Woody Species Diversity and Cover Change Detection of Assabila Community Forest, South Achefer District, West Gojjam, Ethiopia

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Environment and climate change Thesis and Dissertations

2020-12-12 WOODY SPECIES DIVERSITY AND COVER CHANGE DETECTION OF ASSABILA COMMUNITY FOREST, SOUTH ACHEFER DISTRICT, WEST GOJJAM, ETHIOPIA

Mengistu Agegnehu http://hdl.handle.net/123456789/11804 Downloaded from DSpace Repository, DSpace Institution's institutional repository `

BAHIR DAR UNIVERSITY

COLLEGE OF AGRICULTURE AND ENVIRONMENTAL SCIENCES GIRATUAT PROGRAM

WOODY SPECIES DIVERSITY AND COVER CHANGE DETECTION OF ASSABILA COMMUNITY FOREST, SOUTH ACHEFER DISTRICT, WEST GOJJAM, ETHIOPIA

M. Sc. Thesis BY Mengistu Agegnehu Chekol

October 2020 Bahir Dar, Ethiopia

BAHIR DAR UNIVERSITY

COLLEGE OF AGRICULTURE AND ENVIRONMENTAL SCIENCES POST GIRATUAT PROGRAM

WOODY SPECIES DIVERSITY AND COVER CHANGE DETECTION OF ASSABILA COMMUNITY FOREST, SOUTH ACHEFER DISTRICT, WEST GOJJAM, ETHIOPIA

M. Sc. Thesis

Mengistu Agegnehu

ATHESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE (M.Sc.) In ENVIRONMENT AND CLIMATE CHANGE

October 2020 Bahir Dar, Ethiopia

THESIS APPROVAL SHEET

As member of the Board of Examiners of the Master of Sciences (M. Sc.) thesis open defiance examination, we have read and evaluated this thesis prepared by Mr Mengistu Agegnehu Chekol entitled “Woody Species Diversity and Cover change detection of Assabila Community Forest in South Achefer District, West Gojjam, Ethiopia” and examined the candidate. We hereby certify that the thesis is accepted as fulfilling the requirement for the award of the degree of Master of Sciences (M. Sc.) in Environment and Climate Change.

Board of Examiners

1. Beyene Belay (PhD) ______

Name of External Examiner Signature Date

2. Eyayu Molla (PhD) ______

Name of Internal Examiner Signature Date

3. Solomon Addisu ______

Name of Chairman Signature Date

DECLARATION

This is to certify that this thesis entitled “WOODY SPECIES DIVERSITY AND COVER CHANGE DETECTION OF ASSABILA COMMUNITY FOREST, WEST GOJJAM, ETHIOPIA”. This thesis has been submitted in partial fulfilment of the requirements for the award of the degree of Master of Science in Environment and Climate Change to the Graduate Program of College of Agriculture and Environmental Sciences, Bahir Dar University by Mengistu Agegnehu Chekol (ID. No. BDU 1100805R) is an authentic work carried out by him under our guidance. The matter embodied in this project work has not been submitted earlier for the award of any degree or diploma to the best of our knowledge and belief.

Name of the Student

Mengistu Agegnehu Chekol Signature ______Date ______

Name of the supervisors

1. Dessie Assefa (PhD) Signature ______Date ______

Main supervisor

2. Mulatie Mekonnen (PhD) Signature ______Date ______

Co-supervisor

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ACKNOWLEDGMENT

First and foremost, I would like to express my deepest commend, endless praises and thanks to the Almighty God and his Mother Saint Virgin Mary for giving me full health, strength, chance, patience, wisdom, love and protection throughout my life.

I am very grateful to my main advisor, Dr Dessie Assefa, for his unreserved scholastic guidance, friendly treatment, and continuous encouragement at all stages of my work. I would like to express my deepest gratitude to Dr Mulatie Mekonnen for his scientific guidance, encouragement, and support especially on the forest cover change detection part of my study.

I am also very thankful to former Amhara National Regional State REDD+ Regional Coordinator Mr Sintayehu Deresse, who provides financial support during data collection and encourages until the end of this work. I would like to thank also Mr Alelign Gardew and Mr Mulugeta Anley for their technical support on Geographical information system (GIS) tools and analysis.

I am very much indebted to my beloved girlfriend Mulluye Melese for her financial support, encouragements, wonderful advice and endless supports; without her, this research would not be complete.

I would like to express my deepest thanks and respect to all my beloved family for their well- wishers for the countless blessings, spiritual and moral support, and constant encouragement not only during this thesis work but also throughout my life.

Finally, I wish to express my heartfelt appreciations to my best friends for their unreserved cooperation in sharing their idea and unreserved assistance during data collection and data analysis.

DEDICATION

I dedicated this thesis manuscript to my beloved girlfriend (Mulluye Melese), who gave me total financial support and full encouragement when I was working on this program.

LIST OF ACRONYMS/ABBREVIATIONS

EFAP Ethiopian forest Action program UNEP United Nation Development program FAO Food and Agriculture program NGOs None governmental organizations CBD Convention on Biological Diversity CSE Conservation Strategy of Ethiopia IBC Institute of biodiversity conservation NDVI Normalized difference vegetation index NIR Near Infra-red GPS Global positioning system GIS Geographical information system TM Thematic mapper TIRS Thermal Infrared sensor DBH Diameter at breast height IVI Important value index MSS Multispectral Scanner OLI Operational Land Imager GCPs Ground control points MARFCC Mean annual rates of forest cover change Sc Similarity coefficient Ds Dissimilarity BA Basal area PFM Participatory forest management H’ Diversity index EH Evenness REDD+ Reduced Emission from Deforestation, Forest Degradation and enhancement of forest production

TABLE OF CONTENTS Contents Page THESIS APPROVAL SHEET ...... ii DECLARATION ...... iii ACKNOWLEDGMENT ...... iv DEDICATION ...... v LIST OF ACRONYMS/ABBREVIATION ...... vi LIST OF TABLES ...... x LIST OF FIGURES ...... xi LIST OF APPENDICES TABLES ...... xii ABSTRACT...... xiii Chapter 1. INTRODUCTION ...... 1 1.1 Background and justification ...... 1 1.2. Statement of the Problem ...... 3 1.3. Objectives of the Study ...... 4 1.3.1. General objectives ...... 4 1.3.2. Specific objectives ...... 4 1.4. Research Questions ...... 4 1.5. Significance of the Study ...... 4 Chapter 2. LITERATURE REVIEW...... 6 2.1. Concept of Biodiversity ...... 6 2.1.1. Values of biodiversity ...... 7 2.1.2. The value of plant diversity ...... 8 2.2. Vegetation Type of Ethiopia ...... 9 2.2.1. Trends and rate of deforestations in Ethiopian vegetation ...... 10 2.2.2. The threats to the conservation of Ethiopian vegetation ...... 11 2.3. Regeneration of Woody Species ...... 11 2.4. Contribution of Area Enclosures to Regeneration ...... 12 2.5. Diversity Indices ...... 12 2.5.1. Simpson's diversity indices ...... 13 2.5.2. Shannon - Wiener Index of Diversity ...... 14 2.6. Forest Covers Change Detection by using Remote Sensing...... 14

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2.6.1 Image correction ...... 15 2.6.2 Image classification ...... 15 2.6.3 Accuracy assessment of image classification ...... 16 2.6.4 Normalized Difference Vegetation Index (NDVI)...... 16 Chapter 3. MATERIALS AND METHODS ...... 18 3.1. Description of the Study Area ...... 18 3.1.1. Location ...... 18 3.1.2 Climate ...... 19 3.1.3 Vegetation...... 19 3.1.4 Land use and soil ...... 19 3.2 Methods of Data Collection ...... 19 3.2.1 Reconnaissance survey and site selection...... 19 3.2.2 Study site boundaries delineation ...... 20 3.2.3. Sampling design for the study of woody plant diversity ...... 20 3.2.4. Data and methods of forest cover change detection ...... 22 3.3. Data Analysis ...... 23 3.3.1. Diversity analysis ...... 23 3.3.2. Structural analysis ...... 24 3.3.3. Measurement of similarity and dissimilarity analysis ...... 26 3.3.4. Cover change detection Analysis ...... 26 Chapter 4. RESULT AND DISCUSSION ...... 31 4.1 Woody Plant Species Diversity of Assabila Community Forest ...... 31 4.1.1. Species diversity ...... 34 4.1.2. Species evenness ...... 34 4.2 Similarity and Dissimilarity Analysis ...... 35 4.3 Vegetation Structure ...... 36 4.3.1 Density of woody species ...... 37 4.3.2 Basal area ...... 38 4.3.3 Frequency ...... 40 4.3.4 Important value index...... 40 4.4. Tree and Shrub Density ...... 42 4.4.1 DBH distribution ...... 42 4.4.2 Height distribution ...... 44 4.5 Regeneration Status ...... 45 4.6. Forest Cover Change...... 46

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4.6.1 Rates of forest cover change ...... 50 Chapter 5. CONCLUSION AND RECOMMENDATION...... 52 5.1 Conclusion...... 52 5.2 Recommendation ...... 53 6. REFERENCES ...... 54 7. APPENDICES ...... 66 BIOGRAPHICAL SKETCH ...... 82

LIST OF TABLES

Table 3.1. Landsat Image Attributes ...... 27 Table 4.1. List of woody plant families recorded and number of genera and species represented in them...... 33 Table 4.2. Sorensen’s Coefficient of Similarity in the three strata...... 35 Table 4.3. Density, Relative Density, Basal area, Relative dominance, Frequency and Relative frequency of common woody species in Assabila community forest ...... 38 Table 4.4. Comparison of the basal area of Assabila community forest with other forests in Ethiopia ...... 39 Table 4.5. Species, which had highest and lowest importance, value index in Assabila community forest...... 41 Table 4.6. Tree density of Assabila community forest and other dry Afromontane forests ...... 42 Table 4.7. NDVI value of three different year imagery of the study area ...... 46 Table 4.8. Results of the forest cover classification for 1999, 2009 and 2019 images showing the area and percentages of each forest classes...... 49 Table 4.9. The mean annual rates of forest cover change...... 50

LIST OF FIGURES

Figure 3.1. Map of Assabila community forest site ...... 18 Figure. 3.2. Sampling design of the study area...... 21 Figure. 3.3. General Procedure of the Study ...... 30 Figure 4.1. Growth form of vegetation in percent on Assabila community forest...... 31 Figure 4.2. Species richness curve of the whole vegetation...... 35 Figure 4.3. Plant growth forms of Assabila community forest...... 37 Figure 4.4. DBH class distributions of woody species in Assabila community forest...... 43 Figure 4.5. Percentage distributions of trees in height classes for Assabila community forest. .. 44 Figure 4.6. Regeneration status of Sapling and seedling vegetation in Assabila community forest...... 46 Figure 4.7. Comparison of maximum value of NDVI in each study years...... 47 Figure 4.8. Forest cover map of 1999, 2009 and 2019 ...... 49 Figure. 4.9. Rates of forest cover change in Assabila community forest ...... 51

LIST OF APPENDICES TABLES

Appendix Table1. Lists of woody species recorded in Assabila community forest with corresponding family, vernacular name and plant forms...... 66 Appendix Table 2. Shannon- Wiener Diversity (H’) index and the average evenness values ...... 68 Appendix Table 3. Density of woody species in Assabila community forest ...... 70 Appendix Table 4. Mean basal area (BA) in m2 and relative dominance of woody species ...... 72 Appendix Table 5. Species frequency and their rank of Assabila community forest...... 74 Appendix Table 6. Importance value indices (IVI) of woody species in the study forest...... 76 Appendix Table 7. Location of quadrats in Assabila community forest...... 78 Appendix Table 8. Ground truth boundary data ...... 80

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ABSTRACT

This study was conducted on Assabila community forest in South Achefer District, West Gojjam Zone, Ethiopia to determine woody plant species diversity, structure and evaluates forest cover change of the community forest. A systematic sampling method with nested plots was used to collect vegetation data. Accordingly, 36 quadrants each with 400 m2 (20 m X 20 m) were used for trees and 5 m X 5 m subplots (5 *36= 180) for collection of sapling and seedling data. Stratification was done in the study forest to take accurate and reliable data from the field as well as to maintain the homogeneity of the area. Based on vegetation density the study forest was classified into highly populated, medially populated and lowly populated forest. The sampling plots were placed at every 100 m intervals along the transect lines laid at 200 m apart in the south to north direction. Concerning the vegetation structure of the community forest, all trees and shrubs with a diameter at breast height (DBH) >2.5 cm and height >2 m were measured for height and diameter analysis. The result showed that a total of 44 woody species belonging to 31 genera and 26 families were identified in Assabila community forest. From these species, 28 (68 %) were trees, 9(19%) were saplings, and 7 (13%) were seedlings. Of all the families, Fabaceae was the most dominant family which contributing 16 species. The forest had average Shannon-Wiener Diversity Index (H`) and the average evenness values of 2.69 and 0.71, respectively. The analysis of diameter at breast height (DBH) and height class distribution of the study forest showed Bell-shaped attributing poor regeneration and total basal area of all tree species were 11.8 m2/ha-1. Croton macrostachyus, Carissa edulis, Acacia lahai, Maytenus arbutifolia were the most frequently occurred species. As the result of forest cover change detection, the area was decreased from 113 ha to 92 ha from 1999 – 2019. Generally, the study forest had low species diversity and poor regeneration potential because of the unwise use of the forest resources by the nearby village dwellers and poorly protected by all the concerned bodies. Therefore, conservation of species, ecosystem restoration and sustainable use of the forest genetic resources are recommended as a result of this study.

Keywords: Community forest, Assabila, Species composition, Vegetation structure, Forest cover change detections, Woody plant species diversity.

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Chapter 1. INTRODUCTION

1.1 Background and justification

Loss of forest cover and biodiversity due to anthropogenic activities is a growing concern in many parts of the world (Amanuel Ayanaw & Gemedo Dalle, 2018). Africa’s forest cover is estimated to be 650 million ha, constituting 17 percent of the world’s forests including a number of global biodiversity hotspots (Kedir Aliyi. et al., 2015). Ethiopia is one of the tropical countries with diverse flora and fauna resulted from its topography and diverse climatic conditions which led to the emergence of habitats that are suitable for the evolution and survival of various plant and animal species (Birhanu Kebede, 2017). The altitude of the country ranges from 125 meters below sea level at the Dallol depression in the North-East, to the highest peak in the North-West of the country at Mount Ras Dejen, which is 4,620 mean above sea-level (Admassu et al., 2016). These contributed to the emergence of a variety of habitats suitable for the evolution and survival of various plant and animal species. As a result, the country is regarded as one of the most important countries in Africa concerning endemism of plant and animal species (EFAP, 1994).

Ethiopia has the fifth largest floral diversity in tropical Africa Motuma Didita et al. (2010) as the diverse topography, climate and landforms have given rise to the development of wide floral and faunal diversity rich with endemic elements. The flora of Ethiopia and Eritrea included about 6000 higher plant species with 10% endemism (Hedberg et al., 2009). Furthermore, woody plant species in the flora of Ethiopia and Eritrea were estimated to be 1100; out of these, about 300 are tree species (Demel Teketay et al., 2000). Vegetation types in Ethiopia are highly diverse ranging from afro-alpine to desert vegetation (EFAP, 1994).

Even though forest having lots of all-rounded benefits and services, the rich biodiversity resources, including forests, are being destroyed at an alarming rate largely due to human related disturbances (Ermias Aynekulu, 2011). Although there is controversy over the precise figures of the former forest cover in Ethiopia, historical sources indicate that some 35-40% of the land area might have been once covered with forests (EFAP, 1994). With the inclusion of savannah woodlands, the estimate rises to some 66% of the country. As a result of continuous deforestation, most of the forests have disappeared, and deforestation continues unabated at a

1 very alarming rate. Today, only a few forest patches exist, most of which are in various several stages. In most of the northern parts of the country, remnant forests can only be seen around churches and monasteries where, by tradition, trees are not cut by people (Alemayehu Wassie et al., 2005).

Recent report showed that the average annual loss of forest or deforestation rate is around 92,000 ha yr-1 (MEFCC, 2017), the natural high forests of the country have been forecasted to be gone in a few decades. The forests are disappearing even before we have a chance to study and document them properly. Extensive deforestation in the Ethiopian highlands has led to small and isolated forest patches around churches and in poorly accessible mountain areas and these patches are located within a landscape matrix of intensively used agricultural land. The thousands of church forests in the dry Ethiopian mountains can be considered a special case of sacred groves, traditionally managed small forest patches with considerable potential for conservation (Bhagwat and Rutte, 2006).

Satellite remote sensing datasets or Satellite imagery is an important means for forest cover change analysis. Remote sensing techniques provide a source of data that updates land cover information to monitor ecosystem changes over time (Potter C et al., 2007). The forest change detection analysis has the advantages of determining the nature, biodiversity, extent, and rate of land-cover changes, as well as aiding future planning and land management, such as plantation, urbanization, water management and extending the land (Rogan J. and Miller J, 2006). Monitoring and analysis for change detection is the most adopted application of the satellite data (Qingsheng Li et al., 2017). Landsat satellite datasets have been used for change detection analysis (Tarantino E et al., 2015). Change detection analysis can enhance the land- use planning within a framework of laws and policies to guide forest zone allocation. The change detection assessment has become central to diverse facets of the natural environment (Hegazy. I and Kaloop. M, 2015). Information on forest cover change is highly needed to encourage forest management and to inform appropriate expansion.

Forest coverage must be estimated periodically to detect the time series changes. The change detection analysis has the advantage to visualize the dynamics of changes in forest and deforestation processes (Santika. T et al., 2017). In Ethiopia three major factors, affect

LULCC: resettlement programs, population growth and increasing agricultural investments (Degife et al., 2018). From 1995 to 2010, Ethiopia lost about 141,000 ha of forest (FAO, 2011). Ethiopian flora and fauna show a declining trend due to extensive deforestation (Tolera et al., 2008). This study therefore applied the combination of GIS and RS techniques and direct forest inventory using systematic survey techniques to analyze woody plant species diversity and spatiotemporal dynamics of Assabila community forest priority area. Therefore, the present study will aim to generate data on woody species diversity, cover change detection and regeneration status of the forest to fill knowledge gaps and to know area coverage of the selected forest for decision makers and other interested groups.

1.2. Statement of the Problem

Forest degradation has been a serious challenge to Ethiopia. Many forest species in our country are at the red light to endanger because of inappropriate management of forest resource, deforestation of trees for farmland expansion and burning of forest species by wildfire. Like other Ethiopian high land forests, the Assabila community forest is under severe pressures of human based activities. The community living around the forest cut trees for a fence, construction materials, firewood consumption, and charcoal. Farmland expansion and overgrazing are the other activities practiced by the community and causes for deforestation. To reduce this serious forest degradation and deforestation as well as to achieve sustainable forest management and development, availability of accurate data in forest resource is essential (FAO, 2007).

Knowledge of species diversity and structure of forest resources is also useful in identifying important elements of plant diversity, protecting threatened and economically important species and monitoring the state of reference among others (Segawa and Nkuutu, 2006). Reduction in forest cover has a number of consequences including soil erosion and reduction capacity for carbon sequestration, loss of biodiversity and instability of ecosystems and reduced availability of various wood and non-wood forest products and services (Shambel Batiwalu, 2010). This study was carried out to generate valuable data on woody plant species diversity, cover change detection and regeneration status to contribute to the conservation, management and sustainable utilization of these forest resources.

1.3. Objectives of the Study

1.3.1. General objective

The general objective of this study was to investigate the diversity and structure of woody plant species and to detect the forest cover changes of the Assabila community forest.

1.3.2. Specific objectives

This research has the following specific objectives  To determine woody plant species diversity of the study area.  To know the forest cover change in the study area.  To evaluate the forest structure of woody plant species

1.4. Research Questions

The research mainly focuses on the following key research questions

1. What woody plant species are available in the study area? 2. How many hectares of forest are changing into other land use systems in three decades in the study area? 3. What is the forest structure of woody plant species in the study area?

1.5. Significance of the Study

The community forest is under severe pressure by the surrounding community in doing such activities like overgrazing, farmland expansion, cutting trees for firewood consumption and farmland fence. To alleviate such problems, organized data are necessary to create community awareness and propose an alternative management plan on the conservation and sustainable utilization of forest resources. Thus, species information documentation and description of this forest are important to develop sustainable forest management strategies. Furthermore, this study is initiated to document and provide primary information to policy- makers, environmentalists, governmental and non-governmental organizations and also other development agents about woody species diversity, forest cover change detection and

4 structure of the community forest. The study will also contribute to understanding the current status and woody plant diversities and also increasing the forest coverage of the region.

Chapter 2. LITERATURE REVIEW

2.1. Concept of Biodiversity

No other natural resource in the world has occupied the worldview of humans and harnessed for utilization and management more than biodiversity (Hussien Adal, 2014). Biological diversity or ‘biodiversity’ has been defined 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 part; this includes diversity within species, between species, and of ecosystems (Amanuel Ayanaw & Gemedo Dalle, 2018).

The number of species of plants, animals, and microorganisms, the enormous diversity of genes in these species, the different ecosystems on the planet, such as deserts, rainforests, and coral reefs are all part of a biologically diverse earth. This variety provides the building blocks to adapt to changing environmental conditions in the future (Institute of Biodiversity conservation, 2005). Biodiversity refers to all aspects of variety in the living world, including the variety of species on the planet, the amount of genetic variation that exists within a species, the diversity of communities in an ecosystem and the rich variety of landscapes that occur on the planet (Convention on Biological diversity, 2009). Species diversity has been identified as one of the key indices of sustainable land-use practices and considerable resources are expended to identify and implement strategies that will reverse the current decline in biodiversity at local, regional and international scales (Shackelton, 2000).

Diversity could be viewed in terms of alpha, beta and gamma diversity. Alpha diversity refers to the diversity or number of species within a particular habitat or community. Beta diversity is the rate and extent of change in species richness between communities across an environmental gradient over a relatively small distance. It is often estimated by calculating species turn over. Gamma diversity is, on the other hand, the diversity of species or number of species across a very large area such as a biome or continent and is dependent on the alpha and beta diversities (Rosenzweing, 1995).

The idea of species diversity involves two relatively distinct concepts: species richness and evenness. Species richness refers to the total number of species in a community whereas evenness is the relative abundance of species within the sample or community making up the richness of an area (Kent and Cooker, 1992; Krebs, 1999). Ecologists investigating terrestrial systems often focus on species diversity of plant communities since green plants usually account for an overwhelming proportion of the biomass in a given system. In forests, biological diversity allows species to evolve and dynamically adapt to changing environmental conditions (including climate), to maintain the potential for tree breeding and improvement (to meet human needs for goods and services, and changing end use requirements) and to support their ecosystem functions (FAO, 2010).

Diversity is, thus, measured by recording the number of species and their relative abundances. Patterns of plant species diversity have often been noted for prioritizing conservation activities because they reflect the underlying ecological processes that are important for management (Lovett et al., 2000). Environmental degradation in Ethiopia, whether at a local level or ecosystem level, leads to desertification and its manifestations, which eventually become the overriding cause for loss of biodiversity. These disruptions have meant that much endemic biodiversity has been lost and more are threatened (Zerihun Woldu, 2008).

2.1.1. Values of biodiversity

Biodiversity is the most precious gift of nature humankind is blessed with. As all the organisms in an ecosystem are interlinked and interdependent, the value of biodiversity in the life of all the organisms including humans is enormous. Some of the major values of biodiversity are Environmental Value, Social value, Ecosystem service, Economic value, Consumptive use-value, and Productive use-value, Ethical and Moral Value and Aesthetic Value as a result Humans cannot exist without biodiversity as we use it directly and indirectly in several ways. Direct use includes things like food, fibers, medicines, and biological control, whilst indirect uses include ecosystem services such as atmospheric regulation, nutrient cycling, and pollination (Mohammed Ahammed, 2010). There are also non-use values of biodiversity, such as option value (for future use or nonuse), bequest value (in passing on a resource to future generations), existence value (value to people irrespective of use or non-

7 use) and intrinsic value (inherent worth, independent of that placed upon it by humans) (Gaston and Spicer, 2004).

2.1.2. The value of plant diversity

As described by Shambel Alemu (2011), various plant species have different use values depending on socio-economic conditions of a given community. Natural forests are important sources of non-timber forest products such as fruits, fodder, honey, herbal medicine, as sources of tools and construction materials, timber and food for local communities. Forests also shelter several animal species including those that are endemic to Ethiopia alone. In Ethiopia, the majority of the woody species have economic uses. This has tended to promote unsustainable utilization of tree and shrub species, such unsustainable utilization of few species, especially timber and fuelwood species put them in the endangered category. In most cases, the major destructive factor of plant diversity is deforestation caused by agricultural expansion and fuel wood scavenging (Ababu Anage, 2009). This could be probably due to an increased human population and their encroachments on natural habitats of every ecosystems on which they depend. Hence, there is less awareness of sustainable forest management in the local community.

As defined by Goldsmith et al. (1986), vegetation is an assemblage of plants growing together in a particular location and characterized either by its component species or by the combination of structural and functional characters that determine the appearance, or physiognomy of vegetation. The varied topography, the rift valley, and the surrounding lowlands have given Ethiopia a wide spectrum of habitats and a large number of endemic plants and animals (EFAP, 1994; Demel Teketay, 1999 and Zerihun Woldu, 1999). Grossman et al. (1998), presents vegetation is dynamic and often requires a high degree of variability. It is also measured for both inventory and monitoring purposes and can be used as a strong indicator of the ecological functioning. Thus, the classification of vegetation can serve as an important component of a larger strategy to understand and conserve this natural resource.

2.2. Vegetation Type of Ethiopia

Ethiopia is regarded as one of the most important countries in Africa concerning the endemism of plant and animal species (EFAP, 1994). The vegetation type of Ethiopia is considered extremely complex, where the complexity is due to the great variations in altitude. The difference in altitude, in turn, results in great variations of the spatial distribution of vegetation in the country (Zerihun Woldu, 1999). Different researchers have studied the vegetation of Ethiopia at different times (Motuma Didita, 2007; Sisay Nune, 2008 and Abyot Dibaba et al., 2014). However, a recent study by Friis et al. (2010) has further classified the various natural vegetation of Ethiopia into 12 major types with some modifications and they are now considered as natural ecosystems of the country. These 12 include: Desert and semi- desert scrubland (DSS), Acaci-commiphora woodland and bushland (ACB), Wooded grassland of the western Gambella region (WGG), Combretum-Terminalia woodland and wooded grassland (CTW), Dry evergreen Afromontane forest and grassland complex (DAF), Moist evergreen Afromontane forest (MAF), Transitional rainforest (TRF), Ericaceous belt (EB), Afroalpine belt (AA), Riverine vegetation (RV), Fresh-water lakes vegetation including lake shores, marshes, swamps and flood plains vegetation (FLV) and Salt-water lakes vegetation including lake shores, salt marshes and pan vegetation (SLV). Of these six are categorized as forest vegetation, i.e., the Ericaceous belt, the Dry Evergreen Afromontane forest and grassland complex, the Moist Evergreen Afromontane Forest, the Transitional Rainforest, the Combretum-terminalia woodland and wooded grassland, the Acacia- Commiphora woodland and bushland, and the Riverine vegetation.

These major vegetation types are considered natural ecosystems of the country. The vegetation of the present study area falls in the dry evergreen Afromontane forest vegetation type in the country (Friis, 1992; Tamrat Bekele, 1994). The Ethiopian highlands cover more than 50% of the country's land area with Afromontane vegetation (Tamrat Bekele 1993 and 1994; Yalden, 1983), of which dry Afromontane forests form the largest part.

2.2.1. Trends and rate of deforestations in Ethiopian vegetation

Currently, the high land forest coverage is decreased as compared to the previous. Large portions of the high lands of Ethiopia were covered with forests having wide coverage than at present (Friis, 1986). According to Tamrat Bekele (1993), the presence of several isolated forest trees, even on farmlands or patches of forests around churchyards and religious burial grounds in this country indicate the occurrence of a vast expanse of forests earlier.

However, the country’s high forests and woodland coverage have been declining both in size and quality. This is due to the increased use of forest lands for farmlands; unwise use and excessive utilization of forest products without considering the ecological and economic consequences (EFAP, 1994). By the early 1950s, high forests were reduced to 16% of the total land area. Forests on the entire country then went to decline at a faster rate and reached 3.6% by 1980, 2.6% by 1987 and an estimated 2.4% in 1992 (Sayer et al., 1992). In Ethiopia, the annual deforestation rate was estimated at 150,000 - 200,000 ha-1 (EFAP, 1994). Recent reports also showed that 92,000 ha-1(MEFCC, 2017). If the existing rate of deforestation and trend of exploiting the remained scarce forests continuous, there will be little hope of having any forest worth mentioning after a few years (EPA, 1997a and Sebsebe Demissew, 1998).

In Ethiopia, the excessive utilization of natural resources increases incessantly especially forests without well-organized forest management. The occurrence of burning fragile ecosystems, forest fires, overgrazing, farmland expansion, lack of attainable forest legislation and the sustainable institutional organization has resulted in total deforestation and degradation of fertile cultivable land and soil fertility, exposing the country to drought and famine. Thus, the earlier fertile Ethiopia is now on its way changing into a ‘stone desert’ (UNEP, 1995). According to Gebremarkos Woldesillassie (1998), the drought and famine that repeatedly visited Ethiopia for the last few decades is the actual result of shortages of rainfall and change of temperature related to deforestation, environmental degradation, and desertification. The fast rate of deforestation in this country also seriously exposed the fertile soil to the highest magnitude of erosion. Reversing the trend is possible as long as appropriate measures are taken by the concerned governmental sector and the whole society.

2.2.2. The threats to the conservation of Ethiopian vegetation

The forest resources of Ethiopia are under severe pressure because of inhabitants, farmland expansion and overgrazing. The loss of forest resources is severe in the Ethiopian highlands where most of the vast mountain massifs in the heart of the country lie above 1500 m. a. s. l (Diriba Geleti, 2006). These highlands cover about 44% of Ethiopian land area; accommodate 88% of the total population because of their agricultural potential and low prevalence of diseases, contain about 95% cultivated land and more than 67% of the livestock (EFAP, 1994). The location of Ethiopian high forests on the zone of these densely populated highlands and their unique ecology make them endangered and more susceptible to strong deforestation of forests (Shiferaw Dessie and Taye Bekele, 2002). Another threat is the conversion of high forest sites to tea plantations and large-scale farming as an investor.

According to FAO (1985), the major threats to the conservation of the Ethiopian vegetation are increasingly intensive use of forest lands for agriculture and livestock, need of fuelwood and construction materials, forest fires and human settlement. These major causes of forest destruction are very much interrelated and most are ultimately initiated by the rapid population growth in the country.

2.3. Regeneration of Woody Species

Regeneration is a complex ecosystem process involving asexual and sexual reproduction, dispersal and establishment in relation to environmental factors (Haileab Zegeye, Demel Teketay and Ensermu Kelbessa, 2011). The study of regeneration ecology of forests is essential to gather information on the presence and absence of persistent soil seed banks or seedling banks, quantity and quality of seed rain, the longevity of seeds in the soil, losses of seeds to predation and deterioration, source of re-growth after disturbances, etc. Forest plants have been reported to possess various pathways of regeneration. When forest areas are prevented from any dangerous physical contaminations, regeneration status of the forest becomes well-improved (Shambel alemu, 2011). Potential causes of seedling mortality include abiotic stresses such as shade and drought and biotic influences such as herbivores, trampling, disease or root competition (Zelalem Teshager, 2017). According to Mohammed Nuru (2019) seed supply and Predation are controls and also ecological disturbances and

11 establishing area closures are factors of stimulating Natural regeneration. (Mohammed Nuru, 2019) also Noted that, the regeneration status of species in a vegetation community can be predicted from the population dynamics of seedlings and saplings. The population structure characterized by the presence of sufficient number of seedlings, saplings and adult species show satisfactory regeneration behavior, while inadequate number of seedlings and saplings of woody species in a vegetation indicates poor regeneration.

2.4. Contribution of Area Enclosures to Regeneration

Area enclosures are areas closed off to human and livestock interferences in order to promote natural regeneration and reduce environmental degradation (Betru Nedessa et al., 2005). Area enclosures are usually established for site rehabilitation in steep, eroded and degraded areas normally identified together with the local communities and governmental offices (Descheemaeker et al., 2006). To avoid the environmental problems, communities have started to limit human and domestic animal interference in some degraded areas to prevent further degradation and promote natural regeneration (Emiru Brihane, 2002). The primary objective of establishing area closures is to improve the overall ecosystem conditions so that they can reverse ecosystem goods and services. Moreover, establishing area closures is advantageous since it is a quick and cheap method for the rehabilitation (Tefera Mengistu, 2001). The above author also noted that establishing area closures is becoming a promising alternative to combat desertification, to protect severely degraded landscapes, to minimize surface run-off and to create conducive atmosphere for human, livestock and wildlife by conserving the vegetation resources such as trees, shrubs, forbs and grasses.

2.5. Diversity Indices

Biological diversity can be quantified in different ways. A diversity index is a mathematical measure of species diversity in a community. The two main factors taken into account when measuring diversity are richness and evenness. So that diversity index must be sensitive to both factors, thus must also be sensitive to the different number of species in two or more communities (Mueller Dombois and Ellenberg, 1974).

Diversity and equitability of species in a given vegetation community are used to interpret the relative variation among and within the community and help to explain the underlying reasons

12 for such a difference (Kent and Coker, 1992). Species diversity is described based on two concepts (factors), the total number of species in the community (species richness) and the relative abundance of species (species evenness) within the sample or community.

Species richness is a measure of the number of different species in a given site and can be expressed in a mathematical index to compare diversity between sites (Zerihun Woldu, 1985). Species richness index is of great importance in assessing taxonomic and ecological values of a habitat. Evenness is a measure of the relative abundance of the different species making up the richness of an area. Thus, species diversity is a product of species richness and evenness or equitability. Among many species’ diversity indices, the widely used indices include Simpson's index, Shannon-Weiner index, and Sorenson index of similarity (Mueller Dombois and Ellenberg, 1974).

2.5.1. Simpson's diversity indices

Simpson’s index is not logarithmic and therefore is more sensitive to shifts in dominant plant species. In essence, equal value is given to the presence of any species, allowing the abundance of those species to increase the diversity value for a given plant community. Simpson's Diversity Index can refer to anyone of three closely related indices namely: Simpson's Index (D), Simpson's Index of Diversity (1-D) and Simpson's Reciprocal Index (1/D).

i. ------(1)

Where, n= the number of organisms of a particular species N= the total number of organisms of all species. The value of this index ranges between 0 and 1, where, zero represents no diversity and 1, for maximum diversity. Similarly, when Simpson index (D) is used for a direct measure of dominance, the value ranges from 0 to 1; zero represents no dominance (maximum diversity) and 1 represents maximum dominance (only one species in the sample or no diversity)

(Berger and Parker, 1970). This index (D) measures the probability that two individuals randomly selected from a sample will belong to the same species. ii. Simpson's Index of Diversity (1-D) has a value ranging between 0 and 1, but now the greater the value, the greater the sample diversity. In this case, the index represents the probability that individuals randomly selected from a sample will belong to different species. iii. Simpson's Reciprocal Index (1/D) represents the number of species contained in a sample. The value of this index starts with 1 as the lowest possible figure. The higher the value, the greater the diversity is.

2.5.2. Shannon - Wiener Index of Diversity

It is the most applicable index of diversity (Greig-Smith, 1983). Like Simpson's Index, Shannon's Index accounts for both abundance and evenness of the species present. The Shannon Diversity (observed diversity) Index (H') is calculated using the following formula:

H’= (Shannon and Wiener, 1949) …………………... 2

Where, S = total number of species, Pi = the proportion of individuals or the abundance of the ith species expressed as a proportion of total cover. Ln = log base n. Due to its logarithmic nature, the Shannon-Wiener Index is sensitive to uncommon plant species and less sensitive to very common species (Krebs, 1989) for it gives more value to the presence of each species than to its abundance. In addition, the measurement of Similarity and Dissimilarity of species composition of quadrates or samples will be assessed with Jaccard and Sorensen index.

2.6. Forest Covers Change Detection by using Remote Sensing.

The use of remote sensing data in recent times has been of immense help in monitoring the changing pattern of forest cover. It provides some of the most accurate means of measuring the extent and pattern of changes in cover conditions over a period of time (Miller et al., 1998). According to (Mass, 1999) Satellite data have become a major application in forest change detection because of the repetitive coverage of the satellites at short intervals.

Change detection as defined by (Singh, 1986) is a process of identifying changes in the state of an object or phenomenon by observing images at different times. According to the (IGBP/IHDP, 1999) change, detection studies seek to know (i) pattern of forest cover change, (ii) processes of forest cover change, and (iii) human response to forest cover change. Forest cover changes are often influenced by various natural factors: climate variability and climate change; and anthropogenic factors: population growth and their activities such as high urbanization, timber production and pasture development (Boakye et al., 2008). According to Deb & Mishra (2016), Changes in forest cover impacts the catchment processes by effecting various hydrological variables: streamflow, sediment yield, runoff coefficient and evapotranspiration; and biochemical cycles, biodiversity at local, regional and global scale and increase the probability of occurrence of flood and other natural calamities. Inventory and monitoring the forest cover and other LULC change are therefore of major concern in current environmental change research for the natural resource management.

2.6.1 Image correction

Image is a group of pixels with certain values that represents the amount of emission or reflection of the spatial object recorded by sensors. When an image is to be utilized, it is necessary to conduct geometric and radiometric correction attached in the image (Danoedoro, 2012). According to Coppin et al. (2004), the image preprocessing is necessary to establish direct links between the images and biophysical phenomena, to remove image noise and data acquisition error from the image noise affects the change detection capacities or even create false change phenomena.

2.6.2 Image classification

Danoedoro (2012), described image classification (multispectral classification) as a method that is designed to derive thematic information that mostly used mappings of land cover and land use by grouping phenomena by certain criteria. In manual classification, some criteria are used such as the similarity of tone or color, texture, shape, pattern, relief and others, which are applied as a whole set at the same time.

However, in multispectral classification, only one criterion is used, spectral values (brightness value) in some bands at once. Two kinds of image classification are widely used; supervised and unsupervised classification. Supervised classification comprises a group of algorithms, which are based on an input object’s sample (training area). Whereas unsupervised classification lets the computer to group the pixels without being interfered by the operator. According to (Danoedoro, 2012), the result of image classification is also a thematic map that needs to be validated. The evaluation of the accuracy of the classification can be applied in two aspects: the depth of information (detail of information) and truth in reality. Accurate results of image classification with the reality equal to the accuracy of land cover and land use compared to real ground cover.

2.6.3 Accuracy assessment of image classification

Accuracy assessment is the key to spatial data related work (Congalton, 2001). Accuracy assessment is needed to know the reliability of the image classification to compare quantitatively with other methods, used in some analysis and decision-making process. According to (Congalton, 2001), one of the techniques for the accuracy assessment is quantitative accuracy assessment. The key in this quantitative accuracy assessment is the application of the error matrix. An error matrix is an effective way of describing the accuracy because the error matrix describes both commission and omission error for each class.

2.6.4 Normalized Difference Vegetation Index (NDVI)

Several vegetation indices have been developed of which, NDVI is the most commonly used one despite the development of many new indices that take into account soil behavior (Bannari et al., 1995 and Myeong et al., 2006). It is used to distinguish healthy vegetation from others or from non-vegetated areas (Manandhar et al., 2009) using red and near-infrared reflectance values and this was integrated into the post-classification analysis to discriminate between the green cover and barren land. Theoretically, the NDVI threshold value ranges between -1 to +1.

The Normalized Difference Vegetation Index (NDVI) computed as (NIR-Red)/ (NIR + Red) where NIR and Red are the amount of near infrared and red light, respectively, reflected by a

16 surface and measured by satellite sensors (Pettorelli et al., 2011). NDVI values range from -1 to 1; in general terms, very low values of NDVI (0.1 and below) correspond to barren areas of rock, sand, water or snow. Moderate values (0.2– 0.3) represent shrubs and grassland, while high values (0.6 – 0.8) indicate temperate and tropical rainforests (Gascon et al., 2016).

Chapter 3. MATERIALS AND METHODS

3.1. Description of the Study Area

3.1.1. Location

The study was conducted in Amhara National Regional State, West Gojjam Zone of South Achefer district. Assabila community forest is geographically located between 11o17’54” – 11o23’55” N latitude and 36o55’31” – 36o59’07” E longitude. The forest area covers 113.14 ha, which is located in Afrefida kebele (the smallest administrative unit in Ethiopia).

Figure 3.1. Map of Assabila community forest site

The study area is dissected by different streams and rivers and the forest is found within an altitude of 1958 to 2033 m. a. s. l (Wondim Awoke & Desselgn Molla, 2019).

3.1.2 Climate

Agro ecologically, the district comprises 13% low land and 87% mid-high land area (Wondim Awoke& Desselgn Molla, 2019). As reported by (Yhenew G. Selassie & Getachew Ayanna, 2013), The districts has a mean monthly minimum and maximum temperatures of 8.40C and 280C respectively. The area also experiences mono-modal rainy season from June to September and also has a mean total annual rainfall of 1530 mm.

3.1.3 Vegetation

The vegetation type of the study forest is a dry evergreen afro-mountain vegetation type, that contains different types of endogenous trees in which largely dominated by, Croton macrostachyus, Carissa edulis, Mimusops kummel, Acacia abyssinica, Albiza schimperiana, Acacia lahai, Maytenus arbutifolia, Vernonia myriantha, Grewia ferruginea and Cordia africana. During the time being, the forest has experienced long and intensive deforestation, exploitation and forest degradation. However, the current practice of management system of the forest seems at a good position, since it is protected by the local community because of creating awareness of the local government in the water shade principle.

3.1.4 Land use and soil

According to South Achefer district Rural Land Administration Office (2018), Patterns of land use in this district shows that 51.5% of its land is cultivated, 21.33% grazing land, 7.5% forest, 14.33% woodland and bushland, and 5.35% non-cultivated including water bodies and rocky mountain areas. The soil of the study area is grouped as Nitosols (Ehenew G. Selassie & Getachew Ayanna, 2013).

3.2 Methods of Data Collection

3.2.1 Reconnaissance survey and site selection

Reconnaissance survey was carried out on the second week of September 2019 for five days to have familiarity with the vegetation, the topography of the area and to decide sampling techniques of the study area. Before data collection, natural resource experts and farmers in the district were communicated to identify the potential study area.

After selecting the proposed site, the preliminary survey was conducted to record the latitude, longitude, aspect, slope, vegetation density and elevation of the study area. The survey was made across the forest to obtain an impression in site conditions and physiognomy of the vegetation. Furthermore, during this period, secondary information regarding the study area was collected from the District Offices of Agriculture and Rural land administration and use.

3.2.2 Study site boundaries delineation

Delineation of the forest boundary is the first step in forest inventory and woody species diversity analysis (Bhishma et al., 2010). Many tools are available for identifying and delineating project boundaries such as aerial photos, global positioning system (GPS), topographic maps, google earth land records and others. The boundaries of the study forest area were delineated by taking geographic coordinates with GPS at each turning point. The GPS points that were taken from the study site to indicate each sample quadrats were recorded.

3.2.3. Sampling design for the study of woody plant diversity

Stratification was done in the study forest site to take reliable data from the field as well as to maintain the homogeneity of the area. Vegetation density is one of the major parameters to classify the study area. By considering vegetation density, the study area was classified into three strata, namely strata (A), Strata (B) and Strata(C). To know the density of vegetation/ha, two (20mx20m) plots were randomly selected in each stratum. Based on this sampling approach, 1764, 2254 and 968 vegetation/ha has been recorded for each stratum respectively.

As a result, stratum (A) has medially populated, stratum (B) has highly populated and stratum (C) has sparsely populated. The actual data collection was conducted from January 15, 2020 to February 15, 2020. Then a systematic transect (Nested plot) sampling technique was used in the three strata, which are different in their vegetation density and distribution pattern. In each of the stratum, two parallel line transects were laid at 200 m interval that lies with parallel to the slope of the stand.

To avoid the effect of disturbances, the first and the last line transects were laid at indents of 50 m from the edges. The locations of the quadrats were marked by GPS and slope along transects were measured using clinometers.

Along the transect lines, a total of 36 quadrats of 20 m x 20 m (400 m2) were used. From these, 10 quadrants for the stratum (A), 12 quadrants for stratum (B) and 14 quadrants for stratum (C) was laid down at a 100 m interval for trees following the Braun-Blanquet approach (Mueller Dombo and Ellenberge, 1974; Kent and Coker, 1992). To collect data on seedlings and saplings, five subplots of 5 m x5 m (25 m2) size were located at the four corners and one at the center of the main plots.

Figure 3.2. Sampling design of the study area

A hundred - meter tape has been used to demarcate the boundaries of sampling plots and to take biophysical data. Woody plants at each quadrat were recorded and documented in its local name with notebooks. For plant species that were difficult to identify in the field, specimens were properly photographed, collected, pressed and dried by using pressing materials (cartoon, newspaper, scissors, rope) and brought to the office for asking senior

21 experts and accessing internets to identify their vernacular and scientific names using the published volumes of the flora of Ethiopia and Eritrea (Hedberg and Edwards, 1989; Edwards et al., 2000; Azene Bekele, 2007). Diameter at breast height (DBH) and height of trees and shrubs were measured and record using caliper, diameter tape and hypsometer (sometimes visual estimation is used depending on the size of the woody plant species) respectively.

Physiographic variables such as altitude and the slope were recorded for each sampling plot using GPS and clinometer respectively. Pictures of plant specimens, vegetation stands and other multi-state data required in the form of pictures were taken with a digital camera. For trees and shrubs that are branched around the breast height and difficult to measure directly by using caliper, the DBH were measured separately by diameter tape and consider in two trees. The undergrowth of woody species was categorized as a seedling with a height less than 1 m, saplings with a height between 1 m and 2 m and height greater than 2 m will be considered as trees and shrubs (Singhal, 1996). Woody plant species with a diameter greater than 2.5 cm at 1.3 m DBH was measured and height > 2 m counted as trees or shrubs following (Singhal, 1996).

3.2.4. Data and methods of forest cover change detection

In this study, primary data were derived from free downloaded satellite images and preliminary field survey using handheld GPS (Garmin 64). Satellite data for 1999 and 2009 years (Table 3.1) consisting of multi-spectral LandSat-5 acquired by Multispectral Scanner (MSS) and the new Thematic Mapper (TM) sensors which was acquired from GLOVIS website (http://glovis.usgs.gov) and Satellite data for 2019 year consisting of multispectral LandSat-8 acquired by Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) which was acquired from https://earthexplorer.usgs.gov. Forest cover change of the study area was detected by performing the NDVI. The forest cover change detection was essentially done by using ERDAS and Arc GIS software for image analysis and mapping. During image selection, cloud and unwanted shade-free imagery was set as criteria as their presence could substantially reduce the accuracy of the classification. In addition to this cloud-free images that provide in drier periods of the year were considered to make the analysis was more efficient.

3.3. Data Analysis

The data were analyzed using a range of analytical tools and software appropriate to the collected data.

3.3.1. Diversity analysis

Different index of analysis methods was available to analyze woody plant species diversity, richness, and evenness but data were analyzed by using the Shannon Wiener index of analysis because of its power to combine species richness with evenness better than other indices (Zewdie Achiso, 2014). The quantitative index of species diversity, richness and evenness was measured using a diversity index formula by (Shannon and wiener, 1949).

Shannon wiener index (H’) = ------(3) Where: Pi = the probability or proportion of individuals in the ith species s = the number of species/plots ln = natural logarithm.

The minimum value of H’ is zero (0), which is the value for a community with a single species and increases as species richness and evenness increase (Manuel and Molles, 2007). The species evenness that measures the equity of species in a given sample area is represented by 0 and 1. Where 0 indicates the abundance of few species and 1 indicates where all species are equally abundant (Whittaker, 1975).Shannon’s Equitability (EH) or Evenness is determined by ------(4)

Where; S is the number of species recorded (Shannon and Wiener, 1949). Shannon evenness measure or measure of equitability increases the effectiveness of the Shannon diversity measured by considering each species’ relative abundance. If the species are evenly distributed, then the H’ value would be high. So, the H’ value allows us to know not only the number of species but also how the abundance of the species is distributed among all the species in the community (Frosini, 2006; Dinkissa Beche and Shambel Alemu, 2011).

3.3.2. Structural analysis

Analysis of population structure is an extremely useful tool for planning management activities and assessing the impact of resource extraction (Peters, 1996). The structure of the vegetation was described based on the analysis of species density, DBH, height, basal area, frequency and important value index (IVI). The regeneration status of the trees, shrubs, and lianas was also determined by computing density ratios between seedlings and mature individuals, seedlings and saplings, and sapling and mature individuals. The Diameter at Breast Height (DBH) and tree height will be classified into DBH classes and height classes.

DBH = (퐶/휋) Where C is circumference and 휋 is 3.14 ……………………… (5)

The data were summarized using the Microsoft Office Excel spreadsheet. Important value Index (IVI) describes the structural role of a species in a stand and used to compare the ecological significance of species in a given forest type and also a good index for summarizing vegetation characteristics and ranking species for management and conservation practices (Tamrat Bekele, 1993; Simon Shibru and Girma Balcha, 2004). The importance value index (IVI) for each woody plant species was calculated using the formula indicated below (Kedir Aliyi et al., 2015).

Importance Value Index = Relative density + Relative frequency + Relative dominance ……… (6)…...... (Kedir Aliyi et al., 2015).

Basal area

It is the cross-sectional area of all of the stems in a stand at breast height (1.3 m above ground level). This basal area per unit area is used to explain the crowdedness of a stand of forests.

It can be expressed in square meter/hectare (Mueller Dombois and Ellenberg, 1974; Markos Kuma and Simon Shibiru (2015). The basal area is also used to calculate the dominance of species. BA = 휋DBH2/4 …………………… (7) …………………… (Markos Kuma and Simon Shibiru, 2015))

Where BA= Basal Area, DBH is the diameter at breast height.

Relative dominance = ……………... (8)

Where Dominance is average basal area per Tree times the number of Tree species Or Dominance = total cover or basal area of species A/area sampled.

Woody Species Density

Density is defined as the number of plants of a certain species per unit area.

X 100 ------(9)……… (Pascal, 1988)

X 100 ------(10)

Frequency

Frequency is defined as the probability or chance of finding a plant species in a given sample area or quadrat. It is calculated with the formula:

X 100----- (11) … (Pascal, 1988)

The frequencies of the tree and shrub species in all thirty-six quadrats were computed.

X 100 ------(12)

Important value Index (IVI) is an index computed from Relative Density, Relative Dominance and Relative Frequency, which describes the structural role of a species in a stand. It is useful for making comparisons among stands in reference to species composition and stand structure

(Tamrat Bekele, 1993; Simon Shibru and Girma Balcha, 2004). It is used to compare the ecological significance of species in a given forest type and a good index for summarizing vegetation characteristics and ranking species for management and conservation practices.

IVI is a unit less score combining the above three measures giving each equal weight. The individuals of a species with higher IVI values are dominant over individuals of species with relatively lower IVI values.

3.3.3. Measurement of similarity and dissimilarity analysis

Similarity indices measure the degree to which the species composition of quadrates or samples is alike, whereas, dissimilarity coefficient assesses which two quadrates or samples differ in composition (Dinkissa Beche and Shambel Alemu, 2011). Jaccard and Sorensen are the most common binary similarity coefficients because they rely on the presence or absence of data.

Sorensen's coefficient is expressed as:

X 100 (Kent and Coker, 1992) ------(13)

Where, a = number of species common to both quadrats/samples, b = number of species unique to quadrat/sample 1, c = number of species unique to quadrant/sample 2. Often, the coefficient is multiplied by 100 to give a percentage similarity index. Dissimilarity is then computed as:

------(14)

3.3.4. Cover change detection Analysis

Cover change detection is a process of identifying changes in the state of an object or phenomenon by observing images at different times. Remote sensing and Geographical information systems (GIS) are well-established, efficient, cost effective and accurate alternative to study the forest cover analysis and its change over a time period. Satellite data

26 plays a major role to identify the forest cover change due to its spatial and temporal coverage at short time interval (Deb & Mishra, 2016).

Image acquisition

To assess landscape level changes in forest cover, two images were downloaded in 30 meters- resolution Landsat Images from the Earth Explorer website of the United States Geological Survey. Landsat satellite sensors capture spectral information about the electromagnetic radiation that is reflected off of the Earth’s surface, including bands of visible light, near- infrared, and shortwave infrared radiation that which, used in this study in addition to other data such as thermal radiation which I excluded from my analyses (Campbell and Wynne, 2011). We selected these particular images among other available ones because they had minimal cloud cover, which is critical for accurate analyses of ground cover. Both images date to the dry season on (December) of their respective years as recommended by Professor Travis Reynolds of Colby College to emphasize permanent forest vegetation as opposed to seasonal fluctuations in agricultural or other vegetative productivity.

The acquisition dates of the three Landsat images employed in this change detection process fall within the same season. In order to align the images to study area coordinates, geo- referencing was performed using Ground Control Points (GCPs).

Table 3.1. Landsat Image Attributes

Spatial Cloud Bands Path/ resolution Acquisition Satellite Sensor cover used Row (pixel date (%) size) Multispectral Scanner Visible (MSS) and the 30 m × Landsat-5 (B3) and 170/52 0 09/12/1999 new Thematic Mapper 30 m NIR (B4) (TM) Multispectral Scanner Visible 30 m × Landsat-5 170/52 0 04/12/2009 (MSS) and the (B3) and 30 m

new Thematic Mapper NIR (B4) (TM) Operational Land Visible Imager (OLI) and 30 m × Landsat-8 (B4) and 170/52 0 16/12/2019 Thermal Infrared 30 m NIR (B5) Sensor (TIRS)

Normalized difference vegetation index (NDVI)

NDVI is a remote sensing method that allows displaying greenness of vegetation. It is the one that used to distinguish healthy vegetation from others or the non-vegetation environment using red and near-infrared reflectance values (Gandhi et al., 2015). A theoretical threshold values range from -1 to 1. Measured value with negative NDVI is areas other than vegetation cover; such as snow, water or clouds where red reflectance is greater than near infrared.

NDVI calculates according to the following standard formula that involves the red light reflected in the image (band 3) and the near-infrared image (band 4) for bare soil with reflectance values roughly the same in red and near infrared; the NDVI has close to 0. Vegetation formations have positive NDVI values, generally, between 0.1 & 0.7; the highest values for the densest vegetation cover (Biswajit N and Shukla A. 2013). For this study area, the three years (1999, 2009 & 2019) NDVI is constructed from the red band and near-infrared band. It was determined by using the following algorithm or formula.

Or ……………. (15)

On the other hand, we use different types of Landsat the final images were calculated by each individual with the following formula.

For Landsat 5 or …………… (16)

For Landsat 8 or (Annika K.,2016).. (17)

The above mathematical formula is performed by image analysis software, which is ERDAS, ArcGIS or others. In this study, the NDVI values determined by ArcGIS software i.e. raster calculator in Arc toolbox. The NDVI calculation results in the value from -1 to +1; that is -1

Post calculation

In this process, images of every year were classified and labelled separately. The classified images were then compared to determine the change that had taken place between the two images using a change matrix. This enabled the changed areas to be extracted and by how much through the computation of change maps and change matrix statistics (Sheila et al. 2013). With this information, it was easy to quantify and explain the change in the forest cover. Mean annual rates of forest cover change (MARFCC) between different image dates were computed based on the time series classified images using the equation below:

(Sheila et al., 2013). …… (18)

Where: t1 = the year in which the older image was captured; t2 = the year in which the recent image was captured.

Figure 3.3. The conceptual framework procedure of the study.

Chapter 4. RESULT AND DISCUSSION

4.1 Woody Plant Species Diversity of Assabila Community Forest

A total of 44 woody species belonging to 31 genera and 26 families were identified in Assabila community forest (Table 4.1). From the recorded species trees were majorly occurred (28), saplings (9) and seedlings (7) were also second and third respectively (Figure 4.1). Of all the families, Fabaceae was the most dominant family comprising of 6 species followed by Moraceae comprising 4 species, Asteraceae, Anacardiaceae and Boraginaceae comprise 3 species, Rutaceae, Apocynaceae, Celastraceae, Combretaceae and Rosacea each represented by two species. The remaining16 families were represented by single species (Table 2).

Figure 4.1. Woody species diversity in Assabila community forest

Similar to the present study, Ermias Aynekulu (2011), Kitessa Hundera and Tsegaye Gadissa (2008), and Haileab Zegeye (2005) at Hugumburda Forest, Belete forest, Tara Gedam Forest, respectively indicated the dominance of Fabaceae family. This might be attributed due to the adaptation potential of Fabaceae families to wider agro-ecologies. Another study by, Ensermu Kelbessa and Teshome Soromessa, (2008) reported that, the dominance of Fabaceae and Asteraceae families could be attributed to their efficient and successful dispersal mechanisms and adaptation to a wide range of ecological conditions.

The number of species (44) recorded in the study area (Assabila community forest) was lower than that of reported for Abebaye and Tara Gedam Forest =143 (Haileab Zegeye, 2005), Wof Washa = 252 (Demel Teketay and Tamrat Bekele, 1995), Bonga forest = 51 (Abayneh Derero et al., 2003), Jibat =54 (Tamrat Bekele, 1994), Chilmo = 90 (Tadesse Woldemariam et al., 2000), Belete forest =79 (Kitessa Hundera and Tsegaye Gadissa, 2008) and Hugumburda forest =79 (Ermias Aynekulu, 2011).

Nevertheless, a similar study by Alemayehu Wassie (2002) in some Church forests in South Gonder, (the number of species in Dengolt = 36, Gibtsawit = 35, Debresena =34, Hiruy = 31), showed that Assabila community forest has the highest number of species. However, as we seen in the result it indicates that the low species diversity of the study area. This may be due to vulnerability of the plant species b/c of free grazing by animals and also cutting of trees for different purposes. As Kibret Mamo (2008) reported that, the reduction of species diversity in the entire forest could be an indication of the increased vulnerability of the plant species by animals and/or human intervention at maturity or early stage of regeneration. This might indicate that individuals in the community forest were either harvested at an early growth stage by the local inhabitants and/or their domestic animals (Wondie et al., 2014). Similarly, Tessema et al. (2011) suggested that, heavy grazing/browsing might cause a reduction of plant species density and diversity over time.

Table 4.1. List of woody plant families recorded and number of genera and species represented in them. No Family Genera No of Species 1 Anacardiaceae 1 3 2 Apocynaceae 1 2 3 Asteraceae 2 3 4 Bignoniaceae 1 1 5 Boraginaceae 1 3 6 Capparidaceae 1 1 7 Combretaceae 1 2 8 Celastraceae 1 2 9 Euphorbiaceae 1 1 10 Fabaceae 3 6 11 Flacourtiaceae 2 1 12 Loganiaceae 1 1 13 Melianthaceae 1 1 14 Meliaceae 1 1 15 Moraceae 2 4 16 Myrtaceae 1 1 17 Olacaceae 1 1 18 Phytolacaceae 2 1 19 Rosaceae 1 2 20 Rutaceae 1 2 21 Sapotaceae 1 1 22 Sapindaceae 1 1 23 Santalaceae 1 1 24 Simaroubaceae 1 1 25 Tiliaceae 1 1 26 Verbenaceae 1 1 Total 31 44

4.1.1. Species diversity

A combination of the number of species and their relative abundance defines species diversity. The values of species diversity depend upon levels of species richness and evenness (Dinkessa Beche, 2011). The overall average Shannon-Wiener Diversity Index (H`) and the average evenness values for the entire forest were 2.69 and 0.71 respectively, which is higher than Harenna forest (2.60) (Feyera Senbeta, 2006). Shannon diversity index considered as high when the calculated value is ≥ 3.0, medium when it is between 2.0 and 3.0, low between 1.0 and 2.0, and very low when it is ≤1.0 (Cavalcanti and Larrazabal, 2004). In this respect, Assabila Community forest had medium diversity index (2.69).

The result showed that in Appendix (2), only a few species were dominating the vegetation of the study area in their abundance while many of the species were very rare or low in their abundance. Such a result reflects either adverse environmental situations or random distribution of available resource in the study area (Feyera Senbeta et al., 2007 and Tatek Dejene, 2008). It can be further inferred about this study result from the above two authors point of views in that the woody plants were distributed unevenly may be due to inability of individuals to cope up harsh environmental condition, human disturbance, livestock trampling and grazing, and other biotic and abiotic impairments in the area.

4.1.2. Species evenness

A low equitability/evenness value means that there is the dominance of one or few species in the community. While high equitability/evenness means that, there are uniform distributions among the species in a given ecological setting (Cavalcanti and Larrazabal, 2004). In agreement with the above statements, seven sample plots were taken randomly and decided the species diversity curves of the vegetation of Assabila community forest.

Figure 4.2. Species richness curve of the whole vegetation.

The result showed that (Figure 4.2), species richness across quadrats was good at quadrant 1, 5 and 10 due to this pattern of diversity curve was raised up and in the quadrant 15, 20 and 25 had poor species richness and diversity curve was step down and also in quadrant 30 and 35 good species richness and the diversity curve again raised up.

4.2 Similarity and Dissimilarity Analysis

The Sorenson’s similarity coefficient was used to detect similarities and dissimilarities among the strata in plant vegetation. The distribution of species among the strata showed significant similarity. The results in (Table 4.2) showed that the three strata had a similarity index greater than 60%, which implies that more than 60% of the species were common among the three habitats.

Table 4.2. Sorensen’s Coefficient of Similarity in the three strata.

Strata Similarity % Dissimilarity % Strata (I-II) 0.72 72% 0.28 28% Strata (I-III) 0.63 63% 0.37 37% Strata (II - III) 0.68 68% 0.32 32%

High similarity coefficient (Sc= 72%) was observed between strata I and II and low similarity (Sc = 63%) was observed between strata -I and strata -III. Thus, the dissimilarity accounts for 28% for the most similar strata (Strata-I and -II) and 37% for those that share the least similarities (strata-I and III). The relatively higher similarity between (Strata I and II) is probably due to the collective similar environmental factors such as soil nutrients, moisture, aspects, the intensity of grazing etc. According to Magurran (2004), many vegetation samples have similar species composition and relative species abundance due to their occurrence in similar environments. In addition to the above author Fekadu Gurmessa (2010), Shambel Bantiwalu (2010), Dinkessa Beche (2011) and Shambel Alemu (2011) were reported that environmental factors such as aspect, slope, and soil physical and chemical properties have sound effects on patterns of vegetation similarities in plant populations.

4.3 Vegetation Structure

In the analysis of vegetation structure, the growth stages of trees as seedlings, saplings and mature trees as the distribution of size classes within a population can be one of the elements of diversity that allow or deny the chance of rapid recovery after disturbances (Harper, 1982 and Shambel Bantiwal, 2010). The population structures of trees have a significant implication to their management, sustainable use and conservation. The structural patterns that are obtained from measured data can be used for checking the variations in population dynamics that may arise from inherent characters or human interventions and their livestock.

Description of growth forms of species recorded from Assabila community forest is presented in (Figure 4.3). Accordingly, the highest proportion (68%) was trees. In addition, saplings and seedlings comprised 19.2% and 12.8%, respectively, of the total. This might be due to low regeneration status and less sapling population because of disturbance of the study forest by grazers, browsers and also d/f environmental factors. Similar to the present study Zelalem Teshager et al. (2018) reported that, the distribution of mature individuals is greater than seedlings and sapling indicate that the regeneration status of the forest is at the low state. Compared to matured individuals, there were less sapling populations implying the perishing- off of most seedlings before reaching sapling stage due to factors such as closed canopy, human intervention, browsers, grazers, climatic and nature of the seeds.

Figure 4.3. Plant growth forms of Assabila community forest.

4.3.1 Density of woody species

A total of 6775 individuals of woody species were encountered from 36 studied quadrats (Appendix 3). The Six most abundant woody species in their order of highest density were Croton macrostachyus, Carissa edulis, Acacia lahai, Maytenus arbutifolia, Vernonia myriantha and Maytenus obscura. Whereas, species like Ximenia americana, Rubus apetalus and Dovyalis abyssinica were poorly reckoned in this study (Appendix 3). Similar to Yohannes Mulugeta et al., (2015) the present study showed that the density distribution of woody species in different DBH and height classes showed a Bell-shape pattern, which showed a type of frequency distribution in which a number of individuals in the middle classes were high, and decreased towards the lower and higher diameter/height classes. As Tesfaye Atsbha et al. (2019) reported that, the density of woody species in protected forest (area enclosure) site is higher than that of open grazed forest. As the above author explained, the study forest was no more protected from disturbance due to this had low density. However, species that are found in open grazed lands are more tolerance to disturbance and

37 very important in the recovery of degraded vegetation in the forest area. As we seen in (Table 4.3) below some common woody species in the Assabila community forest are listed here and the details are found in (appendix 3).

Table 4.3. Density, Relative Density, Basal area, Relative dominance, Frequency and Relative frequency of common woody species in Assabila community forest.

No Species Density R. Density BA in R. Dominance Frequency R.f m2/ha 1 Crotonmacrostachyus 42.33 22.495 1.7435 14.842 100.00 8.70 2 Carissa edulis 31.31 16.635 0.8326 7.087 97.22 8.45 3 Acacia lahai 17.92 9.52 1.0105 8.602 94.44 8.21 4 Maytenus arbutifolia 17.19 9.137 0.024 0.204 91.67 7.97 5 Acacia abyssinica 6.81 3.616 1.686 14.352 86.11 7.49 6 Maytenus obscura 11.39 6.052 0.0146 0.124 86.11 7.49 7 Vernonia myriantha 12.28 6.524 0.0646 0.55 86.11 7.49 8 Ritchiea steudneri 2.56 1.358 0.0644 0.548 77.78 6.76 9 Capparis tomentosa 4.61 2.450 0.0141 0.120 72.22 6.28 10 Calpunia aurea 6.75 3.587 0.0994 0.846 69.44 6.04 11 Mimusops kummel 4.58 2.435 1.7159 14.606 61.11 5.31 12 Albiza schimperiana 3.69 1.963 1.4923 12.703 66.67 5.80

4.3.2 Basal area

Basal area is used to explain the crowdedness of a stand of forests. A stand of large trees is more stocked than with the same number of trees of smaller diameter (Shambel Bantiwalu, 2010). The total basal area of all tree species in Assabila community forest was found to be 11.8 m2 ha-1. According to Shambel Bantiwalu (2010), the normal basal area of virgin tropical forest in Africa is 23 - 37 m2 ha-1. Basal area provides a better measure of the relative

38 importance of the species than simple stem count (Fekadu Gurmessa, 2010). Therefore, species with the largest contribution in the basal area can be considered as the most important woody species in the forest. The present study result showed that Croton macrostachyus, Mimusops kummel, Acacia abyssinica, Albiza schimperiana, Acacia lahai and Cordia africana were the most dominant species in their basal area. They constitute 72.2% of the total basal area (Table 4.3).

Table 4.4. Comparison of the basal area of Assabila community forest with other forests in Ethiopia

Forest Basal area (m2ha-1)

Wof - Washa 101.80 Menagesha Amba Mariam 84.17 Dindin 49.00 Denkoro 45.00 Menagesha- Suba 36.10 Sanka Meda 34.71 Chilimo 30.10

Assabila community forest (present study) 11.8

Source (Shamble Bantiwalu, 2010).

From this comparison, all the forests had greater basal area than Assabila community forest (Table 4.4). Woody species belonging to higher DBH class in Assabila community forest were fewer but contributed considerably to the total basal area. The basal area of a stand is simply the sum of the basal area value of all trees in that stand. The basal area increment response of trees is correlated with climatic and topographic factors (Spiecker et al., 1996). The total basal area of Assabila community forest is 11.8 m2 ha-1 w/c is small when we compare to the above forest. This might be the result of the extraction of large trees for charcoal making, construction, timber production and fuelwood.

4.3.3 Frequency

Frequency is the number of quadrats in which a given species occurred in the study area. It gives an approximate indication for homogeneity and heterogeneity of vegetation. The frequency of all the woody species in the study area is given in (Table 4.3).

Croton macrostachyus was the most frequently occurred species with 100% in all quadrats. The trees/shrubs species with more than 60% distribution were Carissa edulis (97.22%), Acacia lahai (94.44%), Maytenus arbutifolia (91.67%), Acacia abyssinica (86.11%), Maytenus obscura (86.11%), Vernonia myriantha (86.11%), Ritchiea steudneri (77.78), Capparis tomentosa (72.2%), Calpunia aurea(Alt.) Benth (69.4%), Grewia ferruginea Hochst.ex.A.Rich (69.4%), Albiza schimperiana Oliv (66.7%), Mimusops kummel (61.1%) and Premna schimperi (61.1%).

The woody species with the least occurrence and their contribution were Ximenia Americana (5.6%), Rubus apetalus (5.6%), Acacia brevispica (8.3%) as shown in (Appendix 5)

4.3.4 Important value index

Importance value index combines data for three parameters (relative frequency, relative density and relative dominance). That is why ecologists consider it as the most realistic aspect in vegetation study (Fekadu Gurmessa, 2010; Shambel Bantiwalu, 2010 and Shambel Alemu, 2011). It is useful to compare the ecological significance of species (Lamprecht, 1989). Species, which had highest importance value index for woody species in Assabila community forest (1 -10) is shown in (Table 4.5). Croton macrostachyus (46.03), Carissa edulis (32.18), Acacia lahai Steud (26.33), Acacia abyssinica (25.46), Mimusops kummel (22.36), Albiza schimperiana (20.46) and Maytenus arbutifolia (17.31) all got IVI value above 17. They all summed up to give 190.13 IVI value (53.37%). The reason why Croton macrostachyus has the highest IVI value was that it had the highest relative density (22.49%). Higher IVI value for Acacia lahai and Carissa edulis with 8.60 and 7.09 respectively were due to their high relative dominance.

Table 4.5. Species, which had highest and lowest importance, value index in Assabila community forest.

No Species Name Relative Relative Relative Important value Density Dominance frequency Index (IVI)

According to Yohannes Mulugeta et al. (2015) reported that, Analysis of importance value index (IVI) is used for setting conservation priority. Those species with lower IVI values (11- 16) in (Table 4.5) need high conservation efforts while those with higher IVI values need monitoring management. The present study result indicates that much of IVI was attributed by a few species. These species were those well adapted to the high human pressure (disturbance), natural and environmental factors. Species like Ximenia Americana (0.58), Rubus apetalus (0.67), Dovyalis abyssinica (0.79), Ekebergia capensis (0.79), Combretum molle and Rhus glutinosa had the lowest IVI value indicates high priorities for conservation.

4.4. Tree and Shrub Density

Tree and shrub density, expressed as the number of individuals with DBH greater than 2.5 cm, was 324 ha-1 and those individuals with DBH between 10 and 20 cm and with DBH greater than 20 cm were 248 ha-1 and 112 ha-1, respectively (Table 4.6). The ratio described as a/b is taken as the measure of size class distribution (Breitenbach, 1963 and Fekadu Gurmessa, 2010). Accordingly, the ratio of individuals with DBH between 10 & 20 cm (a) to DBH >20 cm (b) was 2.21 for Assabila community forest. This indicates that the proportion of medium sized individuals (DBH between 10 and 20 cm) is greater than the large sized individuals (DBH >20cm) but the ratio is relatively lower than the results obtained for other forests (Chilimo and Menagesha Suba) but larger than Wofwasha and Denkoro Chaka.

Table 4.6. Tree density of Assabila community forest and other dry Afromontane forests.

Forest DBH between 10 and 20 cm(a) DBH >20 cm(b) a/b Chilmo 638 250 2.6 Menagesha suba 484 208 2.3 Wof-Washa 329 215 1.5 Denkorochaka 206 104 1.9 Assabila Community 248 112 2.21 forest (study site)

4.4.1 DBH distribution

Similar to Zelalem Teshager et al. (2018) the DBH distribution of the study forest showed different patterns (Figure 4.4). The distribution of the forest species in the present study showed high number of individuals in the second DBH classes, whereas small values in the rest DBH classes of the study forest. This shows the forest was under heavy degradation b/c of different factors so, the DBH distribution of the forest shows relatively a Bell-shape pattern (Figure 4.4). Which revealed that a type of frequency distribution in which a number of

42 individuals in the middle classes were high, and decreased towards the lower and higher diameter classes. On the contemporary, Bell-shape pattern is the reflection of a discontinuous or irregular recruitment.

A total of 4612 individuals with height >2 m and DBH >2.5 cm was recorded in Assabila community forest. Following Yohannes Mulugeta et al. (2015) for DBH analysis, eight DBH classes were established i.e class (I) 2.6-10 cm (1186), class (II) 10.01-20.0 cm (1992), class (III) 20.01- 30.0 cm (785), class (IV) 30.01- 40.0 cm (343), class (V) 40.01- 50.0 cm (124) class (VI) 50.1-60.0 cm (93), class(VII) 60.1- 70cm (54) and class (VIII) > 70cm (35). Few individuals of Ficus vasta, Ficus thonningii, Ficus sur, Ficus sycomorus, Albiza schimperiana and Mimusops kummel trees are in the higher DBH classes. Acacia Abyssinica, Ritchiea steudneri, Croton macrostachyus, Cordia Africana, Stereospermum kunthianum, dominated the middle DBH class trees. The lower classes were dominated by Maytenus arbutifolia, Vernonia myriantha, Maytenus obscura, Malpurnia aurea, carissa edulis and acacia lahai.

Figure 4.4. DBH class distributions of woody species in Assabila community forest.

4.4.2 Height distribution

Like that of DBH distribution of trees, the height distribution of trees was also classified into five height classes, class (1) ≤ 4 m (1251), class (2) 5 -8 m (1876), class (3) 9 -12 m (737), class (4) 13 -16 m (464), and class (5) >16 m (284), were established with similar to (Zelalem Teshager et al., (2018). As the result showed in (Figure 4.5), the numbers of individuals in each height class also different patterns of distribution. Of the five height classes established to describe the structure of plant communities, the majority of individuals contributing to the first height class came from individuals from Acacia lahai, Bersama abyssinica, Carissa edulis, Vernonia myriantha, Calpurnia and Capparis tomentosa. For the second height class croton Macrostachyus and Acacia Abyssinica were contains the highest contribution of the height class. The third up to the five height classes were contributed mostly by Albiza schimperiana, Milletia feruginea and Mimusops kummel.

Figure 4.5. Percentage distributions of trees in height classes for Assabila community forest.

4.5 Regeneration Status

The population structure characterized by the presence of sufficient population of seedlings, saplings and adults, indicates successful regeneration of forest species and the presence of saplings under the canopies of adult trees indicates the future composition of a community. Regeneration status of trees can be predicted by the age structure of their populations (shamble Alemu, 2011). Close to the above author a total of 2163 individuals; 1295 saplings and 868 seedlings species were counted from all quadrants. Accordingly, the following species made the largest contribution to the seedling counts per hectare: Croton macrostachyus, Maytenus arbutifolia, Acacia lahai, Maytenus obscura, Calpunia aurea, Vernonia myriantha, Carissa edulis. On the other hand, species with largest contribution to sapling counts were Calpunia aurea, Maytenus arbutifolia, Acacia lahai, Brucea antidy senterica, Carissa edulis,Vernonia myriantha, Rosa abyssinica, Grewia ferruginea,etc (figure 4.6 ). Additionally, Dhaulkhandi et al. (2008) also states that the density values of seedlings and saplings are considered regeneration potential of the species. The presence of good regeneration potential shows stability of the species to the environment. The data analysis revealed that the density values for seedlings and saplings of the population structure of the study forest are low and deviates from the normal patters of the population. This might be due to high disturbances existing in the forest. This implies a need to develop and implement effective forest management regimes in the area to promote healthy regeneration and the sustainable use of these species.

Generally, in this seedling and sapling assessment Croton macrostachyus, Maytenus arbutifolia, Maytenus obscura, Acacia lahai and Carissa edulis were with good recruitment status relative to other species. On the other hand, some species like Vernonia amygdalina, Ficus vasta, Ficus sychomorous, Syzigium guineense, etc showed no seedling and sapling. This may be due to the current climate change impacts caused by different anthropogenic activities and uncontrolled browsing effect of animals in the study area. As shambelAlemu, (2011) reported Regeneration of a particular species is poor if seedlings and saplings are much less than the mature trees. Closely, to the above author the present study shows poor regeneration due to frequent disturbance of seedling and sapling vegetation by animals/humans and also different environmental factors.

Figure 4.6. Regeneration status of sapling and seedling vegetation in Assabila community forest.

4.6. Forest Cover Change

Forest change detection analysis is critical for understanding forest cover change, documentation of which is necessary to spur conservation actions to prevent further decline. In this study, the remote sensing data is used extensively for forest cover change monitoring. The NDVI value of the image 1999, 2009 and 2019 was executed and the result is shown in (Table 4.7).

Table 4.7. NDVI value of three different year imagery of the study area. Imagery Year NDVI value Min Max 1999 0.10 0.80 2009 0.03 0.63

2019 0.01 0.55

The NDVI value of forest cover change detection statistical trends are categorized in the following manner no vegetation, less vegetation, moderated vegetation, dense vegetation, highly dense vegetation (Biswajit N and Shukla A., 2013). Like to the above author in the present study, the greenness range is divided in to four discrete classes by slicing the range of NDVI values by fixing the thresholds for NDVI classification. Those classes are no forest (NDVI value less than 0.1), less forest (0.1- 0.24), moderate forest (0.24-0.44) and dense forest (above 0.44). This range applied for all the three different years’ image classification. This technique is important for comparison of forest cover change from multiple dates of Landsat derived NDVI imageries (Solomon Gebreyohannis, 2015). The historical decline of native forest cover has diminished the provision of ecosystem services that are critical to both the local ecosystem and nearby communities.

Figure 4.7. Comparison of maximum value of NDVI in each study years.

As similar to Biswajit N and Shukla A. (2013) report, the NDVI value of the present study in the three consecutive decades is decreasing manner. This might be cutting of trees for farmland expansion, charcoal production, timber production, firewood etc. According to Reusing M. (2000) study, the forest degradation in Ethiopia is closely linked to the ongoing population growth; more people generally lead to an increasing demand on land for living and

47 for agricultural production. In our study the NDVI graph indicated that not only the deforestation but also forest degradation was the main problem for forest resource through the consecutive decade. From the observation of the study forest, the natural regeneration of the forest resources is difficult due to high populations of grazing and browsing livestock within the forests. The maximum NDVI values of the first decade had highest dense forest which is the value near to 1, that means has high canopy effect and has diversified forest population with different strata. On the same to this study, (Eric K. and Adubofour F, 2012), forest areas turn to have higher NDVI values due to their greater green biomass.

As we seen in the above (figure 4.7). The classification of 1999 image is under NDVI values of (0.1- 0.80). In this period no forest class is none in the study area because the NDVI value is 0.1 and above. The NDVI derived values above 0.44 and 0.24-0.44 of 1999 image shown in the study area were covered by dense forest and moderate forest (shares 59.91% and 33.41% respectively of the total land of the study area) and NDVI value 0.1- 0.24 represents less forest (shares 6.68% of land). Dense forest is dominant in the study area. In some extent, NDVI values decreased in the year 2009, which ranges from 0.03 to 0.63. In this period there is an additional one class (No forest). In the year 2009, less and moderate type of forest is identified, and its coverage was 19.57% and 37.71% respectively. Even if there is a decrement of a certain amount, 41.21% of the study area was covered by dense forest and still, it is a dominant class. The newly added no forest class also covered the least proportion of the area 1.51%. When we see the NDVI value of 2019, it decreased with ranges from 0.01 to 0.55. The none forest and less forest class of 2019 were increased and it covers 19.25% and 26.73% respectively. In contrast, the dense forest and moderated forest decreased and it covers 24.75% and 29.27% respectively. In general, forest degradation indicates loss of dense forest coverage and increase the moderate, less and none forest land. Due to these problems, some indigenous tree species became threatened like Rubus apetalus, Osyris quadriparitita, Rhus glutinosa and Milletia feruginea from our study area. With native forest contraction, communities risk losing a variety of ecological, economic, and cultural ecosystem services.

Figure 4.8. Forest cover map of 1999, 2009 and 2019

Table 4.8. Results of the forest cover classification for 1999, 2009 and 2019 images showing the area and percentages of each forest classes.

1999 2009 2019 Forest class Area_ha % Area_ha % Area_ha % None Forest -- -- 1.71 1.51 21.78 19.25 Less Forest 7.56 6.68 22.14 19.57 30.24 26.73 Moderate Forest 37.8 33.41 42.66 37.71 33.12 29.27 Dense Forest 67.78 59.91 46.63 41.21 28.00 24.75 Total 113.14 100 113.14 100 113.14 100

4.6.1 Rates of forest cover change

The area under the dense forest decreases from 67.78 ha in 1999 to 46.63 ha in 2009 and declines further to 28.00 ha in 2019. The mean annual forest cover change indicates decreasing rates from year to year. On the other hand, none forest and less forest land cover increase from year to year. Similar study Abyot Yismaw et al. (2014) reported that, the rate of forest cover change from year 1973 to 1986 is -245.2 ha per year (6044.4 ha –2855.9 ha/13 years) and from year 1986 to 2003, it was -24 ha annually Besides, considering the annual rate of forest cover change between1973 and 2003, the computed result is -120 ha per year.

Table 4.9. The mean annual rates of forest cover change.

Forest class 1999-2009 2009-2019 1999-2019 Dense forest -3.12 -4 -2.93 Moderate forest 1.28 -2.24 -0.62 Less forest 19.29 3.66 15 None forest 117.37

The result shows that the dense forest covers recorded negative values and the coverage area has decreasing rate through each consecutive study years. High dense forest made the highest conversion to moderate, less and none forest cover in each year through gradual change. Based on the forest cover change detection analysis result, this community forest cover change has impact to facilitate the environment and climate change effect. The historical decline of native forest cover has diminished the provision of ecosystem services that are critical to both the local ecosystem and nearby communities (Annika, 2016). The negative values represent the declined in the proportion of forest land cover in that particular time where positive values correspond to the increased in the proportion of land covers in that particular time of the study. The expansion of none forestland has exerted a negative impact on the other forest land cover categories in the study area particularly on the forest resource (Obang Owar et al., 2017).

Figure 4.9. Rates of forest cover change in Assabila community forest

This finding indicates that the rate of cover change of natural forests has been increasing throughout the study period, which is primarily because of human activities. On the other hand, less forest cover increased in the first period and reduced the second period. However, the overall mean annual rate of deforestation computed on dense forests is low (-2.93% compared to the moderate forest cover annual rate of -0.62%). Due to the result of this change, the forest density was decreased through in fast rate and degradation of biodiversity accelerated. According to Annika K. (2016), study said that, the trends in forest cover change are linked to a myriad environmental, sociopolitical, and economic factor that must be addressed in order to plan effective conservation of native forest lands.

This forest land was surrounded by the expansion of settlements, and the community utilization of forest product such as firewood, charcoal production, house construction and other interior decorator increased through the years. Similarly, Wachiye et al. (2013) said that, the degradation forest has been because of mostly poaching of valuable indigenous trees for charcoal burning to provide charcoal to the growing of population working in the tea industry and fuelwood for the squatters and general community living around the forest.

Chapter 5. CONCLUSION AND RECOMMENDATION

5.1 Conclusion

The study provides useful information on the present condition of the woody species diversity, structure and regeneration status of Assabila Community forest. A total of 44 woody species belonging to 31 genera and 26 families were documented in the study forest and had medium species diversity according to the standard. Fabaceae, was the most dominant family contributing to the large proportion of species might have well developed adaptation potential to wider agro ecology.

In the analysis of vegetation structure trees accounted for the largest proportions of the growth forms of woody species followed by saplings and seedlings. Both the cumulative diameter and height class frequency distribution patterns of woody individuals were resulted in a Bell- shape implies that density of woody species in the middle classes were high, and decreased towards the lower and higher diameter and height classes indicating a discontinuous or irregular recruitment of individuals in the forest. The Forest had 11.8 m2/ha-1 total basal area but most of the basal area was contributed by a few large sized individuals.

The study area currently is experiencing a high rate of degradation because of the unwise use of the forest resources from nearby village dwellers. This shows the forest is poorly protected by all the concerned bodies including local communities. The main cause of deforestation in the study forest are increasing demand for farm land expansion, cutting of trees for charcoal production/ fencing, clearing of forest for construction and timber production, fire wood demanding, free livestock grazing, etc. In the studying community forest for the three-decade 39.78 ha dense, 4.68 ha moderate forest was losing from its original NDVI class. In contradict there is an increasing of less forest by 22.68 ha and also 21.78 ha of forest are changing in to none forest NDVI class b/c of deforestation and forest degradation.

Generally, the regeneration status of the Community forest is not good b/c of the uncontrolled intervention of humans and browsing effects of their livestock. Accordingly, in this seedling and sapling assessment Croton macrostachyus, Maytenus arbutifolia, Maytenus obscura, Acacia lahai and Carissa edulis were with good recruitment status relative to other species.

On the other hand, some species like Vernonia amygdalina, Ficus vasta, Ficus sychomorous, Syzigium guineense, etc showed no seedling and sapling.

5.2 Recommendation

Based on the results of the study and observation made during the field study, the following recommendations were forwarded.

o Although the present study can contribute towards the understanding of plant species diversity and structure further study on soil seed bank, detailed land use and land cover changes over the water shade are important. o Based on the prioritization, in situ and ex situ conservation methods have to be implemented for the conservation of species having low IVI values and poor regeneration status o The local communities minimize livestock grazing, tree cutting and other human disturbances in order to allow the natural regeneration of woody species in the forests. o Establishing participatory forest management in Assabila community forest may help for the conservation of the threatened forest species diversity of the area and improving the livelihood of the community. o It is necessary to the local community for providing fuel saving improved stoves and other alternative energy sources in order to reduce the dependency of the community on the forest for firewood and charcoal. o There is a need to formulate a proper legal framework for the protection of the forest and demarcate the forest boundary. o To preserve the forest resources from further destruction, farmers should be encouraged to plant fast growing trees on their farm boundaries and homesteads to decrease the burden of natural forest or conserve degraded forest lands instead of cutting trees from the existing forest.

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7. APPENDICES

Appendix Table1. Lists of woody species recorded in Assabila community forest with corresponding family, vernacular name and plant forms.

No Scientific Name Family Name Local Name Plant form 1 Acacia abyssinica Hochst.ex.Benth Fabaceae Bazira Girar Tree 2 Acacia lahai Steud. &Hochst. ex Benth Fabaceae Cheba Tree 3 Acacia brevispica Harms Fabaceae Kontir Tree 4 Acacia seyal De Fabaceae Key Girar Tree 5 Acacia sieberiana DC. Fabaceae Nech Girar Tree 6 Albiza schimperiana Oliv. Fabaceae Sesa Tree 7 Bersama abyssinica Fresen. Francoaceae Azamira Tree 8 Bridelia micrantha Phyllanthaceae YenebirTifer Tree

9 Brucea antidysenterica J.F. Miller Simaroubaceae Waginose Shrub 10 Buddleja polystachya Fresen. Buddlejaceae Anfar Tree 11 Calpunia aurea(Alt.) Benth. Fabaceae Zigita Shrub 12 Capparis tomentosa Lam. Capparaceae Gemero Shrub 13 Carissa edulis (Forssk.)Vahle. Rutaceae Agam Tree 14 Clausena anisata (Willd.) Bent. Rutaceae Limich Shrub 15 Combretum molle (R.Br. ex Don.) Engl. Combretaceae Abalo Shrub & Diel 16 Cordia africana Lam. Boraginaceae Wanza Tree 17 Croton microstates Del. Hochest.ex.Del. Euphorbiaceae Bisana Tree 18 Dovyalis abyssinica Salicaceae Korshim Tree 19 Ekebergia capensis Sparrm. Meliaceae Lol Tree 20 Ficus carica L. Moraceae Beles Tree 21 Ficus sur Forssk. Moraceae Shola Tree 22 Ficus sycomorus L. Moraceae Bamba Tree 23 Ficus thonningii Blume Mulberry Chbiha Tree

24 Ficus vasta Forssk Moraceae Warika Tree 25 Grewia ferruginea Hochst.ex.A.Rich Malvaceae Lenkuata Shrub 26 Maytenus arbutifolia (Hochst Celastraceae Atat Shrub ex.A.Rich.)Wilczex. 27 Maytenus obscura (A.Rich.) Cuf Celastraceae Koba Tree 28 Milletia feruginea (Hochst.) Bak Sapotaceae Birbira Tree 29 Mimusops kummel A.DC. Sapotaceae Eshe Tree 30 Osyris quadripartita Decn. Santalaceae Keret Tree 31 Phytolacca dodecandra L.Her Phytolaccaceae Endod Shrub 32 Pittosporum viridifolium Sims Pittosporaceae Dingay Seber Tree 33 Premna schimperi Engl. Lamiaceae Checho Shrub 34 Rhus vulgaris Meikle Anacardiaceae Kamo Tree 35 Rhus glutinosa Hochst. ex A. Rich Anacardiaceae Embus Tree 36 Ritchiea steudneri Gilg. Vitaceae Kesila Shrub 37 Rosa abyssinica Lindley Rosaceae Kega Tree 38 Rubus apetalus Poir. Rosaceae Enjori Tree 39 Stereospermum kunthianum Cham Bignoniaceae Zana Tree 40 Syzigium guineense (Willd.) DC. Myrtaceae Dokima Tree 41 Vernonia amygdalina Del.In Caill. Asteraceae Girawa Tree 42 Vernonia leopoldii(Sch- Rubiaceae Chibo/zenezea Shrub Bip.exWalp.)Vatke 43 Vernonia myriantha Hook.f. Asteraceae Dengerita Shrub 44 Ximenia americana Olacaceae Enkoy Tree

Source: Azene Bekele (1993). Azene Bekele (2007), Dharani N. (2005) and Wolde Michael Kelecha (1987) were used for identification and classification of woody plant species.

Appendix Table 2. Shannon- Wiener Diversity (H’) index and the average evenness values

No Scientific Name Freque Pi Lnpi pilnpi Diversity Evenness ncy H'=2.69 En=0.71 LN(S)=3.78 1 Acacia abyssinica 245 0.04 -3.32 -0.12 2 Acacia lahai 645 0.1 -2.35 -0.22 3 Acacia brevispica 6 0 -7.03 -0.01 4 Acacia seyal 10 0 -6.52 -0.01 5 Acacia sieberiana 10 0 -6.52 -0.01 6 Albiza schimperiana 133 0.02 -3.93 -0.08 7 Bersama abyssinica 66 0.01 -4.63 -0.05 8 Bridelia micrantha 14 0 -6.18 -0.01

9 Brucea antidysenterica 91 0.01 -4.31 -0.06 10 Buddleja polystachya 6 0 -7.03 -0.01 11 Calpunia aurea 243 0.04 -3.33 -0.12 12 Capparis tomentosa 166 0.02 -3.71 -0.09 13 Carissa edulis 1127 0.17 -1.79 -0.3 14 Clausena anisata 16 0 -6.05 -0.01 15 Combretum molle 5 0 -7.21 -0.01 16 Cordia africana 54 0.01 -4.83 -0.04 17 Croton macrostachyus 1524 0.22 -1.49 -0.34 18 Dovyalis abyssinica 4 0 -7.43 0 19 Ekebergia capensis 4 0 -7.43 0 20 Ficus carica 30 0 -5.42 -0.02 21 Ficus sur 28 0 -5.49 -0.02 22 Ficus sycomorus 9 0 -6.62 -0.01 23 Ficus thonningii 28 0 -5.49 -0.02

24 Ficus vasta 4 0 -7.43 0 25 Grewia ferruginea 98 0.01 -4.24 -0.06

26 Maytenus arbutifolia 619 0.09 -2.39 -0.22 27 Maytenus obscura 410 0.06 -2.8 -0.17

28 Milletia feruginea 34 0.01 -5.29 -0.03

29 Mimusops kummel 165 0.02 -3.72 -0.09 30 Osyris quadripartita. 7 0 -6.88 -0.01 31 Phytolacca dodecandra 26 0 -5.56 -0.02 32 Pittosporum viridifolium 12 0 -6.34 -0.01 33 Premna schimperi 91 0.01 -4.31 -0.06 34 Rhus vulgaris 79 0.01 -4.45 -0.05 35 Rhus glutinosa 7 0 -6.88 -0.01 36 Ritchiea steudneri 92 0.01 -4.3 -0.06 37 Rosa abyssinica 123 0.02 -4.01 -0.07 38 Rubus apetalus Poir. 3 0 -7.72 0 39 Stereospermum kunthianum 38 0.01 -5.18 -0.03 40 Syzigium guineense 17 0 -5.99 -0.02 41 Vernoniaamygdalina 36 0.01 -5.24 -0.03 42 Vernonia leopoldii 5 0 -7.21 -0.01 43 Vernonia myriantha 442 0.07 -2.73 -0.18 44 Ximenia americana 3 0 -7.72 0 Total 6775 -228.5 -2.69

Appendix Table 3. Density of woody species in Assabila community forest.

Relative No Species_Name Frequency Density density 1 Acacia abyssinica 245 6.81 3.616 2 Acacia lahai 645 17.92 9.520 3 Albiza schimperiana 133 3.69 1.963 4 Acacia seyal 10 0.28 0.148 5 Acacia sieberiana 10 0.28 0.148 6 Bersama abyssinica 66 1.83 0.974 7 Bridelia micrantha 14 0.39 0.207 8 Brucea antidysenterica 91 2.53 1.343 9 Buddleja polystachya 6 0.17 0.089 10 Calpunia aurea 243 6.75 3.587 11 Combretum molle 5 0.14 0.074 12 Capparis tomentosa 166 4.61 2.450 13 Carissa edulis 1127 31.31 16.635 14 Clausena anisata 16 0.44 0.236 15 Cordia africana Lam. 54 1.50 0.797 16 Croton macrostachyus 1524 42.33 22.495 17 Dovyalis abyssinica 4 0.11 0.059 18 Ekebergia capensis 4 0.11 0.059 19 Rhus glutinosa 7 0.19 0.103 20 Ficus carica. 30 0.83 0.443 21 Ficus sur 28 0.78 0.413 22 Ficus thonningii 28 0.78 0.413 23 Ficus vasta 4 0.11 0.059 24 Ficus sycomorus 9 0.25 0.133 25 Grewia ferruginea 98 2.72 1.446 26 Maytenus arbutifolia 619 17.19 9.137

27 Maytenus obscura 410 11.39 6.052 28 Milletia feruginea 34 0.94 0.502 29 Mimusops kummel 165 4.58 2.435 30 Osyris quadripartita 7 0.19 0.103 31 Phytolacca dodecandra 26 0.72 0.384 32 Pittosporum viridifolium 12 0.33 0.177 33 Premna schimperi 91 2.53 1.343 34 Rhus vulgaris 79 2.19 1.166 35 Ritchiea steudneri 92 2.56 1.358 36 Rosa abyssinica 123 3.42 1.816 37 Rubus apetalus 3 0.08 0.044 38 Stereospermum kunthianum 38 1.06 0.561 39 Syzigium guineense 17 0.47 0.251 40 Vernonia amygdalina 36 1.00 0.531 41 Acacia brevispica 6 0.17 0.089 42 Vernonia leopoldii 5 0.14 0.074 43 Vernonia myriantha 442 12.28 6.524 44 Ximenia americana 3 0.08 0.044 Total 6775 188.19

Appendix Table 4. Mean Basal area (BA) in m2 and relative dominance of woody species

BA in Relative Relative No Species Name m2/ha Dominance Frequency frequency 1 Acacia abyssinica 1.6860 14.352 0.036 3.616 2 Acacia lahai 1.0105 8.602 0.095 9.520 3 Albiza schimperiana 1.4923 12.703 0.020 1.963 4 Acacia seyal 0.0166 0.141 0.001 0.148 5 Acacia sieberiana 0.0995 0.847 0.001 0.148 6 Bersama abyssinica 0.6290 5.354 0.010 0.974 7 Bridelia micrantha 0.0819 0.697 0.002 0.207 8 Brucea antidysenterica 0.0052 0.044 0.013 1.343 9 Buddleja polystachya 0.0633 0.539 0.001 0.089 10 Calpunia aurea 0.0994 0.846 0.036 3.587 11 Combretum molle 0.0011 0.010 0.001 0.074 12 Capparis tomentosa 0.0141 0.120 0.025 2.450 13 Carissa edulis 0.8326 7.087 0.166 16.635 14 Clausena anisata 0.0013 0.011 0.002 0.236 15 Cordia africana 0.8373 7.127 0.008 0.797 16 Croton macrostachyus 1.7435 14.842 0.225 22.494 17 Dovyalis abyssinica 0.0009 0.007 0.001 0.059 18 Ekebergia capensis 0.0013 0.011 0.001 0.059 19 Rhus glutinosa 0.0003 0.002 0.001 0.103 20 Ficus carica 0.1031 0.877 0.004 0.443 21 Ficus sur 0.1212 1.032 0.004 0.413 22 Ficus thonningii 0.0871 0.742 0.004 0.413 23 Ficus vasta 0.1400 1.192 0.001 0.059 24 Ficus sycomorus 0.1451 1.235 0.001 0.133 25 Grewia ferruginea 0.0123 0.105 0.014 1.446 26 Maytenus arbutifolia 0.0240 0.204 0.091 9.137

27 Maytenus obscura 0.0146 0.124 0.061 6.052 28 Milletia feruginea 0.1112 0.946 0.005 0.502 29 Mimusops kummel 1.7159 14.606 0.024 2.435 30 Osyris quadripartita 0.0064 0.054 0.001 0.103 31 Phytolacca dodecandra 0.0044 0.037 0.004 0.384 32 Pittosporum viridifolium 0.0079 0.067 0.002 0.177 33 Premna schimperi 0.0100 0.086 0.013 1.343 34 Rhus vulgaris 0.0118 0.100 0.012 1.166 35 Ritchiea steudneri 0.0644 0.548 0.014 1.358 36 Rosa abyssinica 0.0333 0.283 0.018 1.815 37 Rubus apetalus 0.0169 0.144 0.000 0.044 38 Stereospermum kunthianum 0.0979 0.834 0.006 0.561 39 Syzigium guineense 0.1040 0.885 0.003 0.251 40 Vernonia amygdalina 0.1053 0.896 0.005 0.531 41 Acacia brevispica 0.1138 0.969 0.001 0.089 42 Vernonia leopoldii 0.0100 0.085 0.001 0.074 43 Vernonia myriantha 0.0646 0.550 0.065 6.524 44 Ximenia Americana 0.0065 0.055 0.000 0.044 Total 11.748

Appendix Table 5. Species frequency and their rank of Assabila community forest.

No Species Frequency Rf Rank 1 Crotonmacrostachyus 100.00 8.70 1 2 Carissa edulis 97.22 8.45 2

3 Acacia lahai 94.44 8.21 3 4 Maytenus arbutifolia 91.67 7.97 4 5 Acacia abyssinica 86.11 7.49 5 6 Maytenus obscura 86.11 7.49 6 7 Vernonia myriantha 86.11 7.49 7 8 Ritchiea steudneri 77.78 6.76 8 9 Capparis tomentosa 72.22 6.28 9 10 Calpunia aurea 69.44 6.04 10 11 Grewia ferruginea 69.44 6.04 11 12 Albiza schimperiana 66.67 5.80 12 13 Mimusops kummel 61.11 5.31 13 14 Premna schimperi 61.11 5.31 14 15 Brucea antidysenterica 55.56 4.83 15 16 Cordia africana 52.78 4.59 16 17 Rosa abyssinica 52.78 4.59 17 18 Bersama abyssinica 47.22 4.11 18 19 Rhus vulgaris 41.67 3.62 19 20 Ficus sur 38.89 3.38 20 21 Stereospermum kunthianum 38.89 3.38 21 22 Vernonia amygdalina 36.11 3.14 22 23 Ficus thonningii 33.33 2.90 23 24 Phytolacca dodecandra 33.33 2.90 24 25 Ficus carica 30.56 2.66 25 26 Milletia feruginea 25.00 2.17 26 27 Acacia seyal 13.89 1.21 27

28 Bridelia micrantha 13.89 1.21 28 29 Ficus sycomorus 13.89 1.21 29 30 Pittosporum viridifolium 13.89 1.21 30 31 Syzigium guineense 13.89 1.21 31 32 Buddleja polystachya 11.11 0.97 32 33 Combretum molle 11.11 0.97 33 34 Clausena anisata 11.11 0.97 34 35 Rhus glutinosa 11.11 0.97 35 36 Ficus vasta 11.11 0.97 36 37 Osyris quadripartita 11.11 0.97 37 38 Vernonia leopoldii 11.11 0.97 38 39 Acacia sieberiana 8.33 0.72 39 40 Dovyalis abyssinica 8.33 0.72 40 41 Ekebergia capensis 8.33 0.72 41 42 Acacia brevispica 8.33 0.72 42 43 Rubus apetalus 5.56 0.48 43 44 Ximenia Americana 5.56 0.48 44 1150.00

Appendix Table 6. Importance value indices (IVI) of woody species in the study forest.

No Species Name Relative Relative Relative Important value Density Dominance frequency Index (IVI)

1 Acacia abyssinica 3.616 14.352 8.70 25.46 2 Acacia lahai 9.520 8.602 8.45 26.33 3 Albiza schimperiana. 1.963 12.703 8.21 20.46 4 Acacia seyal 0.148 0.141 7.97 1.50 5 Acacia sieberiana 0.148 0.847 7.49 1.72 6 Bersama abyssinica 0.974 5.354 7.49 10.43 7 Bridelia micrantha 0.207 0.697 7.49 2.11 8 Brucea antidysenterica 1.343 0.044 6.76 6.22 9 Buddleja polystachya 0.089 0.539 6.28 1.59 10 Calpunia aurea 3.587 0.846 6.04 10.47 11 Combretum molle 0.074 0.010 6.04 1.05 12 Capparis tomentosa 2.450 0.120 5.80 8.85 13 Carissa edulis 16.635 7.087 5.31 32.18 14 Clausena anisata 0.236 0.011 5.31 1.21 15 Cordia africana 0.797 7.127 4.83 12.51 16 Croton macrostachyus 22.495 14.842 4.59 46.03 17 Dovyalis abyssinica 0.059 0.007 4.59 0.79 18 Ekebergia capensis 0.059 0.011 4.11 0.79 19 Rhus glutinosa 0.103 0.002 3.62 1.07 20 Ficus carica 0.443 0.877 3.38 3.98 21 Ficus sur 0.413 1.032 3.38 4.83 22 Ficus thonningii 0.413 0.742 3.14 4.05 23 Ficus vasta 0.059 1.192 2.90 2.22 24 Ficus sycomorus 0.133 1.235 2.90 2.58 25 Grewia ferruginea 1.446 0.105 2.66 7.59 26 Maytenus arbutifolia 9.137 0.204 2.17 17.31

27 Maytenus obscura 6.052 0.124 1.21 13.66 28 Milletia feruginea 0.502 0.946 1.21 3.62 29 Mimusops kummel 2.435 14.606 1.21 22.36 30 Osyris quadripartita 0.103 0.054 1.21 1.12 31 Phytolacca dodecandra 0.384 0.037 1.21 3.32 32 Pittosporum viridifolium 0.177 0.067 0.97 1.45 33 Premna schimperi 1.343 0.086 0.97 6.74 34 Rhus vulgaris 1.166 0.100 0.97 4.89 35 Ritchiea steudneri 1.358 0.548 0.97 8.67 36 Rosa abyssinica 1.816 0.283 0.97 6.69 37 Rubus apetalus 0.044 0.144 0.97 0.67 38 Stereospermum kunthianum 0.561 0.834 0.97 4.78 39 Syzigium guineense 0.251 0.885 0.72 2.34 40 Vernonia amygdalina 0.531 0.896 0.72 4.57 41 Acacia brevispica 0.089 0.969 0.72 1.78 42 Vernonia leopoldii 0.074 0.085 0.72 1.12

43 Vernonia myriantha 6.524 0.550 0.48 14.56 44 Ximenia Americana 0.044 0.055 0.48 0.58

Appendix Table 7. Location of quadrats in Assabila community forest.

Quadrant Location Altitude No. of species counted No X Y 1 274626 1252611 2018 186 2 274584 1252583 2020 162 3 274551 1252508 2022 149 4 274523 1252469 2024 212 5 274514 1252419 2028 142 6 274472 1252433 2033 152 7 274509 1252507 2033 118 8 274511 1252617 2020 98 9 274569 1252730 2026 93 10 274652 1252721 2014 99 11 274668 1252791 2017 117 12 274635 1252867 2017 177 13 274527 1252845 2024 185 14 274392 1252920 2019 168 15 274161 1252945 2019 186 16 274965 1252991 2005 202 17 275239 1253137 1999 237 18 275168 1252930 2001 213 19 275071 1252895 2009 209 20 274920 1252841 2014 241 21 274807 1252788 2019 197 22 274699 1252670 2020 238 23 275552 1255170 1958 245 24 275436 1253229 1983 309 25 275589 1253356 1976 254 26 275715 1253513 1969 232

27 275765 1253608 1978 233 28 275623 1253659 1978 214 29 275501 1255701 1984 227 30 275185 1253703 1995 211 31 275103 1253796 1998 224 32 274962 1253807 2001 226 33 274630 1253873 2011 221 34 274453 1253979 2016 124 35 274156 1254020 2023 236 36 274156 1254020 2023 38

Appendix Table 8. Ground truth boundary data

No X Y z 1 274082 1254106 2 274089 1234015 3 274199 1253939 4 274356 1253918 5 274593 1253837 6 275068 1253613 7 275210 1253575 8 275446 1253529 9 275427 1253335 10 275322 1253264 11 275341 1253201 12 275170 1253222 13 274945 1253087 14 274665 1253001 15 274444 1253034 16 274382 1253039 17 274309 1253009 18 274270 1252936 19 274435 1252858 20 274439 1252781 21 274524 1252777 22 274370 1252420 23 274529 1252375 24 274771 1252624 25 275027 1252797 26 275225 1252906

27 275567 1253064 28 275535 1253281 29 275989 1253500 30 276008 1253715 31 275769 1253815 32 275475 1253982 33 275150 1254002 34 274937 1254062 35 274653 1254098 36 274477 1254092 37 274253 1254094

BIOGRAPHICAL SKETCH

The author was born in Achefer district, a West gojjam zone of Amhara Regional State, Ethiopia, in march 1989. He attended the first primary Education in Achefer district (up to grade 6th) “Kat Dikuli” primary first level school and 7th and 8th education at “Abadira” Full primary School. Then he has followed Dangila Senior secondary and Preparatory School and then he got his BSc Degree in Natural Resource Management from Bahir Dar University on July 2013. After his graduation he joined North Achefer district Agricultural and rural land Administration and use office as soil and water conservation expert. On November 2016, he was joined Amhara regional state REDD+ project in Wadela district as forest expert. Then he joined Amhara regional state Environment, Forest, Wild life protection and Development Authority (EFWPDA) as forest expert up to now. The author has joined at Bahir Dar University, College of Agriculture and Environmental Sciences, Department of Natural Resources Management in the regular program as a candidate for Master of Science degree in Environment and Climate change in 2018/19 by self-sponsored.