 5 Rinorea oblongifelia Mpawuo Violaceae green 134 Sterculia oblonga Ohaa Malvaceae pink 16 Sterculia rhinopetala Wawabima Malvaceae pink 64 Sterculia tragacantha Sofo Malvaceae green 17 Strombosia glaucescens Afena Olacaceae pink 23 Terminalia superba Ofram Combretaceae red 3 Tetrapleura tetraptera Prekese Leguminosae green 5

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Trichilia monadelpha Tenuro Meliaceae green 4 Triplochiton scleroxylon Wawa Malvaceae scarlet 10 Uapaca guineensis Kontan Euphorbiaceae green 5 Xylia evansii Samatawa Leguuminosae Blue 21 Zanthoxylum gilletii Ekwuo Rutaceae Green 6 kwakuo ntrowa kwakuo ntrowa 1 kanweno Kanweno 22 fima Fima 28

Total 1064

Appendix C: Sub grouping of species in Post exploitation system

Star Scientific name Local name Family rating count Albizia adianthifolia Panpanaa Leguminosae green 3 Albizia ferruginea Awiamfosamena Leguminosae red 1 Albizia zygia Okoro Leguminosae pink 2 Alstonia boonei Nyamedua Apocynaceae green 12 Amphimas pterocarpoides Yaya Leguminosea red 2 Antiaris toxicaria Kyenkyen Moraceae red 8 Baphia ntida Odwen Leguminosae green 71 Blighia sapida Akyen Sapindaceae green 16 Blighia unijugata Akyenbire Sapindaceae green 7 Bussea occidentalis Kotoprepre Leguminosae green 5 Carapa procera Kwakuobisi Meliaceae green 20 Ceiba pentandra Oyina Bombacaceae red 6 Celtis adolfi-friderici Esa kosua Ulmaceae pink 3 Celtis philippensis Esa fufuo Ulmaceae green 20 Celtis zenkeri Esa kokoo Ulmaceae pink 79 Cleistopholis patens Ngonenkyene Annonaceae green 1 Coffea canephora Kwayekofi Rubiaceae pink 6 Cola giganteao Otaapuuo Malvaceae green 23 Copaifera salikounda Ntedua Leguminosea red 2 Corynanthe pachyceras Panprama Rubiaceae green 17 Cylicodiscus gabunensis Deyan Legumeinosea pink 2 Distemonanthus benthamianus Bonsamdua Leguminosae red 1 Entandrophragm candollei Cedar Meliaceae scarlet 8 Entandrophragma angolense Edinam Meliaceae scarlet 31 Entandrophragma cylindricum Penkwa Meliaceae scarlet 3 Ficus exzsperata Nyankyerene Apocynaceae green 11

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Funtumia elastica Funtum Apocynaceae green 185 Guarea cedrata Kwabohuro Meliaceae red 44 Hannoa klaineana Hotrohotro Simaroubaceae green 5 Heritiera utilis Nyankomaa Malvaceae red 12 Hymenostegia afzelii Takrowa Leguminousae green 46 Trichilia prieuriana Kakaadikro Apocynaceae 34 Rauvelfia vomitoria Kakapenpen Apocynaceae green 2 Khaya ivorensis Mahoganay Meliaceae scarlet 9 Lannea welwitschii Kumnini Anarcadiaceae green 1 Macaranga barteri Opam Euphoebiaceae green 3 Mansonia altissima Oprono Malvaceae red 13 Musanga cecropioides Odwuma Cecropiaceae green 1 Myrianthus arboreus Nyankomaa bire Cecropiaceae green 1 Myrianthus libericus Nyankomaanini Cecropiaceae green 5 Napoleonaea vogelii Obuaa Lecythidaceae green 8 Nesogordonia papaverifera Danta Malvaceae pink 37 Newbouldia laevis Sisimasa Bignoniaceae green 1 Ongokea gore Bodwe Olacaceae pink 2 Petersianthus macrocarpus Esia Lecythidaceae pink 6 Piptadeniastrum Dahoma Legumenosae scarlet 6 Pouteria albidum Asanfena Sapotaceae scarlet 9 Pterygota macrocarpa Kyereye Malvaceae scarlet 24 Pycanthus angolensis Otie Myristcaceae red 14 Ricinodendron heudelotii Owama Euphorbiaceae pink 2 Rinorea oblangifelia Mpawuo Violacae green 77 Sterculia oblonga Ohaa Malvaceae pink 19 Sterculia rhinopetala Wawabima Malvaceae pink 147 Sterculia tragacantha Sofo Malvaceae green 20 Strombosia glaucescens Afena Olaccaceae pink 28 Terminalia ivorensis Emire Combretaceae pink 1 Terminalia superba Ofram Combretaceae pink 2 Trichilia monadelpha Tenure Meliaceae green 6 Triplochiton scleronxylon Wawa Malvaceae scarlet 22 Xylia evansii Samamtawa Leguminosae blue 29 Zanthoxylum gilletii Ekwuo Rutaceae green 2 Awewenkura wewenkura 2 Kanweno Kanweno 11 Fima Fima 22

Total 1218

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Appendix D: sub grouping of species in the Nature reserve

Star No. of Scientific name Local name Family rating counts Ahrysophyllum albidum Akasaa Sapotaceae green 1 Albizia adianthifolia Panpanaa Leguminosae green 2 Albizia aygia Okoro Leguminosae pink 26 Alstonia boonei Nyamedua Apocynaceae green 2 Amphimas pterocarpoides Yaya Leguminosea red 14 Antiaris toxicaria kyenkyen Moraceae red 6 Baphia nitida Odwen Leguminosea green 68 Blighia sapida Akyen Sapindaceae green 24 Blighia unijugata Akyenbirie Sapindaceae green 13 Bonbax buonopozene Akonkodie Bombacaceae pink 6 Bridelia atroviridis Opamkotokrodu Euphoebiaceae green 2 Bussea occidentalis Kotoprepre Legumeinosae green 3 Calpocalyx brevibracteatus Atrotere Leguminosae green 8 Ceiba petandra Oyina Bombacaceae red 4 Celtis milbraedii Esa Ulmaceae pink 1 Celtis philippensis Esa fufuo Ulmaceae green 15 Celtis zenkeri Esa kokoo Ulmaceae pink 50 Coffea canephora Kwayekofi Rubiaceae pink 6 Cola gigantea Waterpuo Malvaceae green 9 Cola ntida Bisi Malvaceae pink 5 Copaifera salikounda Ntedua Leguminosea red 4 Corynanthe pachyceras Panprama Rubiaceae green 5 Daneilla ogea Hyedua Leguminousae red 3 Distemonanthus benthamianus Bonsamdua Leguminosae red 3 Enthandrophragma angolense Edinam Meliaceae scarlet 6 Enthandrophragma candollei Cedar kokoe Meliaceae scarlet 1 Ficus exasperata Nyankyerene Moraceae green 12 Funtumia elastica Funtum Apocynaceae green 54 Guarea cedrata Kwabohuro Meliaceae red 4 Hannoa klaineana Hotrohotro Simaroubaceae green 2 Hymenostegia afzelii Takrowa Leguminosae green 98 Trichilia prieuriana Kakaadikro Apocynaceae green 10 Khaya ivorensis Mahogany Meliaceae scarlet 3 Lannea welwitschii Kumnini Anarcadiaceae green 5 Macaranga barteri Opam Euphoebiaceae green 2 Macaranga hurifolia Opamnini Euphoebiaceae green 2 Dialium aubrevilla Duabankye Moraceae pink 1 Mammea africana Bonpagya Guttiferae blue 3 Mansonia altissima Mansonia Malvaceae red 1 Morinda lucida Konkroma Rubiaceae green 6 Musanga cecropioides Odoma Moraceaea green 1

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Myriadnthus arboreus Nyakomaabidire Malvaceae green 1 Myrianthus libericus Nyankomaanini Malvaceae green 15 Napoleonaea vogelii Obuaa Lecythidaceae green 47 Nesogordonia papaverifera Danta Malvaceae pink 60 Ongokea gore Bodwe Olacaceae pink 3 Petersianthus macrocarpus Esia Lecythidaceae pink 19 Piptadeniastrum africanum Dahoma Leguminousae scarlet 1 Pouteria altissima Asanfena Sapotaceae scarlet 3 Pterygota macrocarpa Kyereye Malvaceae scarlet 25 Pychanthus angolensis Otie Myristcaceae red 13 Ricinodendron heudelotii Owama Euphorbiaceae pink 2 Rinorea oblongifelia Mpawuo Violaceae green 64 Sterculia oblonga Ohaa Malvaceae pink 16 Sterculia rhinopetala Wawabima Malvaceae pink 3 Sterculia tragacantha Sofo Malvaceae green 8 Strombosia pustulata Afena Olacaceae pink 10 Terminalia superba Ofram Combretaceae red 11 Triplochiton scleroxylon Wawa Malvaceae scarlet 15 Xylia evansii Samatawa Leguminousea blue 25 Zanthoxylum gilletii Ekuo Rutaceae green 3 Sahwerewa Sahwerewa 14 Ntorme Ntorme 6 Kanweno Kanweno 3 Fima Fima 20

Total 878

Appendix E: Star rating and meaning

Black star species are globally rare and high priorities for careful management

Gold star species are globally restricted

Blue star species are of some rarity value in Ghana

Scarlet star species are threatened, in Ghana at least by over exploitation

Red star species are heavily exploited in Ghana

Pink star species are of some commercial interest

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Appendix F: FIP species classification

Class 1 species: Tree species recorded as having been exploited from Ghana between the periods of 1973-1988. These include species of major economic importance

Class 2 species: species not exported but which grows to a size of 70cm diameter and occurs at a frequency of grater than 1 tree per km2 Previously, class 1 and 2 species constituted the timber potential of the Ghanaian high forest

Class 3 species: species not attaining 70cm diameter or occurring at a frequency of less than 1 per km2

Source: Ghartey, (1989)

Appendix G: General linear model (GLM) test (ANOVA-one way) for significance of the carbon means in the four systems Source DF sum of Squares Mean Square F Value P value

Model 3 1.04695312 0.34898437 3.71 0.0143

Error 92 8.64692083 0.09398827

Corrected Total 95 9.69387396

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Appendix H: Normal distribution graphs of mean carbon

12 12 10 10 8 8 6 6 4 4 Frequency Frequency 2 2 0 0 389.5140982 616.2208255 86.75449418 383.3002443 Mean C meanC

Appendix I: General linear model (GLM) test (ANOVA-one way) for significance of the carbon means in the four systems

Source of variation DF Sum of squares Mean square F value P value

Model(between 3 1385028.55 461676.18 61.80 0.0001 groups)

Error(within 92 687300.17 7470.65 groups)

Corrected total 95 2072328.73

Assessing the impacts of Silvicultural treatment systems on Ecosystem services: a case of Carbon sequestration and Biodiversity conservation

Appendix J: Field data sheet

Impacts of silvicultural treatments on ecosystem services

Field data sheet………………………….. Name of recorder...... Date...... X cor...... Y cor...... Slope...... Remarks...... Plot number...... Treatment type......

For species For carbon inventory diversity ( nested plots) (500m2) DBH (5- DBH(15- Species local No. 15m) 50m) DBH(50+) name DBH Frequency